Overweight/obesity and cardiovascular diseases are associated with high rates of mobility and mortality. We investigated inflammatory lesions and ionic transporters in the left ventricle of male rats chronically fed a high-fat diet (HL). They developed moderate overweight with early expansion of visceral adiposity. The inflammatory markers interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-), Na+-transporting ATPases, sarco-endoplasmic reticulum Ca2+-ATPase (SERCA2a), and the abundance of Angiotensin II receptors were studied together with lipid and glycemic profiles, and the left ventricle echocardiographic parameters fractional shortening (FS) and ejection fraction (EF). HL rats doubled IL-6 and TNF-, which returned to normal levels when signaling coupled to Angiotensin II type 2 receptors (AT2R) was supraregulated by Ang-(3–4) administration. The decreased glucose tolerance was accompanied by significantly reduced plasma triglycerides due to intense hepatic steatosis (Crisóstomo et al. Metabolism Open. 2022, 14, 100176). AT2R (and AT1R) abundance did not change. (Na++K+)ATPase and ouabain-resistant Na+-ATPase were downregulated and upregulated, respectively, normalized with Ang-(3–4). SERCA2a lost its most critical regulatory property, inhibition by excess substrate. The ultrasound showed no changes in FS and EF. We conclude that very moderate overweight can cause silent initial molecular tissue damage in heart, which can evolve to cause systolic dysfunction.

This article aimed to evaluate, using statistical tools, the generation and composition of urban solid waste (MSW) in the city of Belém, Pará, from nine urban collection sectors in the municipality, and the average nominal income of the population. Approximately 900kg of urban solid waste were col-lected from the municipality in the period from 2021 to 2022. Statistical tests were carried out on hypotheses with 5% significance in comparative evaluations, and their respective average nominal incomes. The results indicated that the organic fraction corresponds to 55.57% of the waste generated in the municipality, 14.26% is inert waste (potentially contaminating) and 0.67% is miscellaneous waste. It was observed that the waste generated by families with high purchasing power tends to contain higher percentages of recyclables, while in families with lower purchasing power the highest percentages tend to be organic. Knowledge of the generation of MSW is fundamental for the choice and dimensioning of operations and processes involved in the management chain at the municipal level. The decentralization of services offered based on solid waste management would enable greater success in serving the population.

The purpose of this research is to examine the benefits and limitations of implementation of novel digital academic advising systems using automated collection and reporting processes for ABET student outcomes data based on principles of authentic OBE for effective developmental advising. We examine digital developmental advising models of undergraduate engineering programs in two universities that employ customized features of the web-based software EvalTools® - Advising Module based on assessment methodology incorporating the Faculty Course Assessment Report, Performance Indicators and hybrid rubrics classified according to the affective, cognitive and psychomotor domains of Bloom’s learning model. A case study approach over a six-year period is adopted for this research. The two case studies present results of samples of developmental advising activity employing sequential explanatory mixed methods models using a combination of quantitative and qualitative analyses of a) detailed students’ outcomes and Performance Indicators information; and b) self-evaluation of their professional development and lifelong learning skills. The findings of this study show that digital advising systems employing the Faculty Course Assessment Report using Performance Indicators and hybrid rubrics can provide comprehensive and realistic outcomes data to help both developmental advisors and students easily identify the specific cause of performance failures, implement practical recommendations for remedial actions and track improvements. Inherent strong skills can also be identified in academically weak students by observing patterns or trends of relatively better performing outcomes to reinforce natural affinity for learning specialized competencies and pursue related and successful career paths.

Change Detection (CD) of aerial images refers to identifying and analyzing changes between two or more aerial images of the same location taken at different times. The CD is a highly challenging task due to the need to distinguish relevant changes, such as urban expansion, deforestation, or post-disaster damage assessment, from irrelevant ones, such as light conditions, shadows, and seasonal variations. Many CD papers have recently been published, where most of the papers that proposed a new model contained a comparison between their proposed and state-of-the-art (SOTA) models. While many recent studies propose new deep learning (DL) models for improving CD performance, their comparative analyses are often restricted, lacking comprehensive insights into the proposed models' real-world generalizability, robustness, and performance trade-offs across diverse change characteristics. This paper presents a novel generic framework to systematically benchmark and assess DL-based CD models through three parallel pipelines: 1) cross-testing models on diverse benchmark datasets to evaluate generalization, 2) robustness analysis against different image corruptions, and 3) multi-faceted contour-level analytics evaluating detection sensitivity to change size/complexity. The framework is applied to comparatively evaluate five state-of-the-art DL-based CD models - Changeformer, BIT, Tiny, SNUNet, and CSA-CDGAN. Extensive experiments unveil each model's strengths, limitations and biases, highlighting their relative proficiencies in generalizing across data distributions, resilience to noise corruption, and discriminative capabilities for changes of varying characteristics. The proposed benchmarking framework demonstrates significant potential for guiding the selection of suitable CD models tailored to specific application requirements by comprehensively evaluating their generalizability, robustness, and detection capabilities across diverse real-world scenarios. This systematic evaluation approach can drive future research into developing more robust and versatile CD solutions aligned with practical needs.

Precancerous and cancerous lesions of the uterine cervix are known to be associated with Human Papillomavirus (HPV) infection. The screening of HR-HPV infection in female population has led to the discovery of several cases of double cervix, a congenital malformation that is very rare. The purpose of this study was to evaluate high risk (HR) HPV infections in women with double cervix within the National Cervical Cancer Screening program of the Lazio Region (Italy). From June 2021 to March 2024 a total of 142,437 samples were analyzed by Seegene’s Anyplex TM II HR-HPV method which identifies 14 HR-HPV genotypes. For each woman identified with a double cervix, two separate samples were taken from both cervixes and analyzed separately. Twenty-seven women with double cervix were identified (0.019%): 20 women were tested as negative for both cervices, while the remaining four (namely A, B, C and D) resulted positive. By genotyping, the following results were obtained: A) both samples showed genotype 31; B) one cervix was negative while the other showed genotype 58. C) one cervix was positive for HPV 18 and 31 while for 18, 31 and 33 in the other. D) one cervix showed genotype 66 while the other carried the 66 and 68 genotypes. Double cervix is a very rare condition, where the presence HR-HPV genotypes is not homogeneous. As already described, our study confirms that different genotypes can be detected in double cervix malformation, suggesting the need to perform HPV screening on brushing samples from both cervices.

The emergence of the Internet of Things (loT) has led to remarkable advancements in various sectors such as urbanization, manufacturing, and healthcare, intending to enhance the quality of life and contribute to the world economy. However, by the conclusion of 2024, ti is projected that the global network of the Internet of Things (IoT) will encompass over 207billion interconnected devices. The immense data collected and exchanged are often stored in centralized servers, leading to vulnerabilities such as data breaches. Blockchain technology, originally developed for cryptocurrency transactions, provides a possible solution for these challenges with its decentralized, immutable, and encrypted features. This survey provides a comprehensive discussion regarding the fusion of Blockchain technology and the loT, covering architectural alignment, application, security, and limitations. The contributions of this survey include an overview of Blockchain technology and its secure applications, the integration of Blockchain into IoT , an examination of suitable methods for Blockchain integration,and a taxonomy or security and limitations of loT and blockchain integration.

Fetal Alcohol Spectrum Disorders (FASD) represent a continuum of lifelong impairments resulting from prenatal exposure to alcohol, with significant global impact. The “spectrum” of disorders includes a continuum of physical, cognitive, behavioral, and developmental impairments, which can have profound and lasting effects on individuals throughout their lives impacting their health, social interactions, psychological well-being and every aspect of their lives. This narrative paper explores the intricate relationship between oxidative stress and epigenetics in FASD pathogenesis and its therapeutic implications. Oxidative stress, induced by alcohol metabolism, disrupts cellular components, particularly in the vulnerable fetal brain, leading to aberrant development. Furthermore, oxidative stress is implicated in epigenetic changes, including alterations in DNA methylation, histone modifications, and microRNA expression, which influence gene regulation in FASD patients. Moreover, mitochondrial dysfunction and neuroinflammation contribute to epigenetic changes associated with FASD. Understanding these mechanisms holds promise for targeted therapeutic interventions. This includes antioxidant supplementation and lifestyle modifications to mitigate FASD-related impairments. While preclinical studies show promise, further clinical trials are needed to validate these interventions' efficacy in improving clinical outcomes for individuals affected by FASD. This comprehensive understanding of the role of oxidative stress on epigenetics in FASD underscores the importance of multidisciplinary approaches for diagnosis, management, and prevention strategies. Continued research in this field is crucial for advancing our knowledge and developing effective interventions to address this significant public health concern.

Whole genome duplication (WGD) or polyploidization can occur at the cellular, tissue, and organismal levels. At the cellular level, tetraploidization has been proposed as a driver of aneuploidy and genome instability, and correlates strongly with cancer progression, metastasis, and development of drugs resistance[1–6]. WGD is also a key developmental strategy for regulating cell size, metabolism, and cellular function[1,7–10]. In specific tissues, WGD is involved in normal development (e.g. organogenesis), tissue homeostasis, wound healing, and regeneration[10–17]. At the organismal level, WGD propels evolutionary processes such as adaptation[18], speciation[19], and crop domestication[20]. An essential strategy to further our understanding of the mechanisms promoting WGD and its effects is to compare isogenic strains that differ only in their ploidy. Caenorhabditis elegans (C. elegans) is emerging as an animal model for these comparisons, in part because relatively stable and fertile tetraploid strains can be produced rapidly from nearly any diploid strain[7]. Here we review the use of Caenorhabditis polyploids as tools to understand important developmental processes (e.g. sex determination, dosage compensation, and allometric relationships)[21–27] and cellular processes (e.g. cell cycle regulation, chromosome dynamics during meiosis)[28–32]. We also discuss how the unique characteristics of the C. elegans WGD model will enable significant advances in our understanding of mechanisms of polyploidization and its role in development and disease.

We introduce a novel microscopic image dataset augmented with segmentation and detection labels specifically designed for microplastic analysis in sewage environments. Recognizing the increasing concern over microplastics — particles of synthetic polymers smaller than 5mm — and their detrimental effects on marine ecosystems and human health, our research focuses on enhancing detection and analytical methodologies through advanced computer vision and deep learning techniques. The dataset comprises high-resolution microscopic images of microplastics collected from sewage, meticulously labeled for both segmentation and detection tasks, aiming to facilitate accurate and efficient identification and quantification of microplastic pollution. In addition to dataset development, we present example deep learning models optimized for segmentation and detection of microplastics within complex sewage samples. The models demonstrate significant potential in automating the analysis of microplastic contamination, offering a scalable solution to environmental monitoring challenges. Furthermore, we ensure the accessibility and reproducibility of our research by making the dataset and model codes publicly available, accompanied by detailed documentation on GitHub and LabelBox. The dataset and example deep learning models are publicly available at the following GitHub link. https://anonymous.4open.science/r/Microplastics-in-Sewage-1BEF

The forage fish, a crucial source of nutrition in developing countries, are unfortunately primarily used for fishmeal and aquafeed production for aquaculture that mainly serves consumers in developed countries. Industrial fish use leaves a meager portion of the catch for direct human consumption in the fish-producing countries, leading to inflated fish prices locally. Overfishing forage fish necessary to support the fish reduction industry disrupts the ecosystem, diminishes the catch of larger fish, such as mackerel, and marginalizes local artisanal fishermen. This article appraises roughly the financial and nutritional impacts of elevated fish prices on low-income consumers in forage fish-producing countries, drawing attention to the ethical implications of this situation. A hypothetical 10% boost in the annual supply of fresh forage fish to the current global supply of 5 million MT markets could save consumers hundreds of millions of US dollars globally, tens of mil-lions of US dollars nationally, and several dollars for a family while improving the nutrition of families that depend on forage fish. The numbers suggest that even a modest boost to the supply of forage fish in fresh fish markets could significantly benefit the fish-producing nations' consumers and the fish-producing nations. In some countries, such as South Africa, the orders of magnitude of these sums approach that of the entire local fish reduction industries' value. The increased fish prices could be considered involuntary subsidies by low-income consumers to the aquafeed and aquaculture industries. In summary, the reduction industry's and aquaculture's current use of captured forage fish warrants further scrutiny, as it inadvertently burdens disadvantaged societies financially and nutritionally. The article proposes alternative protein sources and cultivating non-carnivorous fish among several optional measures to ensure equitable distribution of forage fish resources.

(1) Objectives. Patients with heart failure (HF) who experience hospitalizations for worsening HF (HFH) are at high risk of subsequent events. We aimed to evaluate the impact of an outpatient care management program that includes a clinician-to-clinician e-consultation using an integrated electronic medical record in a healthcare area with a widely dispersed population on delay time in care, hospital admissions, and mortality in a high-risk group of patients with HF and previous episodes of HFH. (2) Methods. We selected 6,444 HF patients who visited the cardiology service at least once between 2010 and 2021. Of these, 4,851 were attended in e-consult, and 2,008 in one-time in-person consultations. In 2,230 HF patients, there was documentation of a previous episode of HFH. Using an interrupted time series regression model, we analysed the impact of incorporating e-consult into the health care model in the group of patients with HFH and evaluated the elapsed time to cardiology care, heart failure (HF), cardiovascular (CV), and all-cause hospital admissions and mortality, calculating the incidence relative risk (iRR). (3) Results. In the group of patients with previous HF hospitalizations, the introduction of e-consult substantially decreased waiting times to cardiology care. The time elapsed to care after e-consult implementation was significantly reduced compared with the previous in-person period (8.6 [8.7] vs 55.4 [79.9] days, p<0.001). In that group of patients, after e-consult implantation, hospital admissions for HF were reduced (iRR [CI95%]: 0,837 [0,840-0,833]), 0,900 [0,862-0,949] for CV and 0,699 [0,678-0,726] for all-cause hospitalizations. There was also lower mortality (iRR [CI95%]: 0.715 [0.657-0.798] due to HF, 0,737 [0.764-0.706] for CV and 0,687 [0.652-0,718] for all-cause). The improved outcomes after e-consultation implementation were significantly higher in the group of patients with previous HFH and were independent of the patient’s clinical characteristics managed during the in-person or e-consultation periods. (4) Conclusions. In HF patients with previous HFH, an outpatient care program that includes an e-consult significantly reduced waiting times to cardiology care and was safe, with a lower rate of hospital admissions and mortality in the first year.

In this study, we explored the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. We first utilized the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, we experimented with the Autoencoder algorithm, an unsupervised deep neural network model, which enhanced the detection accuracy to 33.6% in the best case despite some fluctuations. The results demonstrate deep learning models' strong feature extraction capability and adaptability, highlighting their potential to surpass traditional methods. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. This study underscores the necessity for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The findings provide valuable insights and reference points for future research in financial fraud detection using deep learning methodologies.

The calculation of the isobaric heat capacities of the liquid and solid phase of molecules at 298.15 K is presented, applying a universal computer algorithm based on the atom-groups additivity method, using refined atom groups. The atom groups are defined as the molecules' constituting atoms and their immediate neighbourhood. In addition, the hydroxy group of alcohols are further subdivided to take account of the different intermolecular interactions of primary, secondary and tertiary alcohols. The evaluation of the groups' contributions has been carried out by means of a fast Gauss-Seidel fitting calculus using experimental data from literature. Plausibility has been tested immediately after each fitting calculation using a 10-fold cross-validation procedure. For the heat capacity of liquids, the respective goodness of fit of the direct (R2) and the cross-validation calculations (Q2) of 0.998 and 0.9975, and the respective standard deviations of 8.2 and 9.16 J/mol/K, together with a medium absolute percentage deviation (MAPD) of 2.69%, based on the experimental data of 1133 compounds, proves the excellent predictive applicability of the present method. The statistical values for the heat capacity of solids are only slightly inferior: for R2 and Q2, the respective values are 0.9915 and 0.9875, the respective standard deviations are 12.19 and 14.13 J/mol/K and the MAPD is 4.65%, based on 732 solids. The predicted heat capacities for a series of liquid and solid compounds has been directly compared to those received by a complementary method based on the "true" molecular volume [1] and their deviations elucidated.

Background: Colorectal cancer (CRC) patients experienced multiple chemotoxicity affecting treatment compliance, survival, and quality of life (QOL). Prior research shows clinician-reported chemotoxicity (i.e., grading scales or diagnostic codes) predicts rehospitalization and cancer survival. However, a comprehensive synthesis of clinician-reported chemotoxicity is still lacking. Objectives: We conducted a systematic review and meta-analysis to determine the chemotoxicity's prevalence and risk factors in CRC. Methods: A systematic search from 2009 to 2024 yielded 30 studies for review, with 25 included in the meta-analysis. Results: Pooled prevalences of overall, non-hematological, and hematological moderate-to-severe toxicities were 45.7%, 39.2%, and 25.3%, respectively. The most common clinician-reported chemotoxicities were gastrointestinal (GI) toxicity (22.9%) and neuropathy or neutropenia (17.9%). Significant risk factors at baseline were malnutritional status, frailty, impaired immune or hepato-renal functions, short telomere lengths, low gut lactobacillus levels, age, female sex, aggressive chemotherapy, and low QOL. Age was associated with neutropenia (β: -1.44) and GI toxicity (β:1.85) (Ps 65 y.o.) had higher prevalences of overall (OR: 1.14) and GI (OR: 1.65) toxicities, but a lower prevalence of neutropenia (OR: 0.65) than younger adults (Ps

Background: Emerging and re-emerging infectious diseases that infect wildlife, such as African swine fever, avian influenza, and SARS-CoV-2, have highlighted the necessity for wildlife health surveillance (WHS) due to their direct and indirect impacts on wildlife species, ecosystems, domestic animals, and human health. While global policies and guidelines exist, a critical gap remains in local-to-national implementation of WHS systems. A group of local, national, and global actors in WHS have formed a working group to address this gap. Methods and Findings: The working group reports on a theory of change (ToC) developed to implement WHS from local to global scales. Through brainstorming, plenary exercise, and building on peer-reviewed science and existing surveillance systems, we identified six transformative pathways to be implemented via collaborations across scales and contexts: mindset change, policy and investment, user-driven science, user-driven technologies, capacity enhancement, and mobilization of a global community of practice. Interpretation: This ToC serves as a roadmap to develop effective WHS systems that support adaptive management and implementation. WHS is fundamental to understanding the impacts of health threats to biodiversity and human and domestic animal health. This ToC presents an approach to operationalize integration of wildlife health into collaborative One Health surveillance. Funding: The Science for Nature and People Partnership.

Background & Aims: Reports of a rise in childhood cancer incidence in Australia and globally prompted the investigation of cancer incidence and survival in South Australia (SA) and the Northern Territory (NT) over a 28-year period, with emphasis on Indigenous peoples. Methods: This cross-sectional analysis of two prospective longitudinal databases, the SA and NT Cancer Registries (1990-2017), included all reported cases of childhood cancers. Poisson regression provided estimates of incidence rate ratios and survival was modelled using Cox proportional hazard models for children aged 0.05). With reference to the 1990-1999 era, overall survival improved in subsequent eras in SA (2000-2009: HR 0.53 [0.38-0.73]; 2010-2017: 0.44 [0.28-0.68]); however, remained unchanged in the NT (2000-2009: 0.78 [0.40-1.51]; 2010-2017: 0.50 [0.24-1.05]). In the NT, overall survival of Indigenous patients was significantly lower compared with the non-Indigenous cohort (3.42 [1.92-6.10]). While the survival of Indigenous children with cancer significantly improved in the last two eras (p0.05). Conclusions: The incidence of childhood cancers has remained unchanged over 28-years in SA and the NT. Encouragingly, improved survival rates over time were observed in SA and amongst Indigenous children of the NT. Nevertheless, survival rates in Indigenous children remain lower than non-Indigenous children.

This research proposes a methodology for minimizing losses in transmission lines (LT), considering the reconfiguration of the architecture of the electrical power system (SEP). The implementation of this methodology redirects the power flow with optimal switching through its LTs to guarantee the stability of the voltage, angle, frequency, and power balance to minimize losses that affect the reliability and quality of the system. Optimal transmission switching (OTS) allows various types of analysis to be carried out, where the loadability of the lines, response times, and operating costs, among others, can be improved. This article proposes to minimize the losses in the transmission lines with the OTS by using AC power flows as a mixed integer nonlinear problem (MINLP). Several test scenarios evaluate the method’s effectiveness, determining the optimal topology for corrective control that optimizes power flows in different situations. It has been proven that this approach reduces losses compared to a base case by 99%, even in the face of N-1 or random contingencies, without losing load, maintaining the same active power dispatch, and finally verifying a strategic increase in dispatch of reactive power to maintain operating parameters within stable limits.

The mechanisms of formation of induced intrinsic and impurity radiative states, which consist of intrinsic and impurity electron-hole trapping centers in irradiated Ca2P2O7-Mn and Ca2P2O7 phosphates, were investigated using thermoactivation and vacuum-ultraviolet spectroscopy methods. These centers are excited at photon energies of 4.0 eV and 4.5 eV, which are within the matrix's transparency region. New radiative induced states at 3.06 eV and 2.92 eV are demonstrated to be generated upon the excitation of anions by photons with energies of 5.0 and 5.64 eV. This process is due to charge transfer from the ion to the impurities, specifically Mn^2+ O^2- Mn^2+ and the neighboring ion O^2- (P2O7)^4-. Furthermore, upon the excitation of matrix anions with photon energies exceeding the band gap (8.0 - 8.25 eV), electron trapping by impurities such as Mn^2+ and (P2O7)^4- ions results.

Landsat and Sentinel-2 acquisitions are among the most widely used medium-resolution optical data adopted for terrestrial vegetation applications, such as land cover and land use mapping, vegetation condition and phenology monitoring, and disturbance and change mapping. When combined, both data archives provide over 40 years, and counting, of continuous and consistent observations. Although the spatio-temporal availability of both data archives is well-known at the scene level, information on the actual availability of cloud-, snow-, and shade-free observations at the pixel level is lacking and should be explored individually for each study to correctly parametrize subsequent analyses. However, data exploration is time and resource consuming, thus is rarely performed a-priori. Consequently, the spatio-temporal heterogeneity of usable data is often inadequately accounted for in the analysis design, risking ill-advised selection of algorithms and hypotheses, and thus inferior quality of final results. Here we present precomputed data on the daily 1982‑2023 availability of usable Landsat and Sentinel-2 acquisitions across the globe, sampled at a pixel-level in a regular 0.18°-point grid. The dataset comprises separate Landsat- and Sentinel‑2‑specific data records, and is accompanied by a pixel-specific growing season information to facilitate data exploration. The dataset was derived based on freely available 1982-2023 Landsat surface reflectance (Collection 2) and Sentinel-2 top-of-the-atmosphere reflectance (pre‑Collection-1 and Collection-1) scenes from 2015 through 2023, following the methodology developed in the recent study on data availability over Europe [1]. Growing season information was derived based on 2001‑2019 time series of the yearly 500 m MODIS land cover dynamics product (MCD12Q2; Collection 6) [1]. As such, the dataset presents a unique overview of the spatio-temporal availability of usable daily Landsat and Sentinel-2 data at the global scale, hence offering much needed a-priori information aiding identification of appropriate methods and challenges for terrestrial vegetation analyses at local to global scale.

Fragile X syndrome (FXS), the most common monogenic cause of inherited intellectual disability and autism spectrum disorder, is caused by a full mutation (>200 CGG repeats) in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene. Individuals with FXS experience a wide range of challenges related to social interaction (SI). Animal models, such as Drosophila melanogaster model for FXS where the only ortholog of human FMR1 (dFMR1) is mutated, have played a crucial role in understanding of FXS. The aim of this study was to investigate SI in the dFMR1B55 mutants using the novel Drosophila Shallow Chamber and a Python data processing pipeline based on social network analysis (SNA). In comparison with wild-type flies (w1118), SNA analysis in dFMR1B55 mutants revealed hypoactivity, fewer connections in their networks, a lower ability to efficiently transmit information, fewer alternative pathways for information transmission, a higher variability in the number of interactions they achieved, and tended to stay near the boundaries of the testing chamber. These observed alterations indicate the presence of characteristic strain-dependent social networks in dFMR1B55 flies, commonly referred to as the group phenotype. Finally, a combination of novel research tools is a valuable method for SI research in fruit flies.

Abstract: The objective of these studies was to determine the dependence of the distribution of the microwave field into a grain layer and the efficiency of energy transfer from the magnetron into the grain layer, via a waveguide, on the moisture content. The subject of the research was a semicircular waveguide with slot radiators that supply a microwave field to the grain surrounding the waveguide. The change in the directional diagram of the microwave field in the longitudinal direction of the waveguide was studied using CST Microwave Studio 2019 software. It was found that the change in the moisture content of the processed grain leads to a significant change in both the radiation and total efficiency values of the waveguide. For instance, the radiation efficiency of the waveguide decreases by 15.1% (from 68.729 dB to 58.294 dB) when the moisture content of the processed grain increases from 14% to 26%. The total radiation efficiency also decreases (from 11.27 dB to 21.7 dB). In this case, not only the value of the radiation efficiency but also the shape of its dependence on the magnetron radiation frequency change. Data were obtained on the change in the Q-factor for resonators in which grain layers are processed, depending on the variation in grain moisture content.

The Atlantic Canada Together Enhancing Acute Stroke Treatment (ACTEAST) project is a Modified Quality Improvement Collaborative (mQIC) designed to improve ischemic stroke treatment rates and time-based efficiency in Atlantic Canada. This study specifically evaluates the implementation of the mQIC in Nova Scotia using qualitative methods. The mQIC spanned six months and included two Learning Sessions, multiple webinars, and a per-site virtual visit. Each session was followed by an action planning period to guide implementation efforts over the following 2-4 months. The Consolidated Framework for Implementation Research (CFIR) was utilized to develop a pre-tested, semi-structured interview guide, which aimed to uncover barriers and facilitators to mQIC implementation. Interviews were conducted with 14 healthcare professionals, generating 454 references that were coded into 28 CFIR constructs. Notably, 84% of these references were positively framed as facilitators. Despite this, significant barriers were identified, including resource availability, competing priorities, communication challenges, and difficulties engaging key stakeholders. Some barriers were particularly prominent during specific phases of implementation. The study findings offer valuable insights into the implementation of quality improvement initiatives in stroke care. They underscore the importance of recognizing and addressing context-specific barriers, while also leveraging identified facilitators to drive successful implementation and ultimately improve patient outcomes.

This study examined the effects of incorporating 8 weeks of biweekly upper limb loaded plyometric training (elastic band) into the in-season regimen of handball players. Trial participants were assigned between control (n = 15, age: 18.1±0.5 years, body mass: 73.7±13.9 kg) and experimental (n = 14, age: 17.7±0.3 years, body mass: 76.8±10.7 kg) groups. Measures obtained pre- and post- included a cycle ergometer force-velocity test, ball throwing velocity in three types throw, 1-RM bench press and pull-over, and anthropometric estimates of upper limb muscle volumes. Gains in the experimental group relative to controls included absolute muscle power (W) (Δ23.3%; t-test p<0.01; d=0.083), relative muscle power (W.kg-1) (Δ22.3%; t-test p<0.01; d=0.091), and all 3 types of ball throw (Δ18.6%, t-test p<0.01, d=0.097 on jumping shot; Δ18.6%, t-test p<0.01; d=0.101 on 3-step running throw; and Δ19.1%, t-test p<0.01, d=0.072 on standing throw). Furthermore, a significant improvement by time interactions was observed in both groups on 1-RM bench press and pull-over performance. However, upper limb muscle volumes remained unchanged in both groups. We conclude that adding biweekly elastic band plyometric training to standard training improves measures important to game performance. Accordingly, such exercises can usefully be adopted as a part of handball training.

The incidence of cancer in organ transplant patients is higher than in the non-transplanted population. The incidence increases with increasing age. The use of Fludeoxyglucose positron emission tomography/CT (PET/CT) is sensitive and specific to detect PTLD LD compared with conventional CT imaging. We analyzed the medical data of 127 patients aged over 60 years, who underwent heart transplantation in the above period, who have been practicing early CT-PET for diagnostic purposes between February 2007 and October 2018. Of 127 consecutive patients who underwent CT-PET, SUVs up than 4, were found in 84 patients of which 20 were affected by PTLDs, seven patients were affected by chronic non neoplastic inflammatory diseases, the remainder were affected by other neoplasms. The favorable effect of physical activity programs on cardiorespiratory and psychomotor function occur in all patients. In conclusion, CT-PET at the first doubts about the possibility of the development of neoplasms has allowed a diagnosis and then a treatment more quickly. Education in behavioral norms that improve the patient's quality of life is necessary.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

Abstract: We review the development and application of an organic polymer-gel capable of producing fixed, three-dimensional fluorescent images of complex radiation fields. The gel consists for more than 99% of γ-ray-polymerized (~15% conversion) tertiary-butyl acrylate (TBA) containing ~100 ppm of a fluorogenic compound, e.g. maleimido-pyrene (MPy). The radio-fluorogenic effect depends on copolymerization of the MPy into growing chains of TBA on radiation-induced polymerization. This converts the maleimido residue, which quenches the pyrene fluorescence, into a succinimido moeity (SPy), which doesn't. The intensity of the fluorescence is proportional to the yield of free-radicals formed and hence to the local dose deposited. Because the SPy moieties are built into the cross-linked polymer matrix the image is fixed. The method of preparing the gel and imaging the radiation-induced fluorescence are presented and discussed. The effect is illustrated with fluorescent images of the energy deposited in the gel by beams of X-rays, electrons and protons as well as a radioactive isotope.

The domain of construction engineering and management is evolving rapidly. Optimizing resource positioning is crucial for meeting the challenges of efficiency and productivity. This research presents a novel methodology that incorporates Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies, augmented with a Client Early Warning System (CEWS) and a Decision Support System (DSS). What makes this study unique is that it provides a real-time methodology for resource allocation and project management, tailored to address the complexities of modern construction projects. The application of this integrated technological framework marks a significant leap in construction management, empowering data-driven decision-making and significantly improving project efficiency. Empirically validated through a case study at Helwan University's College of Engineering and substantiated by expert surveys, this research showcases a transformative model for resource management. The findings not only underscore the practical applicability of this approach but also open new horizons for its adoption in global construction practices, offering insights for future research endeavors focused on expanding these methodologies to a wider range of resources.

Understanding the interplay among organophosphorus flame-retardants (OPFRs), microplastics, and freshwater organisms is crucial for unravelling the dynamics within freshwater environments and foreseeing the potential impacts of organic pollutants and plastic contamination. For that purpose, the present research assessed the exposure impact of 10 mg L–1 flame-retardant alumin-ium diethylphosphinate (ALPI), 10 μg mg–1liver microplastics polyurethane (PU), and the combina-tion of ALPI and PU on the freshwater planarian Girardia tigrina. The exposure of G. tigrina to both ALPI and PU revealed a sequential effect, i.e., a decrease in locomotor activity, while other molecular biomarkers (oxidative stress biomarkers and metabolic response) remained unaffected. Despite this fact, it was possible to observe that the range (min-max) of responses of exposed or-ganisms were altered, in the particular case of electron transport system, cholinesterase activity, glutathione S-transferase, catalase and levels of total glutathione and proteins, showing that the energetic costs for detoxification and antioxidant capacity might be causing less energy allocated for the planarian activity. By examining the physiological, behavioural, and ecological responses of planarians to these pollutants, insights can be gained into broader ecosystem-level effects and inform strategies for mitigating environmental risks associated with OPFRs and microplastic pol-lution in freshwater environments.

Atherosclerosis, a complex metabolic-immune disease characterized by a chronic inflammation driven by the buildup of lipid-rich plaques within arterial walls, has emerged as a pivotal factor in the intricate interplay between cancer and cardiovascular disease. This bidirectional relationship, marked by shared risk factors and pathophysiological mechanisms, underscores the need for a comprehensive understanding of how these two formidable health challenges intersect and influence each other. Cancer and its treatments can contribute to the progression of atherosclerosis while atherosclerosis with its inflammatory microenvironment can exert profound effects on cancer development and outcomes. Both cancer and cardiovascular disease involve intricate interactions between general and personal exposomes. In this review we want to summarize the state-of-the-art of translational data and try to show how oncologic studies on cardiotoxicity can broaden our knowledge of crucial pathways in cardiovascular biology with a positive impact on precision cardiology and cardioncology.

Background: The hub-and-spoke model (HSM), offers a framework for efficient healthcare service delivery. This scoping review seeks to establish the implementation and effectiveness of the HSM in Antimicrobial Stewardship (AMS) programs. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) reporting guideline was followed. A systematic search was conducted in four electronic databases (PubMed, Medline, Cochrane library and Google scholar) from inception until January 2024. Studies where the HSM was used for delivering any AMS activity, intervention or action, were included. No study specific filters were applied and all populations, study designs and health settings were included. Data screening and selection was achieved using Rayyan. Three authors independently screened studies, with conflicts resolved by a fourth author. Data were narratively synthesized. Standard appraisal tools were impractical; however, critically evaluation of data collection and outcome reporting was ensured. Results: Out of 1,438 articles, three were included in the scoping review. The primary interventions utilising the HSM in AMS involved reducing antibiotic misuse, training healthcare professionals, case-based learning, establishing AMS programs, developing antibiograms, and formulating policies or guidelines pertinent to AMS. The studies demonstrated significant clinical improvements in AMS. Clinical outcomes from the studies include a significant reduction in antimicrobial usage and improved antibiotic management, with a notable decrease in days on antimicrobial therapy and increased antibiotic de-escalation. Key facilitators for AMS program success: tailored education, collaborative learning, strong leadership, strategic practices, and data-driven decisions. Key barriers: leadership challenges, change resistance, knowledge gaps, inadequate data systems, resource limitations, and technological constraints. Conclusion: The review identified a literature gap in HSM use for AMS programs. Further studies are needed to assess HSM's effectiveness, feasibility, and cost-effectiveness in AMS contexts.

Whether the universe is a computer simulation, or whether we wish to efficiently model our universe in a computer simulation, there would be benefits to modeling it in a fashion analogous to computer spreadsheet, each lattice cell can be conceived as containing all the mathematical formula necessary to continuously compute its state relative to changes in all its neighboring cells, and by progression, in relation to all the cells of entire space-time lattice. Alternatively, the “real” universe may itself be built on a space cell lattice, an irregular foam of space cells, in which each cell may be conceived as a multidimensional cell of distortable space, the shape of which fully describes (a) the four basic forces (gravity, electromagnetic, strong, weak) observed at that cell of space, and (b) the probability (or weight distribution) of any quantum states overlapping the cell and its neighbors. At an appropriate scale, it would appear that this conceptual model would resolve apparent conflicts between general relativity and quantum physics. It would also provide a new interpretation of Planck’s constant as description of the number of space cell events associated with any set of observable events. If formulae operating at a lattice cell level can be improve our ability to understand and model larger scale phenomena, this would be strong evidence in favor of the theory that mathematics is not just a human invention but rather an inherent feature of space-time itself.

Background: Routine childhood vaccination, e.g., for diphtheria-tetanus-pertussis (DTP), might provide an additional protection towards SARS-CoV-2 infection. This concept of heterologous immunity was explored in healthy children receiving both DTP and inactivated SARS-CoV-2 vaccines. Methods: A cross-sectional study was performed on 154 healthy children aged 6–8 years old in Jakarta, Indonesia. Their vaccination status of DTP (including diphtheria-tetanus booster vaccine at 5 years old) and CoronaVac (from 6 years old) were recorded. Peripheral blood samples were collected from all participants, in which anti-diphtheria toxoid IgG, anti-SARS-CoV-2 S-RBD antibodies and T cell-derived IFN-g were measured. Results: Study participants with complete DTP vaccination had significantly higher titer of anti-diphtheria toxoid IgG than the ones without (median=0.9349 versus 0.2113 IU/mL; p<0.0001). Upon stratification based on DTP and CoronaVac vaccination statuses, participants with complete DTP and CoronaVac vaccinations had the highest titer of anti-SARS-CoV-2 S-RBD antibodies (median=1,196 U/mL) and the highest concentration of SARS-CoV-2-specific T cell-derived IFN-g (median=560.9 mIU/mL) among all groups. Conclusion: Healthy children aged 6-8 years old with complete DTP and CoronaVac vaccinations exhibited higher SARS-CoV-2-specific adaptive immune responses, particularly on their T-cell response. This suggests an additional benefit of routine childhood vaccination to generate protection against novel pathogens, presumably via heterologous immunity.

High-Level Synthesis (HLS) tools have revolutionized FPGA application development by providing a more efficient and streamlined approach, significantly impacting digital design methodologies. Despite the capability of FPGAs to customize numerical representations in data paths, most HLS projects have focused on fixed-point precision, while floating-point representations remain limited to vendor-provided single, double, and half-precision formats. This paper proposes a customized floating-point library compatible with HLS to address these limitations. This library allows programmers to define the number of exponent and mantissa bits at compile time, providing greater flexibility and enabling the use of mixed precision. Moreover, this library includes optimized implementations of common components such as vector summation (VSUM), dot-product (DP), and matrix-vector multiplication (MVM). Results demonstrate that the proposed library reduces latency and resource utilization compared to vendor IP blocks, particularly in VSUM, DP, and MVM operations. For example, the MVM operation involving a 32x32 matrix, using vendor IP requires 22 clock cycles, whereas CuFP completes the same task in just 7 clock cycles, using approximately 60% fewer DSPs, 10% fewer LUTs, and 60% fewer FFs.

Distributed tracing is a cutting-edge technology for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and direct storage and utilization of every trace are impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as simplifying application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of the sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

Broadcasting a message from one to many processors in a network corresponds to concurrent reading on a random access shared memory parallel machine. Computing the trees of a forest, the level of each node in its tree and the path between two nodes are problems that can easily be solved with concurrent reading in a time logarithmic in the maximum height of a tree. Solving such problems with exclusive reading requires a time logarithmic in the number of nodes, implying message passing between disjoint pairs of processors on a distributed system. Allowing concurrent reading in parallel algorithm design for distributed computing might be advantageous in practice if these problems are faced on shallow trees with some specific constraints. We show an application to LZC-compressed file decoding, whose parallelization employs these computations on such trees for realistic data. On the other hand, zipped files do not have such advantage since they are compressed by the Lempel-Ziv sliding window technique.

This paper is a further development of our “Proposal of a new double-nozzle technique for in-gas-jet laser resonance ionization spectroscopy” published in journal Atoms earlier in this year. Here, we propose to equip the proposed double-nozzle technique with the RF-only funnel and RF-buncher placed in the gas-jet chamber at 70 mm distance downstream the double-nozzle exit. It allows for highly effective extraction into vacuum heavy ion beams, produced in two-steps laser resonance ionization in the argon supersonic jet. We explored the operation of this new full version of the double-nozzle technique through detailed gas dynamic and then Monte Carlo trajectory simulations, which results are presented and discussed. In particularly, we have shown by calculations that more than 80% of all nobelium-254 neutral atoms, extracted by argon flow from the gas stopping cell through the double-nozzle into the supersonic jet can be extracted into vacuum in a form of pulsed ion beam having low transverse and longitudinal emittance.s dynamic and Monte Carlo simulations

This paper is concerned about the novel exact solitons to the truncated M-fractional (1+1)-dimensional non-linear generalized Bretherton model with arbitrary constants . This model is used to explain the resonant nonlinear interaction between the waves in different phenomenon, including fluid dynamics, plasma physics, ocean waves, and many others. A series of exact solitons, including bright, dark, periodic, singular, singular-bright, singular-dark, and other solitons are obtained by applying the extended sinh-Gordon equation expansion (EShGEE), and the modified (G′/G2)-expansion tech- niques. A novel definition of Fractional derivative provides the solutions distinct from the present solutions. Mathematica software is used to obtain, and verify the solutions. The solutions are shown through 2-D, 3-D, and density plots. The stability process is performed to verify that the solutions are exact and accurate. The modulation instability is used to determine the steady-state stable results to the corresponding equation.

This article summarizes the results of a research conducted to create an intelligent platform supporting small farmers growing vegetables. In Bulgaria, many farmers use relatively small areas for their production. Considering this situation, the platform is designed for regional use, trying to make maximum use of the specific climatic features of the monitored microdistrict. Consequently, special attention is paid to the possibilities of adapting the platform to the specific environment in which the observed plants grow. Thus, the platform is implemented as an agent-oriented system providing needed flexibility. The core of the platform is a personal assistant, which is responsible for tracking the vegetation cycle of the monitored crop and, upon detection of anomalies, preparing warnings to the farmer. The general architecture of the platform is also presented. In a distributed knowledge base, agriculture-specific knowledge are stored. Furthermore, a standardized interface for receiving data from the sensor networks located in the production areas is briefly described. Currently, the platform is being tested for monitoring four varieties of tomatoes grown on the territory of the national research center for potato breeding. Special attention is paid to the measurements carried out. Conclusions have been drawn that will help improve the platform.

We developed a protein to rapidly and accurately diagnose Chagas disease, a life-threatening illness identified by the WHO as a critical worldwide public health risk. Complicating the current health situation are serological test limitations that contribute to most infected persons being unaware of their condition and therefore untreated. To improve diagnostic testing, we developed an immunological mimic of the etiological agent, Trypanosoma cruzi, by combining ten pathogen specific epitopes within the beta-barrel protein structure of Thermal Green Protein. The resulting multi-epitope protein, DxCruziV3, displayed high specificity and sensitivity as the antibody capture reagent in an ELISA platform with an analytical sensitivity that exceeds WHO recommendations. Within an immunochromatographic platform, DxCruziV3 showed excellent performance for the point of application diagnosis in a region endemic for multiple diseases, the municipality of Barcelos in the state of Amazonas, Brazil. In total, 167 individuals were rapidly tested using whole blood from a finger stick. As recommended by the Brazilian Ministry of Health, venous blood samples were laboratory tested by conventional assays for comparison. Test results suggest utilizing DxCruziV3 in different assay platforms can confidently diagnosis chronic infections by T. cruzi. Rapid and more accurate results will benefit everyone but will have the most noticeable impact in resource-limited rural areas where the disease is endemic.

In this paper, we present a rigorous proof demonstrating the stabilization of percolation probability in supercritical regimes for percolation models. We analyze a sequence of expanding compact balls in ℝ

Abstract Sodium glucose cotransporter type 2 inhibitors are a new class of drugs acting on cardiovascular system, renal and metabolism in a multiple way. Indeed, even their principal action is on the transporter of sodium and glucose in the convoluted distal tubule, they have multiple actions as antifibrotic and endothelial protective action. Their principal mechanism consist in the loss of sodium and glucose. Therefore, they have effect on blood pressure and glucose metabolism. In a first period their use has been in the diabetic general population, later on some studies documented their activity in non-diabetic general population, in heart failure in chronic kidney disease. Only in recent years several small studies documented their efficacy in diabetic and non-diabetic kidney transplant patients, larger studies are few and very recent and open new ways for these drugs

The use of substances to improve sports performance is a widespread phenomenon among adolescents. Several anti-doping programs have been developed, mainly based on knowledge-based evidence. The main aim of the present study was to implement an anti-doping intervention in sports high school students, based on psychological framework, such as Socio-Cognitive Theory, through the development of a Serious Game (SG), i.e., a digital learning based on the game. The experimental design included an intervention group (n=167; F=37.7%; Meanage=17.5 years) and a control group (n=112; F=42%; Meanage=17.6). Both of groups completed the same questionnaire in two different occasions (i.e., time 1 and time 2), for measuring doping intention, self-regulatory efficacy to resist social pressure for the use of substances, moral disengagement, and doping knowledge. Data were analyzed through repeated measures of Group X Time ANOVA, demonstrating some degree of efficacy of the intervention, in particular, in terms of decrease of doping intention and the strengthening of doping knowledge. Moreover, we demonstrated that the score obtained during the implementation of the Serious Game partially could represent a coherent measure of the participants’ beliefs regarding doping. These results could be considered a starting point for future research to better develop technological anti-doping interventions.

This study presents the development and evaluation of a Multi-Task Long Short-Term Memory (LSTM) model with an Attention Mechanism designed to predict students' academic performance. The model concurrently addresses two tasks: predicting overall performance (total score) as a regression task and categorizing performance levels (remarks) as a classification task. By processing both tasks simultaneously, the model optimizes computational efficiency and resource use. The dataset includes detailed student performance records across various metrics such as Continuous Assessment, Practical Skills, Demeanor, Presentation Quality, Attendance, and Participation. The model's performance was evaluated using comprehensive metrics. For the regression task, it achieved a Mean Absolute Error (MAE) of 0.0249, Mean Squared Error (MSE) of 0.0012, and Root Mean Squared Error (RMSE) of 0.0346. For the classification task, it attained perfect scores with an accuracy, precision, recall, and F1 score of 1.0. These results highlight the model's high accuracy and robustness in predicting both continuous and categorical outcomes. The Attention Mechanism enhances the model's capabilities by identifying and focusing on the most relevant features. This study demonstrates the effectiveness of the Multi-Task LSTM with Attention Mechanism in educational data analysis, offering a reliable tool for predicting student performance and potential broader applications in similar multi-task learning contexts. Future work will explore further enhancements and wider applications to improve predictive accuracy and efficiency.

The application of a commonly used computer algorithm based on the group-additivity method for the calculation of the liquid viscosity coefficient at 292.15K and the activity coefficient at infinite dilution in water at 298.15K of organic molecules is presented. The method is based on the complete breakdown of the molecules into their constituting atoms, further subdividing them by their immediate neighbourhood. A fast Gauss-Seidel fitting method using experimental data from literature is applied for the calculation of the atom groups’ contributions. Plausibility tests have been carried out on each of the calculations using a 10-fold cross-validation procedure which confirms the excellent predictive quality of the method. The goodness of fit (Q²) and the standard deviation (σ) of the cross-validation calculations for the viscosity coefficient, expressed as log(η), was 0.9728 and 0.11, respectively, for 413 test molecules, and for the activity coefficient log(γ)^∞ the corresponding values were 0.9736 and 0.31, respectively, for 621 test compounds. The present approach has proven its versatility in that it enabled at once the evaluation of the liquid viscosity of normal organic compounds as well as of ionic liquids.

In recent years, the digital economy has played an increasingly important role in enhancing productivity, fostering growth, and facilitating the emergence of digital banking. With the widespread adoption of digital financial services for financial inclusion, the banking sector is experiencing a demand for digital transformation. It is important to note that despite the many advantages of this change, it also disrupts the familiar banking experience that customers have grown to expect, as well as introducing potential risks to maintaining customer protection at previous levels. Many critics have raised concerns regarding customer protection and potential exposure to various risks that may arise from digital financial services, which may result in both financial and non-financial losses. These risks can impact customer retention as dissatisfied customers may switch to alternative service providers. Additionally, reputation can be affected since merely offering digital financial services may not sufficiently safeguard customers. To remain competitive, financial institutions must deliver a secure experience that aligns with customer expectations while considering economic factors and societal needs. This is why it is essential to comprehend the severity of the risk factors that influence the protection of customers in the realm of digitalized banking services and products. In this study, five factors have been identified that influence customer protection when using digital financial services in Pakistan. Based on the analysis conducted using SmartPLS, it appears that all the factors proposed in this study have a significant and positive impact on the protection of customers. In particular, the information provided remains of paramount importance when it comes to determining the level of protection that a customer is entitled to within digital financial services, followed by responsiveness, privacy, authentication, and encryption mechanisms. Due to these findings, the implementation of enhanced information security management principles becomes imperative for the development and progress of the Pakistani digital banking industry.

Chestnut is an interesting productive alternative for farmers in Southern Chile. However, cli-matic variability with the occurrence of late spring and early summer frost have cause damages on young trees. Glycine‒betaine (GB) spraying on shoots of sensitive chestnut induce an increase in tolerance during subsequent chilling stress. Experiment was performed on three years-old trees (cultivar Cittá di Castello) conducted in a leader training system and sprayed with com-mercial GB (120 g hL‒1). In summer trees treated with GB showed a better recovery after the frost (‒2.34°C) occurrence for 3.5 h compared to the control. Whereas trees obtained a 28% greater growth of central axis than untreated control (January). The trunk diameter measured in January showed the highest value under GB (27.2mm) compared with the control (25.7 mm). The GB ap-plication increased by 50 and 47% highest fresh and dry weight respectively in leaves. Under GB trees reached a 28% higher net photosynthesis (AN) compared to the AN in control (11.93 µmol CO2 m‒2 s‒1). Moreover, achieved higher stomatal conductance and transpiration rate was 44 and 31% with GB compared to control trees respectively. Antioxidant performance was differentially affected by GB activating non-enzymatic mechanisms and caused a reduced lipid peroxidation. Glycine‒Betaine has become as an interesting agronomic tool that aimed to increase tolerance to environmental stresses such as low temperatures that cause damage to herbaceous shoots and leaves in young chestnut trees.

Background: The Fat-Augmented Latissimus Dorsi (FALD) flap is an autologous flap that combines Latissimus Dorsi (LD) flap with immediate autologous fat transfer (AFT) in order to improve breast reconstruction (BR) volume. In recent years, our team has described the ergonomic FALD flap, an evolution of this technique which helps to achieve a complete BR in a single surgical step. In this long-term study, we analyze morphological variations of the breast after ergonomic FALD flap reconstruction compared to the traditional transverse FALD flap technique. Methods: Between December 2020 and September 2022 we prospectively enroll patients undergoing BR using FALD flap into 2 groups: Group A included ergonomic FALD flap, while Group B included traditional FALD flap. The primary endpoint of the study was to compare the two groups in terms of breast projection (BP), breast width (BW) and breast height (BH), while the second endpoint concerned the analysis of the aesthetic outcomes. Results: 32 FALD flaps (23 patients) were performed for the Group-A and 31 FALD flaps (25 patients) for Group-B. There were no significant differences regarding demographic variables. After propensity score weighting analysis, Group-A showed a statistically significant better breast projection (p<0.0001) compared to Group-B. (6.05 vs 5.60). Final aesthetic analyses showed to be superior in Group-A concerning breast shape (p=0.001) and global score evaluation (p=0.004). Conclusion: The ergonomic FALD flap offers better long-term aesthetic outcomes for autologous BR with higher breast projection compared to the traditional transverse FALD flap.

Mitral valve disease is a common heart disease in dogs. The aim of this study was to describe the cases of dogs who underwent mitral valve repair using a v-clamp device and to provide their long-term follow-up data on cardiac function after mitral valve repair. Four dogs with mitral valve regurgitation who experienced coughing and dyspnea underwent surgical mitral valve repair between December 2023 and June 2024. The patients were evaluated by transthoracic and transesophageal echocardiography. Echocardiography revealed mitral valve leaflet regurgitation. Mitral valve repair was performed under general anesthesia using a v-clamp device introduced through an introducer wire guide. Echocardiography was conducted at baseline and the 6-month follow-up. Blood analysis results after surgical repair were normal. Follow-up echocardiography revealed no complications related to the procedure, with all dogs demonstrating improved respiratory signs and quality of life after repair. No adverse reactions were reported after surgery. A v-clamp device was used to repair mitral valve regurgitation in four dogs. Mitral valve repair using a v-clamp device is another treatment option for mitral valve disease in dogs.

This study presents, for the first time, the development of fibers with favorable properties for biodegradable wound dressings made from the Ecovio® (EC) polymer blend, composed of poly(lactic acid) (PLA) and poly(butylene adipate-co-terephthalate) (PBAT), incorporated with tea tree essential oil (TTE). TTE presented antimicrobial activity against Pseudomonas aeruginosa and Staphylococcus aureus, achieving minimal inhibitory concentrations of 15 and 7.5 mg/mL, respectively. The TTE was mixed with EC in a binary chloroform and formic acid (85/15 v/v) mixture, leading to homogeneous and wettable fibers by electrospinning. The EC/TTE fibers were characterized confirming the TTE in the fibers. The tests showed that TTE (0.5, 1.0 or 1.5 mL) improved the polymer blend electrospinnability. TTE (1.5 mL or 75 w% concerning the EC) lead to homogeneous fibers with an average diameter of 278 nm. TTE (75 w%) increased the wettability of the EC fibers from 120±2° to 69±1°. Preliminary bacterial adhesion and proliferation assays demonstrated that the EC/TTE fibers have anti-adhesive activity e seems to be more toxic against P. aeruginosa compared to S. aureus after 24 hours of incubation. Compatibility tests with human blood indicated that the EC/TTE fibers accelerate blood coagulation. The EC/TTE fibers exhibit promising chemical and biological properties (in vitro) for the development of wound dressings.

: Rasmussen’s encephalitis (RE) stands as a rare neurological disorder marked by progressive cerebral hemiatrophy and epilepsy resistant to medical treatment. Despite extensive study, the primary cause of RE remains elusive, while its histopathological features encompass cortical inflammation, neuronal degeneration, and gliosis. The underlying molecular mechanisms driving disease progression remain largely unexplored. In this case study, we present a patient with RE who underwent hemispherectomy and has remained seizure-free for over six months, experiencing gradual motor improvement. Further-more, we conducted molecular analysis on the excised brain tissue, unveiling a decrease in the expres-sion of cell cycle-associated genes coupled with elevated levels of BDNF and TNF-α proteins. These findings suggest potential involvement of cell cycle regulators in the progression of RE.

Human microbiome data from genomic sequencing technologies is fast accumulating, giving us insights into bacterial taxa that contribute to health and disease. Predictive modeling of such microbiota count data for classification of human infection from parasitic worms such as helminths can help in detection and management across global populations. Real-world datasets of microbiome experiments are typically sparse containing hundreds of measurements for bacterial species, of which only a few are detected in the bio-specimens that are analyzed. This feature of microbiome data produces the challenge of needing more observations for accurate predictive modeling and has been dealt with previously using different methods of feature reduction. To our knowledge, integrative methods such as transfer learning has not been explored in the microbiome domain yet, as a way to deal with data sparsity by incorporating knowledge of different but related datasets. One way of incorporating this knowledge is by using a meaningful mapping among features of these datasets. In this paper, we claim that this mapping would exist among members of each individual cluster grouped based on phylogenetic dependency among taxa and their association to the phenotype. We validate our claim by showing that models incorporating associations in such a grouped feature space result in no performance deterioration for the given classification task. In this paper, we test our hypothesis using classification models that detect helminth infection in microbiota of human fecal samples obtained from Indonesia and Liberia countries. In our experiments, we first learn binary classifiers for helminth infection detection by using Naive Bayes, Support Vector Machines, Multilayer Perceptrons, and Random Forest methods. In the next step, we add taxonomic modeling using the SMART-scan module to group the data, and learn classifiers using the same four methods, to test the validity of the achieved groupings. We observed a 6% to 23% and 7% to 26% performance improvement based on the Area Under ROC Curve (AUC) and Balanced Accuracy(Bacc) measures, respectively, over 10 runs of 10-fold cross-validation. These results show that using phylogenetic dependency for grouping our microbiota data actually results in a noticeable improvement in classification performance for helminth infection detection. These promising results from this feasibility study demonstrate that methods such as SMART-scan can be utilized in the future for knowledge transfer from different but related microbiome datasets by phylogenetically-related functional mapping, to enable novel integrative biomarker discovery.

In this article, we introduce a new approach to human movement by defining the movement as a static object or a super object in one two-dimensional image. This method can allow researchers to label and describe the total movement as an object isolated from a reference video. This ap-proach allows us to perform various tasks, including finding similar movements in a video, measuring, and comparing movements, generating new similar movements, and defining chore-ography by controlling specific parameters in the human body skeleton. As a result of the pre-sented approach, we can eliminate the need to label images manually, disregard the problem of finding the beginning and the end of a movement, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one il-lustrates how to verify and score a requested movement. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supply-ing sufficient training data for deep learning applications (DL). A Variational Auto Encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate two use cases. These use cases demonstrated the versatility of our innovative concept in measuring, categorizing, inferring hu-man behavior, and generating gestures for other researchers.

Traumatic Brain Injury (TBI) is one of the leading causes of death and disability worldwide. Patient treatment and management pose a challenge for healthcare systems. The prognosis of these patients may be influenced by factors such as sex. This study aims to describe the clinical parameters of patients hospitalized in the Intensive Care Unit (ICU) of a Spanish hospital due to Trauma Brain Injury and to establish differences based on sex. Methodology: An observational, cross-sectional, and analytical study was conducted from March 2015 to December 2019. All patients admitted to the ICU during this period who presented isolated significant Traumatic Brain Injury, defined as an abbreviated injury scale (AIS) ≥ 3, were included. Data on baseline characteristics, mechanism of injury, substance use, and acute management in the prehospital and hospital stages (including the need for hospitalization) were recorded. A descriptive and in-ferential analysis was conducted using the chi-square test. Results: A total of 950 patients were ultimately included. The majority of the patients were men (723) with a mean age of 45.16 (± 17.55). There were statistically significant differences in terms of the mechanism of injury (p

Introduction: Glomerulopathy is a term used to describe a broad spectrum of renal diseases, character-ized by dysfunction of glomerular filtration barrier, especially of podocytes. Several podocyte-associated proteins have been found and proved their usefulness as urine markers of podocyte dysfunction. Two of them are nephrin (NEP) and prodocalyxin (PDC). Purpose: This study aims to evaluate the association of podocyte damage, as it is demonstrated via the concentrations of urinary proteins, with clinical and histological data from patients with several types of glomerulonephritis. Methods: We measured urine levels of two podocyte-specific markers, NEP and PDC (corrected for urine creatinine levels), in patients with a wide range of glomerulopathies. Serum and urine parameters as well as histological parameters from renal biopsy were recorded. Results: In total, data from 37 patients with glomerulonephritis and 5 healthy controls were analyzed. PDC and NEP concentrations correlated between them and with serum creatinine levels (p=0.001 and p=0.013 respectively), and with histological lesions associated with chronicity index of renal cortex, such as severe interstitial fibrosis, severe tubular atrophy and hyalinosis (for PDC/NEP, all p3gr/24h with diffuse fusion of podocyte foot processes (p=0.016) was identified. Conclusions: Podocalyxin and nephrin concentrations in urine represent markers of podocyte dysfunction and in our study they were associated both with serum creatinine and histological chronicity indices.

The prevalence of HIV infections in the province of Papua is rising. The study aimed to examine the effect of a sexual reproductive health-based HIV prevention program on knowledge, subjective norms, and attitudes, as well as the behavior control of reproductive health and HIV prevention. A quasi-experimental study was applied in this study with an equivalent control group. The total of 289 students is divided into the experimental group (n = 134) and the control group (n = 155). The experimental group received the sexual reproductive health-based HIV prevention program. The control group received the routine program from the school. The results found a significant difference in knowledge, subjective norms and attitudes, and behavior control of reproductive health and HIV prevention among intervention groups before and after receiving the Sexual Reproductive Health School-Based HIV Prevention program (p-value

Through a partnership with three Federally Qualified Health Centers (FQHCs), the local health department in Los Angeles County implemented a produce prescription program (PPR) to increase fresh fruit and vegetable purchases among patients with diabetes or prediabetes. The PPR, which began in 2020, provided eligible patients with a $40 voucher every month for 6 months to promote the purchase of fresh produce at large chain grocery stores. This qualitative assessment describes staff experiences with the PPR, documenting the facilitators and barriers they encountered while implementing the program. Fifteen clinic staff (i.e., PPR implementers) were interviewed for this assessment. Thematic analysis and coding were conducted using the ATLAS.ti software; the coding was carried out by two separate coders. Interviewees discussed the importance of having preexisting partnerships and programs to support the PPR at their clinic site. Hidden costs related to implementing the program included a large and unexpected amount of staff time devoted to enrolling patients into the program. Collecting quality data and having limited expertise to rigorously evaluate the program were other challenges. Because patients often share their food with their family, the monthly $40 incentive was generally not enough to support their needs; interviewees suggested giving a higher inventive amount to those with a larger household. Future PPR efforts and similar food incentive programs should consider these and other facilitators and barriers to implementation and sustainability, especially when making adjustments to these programs to improve services and access to food resources.

Children under two years old are at risk for anemia because young children have an increased need for iron for their physical growth and brain development. The purpose of this study was designed to evaluate the effects of a caregiver potential support program on anemia prevention in children six months to two years old attending the subdistrict health-promoting hospital in Thasala District, Nakhon Si Thammarat Province. This study was quasi-experimental. The sample included children aged six months to two years old and their caregivers, who were selected by random sampling and allocated to either the experimental or control group, with 40 pairs per group. The experimental group received a potential support program, while the control group received regular care. Both groups were followed for 12 weeks. The instruments used were the potential caregiver assessment, children’s anemia assessment, and a program to support the potential of primary caregivers. Descriptive statistics, chi-square, and t-tests were used to analyze the data. The results revealed that 11.4% of the children had anemia, and a hematocrit count of less than 33% (range = 30-40, M = 34.89, SD = 1.97). The mean scores of knowledge about anemia and iron supplementation after using the program in the experimental group and control group were significantly different (p < 0.001). The mean scores of knowledge about anemia in the experimental and control group were 15.75, SD = 0.54, and 13.28, SD = 1.43 respectively. The mean scores of knowledge about iron supplementation in the experimental and control group were 10.75, SD = 0.49, and 8.15, SD = 1.54 respectively. It was found that the experimental group had a higher mean score on food care behaviors than the control group for 6-11 months and 1-2 years, with statistical significance. The experimental group also had a higher mean score of care behaviors concerning children’s consumption of medicine than the control group (M = 58.20, SD = 4.05; M = 45.78, SD = 9.66, respectively), (p < 0.001). The mean score of the hematocrit level for the experimental group was found to be higher than the control group after receiving the program (M = 35.80, SD = 1.55; M = 34.83, SD = 2.14, respectively), (p < 0.05). Therefore, healthcare providers should support caregivers' capacity to provide continued care for children to prevent anemia.

Foot drop is a common disability in post-stroke patients and represents a challenge for the clinician. To date, Ankle Foot Orthosis (AFO) combined with conventional rehabilitation is the gold standard of rehabilitation management. AFO has a palliative mechanical action without actively restoring the associated neural function. Functional Electrical Stimulation (FES), consisting in stimulation of the peroneal nerve pathway, represents an alternative approach. By providing a FES device (Bioness L-300, BIONESS, USA) for 6 months to a post-stroke 22-year-old woman with a foot drop, our goal was to quantify its potential benefit on walking capacity. Gait parameters and the temporal evolution of the speed were collected with a specific connected sole device (Feet Me®) during the 10-meter walking, the Time Up and Go, and the 6-minute walking tests with AFO, FES or without any device (NO). As a result, the walking speed changes on 10-meters were clinically significant with an increase from baseline to 6 months in AFO and FES conditions (+0.14m-1 and +0.36m-1), without any changes in NO condition. In addition, speed decreased at about 4-minutes of the 6-minute walking test in NO and AFO conditions, while speed increased in FES conditions at baseline and after 1, 3 and 6 months. Monitoring gait speed in an endurance test after an ecological rehabilitation training program helps to examine walking performance in post-stroke patients and to propose a specific rehabilitation program depending on a fatigue threshold.

Smoking during pregnancy remains one of the most significant risk factors for poor birth outcomes. The Loma Linda University Health Comprehensive Tobacco Treatment Program (CTTP) is the largest maternal tobacco cessation program in San Bernardino County – the largest county in the contiguous US. CTTP is an eight week, incentivized, behavioral intervention for tobacco cessation for pregnant smokers. As part of program evaluation, we used a retrospective cohort design to assess smoking abstinence and to identify predictors of relapse/smoking after enrollment. Among CTTP cohort enrollees (n=233) from 2012-2013 we found: 1) a 28.4% 8 week point prevalence abstinence (PPA), and at a median of 9 months of follow-up after the PPA, 81.6% continued to indicate tobacco cessation, and 2) a high rate of relapse or loss to follow-up (56%). Our modeling of relapse/smoking after enrollment indicated that this was higher in young mothers, non-Hispanic mothers (White, Black), first/third trimester mothers, and rural mothers. The evaluation identified efficacy of the behavioral model at 8 weeks, but that relapse/smoking was occurring in disparity populations. Formative research is needed to expand the programmatic outreach and enrollment of mothers wanting to quit smoking.

To investigate the bioactivities of fresh garlic and its processed product, black garlic, we conducted comparative analyses of antioxidant, anti-inflammatory, innate immune activation and anti-cancer activities, in addition to chemical composition (sugar, amino acid, polyphenol contents) of these materials. Simultaneous assay using neutrophil-like cells showed that fresh garlic exhibited antioxidant and innate immunostimulatory activities, whereas black garlic displayed a potent anti-inflammatory effect. The antioxidant activity index was correlated with phenol and flavonoid contents, while the innate immunostimulatory activity was correlated with fructan content. Furthermore, some black garlics with low fructose content were found to inhibit the proliferation of UM-UC-3 cancer cells, while other black garlics rich in fructose increased UM-UC-3 cell proliferation. It was shown that the thermal processing steps of fresh garlic could change the composition of sugars, antioxidants, and amino acids, which have different effects on neutrophil-like cells and UM-UC-3 cells, as well as on bioactivities.

The calculation of the standard enthalpies of vaporization, sublimation and solvation of organic molecules is presented using a common computer algorithm on the basis of a group-additivity method. The same algorithm is also shown to enable the calculation of their entropy of fusion as well as the total phase-change entropy of liquid crystals. The present method is based on the complete break-down of the molecules into their constituting atoms and their immediate neighbourhood; the respective calculations of the contribution of the atomic groups by means of the Gauss-Seidel fitting method is based on experimental data collected from literature. The feasibility of the calculations for each of the mentioned descriptors was verified by means of a 10-fold cross-validation procedure proving the good to high quality of the predicted values for the three mentioned enthalpies and for the entropy of fusion, whereas the predictive quality for the total phase-change entropy of liquid crystals was poor. The goodness of fit (Q²) and the standard deviation (σ) of the cross-validation calculations for the five descriptors was as follows: 0.9641 and 4.56 kJ/mol (N=3386 test molecules) for the enthalpy of vaporization, 0.8657 and 11.39 kJ/mol (N=1791) for the enthalpy of sublimation, 0.9546 and 4.34 kJ/mol (N=373) for the enthalpy of solvation, 0.8727 and 17.93 J/mol/K (N=2637) for the entropy of fusion and 0.5804 and 32.79 J/mol/K (N=2643) for the total phase-change entropy of liquid crystals. The large discrepancy between the results of the two closely related entropies is discussed in detail. Molecules, for which both the standard enthalpies of vaporization and sublimation were calculable, enabled the estimation of their standard enthalpy of fusion by simple subtraction of the former from the latter enthalpy. For 990 of them the experimental enthalpy-of-fusion values are also known, allowing their comparison with predictions, yielding a correlation coefficient R² of 0.6066.

There is increasing interest in identifying biological and imaging markers for the early detection of neurocognitive decline. In addition, non-pharmacological strategies including physical exercise and cognitive interventions may be beneficial for those developing cognitive impairment. The Feuerstein Instrumental Enrichment (FIE) Program is a cognitive intervention based on Structural Cognitive Modifiability and the Mediated Learning Experience (MLE) and aims to promote problem-solving strategies and metacognitive abilities. The FIE program uses a variety of instruments to enhance the cognitive capacity of the individual as a result of mediation. A specific version of the FIE program was developed for the cognitive enhancement of older adults, focusing on strengthening orientation skills, categorization skills, deductive reasoning and memory. We performed a prospective interventional pilot observational study on older subjects with MCI who participated in 30 mediated FIE sessions (two sessions weekly for 15 weeks). Of the 21 subjects who completed the study, there was a significant improvement in memory on the Neurotrax battery comparing pre- and post-intervention scores (pre: M=95.3, SD=12.2, post: M=101.2, SD=7.9, p<.05). Complete sets of anatomical MRI data for voxel-based morphometry, taken at the beginning and the end of the study, were obtained from 16 participants (mean age 83.5 years). Voxel-based morphometry showed an unexpected increase in grey matter (GM) in the anterolateral occipital border and the middle cingulate cortex. These initial findings of our pilot study support the design of randomized trials to evaluate the effect of cognitive training using the FIE Program on brain volumes and cognitive function.

Introduction Pedestrian road injuries are a significant public health issue in Georgia, Syria, and Tajikistan. This study investigates the trends in mortality, Years of Life Lost (YLLs), and incidence rates of pedestrian road injuries from 1990 to 2021, with a focus on sex-specific differences, using age-standardized data from the Global Burden of Disease (GBD) database. Methods The data for this analysis were obtained from the GBD database, emphasizing age-standardized rates of mortality, YLLs, and incidence of pedestrian-related road injuries in Georgia, Syria, and Tajikistan. The population was divided into male and female cohorts for comparison. Statistical methods included descriptive statistics, independent samples t-tests, and effect size calculations to evaluate the differences between sexes. Temporal trends were analyzed to observe changes over the study period. Results Mortality The analysis revealed that males had a significantly higher mean mortality rate (7.18, SD = 2.94) compared to females (2.26, SD = 0.78). The independent samples t-test confirmed a significant difference (t(62) = 9.141, p < 0.001). The effect sizes were substantial, with Cohen's d at 2.285, Hedges' g at 2.257, and Glass's Δ at 6.281. Over time, the mortality rates exhibited peaks in the early 1990s and mid-2000s, with a general decline afterward. Males consistently showed higher mortality rates than females throughout the period. YLLs (Years of Life Lost) For YLLs, males had a significantly higher mean rate (332.59, SD = 132.31) compared to females (103.35, SD = 32.57), with the t-test indicating a significant difference (t(62) = 9.515, p < 0.001). Effect sizes supported the significance of this difference, with Cohen's d at 2.379, Hedges' g at 2.350, and Glass's Δ at 7.308. The YLL rates showed notable peaks in the early 1990s and mid-2000s, followed by a decline, with males maintaining higher rates than females. Incidence Regarding incidence, males had a higher mean rate (235.90, SD = 36.09) compared to females (89.53, SD = 15.29). The t-test results (t(62) = 21.125, p < 0.001) and effect sizes (Cohen's d = 5.281, Hedges' g = 5.217, and Glass's Δ = 9.574) confirmed a significant difference between the sexes. Incidence rates demonstrated a significant decline from 1990 to 2000, followed by fluctuations, with males consistently experiencing higher rates than females. Conclusion This study underscores significant sex differences in mortality, YLLs, and incidence rates due to pedestrian road injuries in Georgia from 1990 to 2021, with males consistently showing higher rates. These findings emphasize the need for targeted interventions to address the specific vulnerabilities of male pedestrians in Georgia. The data and analysis were facilitated through collaboration with the Global Burden of Disease Study, whose contributions were invaluable.

During the 2008 financial crisis, central banks (such as the Fed) adopted quantitative easing (QE) policy to stimulate their economies and overcome severe economic and financial recessions. However, apart from stimulating the economy by issuing a substantial amount of currency to purchase long-term bonds and suppress interest rates, QE policy also contributed to a boom in the real estate and construction sectors. Therefore, this study employs Data Envelopment Analysis to measure the business performance (BP) of construction companies and explores the impact of QE policy on the BP of construction companies between 2004 and 2015 using ordinary least squares regression. Also, we examine the moderating role of debt ratio on the relationship. Focused on publicly listed construction companies in Taiwan, this research reveals three encouraging find-ings. Firstly, QE policy indeed enhanced the BP of Taiwanese construction companies. Secondly, the performance improvement of construction companies due to QE policy shows a time-diminishing trend, suggesting the importance of seizing the initial policy benefits of QE implementation. Lastly, construction companies with appropriate financial leverage may exhibit better BP. These findings can provide valuable insights for relevant government entities and de-cision-makers in the industry for policy and investment decisions.

Background: The microbiome of the ocular surface has been characterised, but only limited in-formation is available on a possible silent intraocular microbial colonisation in normal eyes. Therefore, we performed next generation sequencing (NGS) of 16S rDNA genes in aqueous hu-mor. Methods: Aqueous humor was sampled from three patients during cataract surgery. Air swabs, patient’s and healthy donor conjunctival swab served as controls. Following DNA extraction, the V3 and V4 hypervariable regions of the 16S rDNA gene were amplified and sequenced fol-lowed by denoising. The resulting Amplicon Sequence Variants were matched to a subset of the Ribosomal Database Project 16S database. The deduced bacterial community was then statis-tically analysed. Results: DNA content in all samples was low (0-1.49 ng/µl) but sufficient for analysis. Main phyla in all samples were Acinetobacteria (48%), Proteobacteria (26%), Firmicutes (14%), Acidobac-teria (8%) and Bacteroidetes (2%). Patients conjunctival control samples and anterior chamber fluid showed similar patterns of bacterial species containing many waterborne species. Non-disinfected samples showed a different bacterial spectrum than the air swab sample. Conclusions: The data confirm the existence of an ocular surface microbiome. A distinct intraoc-ular microbiome was not discernable from the background, suggesting the absence of an intra-ocular microbiome in normal eyes.

The aging of the global population has increased the prevalence of neurodegenerative conditions. Bacopa monnieri (BM), an herb with active compounds, such as bacosides A and B, betulinic acid, loliolide, asiatic acid, and quercetin, demonstrates the potential for brain health. Limited research has been conducted on the therapeutic applications of BM in neurodegenerative conditions. This systematic review aims to project BM's beneficial role in brain disorders. BM has anti-apoptotic and antioxidant actions and can repair damaged neurons, stimulate kinase activity, restore synaptic function, improve nerve transmission, and increase neuroprotection. The included twenty-two clinical trials demonstrated that BM can reduce Nuclear Factor-κB phosphorylation, improve emotional function, cognitive functions, anhedonia, hyperactivity, sleep routine, depression, attention deficit, learning problems, memory retention, impulsivity, and psychiatric problems. Moreover, BM can reduce the levels of pro-inflammatory biomarkers and oxidative stress. Here, we highlight that BM provides notable therapeutic benefits and can serve as a complementary approach for the care of patients with neurodegenerative conditions associated with brain disorders. This review adds to the growing interest in natural products and their potential therapeutic applications by improving our understanding of the mechanisms underlying cognitive function and neurodegeneration and informing the development of new therapeutic strategies for neurodegenerative diseases.

Cervical cancer is caused by a persistent and high-grade infection, caused by the Human Papilloma Virus (HPV), which, when entering cervical cells, alters their physiology and generates serious lesions. HPV 18 - is among those most involved in carcinogenesis in this region, but there are still no drug treatments that cause cure or total remission of lesions caused by HPV. Knowing that L-Asparaginase is an amidohydrolase, which plays a significant role in the pharmaceutical industry, particularly in the treatment of specific cancers, due to its antitumor properties, some studies have demonstrated its cytotoxic effect against cervical cancer cells. However, the commercial version of this enzyme has side effects, such as hypersensitivity, allergic reactions and silent inactivation due to the formation of antibodies. To mitigate these adverse effects, several alternatives have been explored, including the use of L-asparaginase from other microbiological sources, which is the case with the use of the fungus Aspergillus niger, a high producer of L-asparaginase. The study investigated the influence of the type of fermentation, precipitant, purification, characterization and in vitro cytotoxicity of L-asparaginase. The results revealed that semisolid fermentation produced higher enzymatic activity and protein concentration of A. niger. The characterized enzyme showed excellent stability at pH 9.0, temperature of 50ºC, resistance to surfactants and metallic ions, and an increase in enzymatic activity was also noted with the organic solvent ethanol. Furthermore, it exhibited low cytotoxicity in GM and RAW cells, and significant cytotoxicity in HeLa cells. These findings indicate that L-asparaginase derived from Aspergillus niger may be a promising alternative for pharmaceutical production. Its attributes, including stability, activity and low toxicity in healthy cells, suggest that this modified enzyme could overcome challenges associated with antitumor therapy.

Improving HIV testing efficiency saves financial and material resources for health. We conducted a retrospective secondary data analysis of routinely collected HIV risk screening program data in Uganda, from October to November 2019, to determine the performance characteristics of the adolescent and adult HIV risk screening tool in public health facility settings. A total of 19,854 clients had been screened for HIV testing eligibility and tested for HIV. The overall positivity rate (cluster weighted prevalence of HIV) among those screened was 4.5% (95% CI: 4.1%-4.8%) versus 3.71% (95% CI: 3.06-4.50) among those not screened. The sensitivity and specificity of the risk screening tool were 90.7% (95% CI: 88.4%, 92.7%) and 75.8%, (75.2-76.4) respectively. With screening, the number needed to test to identify one PLHIV was reduced from 27 to 22. Although risk screening would have led to a 24.5% (4,825/19,704) reduction in testing volume, 9.3% (68/732) of PLHIV would have been missed, being misclassified as not eligible for testing. The cost per PLHIV identified fell by 3% from $69 without screening to $66.9 with screening. We recommend the use of scientifically validated HIV risk screening tools, and a need to explore the use of HIV self-testing as a test for tirage.

The aim of this study is to describe a research protocol of a multi-country randomized controlled trial to investigate the effect of a smartphone CBT stress management program on improving depression and other health- and work-related outcomes among hospital nurses in Vietnam and Thailand during the COVID-19 epidemic as a part of the COCONATS project. The study is a two-arm, parallel randomized control trial. Hospital nurses (N = 1,500) will be recruited in Vietnam and Thailand, and those who meet the criteria will be randomly allocated to the intervention or control (treatment as usual) group. A seven-week, seven-module smartphone-based stress management program will be developed to teach CBT skills. The intervention group will participate in the program for 10 weeks. Depression as the primary outcome will be measured using the DASS21 at baseline and in 3- and 6-month follow-up surveys. A mixed model repeated measures analysis will be used to test the intervention effect in the total combined sample, on an intention-to-treat basis. This is the first study to investigate the effectiveness of a self-guided smartphone CBT program on improving depression among hospital nurses using a RCT design in South-East Asian middle-income countries during the COVID-19 pandemic.

The inclusion of various chemical substances in personal care products (PCPs) and cosmetic formulations can be associated with disruption and damage to the nervous system. Microplastics, benzophenones, parabens, phthalates and metals are among the most common chemical substances found in cosmetics that have been shown to induce neurotoxic mechanisms. Although cosmetic neurotoxin exposure is believed to be minimal, different exposure scenarios of cosmetics suggest that these neurotoxins remain a threat. Special attention should be paid to early exposure in the first gestation weeks, when critical processes, like the migration and proliferation of the neural crest derived cells start to form the ENS. Importantly, cosmetic’s neurotoxins can cross the placenta barrier and so affect the future embryo, but are also secreted in the breast milk, so that babies remain exposed for longer periods, even after birth. In this review we explore how neurotoxins contained in cosmetic and PCPs may have a role in the pathogenesis of various neurodevelopment disorders and neurodegenerative diseases, and therefore also in congenital enteric aganglionosis as well as in postnatal motility disorders. Understanding the mechanisms of these chemicals used in cosmetic formulations and their role in neurotoxicity is crucial to determining the safety of use for cosmetic products during pregnancy.

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model's language capability. Additionally, GPT-4o's vision and speech capabilities are tested through image classification and object recognition tasks, as well as accent classification. The multimodal evaluation assesses the model's performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning capabilities, excelling in tasks that require few-shot learning. GPT-4o also provides notable improvements in multimodal tasks compared to its predecessors. However, the model shows variability and faces limitations in handling complex and ambiguous inputs, particularly in audio and vision capabilities. This paper highlights the need for more comprehensive benchmarks and robust evaluation frameworks, encompassing qualitative assessments involving human judgment as well as error analysis. Future work should focus on expanding datasets, investigating prompt-based assessment, and enhancing few-shot learning techniques to test the model's practical applicability and performance in real-world scenarios.

Clostridioides difficile is a Gram-positive pathogen known for its toxin production and spore formation. It is primarily responsible for most cases of antibiotic-associated diarrhea. Bacterial persisters are a small subset of the population that exhibits transient tolerance to bactericidal substances, and they are of significant medical concern due to their association with the emergence of antibiotic resistance and difficult-to-treat chronic or recurrent infections. Vancomycin, the predominant antibiotic utilized in the management of C. difficile infection, is extensively applied in the realm of clinical practice. Previous studies have demonstrated a persister-like phenotype with treatments involving this antibiotic. However, the mechanism in C. difficile remains largely unknown, primarily due to the challenge of isolating this small population at any given time. To better characterize C. difficile persister cells, we present a study that enables the enrichment and characterization of persister cells from bacterial cultures in both exponential and stationary phases. Moreover, we could differentiate between triggered (induced by antibiotics, such as vancomycin) and spontaneous (stochastic) persister cells. Additionally, we observed the involvement of toxin-antitoxin systems and Clp proteases in persister cell formation.

Alzheimer's disease (AD) is a stealthy and progressive neurological disorder that is a leading cause of dementia in the global elderly population, imposing a significant burden on both the elderly and society. Currently, the condition is treated with medications that alleviate symptoms. Nonetheless, these drugs may not consistently produce the desired results and can cause serious side effects. Hence, there is a vigorous pursuit of alternative options to enhance the quality of life for patients. Ginkgo biloba (GB), an herb with historical use in traditional medicine, contains bioactive compounds such as terpenoids (Ginkgolides A, B, and C), polyphenols, organic acids, and flavonoids (quercetin, kaempferol, and isorhamnetin). These compounds are associated with anti-inflammatory, antioxidant, and neuroprotective properties, making them valuable for cognitive health. A systematic search across three databases using specific keywords GB in AD and dementia yielded 1702 documents, leading to the selection of 15 clinical trials for synthesis. In eleven studies, GB extract/EGb 761® was shown to improve cognitive function, neuropsychiatric symptoms, and functional abilities in both dementia types. In four studies, however, there were no significant differences between the GB-treated and placebo groups. Significant improvements were observed in scores obtained from the Mini-Mental State Examination (MMSE), Short Cognitive Performance Test (SKT), and Neuropsychiatric Inventory (NPI). While the majority of synthesized clinical trials show Ginkgo biloba has promising potential in these conditions, more research is needed to determine optimal dosages, effective delivery methods, and appropriate pharmaceutical formulations. Furthermore, a thorough assessment of adverse effects, exploration of long-term use implications, and investigation into potential drug interactions are critical aspects that must be carefully evaluated in future studies.

Civil construction is one of the oldest known activities in humanity, reports indicate that builders of the Roman Empire already sought the reuse of materials. Currently, considering the depletion of the supply of natural resources, the recycling of solid waste from construction and demolition (CDW), not only provides new products, but also offers ecological and economic alternative. In this context, this research seeks new variables for the disposal of waste from construction and demolition having in the composition of artificial finishing stones an odd possibility to be analyzed. In this research was developed and analyzed a new composite from the construction and demolition waste using as a binder an orthophthalic polyester resin. The residue was sieved and separated by granulometry by the simplex centroid method, the best compacted mixture was determined statically by ANOVA and the Tukey test. The residue was characterized by X-ray fluorescence and the resin by infrared spectroscopy by Fourier transform. Artificial rock plates were produced with 85% residue and 15% resin, by mass, by vibro system, compression and vacuum, later were cut for mechanical, physical and chemical tests. Analysis was performed by scanning electron microscopy of fractured compositions as well as grain sizes. The artificial stone with the best results presented density of 2,256 g/cm3, water absorption of 0.69%, apparent porosity of 1.55%; bending strength of 34.74 MPa and compression of 111.96 MPa; good results in the alterability and thermal tests. In this satisfactory scenario, the use of this waste in the composition of artificial rocks is promising because it directly meets the concept of sustainable development; replacing the concept of end-of-life of the linear economy, by new circular flows of reuse, restoration and renovation, in an integrated process the circular economy; as well as the quality of the final product with properties similar to the artificial rocks already marketed.

Chronic kidney disease (CKD) is a progressive condition that affects more than 10% of the world's population. Monitoring urine albumin and creatinine ratio (µACR) has become the gold standard for nephropathy diagnosis and control. The objective of the present study was to develop a simple, accurate, sensitive, and rapid point-of-care test (PoCT) device - MYμALBUMIN that community healthcare professionals can use in resource-limited facilities and by patients in their own homes for screening the risk and monitoring the progress of CKD. Albumin and creatinine concentrations in urine samples were determined using spectrophotometric dye (tetrabromophenol blue)-binding and colorimetric Jaffe’assay, respectively. Urine samples were diluted with distilled water (1:80) and mixed separately with albumin and creatinine reaction mixture. The creatinine reaction was incubated at room temperature (25oC) for 30 minutes before analysis. Optical density (OD) was measured at the wavelengths of 625 nm (albumin) and 515 nm (creatinine). All calibration curves (2-60 mg/L and 0.25-2 mg/dL for albumin and creatinine) yielded linear relationships with correlation coefficients (r2) of &gt; 0.999. Good accuracy (%DMV &lt; + 2.88%) and precision (%CV &lt; 3.0%) were observed from both the intra- or interday assays for the determination of albumin and creatinine by MYµALBUMIN. The limit of quantification (LOQ) of albumin and creatinine in urine samples determined by MYµALBUMIN and laboratory spectrophotometer were 2 mg/L and 0.1 mg/dL, respectively, using 37.5 μL urine samples. The device was well applied with clinical samples from 13 CKD patients. The median (range) of %DMV of the central (hospital) laboratory method (immune-based assay) was 3.18 (-10.11 to 14.12)%, with a high correlation coefficient (r2 &gt;0.99). In conclusion, MYμALBUMIN showed satisfactory test performance in terms of accuracy, reproducibility, sensitivity, and specificity. The device would have significant economic benefits besides obviously improving clinical decision-making processes.

This study addresses the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONF) and Recurrences of Faults (RF). To make the predictions, they used predictive models based on Machine Learning, more specifically on MLP (Multi Layer Perceptron) neural networks. The study revealed that, by analyzing data from past failures and considering factors such as altitude, number of failures on the same street and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes to the field of water infrastructure management by suggesting that the data-driven approach can be applied in other cities or in networks of different types, such as energy or communication networks. The conclusions highlight the importance of data collection and analysis in preventing failures and optimizing water utilities’ resources.

(1) Telemedicine in diabetic retinopathy (RD) screening is effective but does not reach the entire diabetes population. The use of portable cameras and artificial intelligence (AI) can help diabetes screening. (2) Methods: We evaluated the ability of two handheld cameras, one based on a smartphone and the other on a smartscope, to obtain images comparing OCT. Evaluation was done in two stages, the first by two retina specialists and the second using an artificial intelligence algorithm that we developed. (3) Results: The retina specialists report that the smartphone images must mydriasis in all cases compared to 73.05% of the smartscope images and 71.11% of the OCT images. Images were ungradable in 27.98% of the retinographs with the smartphone and 7.98% with the smartscope. The detection of any-DR using the AI algorithm showed that the smartphone obtained lower recall values (0.89) and F1scores (0.89) than the smartscope, with 0.99. The detection of Mild-DR using the smartphone also obtained low results (146 retinographs) compared to the smartscope (218 retinographs). (4) Conclusions: The use of handheld devices together with AI algorithms for reading retinographs can be useful for DR screening, although these devices need to improve the ease image acquisition through small pupils.

Background: Patients with mutations in the monocarboxylate transporter 8 (MCT8) suffer from Allan-Herndon-Dudley syndrome (AHDS), characterized by developmental delay and a highly disabling movement disorder. Despite the potential of thyroid hormone derivatives to overcome the transporter defect, current trials did not achieve patient-oriented therapeutic goals. Objectives: Since most neurological symptoms are related to the dopaminergic system, we investigated the role of dopamine and its metabolites in MCT8 deficiency with regard to pathophysiology and potential therapeutic strategies in an observational cohort study. Methods: We present longitudinal data from the DEEPTYPE registry of ten patients with video-documentation, standardized phenotyping, cerebrospinal fluid analysis, treatment response to levodopa/carbidopa supplementation, and neuroimaging data. To establish a cell-based model for pathophysiological studies, we differentiated healthy human induced pluripotent stem cells (hiPSCs) into dopaminergic neurons. Results: Children presented with signs of parkinsonism in childhood, including hypokinesia, hypomimia, inability to sit or stand, rigidity, dystonia, and autonomic dysfunction along the classification of Leuzzi and colleagues. Cerebrospinal fluid homovanillic acid concentrations were decreased (n=11), suggesting isolated dopamine pathway impairment. Six out of seven patients responded favorably to levodopa/carbidopa supplementation and we did not see any adverse drug reactions. Our cell-based studies showed that hiPSC-derived dopaminergic neurons expressed MCT8 and produced quantifiable levels of biogenic amines. Conclusions: Parkinsonism is part of AHDS's clinical presentation and may be amenable to treatment. The precise impact of MCT8 deficiency on the dopamine metabolism needs to be further elucidated, e.g. by using patient iPSC-derived dopaminergic neurons in future studies.

This comprehensive overview of the historical milestones in cell culture underscores key breakthroughs that have shaped the field over time. It begins with Wilhelm Roux's seminal experiments in the 1880s, followed by the pioneering efforts of Ross Granville Harrison, who initiated groundbreaking experiments that fundamentally shaped the landscape of cell culture in the early 20th century. Carrel's influential contributions, notably the immortalization of chicken heart cells, mark a significant advancement in cell culture techniques. Holtfreter, Moscona, and Leighton introduced innovations such as 3D cell culture, laying the groundwork for further exploration. The advent of induced pluripotent stem cells by Takahashi and Yamanaka in 2006 was revolutionary, enabling the reprogramming of differentiated cells into a pluripotent state. Since then, recent innovations include spheroids, organoids, and organ-on-a-chip technologies, aiming to mimic the structure and function of tissues and organs in vitro, pushing the boundaries of biological modeling and disease understanding. In this review, we overview the history of cell culture shedding light on the main discoveries, pitfalls and hurdles that were overcome during the transition from 2D to 3D cell culture techniques. Finally, we discussed the future directions for cell culture research that may accelerate the development of more effective and personalized treatments.

OpenFOAM is a CFD software widely used in both industry and academia. The exaFOAM project aims at enhancing the HPC scalability of OpenFOAM, while identifying its current bottlenecks and proposing ways to overcome them. For the assessment of the software components and the code profiling during the code development, lightweight but significant benchmarks should be used. The answer was to develop microbenchmarks, with a small memory footprint and short runtime. The name microbenchmark does not mean that they have been prepared to be the smallest possible test cases, as they have been developed to fit in a compute node, which usually has dozens of compute cores. The microbenchmarks cover a broad band of applications: incompressible and compressible flow, combustion, viscoelastic flow and adjoint optimisation. All benchmarks are part of the OpenFOAM HPC Technical Committee repository and are fully accessible. The performance using HPC systems with Intel and AMD processors (x86_64 architecture) and Arm processors (aarch64 architecture) have been benchmarked. For the workloads in this study, the AMD processor seems particularly suited resulting in an overall shorter time-to-solution.

Background and objective: Obesity is a growing global epidemic. The composition of the intestinal microbiota can be influenced by several factors. Studies highlight the role of intestinal bacteria in the pathophysiology of obesity. So, the objective of this study was to investigate whether the use of probiotics, together with healthy lifestyle habits, contributes to weight reduction in obese indi-viduals by analyzing the intestinal microbiota profile. Methods: A prospective study was carried out with 45 adults with obesity. Participants underwent guidance on healthy lifestyle habits, re-ceived a probiotic component containing different microbiological strains and were followed for 60 days. Clinical parameters, body composition, biochemical analysis and intestinal microbiota assessment were performed before and after treatment. After 60 days, it was observed that the bacterial strains present in the probiotic were present in the patients' intestinal microbiota. Par-ticipants also showed improvements in physical activity, sleep quality, anxiety management, as well as changes in some eating habits, such as a reduction in the consumption of processed foods and a significant increase in water intake. Results: A reduction in BMI, fasting glucose, insulin, HOMA-IR, LDL cholesterol and triglycerides was observed, in addition to an increase in HDL cholesterol, improvement in bowel movement frequency and stool consistency. Analysis of the intestinal microbiota revealed an increase in microbial diversity and a better balance between the bacterial phyla Firmicutes and Bacteroidetes. Conclusions: The changes related to improving the composition of the intestinal microbiota, dietary habits, increased physical activity, reduced anxiety, and better sleep quality have significantly contributed to weight loss and improvements in physiological parameters in obese individuals.

There is a growing concern about climate change being a serious threat to insurance business sustainability. Knowing if climate warming is a cause for claims and losses increase, and how this cause-effect relationship will develop in the future are two open major questions. In this article, we answer both questions by particularizing the geographical area of Spain, and a precise risk, hailstorm in crop insurance in the line of business of wine grapes. We measure climate change thanks to the Spanish Actuarial Climate Index (SACI). We use a database containing all the claims caused by hail risk in Spain from 1990 to 2022. With homogenized data, we consider as dependent variables the monthly number of claims, monthly number of loss costs equal to one, and monthly total losses. The independent variable is the monthly Spanish Actuarial Climate Index (SACI). We try to explain the former through the latter using regression and quantile regression models. Our main finding is that climate change as measured by SACI explains these three dependent variables. We also give an estimation of the increase of the monthly total losses Value at Risk, corresponding to a future increase in climate change measured in units of SACI. Spanish crop insurance managers should carefully incorporate these conclusions into their decision-making process to keep this line of business sustainable in the future.

Abstract: The purpose of this study was to understand the role of infection in the origin of chromosomal anomalies linked to neurodevelopmental disorders. In patients with neurodevelopmental disorders, DNA’s from viruses and bacteria including known teratogens were tested against chromosome anomalies known to cause the disorders. Results support a theory that parental infections disrupt elaborate multi-system gene coordination needed for neurodevelopment. Genes essential for neurons, lymphatic drainage, immunity, circulation, angiogenesis, barriers, structure, and chromatin activity were all found close together in polyfunctional clusters that were deleted in neurodevelopmental disorders. These deletions account for immune, circulatory, and structural deficits that accompany neurologic deficits. In deleted clusters, specific and repetitive human DNA matched infections and passed rigorous artifact tests. In some patients, epigenetic driver mutations were found and may be functionally equivalent to deleting a cluster or changing topologic chromatin interactions because they change access to large chromosome segments. In three families, deleted DNA sequences were associated with intellectual deficits and were not included in any database of genomic variants. These sequences were thousands of bp and unequivocally matched foreign DNAs. Analogous homologies were also found in chromosome anomalies of a recurrent neurodevelopmental disorder. Viral and bacterial DNAs that match repetitive or specific human DNA segments are thus proposed to interfere with highly active break repair during meiosis, and sometimes delete polyfunctional clusters, and disable epigenetic drivers. Mis-repaired gametes produce zygotes containing rare chromosome anomalies which cause neurologic disorders and accompanying non-neurologic signs. Neurodevelopmental disorders may be examples of assault on the human genome by foreign DNA with some infections more likely tolerated because they resemble human DNA segments. Further tests of this model await new technology.

Riparian zones represent ecotones between terrestrial and aquatic ecosystems and are of utmost importance to biodiversity and ecosystem functions. Modelling/mapping of these valuable and fragile areas is needed for an improved ecosystem management, based on an accounting of changes and on monitoring of their functioning in time. In Europe, the main legislative driver behind this goal is the European Commission’s Biodiversity Strategy to 2020, on one hand aiming at reducing biodiversity loss, on the other hand enhancing ecosystem services by 2020, and restoring them as far as feasible. A model, based on Earth Observation data, including Digital Elevation Models, hydrological, soil, land cover/land use data, and vegetation indices is employed in a multi-modular and stratified approach, based on fuzzy logic and object based image analysis, to delineate potential, observed and actual riparian zones. The approach is designed in an open modular way, allowing future modifications and repeatability. The results represent a first step of a future monitoring and assessment campaign for European riparian zones and their implications on biodiversity and on ecosystem functions and services. Considering the complexity and the enormous extent of the area, covering 39 European countries, including Turkey, the level of detail is unprecedented. Depending on the accounting modus, 0.95%–1.19% of the study area can be attributed as actual riparian area (considering Strahler’s stream orders 3-8, based on the Copernicus EU-Hydro dataset), corresponding to 55,558–69.128 km². Similarly depending on the accounting approach, the potential riparian zones are accounted for about 3-5 times larger. Land cover/land use in detected riparian areas was mainly of semi-natural characteristics, while the potential riparian areas are predominately covered by agriculture, followed by semi-natural and urban areas.

This study evaluated the epidemiological and genetic susceptibility profile caused by coronavirus 2 (SARS-CoV-2) in children ≤3 years of age with acute gastroenteritis (AGE) or acute respiratory infection (ARI), living in the northwestern region of the Amazon (NWAR). Fecal and saliva sam-ples collected from 202 children (n=404) were subjected to detection of SARS-CoV-2 by RT-qPCR. Polymorphisms in the angiotensin-converting enzyme (ACE rs4646994) and ACE2 G8780A; rs2285666) genes were detected by SYBR GREEN real-time PCR and PCR/Alul digestion, respec-tively. Secretory and Lewis status of histo-blood group antigen (HBGA) was performed using phenotyping/ELISA. Frequencies for the AGE group: SARS-CoV-2; 8.9%; ARI group: SARS-CoV-2; 2.9%. The ACE I/I homozygous polymorphism gene was majority. Males with the ACE2 G8780A SNP were more susceptible to ARI symptoms than AGE (OR = 3.85; 95% CI-1.42-10.38; P 0.007). Most children were secretory FUT2 phenotype (92.6%). Surveillance of diarrhea and respiratory infections in children, along with understanding their causes, are crucial for identifying preven-tion and control strategies and for prioritizing and developing new vaccines. Clinical manifesta-tions of COVID-19 in children ≤3 years of age include AGE.

Background: Early diagnosis of postoperative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. Prognostic models based on various data could help in the early diagnosis of postoperative complications after cardiac surgery. Objectives: The purpose of the study was to determine whether an integrated approach based on clinical data along with metabolites and biomarkers had greater predictive value than the models built on less data. Methods: The study included patients (n=62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass), who were retrospectively divided into two groups depending on the presence (n=26) or absence (n=36) of postoperative complications of all types. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers (interleukin-6, procalcitonin, NT-proBNP, high-sensitivity troponin T) and metabolites (8 aromatic and 2 dicarboxylic acids were detected using gas chromatography-mass spectrometry). Results: Patients before surgery, compared with healthy donors, differed in the concentration of metabolites (p &lt; 0.05), and had concentrations of a number of biomarkers exceeding the corresponding reference values. Multivariate PLS-DA models, predicting the presence or absence of postoperative complications, were built using clinical data (ROC-AUC=0.71), concentrations of metabolites and biomarkers (ROC-AUC=0.60), and the entire data set (ROC-AUC=0.75). For comparison we built univariate models using the EuroScore2 (ROC-AUC=0.59) and SOFA (ROC-AUC=0.76) scales; concentrations of lactate (ROC-AUC=0.71), the dynamic changes of 4-hydroxyphenyllactic acid (ROC-AUC=0.69), and the sum of three sepsis-associated metabolites (ROC-AUC=0.70). The proposed complex model using entire data set was comparable to the SOFA scale, which confirms the expediency of searching for new predictive models based on a variety of factors.

Milk serves as a crucial source of natural bioactive compounds essential for human nutrition and health. The gastrointestinal digestion of milk proteins releases various biologically active peptides that positively impact human health and enhance physiological performance. With the growing production of cheese, Greek yogurt, and other dairy products, the volume of unutilized acid whey is significantly increasing. Acid whey, containing almost all whey proteins and approximately one-quarter of casein, presents a valuable raw material for generating peptides with potential health benefits. These peptides exhibit properties such as antioxidant, antimicrobial, anti-inflammatory, anticarcinogenic, antihypertensive, antithrombotic, opioid, mineral-binding, and growth-stimulating activities, contributing to improved human immunity and the treatment of chronic diseases. Enzymatic hydrolysis of milk proteins has been developed as a method to produce these peptides, facilitating their use in food fortification. To incorporate whey into functional beverages, it undergoes fermentation by lactic acid bacteria with proteolytic activity. To specifically enrich these beverages with peptides exhibiting targeted bioactivities, it is essential to select lactic acid bacteria strains possessing the requisite protease profiles. Additionally, the sensory properties of the final product can be enhanced using starter cultures with diverse metabolic capabilities, even in the absence of added flavoring agents. Given the imperative to develop waste-free technologies and enhance the profitability of the dairy industry, there is a growing need to efficiently utilize and transform acid whey into a broad spectrum of valuable products and food additives. This approach is justified not only by the nutritional benefits of the bioactive components in whey but also by the commercial potential of safe and health-enhancing products.

This study delves into the dynamics of 'Variations' and 'Claims' within construction projects, specifically under the FIDIC-Red Book 1999 framework. The study aims to identify, categorize, and devise mitigation strategies for key types of variations and claims aligning with the FIDIC conditions of contract. The research is drawing on inputs from construction industry professionals including contract administrators and project managers, focusing on the MENA region. This choice is driven by the region's extensive adoption of FIDIC standards and its rapidly growing construction sector. Data collection encompassed a questionnaire distributed to 80 industry experts predominantly through interviews that focused on countries like Saudi Arabia, UAE, Kuwait, and Egypt. These locations were chosen to reflect diverse construction practices and the involvement of international firms. Utilizing SPSS-V.25 for statistical analysis, the study uncovers the most prevalent and impactful causes of variations and claims, highlighting the critical need for managerial intervention. A key feature is the integration of scientometric analysis for a quantitative finding. A significant addition to the methodology is the implementation of a k-means clustering analysis. The survey had high internal consistency with a Cronbach's alpha of 0.97, and respondents reported frequent and significant claims like delayed drawings, ambiguous documents, and client changes. The results showed that effective claims management requires clear communication and balanced contracts, while poor design and contract documentation cause variations and claims. Spearman's correlation showed strong positive relationships between certain claim types and causes, emphasizing the importance of dealing with these factors to reduce claims. Most respondents said the survey could predict and reduce claims.

Gastric cancer has become a serious worldwide health concern, emphasizing the cru-cial importance of early diagnosis measures to improve patient outcomes. While tradi-tional histological image analysis is regarded as the clinical gold standard, it is la-bor-intensive and manual. Recognizing this problem, there has been a rise in interest in using computer-aided diagnostics tools to help pathologists with their diagnostic ef-forts. In particular, deep learning (DL) has emerged as a promising solution in this sector. However, current DL models are still restricted in their ability to extract exten-sive visual characteristics for correct categorization. To tackle this limitation, this study proposes the use of ensemble models, which incorporate the capabilities of sev-eral deep learning architectures and use aggregate knowledge of many models to im-prove classification performance, allowing for more accurate and efficient gastric cancer detection. To see how well these proposed models performed, this study com-pared them to other works, all of which were based on the Gastric Histopathology Sub-size Images Database, a publicly available dataset for gastric cancer. This research demonstrated that the ensemble models achieved high detection accuracy across all sub-databases, with an average accuracy exceeding 99%. Specifically, ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet. For the 80 × 80 pixel sub-database, ResNet34 exhibited an accuracy of approximately 93%, VGGNet achieved 94%, and the ensemble model excelled with 99%. In the 120 × 120 pixel sub-database, the ensemble model showed 99% accuracy, VGGNet 97%, and ResNet50 approximately 97%. For the 160 × 160 pixel sub-database, the ensemble model again achieved 99% accuracy, VGGNet 98%, ResNet50 98%, and EfficientNet 92%, high-lighting the ensemble model's superior performance across all resolutions. Overall, the ensemble model consistently provided an accuracy of 99% across the three sub-pixel categories. These findings showed that ensemble models may successfully detect criti-cal characteristics from smaller patches and achieve high performance. The findings will help pathologists diagnose gastric cancer using histopathological images, leading to earlier identification and higher patient survival rates.

Abstract: The SARS-CoV-2 pandemic has reemphasized the urgent need for broad-spectrum antiviral therapies. We developed a computational workflow using scRNA-Seq data to assess cellular metabolism during viral infection. With this workflow we predicted the capacity of cells to sustain SARS-CoV-2 virion production in patients and found a tissue-wide induction of metabolic pathways that support viral replication. Expanding our analysis to influenza A and dengue viruses, we identified metabolic targets and inhibitors for potential broad-45 spectrum antiviral treatment. These targets were highly enriched for known interaction partners of all analyzed viruses. Indeed, phenformin, an NADH:ubiquinone oxidoreductase inhibitor, suppressed SARS-CoV-2 and dengue virus replication. Atpenin A5, blocking succinate dehydrogenase, inhibited SARS-CoV-2, dengue virus, respiratory syncytial virus, and influenza A with high selectivity indices. In vivo, phenformin showed antiviral activity against SARS-CoV-2 in a Syrian hamster model. Our work establishes host metabolism as druggable for broad-spectrum antiviral strategies, providing invaluable tools for pandemic preparedness.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

The impact of interaction SS with MJO on rainfall along Java Island to East Nusa Tenggara has been studied using daily rainfall observation data from 2140 weather observation stations, which are also equipped with GSMaP, OLR, and ERA5 data. There were 61 SS events found, 17 during active MJO and 44 in inactive MJO. Rainfall increases more in the western study area when the active MJO and more in the eastern study area when the inactive MJO is due to SS. SS reduces rainfall in the Jakarta region, regardless of the activity or inactivity of the MJO. SS causes extreme rainfall to only occur in a small part of certain areas, so it tends to significantly reduce the possibility of extreme rainfall. In the southern part of the IMC, SS predominates over MJO in supporting increased water vapor transport. Large-scale synoptic circulations like the monsoon, SS, and MJO can interact with local land-sea wind circulation to produce spatiotemporal variability in rainfall on Java Island. Rainfall mostly increases in the afternoon and decreases in the morning when SS occurs, whether there is MJO or not. Convective instability analysis indicates that SS increases precipitation, most likely by lowering CIN and raising VIMFC.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

The Simultaneous Localization And Mapping (SLAM) scanner is an easy and portable Light Detection And Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO Scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data are used: the Total Station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons are tested: cloud to cloud, cloud to mesh, mesh to mesh, and edge detection board assessment. However, the results of the assessments confirm that the accuracy of indoor SLAM scanner measurements (5 mm) is greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud to cloud provides the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it is confirmed that SLAM scanners are effective tools for data acquisition.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Background: Porteirinha is endemic for visceral leishmaniasis (VL), with intense disease transmission of the disease. We evaluated the impact of canine euthanasia as a single control measure on the incidence of VL in humans and canines. Methods: A prospective observational cohort study was carried out over four years (1998–2002) in eight of the 12 neighborhoods of the city. The dynamics of canine visceral leishmaniasis (CVL) transmission were evaluated for 2 years, before beginning the screening-culling intervention. The comparative morbidity index (CMI) was used to stratify areas with the greatest risk of CVL, and the spatial distribution of human and canine VL cases was compared using univariate and bivariate K functions. Results: Human cases conglomerated in three neighborhoods. Spatial clusters were detected for CVL in 1998, 2000, and 2001, but not in 1999, when greater spatial dispersion occurred. The screening and culling intervention reduced the number of human VL cases and decreased the incidence of CVL, mainly in neighbor-hoods with a high CMI. Conclusions: The systematic euthanasia of seropositive dogs was shown to be an effective control action of the Program for Control of Visceral Leishmaniasis (PCLV) in Brazil. The fundamental role of domestic dogs in the epidemiological chain of VL was reaffirmed.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.