Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection triggers various events from molecular to tissue level, which in turn is given by the intrinsic characteristics of each patient. Given the molecular diversity characteristic of each cellular phenotype, the possible cytopathic, tissue and clinical effects are difficult to predict, which determines the heterogeneity of COVID-19 symptoms. The purpose of this article is to provide a comprehensive review of the cytopathic effects of SARS-CoV-2 on various cell types, focusing on the development of COVID-19, which in turn may lead, in some patients, to a persistence of symptoms after recovery from the disease, a condition known as long COVID. We describe the molecular mechanisms underlying virus-host interactions, including alterations in protein expression, intracellular signaling pathways, and immune responses. In particular, the article highlights the potential impact of these cytopathies on cellular function and clinical outcomes, such as immune dysregulation, neuropsychiatric disorders, and organ damage. The article concludes by discussing future directions for research and implications for the management and treatment of COVID-19 and Long-COVID.

Type 2 diabetes mellitus (T2DM) and late-onset Alzheimer’s disease-dementia (LOAD) are increasing in global prevalence and current predictions indicate they will only increase over the coming decades. These increases may be a result of the concurrent increases of obesity and aging. T2DM is associated with cognitive impairments associated with metabolic factors and increases the cellular vulnerability to develop the age-related increased risk of LOAD. This review addresses possible mechanisms due to obesity, aging, multiple intersections between T2DM and LOAD and mechanisms for the continuum of progression. Multiple ultrastructural images in female diabetic db/db models are utilized to demonstrate marked cellular remodeling changes of mural and glia cells and provide for the discussion of functional changes in T2DM. Throughout this review multiple endeavors to demonstrate how T2DM increases the vulnerability of the brain’s neurovascular unit (NVU), neuroglia and neurons are presented. Five major intersecting links are considered: i. aging (chronic age-related diseases); ii. metabolic (hyperglycemia - advanced glycation end-products and its receptor (AGE/RAGE) interactions and hyperinsulinemia – insulin resistance (a linking linchpin); iii. oxidative stress (reactive oxygen-nitrogen species); iv. inflammation (peripheral macrophage and central brain microglia); v. vascular (macrovascular accelerated atherosclerosis - vascular stiffening and microvascular NVU/neuroglial remodeling) with resulting impaired cerebral blood flow.

Rheometric investigations facilitate the rheological analysis of cementitious suspensions. However, the choice of the rheometric device and raw data conversion affect the results. Contrary to small gap rheometry, where the flow data shear stress

Commercial production of Choromytilus meridionalis (black mussel) and Mytilus galloprovincialis (Mediterranean mussel) contributes to half of the South African marine aquaculture production per year. However, limited ready-to-eat black mussel products are available. The aim of this preliminary study was to investigate the effects of storage (15 days at room temperature) on the total viable counts (TVC), coliforms, proximate composition, and fat oxidation (TBARS) of a small-scale kitchen-based ready-to-eat hot smoked black mussel product preserved with vegetable oil. Three batches of black mussel meat were smoked at 80ºC for 15 minutes. Half of each batch were packaged in sterile glass jars with heated (50ºC) vegetable oil and half without oil. Microbiological analyses were done at days 1, 3, 9 and 15. Proximate composition was determined at day 1 and TBARS at days 1 and 15. Coliforms for all samples throughout the storage period were undetected. TVC plates for mussels stored without oil had no growth at days 1 to 5, however, reached TVC > 5 log10.g-1 at day 15. TVC for mussels with oil increased from 0 at day 1 to > 5 log10.g-1 at day 3 and remained at counts > 5 log10.g-1. There was no significant difference (P > 0.05) in moisture, protein, and ash for mussels stored with oil and those without oil, while significant differences (P < 0.001) in total fat and carbohydrates (10.27 ± 2.10% and 6.80 ± 2.22% with oil, and 5.05 ± 1.49% and 10.59 ± 3.00% without oil) were detected. These differences were due to the presence of additional fats from the vegetable oil. TBARS at day 15 showed no significant difference (P > 0.05) between mussels stored with oil and those without oil. There were no significant differences in TBARS from day 1 to 15 for both oil and without oil. Small-scale kitchen-based smoked black mussels preserved without vegetable oil could be stored for up to 9 days at room temperature.

Smart grids represent the evolution of traditional electrical grids through the integration of digital and communication technologies. These grids are capable of monitoring and managing the distribution of electricity efficiently and resiliently. Artificial Intelligence (AI) plays a crucial role in this context, offering advanced solutions for optimization, forecasting, and energy management. This paper explores the main AI technologies applied to smart grids, highlighting their advantages, challenges, and impacts on energy efficiency and sustainability. The methodology employed includes a comprehensive literature review, analysis of current AI applications in smart grids, and examination of case studies to illustrate the practical implementation and benefits of AI technologies in this field.

The analysis of the key factors which prevent or contribute to the promotion and development of the Power Purchase Agreements (PPAs) in order to meet the sustainability goals represents an important issue which worth being investigated, especially in the situation in which entering in bilateral relations inside this contract has a powerful meaning in achieving new climate targets, developing and improving the use of green energy, and promoting a sustainable green economy. The main goal of this research is to identify and analyze the main factors which help or pre-vent the promotion and development of a PPA, in order to meet the sustainability goals by promoting a green economy. For this research survey based on comparative analysis, questionnaires and interviews with energy experts, Spearman’s correlation matrix and IBM SPSS Statistics for Window are also used. The results suggest that although there are positive interest and an open attitude towards the PPA from both sides (sellers and buyers), but there is still place for improvement, the lack of knowledge or the insufficient measures taken so far being two of the reasons way until now in Romania we do not have a developed PPA market and the level of knowledge regarding this type of mechanism is quite low.

Counter-current spontaneous imbibition of water is a critical oil-recovery mechanism. In the laboratory, the Amott test is a widely used technique to assess efficacy of brine imbibition into oil-saturated core plugs. The classic Amott-cell methodology was designed to estimate ultimate oil recovery, but not the recovery dynamics that provides essential information about the imbibition mechanisms. Retention of oil droplets at the external core surface and delayed onset of oil production are the two key artifacts of the classic Amott experiment. This retention, referred to here as the ``external-surface oil holdup effect'', often results in stepwise recovery curves that obscure true dynamics of spontaneous imbibition. To address these holdup drawbacks of the classic Amott method, we modified the Amott cell and experimental procedure. For the first time, using water-wet Indiana limestone cores saturated with brine and mineral oil, we showed that our improvements of the Amott method enabled accurate and reproducible measurements of oil-recovery dynamics. We used the Generalized Extreme Value (GEV) statistics to describe oil-production histories from water-wet heterogeneous limestone cores with finite initial water saturations, for the first time. We demonstrated that our four-parameter GEV model accurately described the recovery dynamics, and that optimal GEV parameter values systematically reflected the key characteristics of the oil-rock system, such as oil viscosity and rock permeability. These findings gave us a more fundamental understanding of spontaneous, counter-current imbibition mechanisms and insights into what constitutes a predictive model of counter-current water imbibition into oil-saturated rocks with finite initial water saturation.

Abstract: It is assumed that upon SARS-CoV-2 infection granulocytes can undergo potentially destructive oxidative burst. Therefore, the aim of this study was to evaluate some parameters of redox and inflammatory signaling in granulocytes of recovered and of deceased COVID-19 pa-tients. Granulocytes were isolated from the blood of 32 COVID-19 patients on admission to the hospital (16 survived and 16 died within a week). The levels of proteins (immunoassay), eico-sanoids (UPLC-MS) and antioxidants activity (spectrophotometry) were examined. Enhanced activation of Nrf2 and NFκB and the levels of heme oxygenase and proinflammatory cytokines where found in granulocytes of all COVID-19 patients, while Cu,Zn-SOD and Mn-SOD activities were decreased, especially in deceased patients. Moreover, in patients who died increased levels of pro-inflammatory eicosanoids (PGE2 and TXB2) and decreased of anti-inflammatory (15d-PGJ2 and 5-HETE) were observed. However TXB2 was decreased, and IL-2 and IL-10 levels were in-creased in survivors, if compared both to healthy subjects and deceased patients, who did not change their cytokine generation. Therefore, it seems that by triggering transcription factors granulocytes activate redox signaling, leading to the production of pro-inflammatory eicosanoids, while reducing cellular antioxidant capacity via SOD, they express altered response to COVID-19, which might result in the onset of the vicious cycle of systemic oxidative stress in deceased patients.

Abstract: (1) Background: The occurrence of youth aggression has aroused widespread concern, and we would like to explore the reasons for it from both the family and individual sides of the youth. This study constructed a chain mediation model based on Bandura's Social Learning Theory to explore the mechanism of internet gaming disorder and avatar identification in it; (2) Methods: We conducted a randomized survey involving 973 adolescents from various middle schools in China. We used the Harsh Parenting Scale, the Online Gaming Disorder Scale, the Incarnate Identity Scale, and the Buss-Perry Aggression Scale to conduct a randomized survey. Empirical analyses were conducted using SPSS 25.0 and Amos 24.0 software to examine the mediating role of online gaming barriers and avatar identity; (3) Results: There was a two-by-two significant positive correlation between harsh parenting, internet gaming disorder, avatar identification and aggression. The me-diation model between harsh parenting, internet gaming disorder, avatar identification and ag-gression was well fitted ( χ2/df=7.268, GFI=0.961, AGFI=0.923, IFI=0.970, CFI=0.970, TLI=0.951, RMSEA=0.080).; (4) Conclusions: Parental harsh parenting affects adolescent aggression not only through the separate mediating roles of internet gaming disorder and avatar identification, but also through the chain mediating roles of internet gaming disorder and avatar identification.

Many of the fundamental molecules of life share extraordinary pigment-like optical properties in the long-wavelength UV-C spectral region. These include strong photon absorption and rapid (sub pico-second) dissipation of the induced electronic excitation energy into heat through peaked conical intersections. These properties have been attributed to a ``natural selection'' of molecules resistant to the dangerous UV-C light incident on Earth's surface during the Archean. In contrast, the ``thermodynamic dissipation theory for the origin of life'' argues that, far from being detrimental, UV-C light was, in fact, the thermodynamic potential driving the dissipative structuring of life at its origin. The optical properties were thus the thermodynamic ``design goals'' of microscopic dissipative structuring of organic UV-C pigments, today known as the ``fundamental molecules of life'', from common precursors under this light. This ``UV-C Pigment World'' evolved towards greater solar photon dissipation through more complex dissipative structuring pathways, eventually producing visible pigments to dissipate less energetic, but higher intensity, visible photons, up to wavelengths of the ``red-edge''. The propagation and dispersal of organic pigments, catalyzed by animals, and their coupling with abiotic dissipative processes, such as the water cycle, culminated in the apex photon dissipative structure, today's biosphere.

This paper investigates women’s empowerment and their active involvement in governance aimed at promoting sustainable development. The study focuses on examining the roles and processes of co-creation of nature-based solutions (NBS) for water management in Milan. Three research directions were explored: 1) to determine gender-based roles within local governance; 2) to detect semantic groups of NBS based on stakeholders' gender; c) to analyze gender-based linguistic and conceptual patterns of NBS. To achieve our objectives, we converted a sample of audio-video interviews of local stakeholders into textual data along with metadata indicating speakers' roles and gender. Descriptive statistics were applied to explore the governance's roles. We employed a combination of textual analysis and network analysis to identify thematic groups of NBS, and applied a word embedding approach to uncover linguistic patterns indicative of semantic sub-structures. The findings revealed significant demographic diversity. Male stakeholders perceive sustainable development as a process driven by the circular economy and the coordination of territorial authorities, while female stakeholders envision a transformative approach rooted in science, culture, and collaboration. The semantic sub-structure analysis highlighted a tendency among male governance to emphasize specialized skills, whereas female governance placed greater emphasis on problem definition rather than resolution.

This general review assesses the impact of physical exercise on oxidative and nitrosative stress and the counteractive role of antioxidants, drawing from a systematic literature search up to March 2024 in databases like PubMed and Web of Science. The review highlights studies on the effect of exercise on the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) and antioxidant responses in both human and animal models based on rigorous inclusion and exclusion criteria. Due to the variety of exercise types and participants' characteristics, a narrative synthesis approach was adopted. The findings indicate that moderate exercise boosts antioxidant defenses, known as hormesis, whereas excessive physical activity may increase oxidative and nitrosative damage. The review also explores the complex effects of antioxidant supplementation, revealing that while natural dietary antioxidants support health, high-dose supplements could potentially hinder positive adaptations to exercise. Recommendations for athletes and the general active population highlight the importance of balancing exercise and dietary intake to optimize health benefits. The review concludes with a call for more detailed research on tailored exercise and nutrition plans to dissect these intricate relationships further.

In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilized the supervised machine learning approach to predict students’ research competency, as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills— can be used to predict students’ research competency, as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. Findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency.

In this work, we introduce a new and generalized concept (herein referred to as Caputo fractional delta derivative and Caputo fractional delta dini derivative of order $\alpha\in(0,1)$) for Caputo fractional derivatives on an arbitrary time domain $\mathbb{T}$ which is a closed subset of $\mathbb{R}$. Combining the continuous and discrete time domains, we create a unified framework for stability analysis on time scales. Comparison results and stability criteria for the considered Caputo fractional dynamic equations are presented based on a new definition for Caputo fractional delta derivative of a Lyapunov function, contributing to the broader understanding of fractional calculus on time scales. The work also incorporates an illustrative example to demonstrate the relevance, effectiveness and applicability of the established stability results over that of the integer order.

Epithelial ovarian cancer significantly contributes to cancer-related mortality in women, often recurring post-treatment with chemotherapy resistance. Dietary interventions have demonstrated influence on cancer progression; for instance, caloric restriction has exhibited tumor growth reduction and enhanced survival in animal cancer models. This study aims to calculate a transcriptomic signature based on caloric-restriction for ovarian cancer patients and explore its correlation with disease progression. We identified proteins modulated by fasting, intermittent fasting or caloric restriction in human females. Based on their gene expression, we calculated a Non-Fasting Genomic Signature score for ovarian cancer samples from the TCGA database. We examined the association between this genomic profile and clinical characteristics. The non-fasting signature, comprising eight genes, demonstrated higher prevalence in primary ovarian tumors compared to normal tissue. Patients with elevated signature expression exhibited reduced overall survival and increased lymphatic invasion. The mesenchymal subtype, associated with chemotherapy resistance, displayed the highest signature expression. Multivariate analysis suggested the non-fasting genomic signature as a potential independent prognostic factor. Ovarian cancer tumors expressing a “non-fasting” transcriptional profile correlate with poorer outcomes, emphasizing the potential impact of caloric restriction on improving patient survival and treatment response. Further investigations, including clinical trials, are warranted to validate these findings.

This paper formulates the performance evaluation of cloud services as a multicriteria group decision making problem, and presents a fuzzy multicriteria group decision making method for evaluating the performance of cloud services. Interval-valued intuitionistic fuzzy numbers are used to model the inherent subjectiveness and imprecision of the performance evaluation process. An effective algorithm is developed based on the technique for order preference by similarity to ideal solution method and the Choquet integral operator for adequately solving the performance evaluation problem. An example is presented to demonstrate the applicability of the proposed fuzzy multicriteria group decision making method for solving the multicriteria group decision making problem in real world situations.

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong com-pound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the Electromagnetic Interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, common mode rejection ratio , gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain Computer Interfaces and neurofeedback training systems.

Aiming at the problem of low pollutant concentration in the sewage treatment plant due to external water intrusion into the sewage collection system, which in turn leads to low pollutant reduction efficiency. A sewage system in Zhenjiang City is taken as an example. Analyze the situation of external water intrusion in the sewage pipe network, and determine the external water intrusion proportion based on the water quality and quantity method. First, the dry season flow rate of the sewage pipe is obtained according to the monitoring data of the flowmeter. Then, the key research areas are screened out based on the changes in the concentration of water quality characteristic factors. Furthermore, chemical oxygen demand and electrical conductivity are used as the water quality characteristic indicators to characterize shallow groundwater and river water. In addition, the proportions of groundwater and river water intrusion in the sewage pipe network are quantitatively analyzed based on the chemical mass balance equation. At the same time, the dredging detection method is used to assist in the investigation, and finally the engineering rectification of the problems found in the drainage is carried out. The results show that the water quality and quantity method can effectively identify the types of external water and analyze the proportion of external water intrusion, which is of exemplary significance for the evaluation of sewage collection systems.

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

The paper primarily demonstrates that the three internal triangles formed by connecting any point on a midsegment with the vertices of a generic triangle satisfy the geometric Proof of the Pythagorean theorem in Euclidean geometry. Moreover, this study elucidates the Pythagorean relationships inherent within three-dimensional geometric constructs resulting from the arrangement of three-dimensional spatial triangles. This geometric relationship, akin to a generalized extension of the Pythagorean theorem, unveils a unique spatial region characterized by this harmonious area interrelation among triangles.

Neutrophil elastase (NE) is taken up by macrophages, retains intracellular protease activity and induces a pro-inflammatory phenotype. However, the mechanism of NE-induced pro-inflammatory polarization of macrophages is not well understood. We hypothesized that intracellular NE degrades histone deacetylases (HDAC) and Sirtuins, disrupting the balance of lysine acetylation and deacetylation, and resulting in nuclear to cytoplasmic translocation of a major alarmin, High Mobility Group Box 1 (HMGB1), a pro-inflammatory response in macrophages. Human blood monocytes were obtained from healthy donors, or from subjects with cystic fibrosis (CF) or chronic obstructive pulmonary disease (COPD). Monocytes were differentiated into blood monocyte derived macrophages (BMDM) in vitro. Human BMDM were exposed to NE or control vehicle and the abundance of HDACs and Sirtuins was determined by western blotting of total cell lysates or nuclear extracts, or determined by ELISA. HDAC, Sirtuin, and Histone acetyltransferase (HAT) activities were measured. NE degraded most HDACs and Sirtuin (Sirt)1, resulting in decreased HDAC and sirtuin activities, with minimal change in HAT activity. We then evaluated whether the NE-induced loss of Sirt activity or loss of HDAC activities would alter the cellular localization of HMGB1. NE-treatment or treatment with Trichostatin A (TSA), a global HDAC inhibitor, both increased HMGB1 translocation from the nucleus to the cytoplasm, consistent with HMGB1 activation. NE significantly degraded Class I and II HDAC family members and Sirt 1 which shifted BMDM to a pro-inflammatory phenotype.

The Polyurethane (PU) mixture is a high−performance engineering material with different dynamic properties compared to a hot−mixed asphalt mixture (HMA). This paper analyzed whether the Kramers−Kronig (K−K) relation and thermo−rheological simple properties were applicable to the PU mixture. Based on the result discussion, the PU mixture exhibited thermo−rheological simple properties within the test conditions. The time−temperature superposition principle (TTSP) was applicable for the PU mixture to construct the dynamic modulus master curve using the standard logistic Sigmoidal (SLS) model, the generalized logistic sigmoidal (GLS) model, and the Havriliak−Negami (HN) model. The Hilbert integral transformed SLS and GLS models for the phase angle can accurately fit the measured phase angle data with newly fitted shift factors and predict the phase angle within the viscoelastic range. The Core−Core and black space diagrams both displayed single continuous smooth curves, which can be utilized to characterize the viscoelastic property of the PU mixture. The K−K relation is applicable for the PU mixture to obtain the phase angle master curve model, storage modulus, and loss modulus from the complex modulus test results. The phase angle of the PU mixture cannot be derived from the dynamic modulus data and currently needs to be derived from the laboratory measurement.

The circular economy is one of the main strategies for mitigating the environmental impacts of civil construction, due to the generation of construction and demolition waste (CDW). In this transition, evaluating alternatives for using buildings as material banks is a way to make the process of reusing construction components more efficient. Thus, the article aimed to evaluate the state of the art of publications on the relationship between the circular economy in civil construction and the conceptual model of buildings as material banks, to mitigate the environmental impacts of CDW. The authors chose the methodological design of Systematic Literature Review, using the Scopus and Web of Science databases for research, with the following search strings: ("construction" or "civil construction" or "built environment" or "construction industry" ) and ("circular economy" or "circular construction") and ("material banks" or "BAMB" or "buildings as material banks" or "building stocks" or "building materials") and ("construction waste" or "demolition waste" or "CDW" or "construction and demolition waste" or "environmental impacts"). After a screening in which only articles published in journals were selected, from 2013 to 2023, inclusion and exclusion criteria were applied, to evaluate only those that had a direct relationship with CDW management through circular economy strategies and buildings such as banks of material. As a result, 93 articles remained, which were analyzed using quantitative and qualitative approach. The quantitative results showed an increase in publications since 2020, which are concentrated in European countries and China. The predominance of applied studies was also noted, through case studies that evaluate the management of materials and waste in the urban environment. The qualitative analysis, carried out using a SWOT Matrix, highlighted the strengths of the buildings, such as material banks, the potential reduction of resource extraction and urban mining, promoting the circulation of construction products. However, the recycling of waste, such as aggregates, still stands out as the main end-of-life strategy adopted, even without occupying the top of the waste hierarchy.

Research on titanium oxo complexes (TOCs) are usually focused on their structure and photocatalytic properties. Findings from these investigations spurred our interest in exploring also their potential biological activities. In this study, we focused on the synthesis and structure of a compound with the general formula [Ti8O2(OiPr)20(man)4] (1), which was isolated from the reaction mixture of titanium(IV) isopropoxide with mandelic acid (Hman) in a molar ratio of 4:1. The structure (1) was determined using single-crystal X-ray diffraction, while spectroscopic studies provided insights into its physicochemical properties. To assess the potential practical applications of (1), its microcrystals were incorporated into a polymethyl methacrylate (PMMA) matrix, yielding composite materials of the type PMMA + (1) (2 wt.%, 5 wt.%, 10 wt.%, and 20 wt.%). The next stage of our research involved the antimicrobial activity evaluation of the obtained materials. Investigations carried out demonstrated antimicrobial activity of pure (1) and its composites (PMMA + (1)) against both Gram-positive and Gram-negative strains. Furthermore, MTT tests conducted on the L929 murine fibroblast cell line confirmed the lack of cytotoxicity of these composites. Our study identified (1) as a promising antimicrobial agent, also for producing composite coatings.

This paper describes the proposal and development of a proof of concept (PoC) for a data logger system designed for operation and fault detection in bench testing of combustion engines. The main objective was to develop a data logger system that collects data on vibration, sound, temperature, and CO2 levels to identify ignition and combustion faults, thereby contributing to reducing greenhouse gas emissions (GHG). The paper presents (i) the proposal and development and (ii) the open-source code: proof of concept for a data logger system focused on detecting faults and monitoring the operational condition of benchtop combustion engines. The results indicate the feasibility of the PoC for data acquisition and dataset creation for Research and Development purposes.

L-cysteine, a precursor of essential components for plant growth, is synthesized by the cysteine synthase complex, which includes O-acetylserine(thiol) lyase (OAS-TL) and serine acetyltrans-ferase. In this work, we investigated how S-benzyl-L-cysteine (SBC), an OAS-TL inhibitor, affects the growth, photosynthesis, and oxidative stress of Ipomoea grandifolia plants. SBC impaired gas exchange and chlorophyll a fluorescence, indicating damage that compromised photosynthesis and reduced plant growth. Important parameters such as light-saturated net photosynthetic rate (PNmax), light saturation point (LSP), maximum carboxylation rate of Rubisco (Vcmax), electron transport rate (J), and triose phosphate utilization (TPU) decreased as a result. The quantum yield of photosystem II photochemistry (ϕPSII) and the electron transport rate through PSII (ETR) also decreased, as did the stomatal conductance (gs) and the photochemical quenching coefficient (qP). Additionally, SBC decreased the maximum fluorescence yield (Fm), variable fluorescence (Fv), and chlorophyll index, indicating possible damage to the photosynthetic apparatus. SBC triggered oxidative stress by increasing reactive oxygen species, malondialdehyde, and conjugated dienes. We hypothesize that dysfunctions in the sulfur-containing components of the photosynthetic electron transport chain, such as the cytochrome b6f complex, ferredoxin, and iron-sulfur (Fe-S) centers are the cause of these effects, which ultimately reduce the efficiency of electron transport and hinder photosynthesis in I. grandifolia plants. In short, our findings suggest that targeting OAS-TL with inhibitors such as SBC could be a promising strategy for the development of novel herbicides.

Tertiary lymphoid structures (TLSs) have become the focus of antitumor immunity. However, the prognostic significance of TLSs in epithelial ovarian cancer (EOC) treated with primary debulking surgery (PDS) or interval debulking surgery (IDS) remains unelucidated. This retrospective study examined the prognostic value of TLSs in tumor tissues obtained during PDS and IDS. Pathological specimens were collected from patients with EOC who underwent PDS or IDS between 2017 and 2020 at the National Cancer Center Hospital East. The correlation of tumor-infiltrating lymphocyte frequency, TLS presence, and PD-L1 expression with progression-free survival (PFS) and overall survival (OS) and the correlation between TLS and PD-L1 expression were analyzed. Overall, 25 specimens (15 from PDS and 10 from IDS) were analyzed. The presence of TLS was associated with a significantly longer OS in patients who underwent PDS but not in patients who underwent IDS. No correlation was identified between TLS and PFS in patients with PDS or with IDS. However, TLS was positively correlated with PD-L1 positivity. The prognostic impact of TLS, specifically in pre-chemotherapy specimens, and a positive correlation between TLS and PD-L1 indicate the potential of TLS as a meaningful biomarker for understanding antitumor immunity and developing immunotherapy for EOC.

Creating self-learning algorithms, developing deep neural networks and improving other methods that "learn" for various areas of human activity is the main goal of the theory of machine learning. It helps to replace the human with a machine, aiming to increase the quality of production. The theory of artificial neural networks, which already have replaced the humans in problems of detection of moving objects, recognition of images or sounds, time series prediction, big data analysis and numerical methods remains the most dispersed branch of the theory of machine learning. Certainly, for each area of human activity it is necessary to select appropriate neural network architectures, methods of data processing and some novel tools from applied mathematics. But the universal problem for all these neural networks with specific data is the achieving the highest accuracy in short time. Such problem can be resolved by increasing sizes of architectures and improving data preprocessing, where the accuracy rises with the training time. But there is a possibility to increase the accuracy without time growing, applying optimization methods. In this survey we demonstrate existing optimization algorithms of all types, which can be used in neural networks. There are presented modifications of basic optimization algorithms, such as stochastic gradient descent, adaptive moment estimation, Newton and quasi-Newton optimization methods. But the most recent optimization algorithms are related to information geometry, for Fisher-Rao and Bregman metrics. This approach in optimization extended the theory of classic neural networks to quantum and complex-valued neural networks, due to geometric and probabilistic tools. There are provided applications of all introduced optimization algorithms, what delighted many kinds of neural networks, which can be improved by including any advanced approaches in minimization of the loss function. Afterwards, we demonstrated ways of developing optimization algorithms in further researches, engaging neural networks with progressive architectures. Classical gradient based optimizers can be replaced by fractional order, bilevel and, even, gradient free optimization methods. There is a possibility to add such analogues in graph, spiking, complex-valued, quantum and wavelet neural networks. Besides the usual problems of image recognition, time series prediction, object detection, there are many are other tasks for modern theory of machine learning, such as solving problem of quantum computations, partial differential and integro-differential equations, stochastic processes and Brownian motion, making decisions and computer algebra.

Typical modern information systems are required to process copious data. Conventional manual approaches can no longer effectively analyze such massive amounts of data, and thus humans resort to smart techniques and tools to complement human effort. Currently, network security events occur frequently, and generate abundant log and alert files. Processing such vast quantities of data particularly requires smart techniques. This study reviewed several crucial developments of existent data mining algorithms, including those that compile alerts generated by heterogeneous IDSs into scenarios and employ various HMMs to detect complex network attacks. Moreover, sequential pattern mining algorithms were examined to develop multi-step intrusion detection. These studies can focus on applying these algorithms in practical settings to effectively reduce the occurrence of false alerts. This article researched the application of data mining algorithms in network security. The academic community has recently generated numerous studies on this topic.

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

Crop production has become a priority issue in the last few years due to the exponential growth of the world population and the need to substitute chemical fertilizers. This last matter is under the spotlight to achieve a more sustainable approach in a cost-effective way. Biostimulants have gained attention as an alternative to chemical fertilizers. Although they are not considered fertilisers as inputs of nutrients, they stimulate plants’ nutrition and tolerance to stress, among other characteristics. Amino acid-based biostimulants have been found to be effective in literature. This review focuses on the effectiveness of biostimulants, their presence in the global market, and mainly in their production with fish by-products as a source, using enzymatic hydrolysis and autolysis, focusing especially on fish viscera, their possibilities in the agricultural sector and their availability in Europe for possible opportunities. Fish viscera protein hydrolysates for biostimulant production seems a feasible alternative to fishmeal production in Europe, mainly in areas located far away from fishmeal plants.

The research reported on this paper was aimed at improving the overall efficiency of a PDO certified artisanal cheese production process. Being a PDO certified foodstuff by the EU, it is considered to have properties and qualities determined by the geographical environment in which is made, with its production taking place in a specific and determined geographical location, in this case the Serra da Estrela region. In that sense, the authors conducted a mapping according to a systemic perspective of the processes involved in the context of manufacturing and distribution of certified Serra da Estrela cheese. Numerous methods were used throughout the process, such as a systemic design analysis, and techniques derived from ethnographic methods, which led to the collection of data in the field and consequently provided the immersion of the researcher in genuine work situations. Critical points were identified and emphasized in the systemic map with the purpose of encouraging initiatives to address and overcome the gaps and inefficiencies detected. The systemic design analysis triggered the development of design work. Observations following an ethnographic approach identified ergonomic risks in cheese making during the process of cutting excess chips, fostering the emergence of musculoskeletal disorders at the wrist. A tool that fits best to the task at hand was developed. A prototype of the new tool enabled collecting feedback from use in the work context, in order to inform product development. The domains of agricultural production and microbiology, considering the specific microorganisms developed trough the ripening process of the cheese, turned out to be aspects of high importance for the issue under focus, contributing to a broader understanding of the ripening process and its risks, simultaneously improving the efficiency and success of cheese production. If it were not for the systemic analysis, which served as a link between the boundaries of distinct domains such as the risk of microorganism contamination, ergonomics, energy efficiency, legislation and regulation policies, transportation challenges and economic viability, approached throughout the research. Simultaneously creating bridges between them, the various problems might not have been detected in the first place, as they are usually addressed in specialized disciplines, predetermined by the restrictions of each specific area of knowledge. As a consequence of the development of this research, which was based on an analysis that sought to establish possible connections between various disciplines and tried to constantly maintain a holistic perspective, making new connections and observing the issues from a new angle, apart from the already established methodologies. All this allowed to lay out the seeds for the development of a plan to tackle the critical points identified by the systemic analysis reported in this paper.

Following the well-known pandemic, declared on 30 January 2020 by the World Health Organi-zation, the request for new global strategies for the prevention and mitigation of the spread of the infection has come to the attention of the scientific community. Nanotechnology has often managed to provide solutions, effective responses, and valid strategies to support the fight against SARS-CoV-2. This work reports a collection of information on nanomaterials that have been used to counter the spread of the SARS-CoV-2 virus. In particular, the objective of this work is to illustrate the strategies that have made it possible to use the particular properties of nanomaterials, for the production of personal protective equipment (DIP) for the defense against the SARS-CoV-2 virus.

In the present study, a five-year follow-up was performed by remote sensing of the calcium carbonate precipitation in La Gitana karstic lake (located on the province of Cuenca, Spain). The important role that calcium carbonate precipitation plays in the ecology of the lake is well known for its influence on the vertical migrations of phytoplankton, the concentration of bioavailable phosphorus and, therefore, the eutrophication and quality of the waters. Whiting take place between the months of July and August, and it can be studied at this time through its optical properties, with the main objective of offering updated data on a phenomenon traditionally studied and establishing possible relationships between abiotic factors such as temperature and/or rainfall. The atmospheric temperature data collected by the meteorological station suggest a possible relationship between the appearance of the white phenomenon and a pulse of previous maximum temperatures. On the other hand, no apparent relationship was found between rainfall and water bleaching.

This paper investigates whether the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) pandemic – also known as COronaVIrus Disease 19 (COVID-19) – could have been favored by specific weather conditions. It was found that the 2020 winter weather in the region of Wuhan (Hubei, Central China) – where the virus first broke out in December and spread widely from January to February 2020 – was strikingly similar to that of the Northern Italian provinces of Milan, Brescia and Bergamo, where the pandemic has been very severe from February to March. The similarity suggests that this pandemic worsens under weather temperatures between 4°C and 11°C. Based on this result, specific isotherm world maps were generated to locate, month by month, the world regions that share similar temperature ranges. From January to March, this isotherm zone extended mostly from Central China toward Iran, Turkey, West-Mediterranean Europe (Italy, Spain and France) up to the United State of America, coinciding with the geographic regions most affected by the pandemic from January to March. It is predicted that next spring, as the weather gets warm, the pandemic will likely worsen in northern regions (United Kingdom, Germany, East Europe, Russia and North America) while the situation will likely improve in the southern regions (Italy and Spain). However, in autumn, the pandemic could come back and affect the same regions again. The Tropical Zone and the entire Southern Hemisphere, but in restricted southern regions, could avoid a strong pandemic because of the sufficiently warm weather during the entire year. Google-Earth-Pro interactive-maps are provided as supplements.

Objectives: To evaluate the relationship between the type of conization, the volume and dimensions of the cone, with the persistence/recurrence of CIN 2-3, presence of affected margins and post-cone HR-HPV positivity. Methods: 227 women treated with LLETZ and histologically confirmed CIN2-3 were evaluated between 1996 and 2006 and followed up until June 2016. Age, type of resection according to IFCPC 2011, volume, cone dimensions, margins were studied. of resection, post-cone high-risk human papillomavirus (HR-HPV) status and viral load. Determinants of HR-HPV positivity were estimated using unconditional logistic regression and time to recurrence was assessed using Kaplan-Meier curves. Results: Persistent/recurrent CIN2-3 was found in 12 cases (5.2%), type 1 conization was performed in 81 patients, with 5 recurrences (6.1%), type 2 in 101 patients, with 6 recurrences (5 .9%) and type 3 resection in 45 women, with 1 recurrence (2.2%). Volume and dimensions were not associated with recurrence. The Log-Rank Test did not show a significant difference between the 3 treatments (p= 0.18). The free margins in type 1 were 37%, in type 2 resection 61% and in type 3 71% (p=0.001). Resection type 1 had 15% HR-HPV post positive cone, type 2 reached 16% and type 3 had 9%, this difference being non-significant (p=0.58). The incidence of recurrence at 36 months in type 1 resection was 0.08%, in type 2 treatment it was 0.04% and in type 3 resection it was 0.0%. Conclusions : Type 3 resection had a higher proportion of free margins and lower post-cone HR-HPV positivity, with a lower incidence of persistence/recurrence.

In the context of freshwater ecosystems, turbidity and suspended solids play crucial roles, with their levels significantly influenced by anthropogenic activities. This study focuses on assessing and monitoring these parameters in Albufera de Valencia using Sentinel-2 imagery. The primary aim is to establish reliable estimation algorithms that can contribute to effective ecosystem management. The study calibrated and validated algorithms for estimating turbidity and suspended solids. The R783×R705/R490 model proved to have the best performance for estimating turbidity and total solids in the Albufera. However, R783/R490 obtained a higher coefficient of de-termination for the organic part, while the R705 band was selected for the inorganic part. However, to achieve better estimates of turbidity and inorganic matter, more research is needed in the future. The implications of excessive suspended solids are underscored, including the depletion of dissolved oxygen, and reduced primary productivity due to limited light penetration and habitat availability. Collaboration between disciplines such as limnology, optics and water chemistry are crucial to advance water quality estimation models in lakes and lagoons such as Albufera. By integrating expertise and approaches from these diverse fields, new knowledge can be gained and the basis for more effective management and conservation strategies can be laid.

Large population passages of the SARS-CoV-2 in the past two and a half years have allowed the circulating virus to accumulate an increasing number of mutations in its genome. The most recently emerging Omicron subvariants have the highest number of mutations in the Spike (S) protein gene and these mutations mainly occur in the receptor-binding domain (RBD) and the N-terminal domain (NTD) of the S gene. The eCDC and the WHO recommend partial Sanger sequencing of the SARS-CoV-2 S gene RBD and NTD on the PCR-positive samples in diagnostic laboratories as a practical means of determining the variants of concern to monitor a possible increased transmissibility, increased virulence, or reduced effectiveness of vaccines against them. The author’s diagnostic laboratory has implemented the eCDC/WHO recommendation by sequencing a 398-base segment of the N gene for the definitive detection of SARS-CoV-2 in clinical samples, and sequencing a 445-base segment of the RBD and a 490-509-base segment of the NTD for variant determination. This paper presents 5 selective cases to illustrate the challenges of using Sanger sequencing to diagnose Omicron subvariant when the samples harbor a high level of co-existing minor subvariant sequences with multi-allelic single nucleotide polymorphisms (SNPs) or possible recombinant Omicron subvariants containing a BA.1 NTD and a BA.2 RBD, which can only be detected by using specially designed PCR primers. The current large-scale surveillance programs using next-generation sequencing (NGS) do not face similar problems because NGS focuses on deriving consensus sequence.

Global stress is widespread in todays post-pandemic world of political and economic uncertainty. Vibroacoustic technology is a vibrotactile intervention with multiple uses, and its impact on stress lacks interpretation. This research assessed if the vibroacoustic technology of a Vibroacoustic Sound Massage (VSM) can reduce psychological, physiological and cognitive stress. The Perceived Stress Scale (PSS-10), and electrocardiogram (ECG) and electroencephalogram (EEG) biosignals were used to quantify results. Participants were divided into Low Stress and High Stress groups. ECG results show VSM increased parasympathetic activity for all participants, with the Low Stress group more effected. EEG results indicate increased concentration (TBR), reduced arousal and increased relaxation (BAR), and non-significant effects on participant well-being (FAA), though variability in this metric was homogenised after VSM. Together, these results validate VSM as an effective support tool for stress management and further research is required.

Evolution of SARS-CoV-2 has followed similar trends as other RNA viruses, e.g., HIV-1 and influenza A. Rapid initial diversification was followed by strong competition and rapid succession of dominant variants. Host-initiated RNA editing has been the primary mechanism for introducing mutations. A significant number of mutations were detrimental and were quickly purged. Fixed mutations are mostly diversifying mutations selected for host adaptation and immune evasion, with the latter accounting for more of the changes. However, immune evasion often comes at the cost of functionality, so that optimal functionality is still far from being accomplished. Instead, selection for antibody-escaping variants and accumulation of near-neutral mutations has led to suboptimal codon usage and reduced replicative capacity as demonstrated in non-respiratory cell lines. Beneficial adaptation of the virus includes reduced infectivity in lung tissues and increased tropism for the upper airway, resulting in shorter incubation periods, milder diseases, and more efficient transmission between people.

The order Mononegavirales harbors numerous viruses of significant relevance for human health, including both established and emerging infections. Currently, vaccines are only available for a small subset of these viruses and antiviral therapies remain limited. Being obligate cellular parasites, viruses must utilize the cellular machinery for their replication and spread. Therefore, targeting cellular pathways used by viruses can provide novel therapeutic approaches. One of the key challenges confronted by both hosts and viruses alike is the successful folding and maturation of proteins. In cells, this task is faced by cellular molecular chaperones, a group of conserved and abundant proteins that oversee protein folding and help maintain protein homeostasis. In this review, we summarize the current knowledge of how the mononegavirales interact with cellular chaperones, highlight key gaps in our knowledge, and discuss the potential of chaperone inhibitors as antivirals.

Citrus species essential oils (EOs) are significant sources of bioactive compounds and demonstrate diverse therapeutic effects. However, limited documentation exists regarding the phytochemicals associated with their biological activities. This study aims to assess the antioxidant activity, plant inhibitory properties, and brine shrimp cytotoxic effects of EOs derived from three Citrus species: C. sinensis, C. limon, and C. hystrix. Utilizing chemometric analysis and gas chromatography-mass spectrometry (GC-MS) fingerprints, the volatile components contributing to antioxidant activity were elucidated. The peels of the Citrus samples were hydro-distillated to obtain EOs, and subsequently subjected to antioxidant, plant inhibitory, and brine shrimp cytotoxic assays. The results indicated that C. limon EO exhibited the highest antioxidant activity, while C. hystrix and C. sinensis EOs demonstrated pronounced inhibitory effects against Aetrmia salina and Lactuca sativa, respectively. GC-MS analysis facilitated the identification of key compounds in each EO. Principal component analysis (PCA) and hierarchy cluster analysis (HCA) effectively categorized Citrus EOs based on their antioxidant properties, highlighting the proximity of C. limon and C. sinensis. Among the identified compounds, D-limonene, α-Terpineol, Caryophyllene, (+)-3-Carene, β-Pinene, (-)-Spathulenol, trans-p-Mentha-1(7),8-dien-2-ol, and trans-Verbenol were the most discriminating compounds affected the antioxidant activity of C.limon and C. sinensis EOs.

The current study characterizes the transient M/M/1 queue manifold info-geometrically, through devising Fisher Information matrix(FIM) and its inverse(IFIM). Additionally, the impact of stability on the existence of IFIM and explore the geodesic equations of motion has been revealed. More potentially, the paper discusses some info-geometric applications to Machine Learning are highlighted. Closing remarks with some challenging open problems combined with the next phase of research are given.

Complete pathologic response (pCR) predicts the long-term outcome of neoadjuvant treated (NAC) breast cancer (BC) but is reached in <10% of hormone-receptor-positive patients. Biomarkers able to guide adjustment or interruption of an ineffective therapy are desired. Here, we evaluated whether shifts in the serum concentration of thymidine kinase 1 (sTK1) during NAC could be utilized as a biomarker. In the PROMIX trial, women with localized HER2- BC received neoadjuvant epirubicin/docetaxel in six cycles. sTK1 was measured with an ELISA in 54 patients at cycles 1-4 and in a total of 131 patients before and 48h after cycle 1. The prognostic significance of the results was evaluated by log-rank tests of Kaplan–Meier estimates. Treatment resulted in a 2-fold increase of sTK1 before and 3-fold increase 48h after the cycles, except for the first cycle, where half of patients reacted with a decrease (post/pre sTK1- ratio <1.12) and the other half reacted with an increase (ratio >1.12). OS rates in ER+ patients with ratios of >1.12 and <1.12 were 97.7% and 78% (p=0.005), respectively, and DFS rates were 90.7% and 68% (p=0.006), respectively. Thus, response of sTK1 at the first cycle of chemotherapy could be used both as an early biomarker for guidance of chemotherapy and for the study of inherent tumor chemo-sensitivity, which could predict long-term outcome prior to therapy.

Storage, transmission and processing of data become a significant problem when large digital data files or databases are involved, as in the case of decentralized online global databases such as blockchain. Here we propose a novel method that allows the scalability of digital assets, including blockchain databases in the download, validation and confidentiality process, by developing a lightweight blockchain technology called Entropic Blockchain. This is a computer-implemented mathematical method to generate an information-entropic numerical representation of a digital asset, which can be utilized on its own, or as an optically machine-readable entropic barcode, for data secure transmission, processing, labelling, identification, one-way encryption and compression, validation, and digital tamper-proof check.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

Public restrooms are often a hub of microbial contamination, and the examination of bacterial contamination in these facilities can serve as an important indicator of the transmission of infectious diseases. This study was conducted to determine the prevalence of bacterial contamination in public restrooms based on the economic class of the building. Samples were collected from various spots in 32 restrooms found in 10 shopping malls, classifying them into two categories: upper-end restrooms and lower-end restrooms. The findings showed that the level of contamination was higher in the lower-end restrooms, with the seat being the most contaminated area. The most dominant Gram-positive bacteria were coagulase-negative staphylococci species, making up 86% of the identified Gram-positive isolates. The most dominant Gram-negative bacteria identified were Klebsiella pneumoniae and Pseudomonas aeruginosa. The antibiotic sensitivity test results revealed the presence of multi-drug resistant bacteria among the Gram-positive and negative isolates, including Staphylococcus haemolyticus, Staphylococcus kloosi, Acinetobacter baumanii, and Pseudomonas aeruginosa. In conclusion, the study underscores the significance of monitoring bacterial contamination in public restrooms and the need for measures to reduce the spread of infectious diseases. Further research is crucial to gain a complete understanding of the bacterial contamination in public restrooms and their resistance patterns, to ensure the safety and health of the public. The implementation of improved cleaning practices and hands-free designs in restrooms can help reduce the risk of cross-contamination and prevent the spread of diseases.

Lung neuroendocrine tumors (LNETs) and gastroenteropancreatic neuroendocrine tumors (GEPNETs) are two distinct types of neuroendocrine tumors (NETs) that have traditionally been treated as a single entity despite originating from different sources. Although they share certain phenotypic characteristics and the expression of neuroendocrine markers, they exhibit differences in their microenvironment, molecular mutations, and responses to various therapeutic regimens. Recent research has explored the genetic alterations in these tumors, revealing dissimilarities in the frequently mutated genes, role of EGFR in carcinogenesis, presence of transcription factors, and immunogenicity of the tumor and its microenvironment. Spread Through Air Spaces (STAS), a phenomenon unique to lung carcinomas, appears to play a crucial role in LNET prognosis. These distinctions are also evident in the cascade response of lung and GI tract neuroendocrine tumors to somatostatin analogs, Peptide Receptor Radionuclide Therapy (PRRT), chemotherapy, and immunotherapy. Identifying similarities and differences between the two groups may im-prove our understanding of the underlying mechanisms and facilitate the development of more effective treatment strategies.

Background: Despite their generally favorable prognosis, hepatic fibrosis remains a possible complication for hepatitis B virus (HBV) inactive carriers. This study aimed to estimate the prevalence of significant fibrosis and associated risk factors among Omani HBV inactive carriers. Methods: A retrospective study was conducted of 200 Omani HBV inactive carriers visiting a tertiary hospital in Muscat, Oman, between January 2017 and December 2018. Significant fibrosis (stage F2 or higher) was identified using two-dimensional shear-wave elastography. Baseline clinical, laboratory, and radiological data were analyzed to determine associations. Results: Of the total sample, 106 patients (53%) were male, and the mean age was 44.6 ± 9.3 years. The prevalence of significant fibrosis was 20% (n = 40). Male gender (p = 0.007), age ≥60 years (p = 0.024), and fatty changes in liver ultrasound (p = 0.044) were found to be independent factors for significant fibrosis. Conclusion: the prevalence of significant fibrosis was relatively high among a sample of Omani HBV inactive carriers, with several sociodemographic and clinical risk factors identified. Periodic assessment of liver function is needed for patients with these risk factors; moreover, routine biochemical parameters could be useful predictors of disease prognosis. Further research is recommended to substantiate these findings.

We investigate and discuss in detail the structure of the restricted singular value decomposition for a tensor triplet under t-product (T-RSVD). The algorithm is provided with a numerical example illustrating the main result. For applications, we consider color image watermarking processing with T-RSVD.

ω-Transaminases have been immobilized on macrocellular silica monoliths and used as heterogeneous biocatalysts in a continuous flow mode enantioselective transamination reaction. The support was prepared by a sol-gel method based on emulsion templating. The enzyme was immobilized on the structured silica monoliths both by adsorption, and by covalent grafting using amino-functionalized silica monoliths and glutaraldehyde as a coupling agent. A simple reactor set-up based on the use of a heat-shrinkable Teflon tube is presented and successfully used for the continuous flow kinetic resolution of a chiral amine, 4-bromo-α-methylbenzylamine. The porous structure of the supports ensures effective mass transfer and the reactor works in the plug flow regime without preferential flow paths. When immobilized in the monolith and used in the flow reactor, transaminases retain their activity and their enantioselectivity. The solid biocatalyst is also shown to be stable both on stream and during storage. These essential features pave the way to the successful development of an environmentally friendly process for chiral amines production.

We examine the relation between environmental, social, and governance commitment levels (ESG) and firm dividend payer status. Given that larger and more profitable firms are positively associated with both payer status and ESG, it could be that ESG and dividends are complements. However, given that both dividends and ESG relate to firm spending decisions, it may be that the choice is “either/or” and that ESG and dividends are substitutes. We document a positive relation between ESG and dividend payer status in U.S. firms over the period 1991-2016. In particular we find that the proportion of dividend payers is roughly 13% higher for firms with positive ESG compared to those with negative ESG. Including ESG in the models used to predict payer status provides, on average, a nearly 26% improvement in relative forecast accuracy. Our results are robust to estimation techniques and the inclusion of variables known to be determinants of payer status.

The rapid growth of the metropolitan area around Budapest has led to increased traffic, infrastructure problems, and social and economic issues in the suburbs, making the sustainability of these areas a key topic in professional and public discussions. The article aims to explore the intricacies of sustainability in a suburban town by utilizing the Smart Growth framework, which is highly pertinent in Hungary. The focus of scientific and public discussions revolves around urban planning to curb urban sprawl and enhance public transportation usage. The analysis concentrates on compactness, commute, and community, utilizing quantitative survey data between 2018 and 2023. The findings demonstrate that the town under analysis is dense regarding common and regular services but depends significantly on the central area for more specialized services. The local community is tightly interconnected and shows no evidence of atomization. Commuting patterns show that polycentricity leads to new spatial connections and a growing reliance on cars, even while the quantity of vehicles is still growing. The results indicate that assessing the sustainability of suburbs may not be universally applicable, and examination should be conducted at the town level. Secondly, the sustainability of suburban areas may be distinct from the sustainability of denser, urbanized places.

Land degradation, desertification and tree mortality related to global climate change are in the spotlight of Remote Sensing research in recent decades, since extreme climatic events could affect the composition, structure and biogeography of forests. However, the complexity of tree mortality processes requires a holistic approach. Herein, we present the first global assessment and a historical perspective of tree mortality by reviewing both Remote Sensing and ground-based studies. We compiled 254 papers on tree mortality that make use of remotely sensed products, ground-based monitoring and climatic drivers, focusing on their spatial and temporal patterns, in the methods applied, while highlighting research gaps. Forested ecosystems, especially in the Mediterranean area, appear to have experienced often climatic-induced physiological stress under extreme droughts and warming, raising concern that forests may become increasingly vulnerable to tree mortality. Our core results indicated that international publications on tree mortality are on the increase with the main hotspots being North America (39%) and Europe (26%). Wetness indicators appear as the barometer in explaining tree mortality on a local scale, while vegetation indicators derived from multispectral optical sensors are promising for large scale assessment. We observed that almost all of the studies we have reviewed were based on less than 25 years of data and were of local scale. Longer timeframes and regional scale investigations that will include multiple tree species analysis could have a significant impact on future research.

Bettelmatt cheese, a cherished Italian culinary gem hailing from the picturesque Val d'Ossola region, is deeply intertwined with its natural environment. This article explores the intricate relationship between the organoleptic attributes of Bettelmatt cheese and the challenges posed by climate change. The sensory qualities of this cheese, including its flavor profile, texture, and aroma, are profoundly influenced by the alpine pastures where the cows graze, the unique terroir, and the traditional production methods. However, as climate change continues to impact ecosystems and weather patterns, it raises questions about the resilience and adaptability of this traditional cheese. In addition to these challenges, the article would like to explore the complexities of regulation and protection of this cheese, crucial for food and gastronomic sciences.

Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors, including abemaciclib, have been approved for the treatment of hormone receptor-positive, HER2-negative advanced, and metastatic breast cancer. Despite the high therapeutic efficacy of CDK4/6 inhibitors, they are associated with various adverse effects, including potentially fatal interstitial lung disease. Therefore, a combination of CDK4/6 inhibitors with letrozole or fulvestrant has been attempted but has demonstrated limitations in reducing adverse effects, highlighting the need to develop new combination therapies. This study proposes a combination strategy using CDK4/6 inhibitors and tricyclic antidepressants to enhance the therapeutic outcomes of these inhibitors while reducing their side effects. The therapeutic efficacies of abemaciclib and desipramine were tested in different cancer cell lines (H460, MCF7, and HCT-116). The antitumor effects of the combined abemaciclib and desipramine treatment were evaluated in a xenograft colon tumor model. In vitro cell studies have showed the synergistic anticancer effects of combination therapy in the HCT-116 cell line. The combination treatment significantly reduced tumor size compared with control or single treatment without causing apparent toxicity to normal tissues. The combination of abemaciclib and desipramine may be a promising therapeutic strategy as they could reduce side effects and enhance tumor suppression.

The StarDICE experiment strives to establish an instrumental metrology chain with a targeted accuracy of 1 mmag in griz bandpasses to meet the calibration requirements of next-generation cosmological surveys. Atmospheric transmission stands out as a significant source of systematic uncertainty. Specifically, gray extinction induces spurious variations of photometric fluxes. We propose a solution relying on an uncooled long-wave infrared thermal camera to evaluate it. This type of camera can provide real-time insights into atmospheric conditions and detect cirrus clouds. However, achieving accurate measurements with thermal imaging systems necessitates prior calibration due to the influence of temperature-induced effects, which compromise their spatial and temporal precision. Moreover, these systems cannot provide scene radiance values in physical units by default. This study introduces a new calibration process utilizing a tailored forward modeling approach. The method is applied to an uncooled FLIR Tau2 thermal camera. It incorporates sensor, housing, flat-field support, and ambient temperatures, along with raw digital response, as input data. Experimental measurements are conducted inside a climatic chamber, with the camera imaging a thermoregulated blackbody source. Results demonstrate the calibration effectiveness, achieving precise radiance measurements with a temporal pixel root-mean-square error (RMSE) of 0.09 W/m2/srand residual spatial noise of 0.03 W/m2/sr.

Most consumer purchases are influenced by their evaluation of a product's advantages, disadvantages, and emotional components. Research in psychology and marketing indicates that customer emotions play a role at different stages of the purchasing process. This study aims to identify the elements that may influence consumer feelings when purchasing high-end cosmetics. To explore the various aspects of customer emotions, a qualitative study was conducted using in-depth semi-structured interviews with 23 users of luxury cosmetics and health products in Telegram groups. This research uncovered multiple emotional dimensions and pinpointed factors that could elicit emotions in the target market. The next step, based on collective consensus, was to determine the variables impacting customer emotions. At this stage, the panel included 15 experts in marketing, psychology, luxury cosmetics import businesses active on digital platforms, and managers of luxury cosmetics and hygiene groups in online environments. Through a three-step consensus method, experts identified and assessed 36 factors affecting customer emotions based on their significance and perceived impact. These factors were then categorized into three groups: personal variables, group and product variables, and contextual variables.

The paper presents an analysis of the concept of resilience within the context of a modern period example in Croatia, the Cvjetno housing estate in Zagreb, recognized as a cultural and historical complex. While extensive literature exists on the estate's architectural and historical significance, limited research explores its resilience. This study aims to fill this gap by examining Cvjetno estate's resilience to natural hazards, financial and economic challenges, and social crises. It investigates the estate's design, historical context, and physical characteristics through archival documents, historical records, and field research. The analysis focuses on the estate's planned resilience to natural disasters, particularly flooding from the nearby river Sava and seismic risks due to its location in a seismically active area. Drawing from the literature on resilient communities, the study draws on key characteristics and principles of resilience and explores how Cvjetno estate exemplifies these traits. The research contributes to a deeper understanding of resilience in urban planning and provides insights for enhancing resilience in similar historical settlements facing contemporary challenges.

Despite the growing number of published studies on student learning in the laboratory, there is a critical need to improve methodological rigor. Resonating with discussions on research methods, this paper outlines the importance of theory-informed research questions, the minimization of researcher and participant biases, and the use of triangulation and iteration in data collection to establish rigor. An illustrative case is presented within the context of a large interdisciplinary research project aimed at improving laboratory learning at the university level. The project incorporates two research avenues: one focusing on literature and faculty perspectives, and the other on a comprehensive assessment of multidimensional learning in the laboratory. The project employs a mixed methods paradigm and is grounded in a conceptual framework that conceptualizes laboratory work as epistemic practice, requiring a holistic analysis of student learning. The article concludes by discussing the results and implications of the project's findings, which are synthesized to highlight aspects of establishing methodological rigor. The overarching goal is to develop a comprehensive assessment instrument that captures the complexity and richness of the laboratory learning environment. The findings from this research are expected to contribute to the advancement of laboratory education research by providing a model for methodological rigor that can be applied across various scientific and interdisciplinary contexts.

The present study aims to show on the one hand how easily one can get confused when doing research on the internet. Different platforms, fora and homepages supply the visitor with different - and sometimes contradictory - information. On the other hand, we show how to overcome these contradictions and produce valuable research based on almost all sources. For our study, we use the example of the Feit-Thompson conjecture, which is - in opposite to the well-known Feit-Thompson theorem - not widely known. We try to clarify the scientific literature on this topic and we embed our own result into the present state of art. We prove that pq−1p−1 does not divide qp−1q−1 if p and q are twin primes, i.e. p=q+2. Thus we prove that the Feit-Thompson conjecture is true for twin prime numbers.

As an active device, air source heat pumps (ASHP) are widely used for heating and cooling in buildings. However, in cold regions, their efficiency decreases, and frost formation becomes a significant issue during winter. This paper proposes a new scheme that couples air source heat pumps with passive heating. By using software simulations and mathematical models, the new scheme is compared to traditional ASHP systems to analyze its heating performance in rural homes during winter in China’s cold regions. According to the simulation and calculation analysis, on the coldest day of winter, the coupling scheme can provide approximately 99.41kWh of heat to the interior, surpassing the 86.67kWh required to maintain an indoor temperature of 20℃. The system’s power consumption is 36.96 kWh, which is 66.88% lower than that of traditional heat pump heating. The study shows that a coupled system of air source heat pumps and passive heating has better heating performance in cold regions. By incorporating phase change materials and heat storage media to address the shortage of solar energy at night, the system can meet the heating needs throughout the entire day.

Detection and measurement of pain are crucial in designing effective pain management interventions to alleviate suffering and prevent functional decline in the medical domain. Traditional methods of pain identification rely on verbal patient reports or observable signs, which can be imprecise or subjective due to variability in patient self-assessments. Hence, there is a pressing need for automated methods to standardize pain measurement procedures. This paper proposes a novel facial expression-based automated pain assessment system as a behavioral indicator for pain evaluation. Our system integrates a fusion structure comprising Convolutional Neural Networks (CNNs) and Tensor-based extended Quantitative Expression Descriptors Analysis (TXQEDA). This innovative approach allows for joint learning of robust pain-related facial expression features from raw facial images, combining RGB appearance with shape-based latent representation. We extensively evaluated our proposed model using the UNBC-McMaster dataset for pain classification and intensity estimation. Our results demonstrate the efficacy of the proposed technique, achieving an impressive accuracy of 99.10% for pain level classification. Furthermore, our system excels in pain intensity estimation, showcasing its potential to provide precise and reliable assessments of pain severity. Overall, our research underscores the importance of automated pain assessment systems in improving patient care and clinical outcomes. By leveraging advanced deep learning techniques and fusion strategies, our proposed model offers a promising approach for standardizing pain measurement procedures and enhancing the quality of pain management interventions.

Mangrove ecosystems are one of the most productive on our planet but have declined globally by 30-50%. Many species rely on mangrove habitat and thus their conversion to aquaculture farms has led to a noticeable loss in commodities such as wild fish stocks. This study aimed to assess 1) the influence of the presence of aquaculture and 2) restoration projects on the ecosystems ability to provide resources. We collected data on mangrove vegetation (i.e., biomass, richness and abundance), soil nutrients (i.e. organic carbon, aluminium and nitrogen), crab abundance and fishing pressure in six sites. We set up 15 plots in each site and collected data between May and July 2023. Via generalised linear mixed models we found that the abundance and richness of crabs was significantly higher in aquaculture plots than in no aquaculture plots. Aquaculture plots had higher topsoil aluminium, topsoil and subsoil Nitrogen, and had lower topsoil Carbon than non-aquaculture sites. Restored sites had less Nitrogen in the topsoil than unmanaged sites. Biomass did not change between aquaculture, restored and unmanaged plots. We found a negative correlation between crab abundance and richness and mangrove DBH, suggesting that the species of crabs present preferred areas with propagules for feeding (e.g., Grapsidae crabs). The content of nitrogen in the subsoil was positively correlated with mangrove richness, diversity and height, suggesting the importance of nitrogen availability for mangrove growth. The content of aluminium in the subsoil was negatively correlated with the content of organic carbon both in topsoil and subsoil, suggesting the detrimental effect of aluminium on the carbon cycle. Fishing practices were observed in all sites during the data collection period. Even if most of the vegetation parameters were not significantly affected by presence of aquaculture or management for restoration, it is clear that important variables such as aluminium carbon, and nitrogen content in the soil and crab assemblages were highly variable.

Microplastic pollution (MP) in marine environments around the globe is severe and enough precautions have not been taken so far for its prevention. The focus of this study is to quantify MPs from beach sediment and seawater samples and to identify their distribution and types along the western coast of Sri Lanka from the Kelani River estuary to the Mahaoya estuary. Nine sites along this 42 km stretch were selected and random sampling was employed to collect a minimum of 8 sediment samples from each site between October and December 2021. Water samples were also collected, parallel to the sediments, from the ocean surface. FTIR analysis revealed that most of the MPs found were polyethylene(PE), polypropylene(PP), polystyrene(PS), polyethylene terephthalate(PET), and phenol formaldehyde resin. The mean abundance of MPs varied from 2.0 ± 0.6 items/L to 161.0 ± 15.7 items/L in water samples and, from 3.0 ± 0.3 items/m2 to 656.0 ± 34.5 items/m2 in sediment samples. The MPs found were identified in different shapes as fragments (80.2%), pellets (14.9%), fibers (2.7%) and foams (2.5%). Analysis revealed that the beach sediments were contaminated with PS, phenol formaldehyde resin, PET, PP and PE, while surface seawater is dominated by phenol formaldehyde resin, PS, PP and PE.

Coronavirus disease 2019 (COVID-19) displays clinical heterogeneity, but little information is available for patients with mild or very early disease. We aimed to characterize biomarkers that are useful in discriminating the hospitalization risk in a COVID-19 cohort from Northern Italy during the first pandemic wave. We enrolled and followed for four weeks 76 symptomatic SARS-CoV-2 positive patients and age/sex-matched healthy controls. Patients with mild disease were discharged (n.42), and the remaining were hospitalized (n.34). Blood was collected before any anti-inflammatory/immunosuppressive therapy and assessed for soluble C5b-9/C5a, H3-neutrophil extracellular traps (NET), calprotectin, DNase plasma levels by ELISA, and a panel of pro-inflammatory cytokines by ELLA. Calprotectin and NET levels discriminate between hospitalized and non-hospitalized patients, while DNase negatively correlates with NET levels; there are positive correlations between calprotectin and both NET and neopterin levels. Neopterin levels increase in patients at the beginning of the disease and more in hospitalized than non-hospitalized ones. C5a and sC5b-9, and other acute phase proteins correlate with neopterin, calprotectin, and DNase. Both NET and neopterin levels negatively correlate with platelet count. We show that calprotectin, NET, and neopterin are important proinflammatory parameters potentially useful in discriminating between COVID-19 patients at risk for hospitalization.

We perform new measurements of the expansion rate and the sound horizon at the end of the baryon decoupling and derive constraints on cosmic key parameters in the framework of the ΛCDM model, flat wCDM model, the ΛCDM+ΩK model and the phenomenological emergent dark energy (PEDE) model. We keep rd and H0 completely free and use the recent dark energy spectroscopic instrument (DESI) Year 1 and dark energy survey (DES) Year 6 BAO measurements in the effective redshift range 0.3&lt;z&lt;2.33 combined with the compressed form of the Pantheon sample of Type Ia supernovae, the latest 33 observational H(z) measurements based on the differential age method, and the recent H0 measurement from SH0ES 2022 as an additional Gaussian prior. Combining BAO data with the observational H(z) measurements, and the Pantheon SNe Ia data, we get H0=69.65±1.07 km s−1 Mpc−1, rd=147.71±2.18 Mpc in ΛCDM model, H0=70.06±1.07 km s−1 Mpc−1, rd=147.39±2.50 Mpc in PEDE model. The spatial curvature is Ωk=0.025±0.026 and the dark energy equation of state is w=−1.016±0.056, consistent with a cosmological constant. We apply the Akaike information and the Bayesian information criterion test to compare the four models, and see that PEDE model performs better.

In this article, we first defined a stronger form of (r,s)-generalized fuzzy semi-closed sets (briefly, (r,s)-gfsc sets) called (r,s)-g*fsc sets and investigated some of its features. Moreover, we showed that (r,s)-fsc set → (r,s)-g*fsc set → (r,s)-gfsc set, but the converse may not be true. In addition, we explored novel types of fuzzy generalized mappings between double fuzzy topological spaces (U, τ, τ*) and (V, η, η*), and the relationships between these classes of mappings were examined with the help of some illustrative examples. Thereafter, we introduced novel types of higher separation axioms called (r,s)-GFS-regular and (r,s)-GFS-normal spaces with the help of (r,s)-gfsc sets and discussed some topological properties of them. Finally, some novel types of compactness via (r,s)-gfso sets were defined and the relationships between them were introduced.

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, in-cluding fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters, in the combined phase-space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21976 im-precisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “medium-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st- through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems mod-eled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, MC). At least second-order sensitivities should be retained in order to obtain reliable predictions.

Background: Differentiating essential tremor (ET) and functional tremor (FT) remains challenging due to their overlapping clinical presentations. Objective: In this study, we aimed to elucidate the demographic and psychometric differences between the aforementioned tremor types to enhance the diagnostic accuracy and therapeutic strategies. Methods: In this prospective study, we included 96 patients diagnosed with ET or FT and analysed their related demographic data, clinical symptoms, and psychometric evaluation scores. Our statistical analysis involved Pearson’s chi-square tests, Fisher’s exact tests, and logistic regression to assess how the different variables impact tremor diagnosis. Results: Our study demonstrated a higher ET prevalence in males (p = 0.015). Furthermore, we demonstrated that patients with ET displayed a significantly lower body mass index and a lower age of onset compared to those with FT (p = 0.050 and p = 0.023, respectively). Our psychometric assessments revealed higher cognitive and body image scores in patients with ET, whereas those with FT scored higher on depression and anxiety scales. The misdiagnosis rate was 14.5%, emphasising the need for improved diagnostic criteria. Conclusions: We established specific demographic and psychometric distinctions between ET and FT, which could potentially benefit clinicians in making accurate diagnoses and tailoring treatment approaches. These findings support the inclusion of comprehensive psychometric evaluations into standard diagnostic procedures to better differentiate tremor types.

This study concerns the transport and retention of polydisperse micron-sized (16 ± 6 µm) of microplastics (MPls) in porous media under varying flow rate conditions. Sorption kinetics were modeled using first-order reversible and irreversible kinetic sorption models, with sensitivity analyses providing insights into each sorption parameter's effect. Both numerical modeling and experimental measurements were employed to assess sand filter retention rates. The impact of flow rate on sorption reveals variations in distribution coefficient (Kd), mass transfer coefficient (β), and irreversible sorption rate (K1). Lower flow rates are correlated with higher Kd and β values, indicating an increase in sorption and diminished mass transfer rates. The findings revealed that an increase in Kd resulted in a more gradual sorption process with a decrease in peak concentration, whereas changes in β influenced the rate of sorption and peak concentration to a lesser extent compared to Kd. Lower K1 values are associated with higher peak concentrations and decreased retention efficiency. Retention rates were evaluated by a numerical model and found as 28 ± 1% at a flow rate of 31 ml min⁻¹ and 17 ± 1% at 65 ml min⁻¹. The introduction of MPls into soil environments has been noted to modify transport dynamics into soil. As a result, these alterations effects hydrological characteristics of soil, thereby impacting quality of groundwater and agricultural output. The mean absolute error (MAE) of 6% between modeled and observed retention rates suggests minor discrepancies. This study highlights the importance of examining retention efficiency and the accuracy of numerical models in porous media during MPl transport.

Rocky Mountain or Brazilian spotted fever, caused by Ricketsia ricketssi, is a fulminant, seasonal, and neglected disease that occurs in focal points of North America and South America. Rapid detection are essential for a better prognosis and survival rate of the infected individual. However, disease diagnosis still faces challenges as the accuracy of many of the available laboratory tests fluctuates. Therefore, this review aimed to identify methods for antibody or antigen detection, its gaps, and evolution over time. A search was conducted to find all studies in the Pubmed database that described antibody or antigen detection for Ricketsia ricketsii infection. Initially, a total of 403 articles were screened. Of these articles, only 19 fulfilled the pre-established inclusion criteria and were selected. Among the different methods applied, the IFA technique was the one most frequently found in the studies. However, it presented varied results such as low specificity using the indirect method. Other techniques, such as ELISA and immunohistochemistry, were also found, although in smaller numbers and with their own limitations. Although some studies showed promising results, there is a pressing need to find new techniques to develop a rapid and effective Ricketsia ricketssi infection diagnosis.

The recurrent coronavirus outbreaks in China (SARS-CoV and its relative, SARS-CoV-2) have raised speculations that perhaps Asians are somehow more susceptible to these coronaviruses. Here, we test this possibility based on an analysis of the lung-specific expression of ACE2, which encodes the known cell-entry receptor of both SARS-CoV and SARS-CoV-2. We show that ACE2 expression is not affected during tumorigenesis, supporting that the abundant transcriptomes in cancer genomic studies can be informatively used to study ACE2 expression among diverse individuals without cancer. We find that ACE2 expression in the lung increases with age, but is not associated with sex. Further, Asians do not differ from other populations for ACE2 expression and do not harbor unique genetic polymorphisms in the ACE2 locus. Thus, beyond illustrating an innovative method for assessing the potential impacts of demographic factors for non-cancer diseases from large-scale cancer sample datasets, our statistically robust findings emphasize that individuals of all races require the same level of personal protection against SARS-CoV-2.

In my paper, a divisibility rule named the Queen function is de- scribed, which has been generalized for every integer and encompasses all pre- viously established divisibility rules. Subsequently, the paper discusses other areas where this function is instrumental. These include simplifying ratios, generating prime numbers, and more. Towards the end of the paper, a set of hypotheses is also presented. Among these, the most significant is the novel approach involving the application of the Queen function for prime number generation.

Indigenous peoples around the world are revitalizing their ancestral beliefs, practices, and languages, including traditional understandings of health and wellbeing. In the Aotearoa (New Zealand) context, a number of ground-breaking Māori health and wellbeing related models have emerged, each with their own unique applications and offerings. We sought in these qualitative studies to explore and identify some key sources of wellbeing for Māori individuals. Nine interviews were conducted with members of Māori communities to identify key themes of Māori wellbeing. A Reflexive Thematic Analysis was performed on these data, and then a further fifteen interviews were conducted to revise, refine, and reposition the previously generated themes. The Ngaruroro describes wellbeing as the embodied and active process of being in good relation with your 1) here tāngata (social and familial ties), 2) te taiao (the environment), and 3) taonga tuku iho (cultural treasures), while doing what you can to make lifestyle choices that are conducive to the health of your 4) tinana (body) and 5) wairua (spirit), while cultivating a balanced 6) ngākau (inner-system), fulfilling 7) matea (core needs), and exercising your 8) mana (authority). These themes illustrate and reiterate that Māori wellbeing is dynamic, interconnected, and holistic.

Bacillus subtilis NCD-2 showed a promising biocontrol effect against plant soil-borne diseases, and was developed as a commercial microbial fungicide against cotton verticillium wilt in China. Spores were main ingredient of the fungicide and played the crucial role in biological control of plant diseases. Therefore, the key to reducing the cost of fungicide was to find ways to increase the number of spores of strain NCD-2 during fermentation. In this study, 755 substances were evaluated by Phenotype Microarray technology, and 5 kinds of carbon sources and 1 kind of nitrogen source were found to promote the metabolism of strain NCD-2. Among carbon sources L-arabinose showed strongest ability to promote the bacteria growth and sporulation of strain NCD-2. When L-arabinose was used as a single carbon source, it could increase the bacteria concentration and the sporulation efficiency of strain NCD-2 by 2.04 times and 1.99 times, respectively, compared with D-glucose. Moreover, L-arabinose could significantly decrease the autolysis of strain NCD-2 revealed by microscopy observation. The mechanism for promoting the sporulation by L-arabinose was analysed by RNA-seq sequencing. Results showed that total of 790, 923 and 1270 genes were up-regulated and 639, 850 and 1001 genes were down-regulated under L-arabinose supplies when cultured at 8 h, 12 h and 16 h, respectively. Fourteen differentially expressed genes associated with arabinose transportation and sporulation were selected for qRT-PCR analysis, and the result showed basically consistent expression trend with transcriptome. Notably, genes associated with arabinose metabolism, sporulation, spore resistance to heat and spore coat formation were significantly up-regulated, and genes associated with sporulation-delaying protein were significantly down-regulated under L-arabinose supplies. msmX gene which was involved in arabinose transport in Bacillus genus was deleted, and the mutant decreased the growth and sporulation by 53.71% and 86.46%, respectively, when compared to strain NCD-2 wild type. Complementary of the mutant by importing intact msmX gene could restore the growth and sporulation of the mutant strain. In conclusion, arabinose played an important role in regulating the growth and sporulation of strain NCD-2.

In this study, we aimed to reveal the proinflammatory effects of serum 25-hydroxyvitamin D3 (Vit D) deficiency and insufficiency in new-onset type 2 diabetes mellitus (T2DM) and prediabe-tes. We recruited 84 prediabetes patients, 94 new-onset T2DM patients and 113 healthy partici-pants. Levels of C-reactive protein (CRP), fibrinogen, ferritin, interleukin-1β (IL-1β), interleu-kin-6 (IL-6), interleukin-8 (IL-8), tumour necrosis factor-α (TNF-α), nuclear factor kappa-B (NF-κB) and mitogen-activated protein kinase (MAPK) were measured in the serum of the par-ticipants. The ANOVA Bonferonni and Kruskal Wallis Dunn tests were used for the comparison of the inflammation markers and vitamin D levels between the groups. Upon covariance analy-sis with age, gender and BMI; Vit D levels of T2DM group were found to be significantly lower (p<0.003). Proinflammatory markers and CRP were found to be significantly higher in predia-betic and diabetic subjects. In the prediabetes group, IL-1β, IL-6, IL-8, TNF-α and MAPK were significantly higher in those with Vit D insufficiency and deficiency groups. In T2DM group, IL-1β, IL-6, IL-8, TNF-α, NF-κB, MAPK and CRP were significantly higher in those with Vit D insufficiency and deficiency. Our study emphasizes the proinflammatory effects of Vit D on the initiation of inflammation in both prediabetes and diabetes mellitus.

The spike protein (S) of SARS-CoV-2 has been shown to bind to the human angiotensin-converting enzyme 2 (ACE2) receptor with much higher affinity compared to other coronaviruses. The binding interface between the ACE2 receptor plays a critical role in the entry mechanism of SARS-CoV-2 virus. There are specific amino acids involved in the interaction between S protein and ACE2 receptor. This specificity is critical for the virus to establish a systemic infection and cause COVID-19 disease. In the ACE2 receptor, the largest number of amino acids that play a crucial role in the mechanism of interaction and recognition with the S protein are located in the C-terminal part which represents the main binding region between ACE2 and S. This fragment is abundant in coordination residues such as aspartates, glutamates and histidine that could be targeted by metal ions. Zn²⁺ ion binds to the ACE2 receptor in its catalytic site and modulates its activity, but it could also contribute to the structural stability of the entire protein. The ability of the human ACE2 receptor to coordinate metal ions, such Zn²⁺, in the same region where it binds to the S protein could have a crucial impact in the mechanism of recognition and interaction of ACE2-S with consequences on their binding affinity that deserve to be investigated. To test this possibility, this study aims to characterize the coordination ability of Zn²⁺, and also Cu²⁺ for comparison, with selected peptide models of the ACE2 binding interface using spectroscopic and potentiometric techniques.

A significant proportion of SARS-CoV-2 infections in Africa are identified as asymptomatic, facilitating the silent spread of the virus especially in populated urban cities. With the surge of the highly transmissible Omicron variant, the inclusion of asymptomatics in epidemiological surveys is key in estimating true infections and seroprevalence in the population. The aim of the study was to determine seroprevalence, active infection and circulating variants in Accra, the capital city of Ghana during the Omicron wave. The study was a cross-sectional survey conducted in 22 municipalities in December 2021. Naso-oropharyngeal swabs and serum samples were collected from 1027 individuals aged 5 years and above, for detection of infection by RT-qPCR and estimation of total antibodies using the WANTAI ELISA kit. Our results show 10% SARS-CoV-2 prevalence, with the Omicron and Delta variants accounting for 44.1% and 8.8% of infections, respectively. Omicron was most prevalent (48.9.%) among the 20–39-year-olds. Asymptomatic individuals accounted for 75.2% of infections. Seropositivity within the population was 86.8%, with the 60+ year group having significantly higher likelihood of exposure (OR 10.22: 95% CI: 3.51-29.73; p<0.001). This high seroprevalence appears to have been as a result of increased vaccination among this group (OR 2.7: 95% CI 1.78-4.09, p < 0.001). The high seropositivity of SARS-CoV-2 in the capital could be a good indication of herd immunity among the population and while the low infection rate supports the role of vaccination in reducing viral transmission.

After the increasing number of SARS-CoV-2 infections all over the world, researchers and clinicians are struggling to find a vaccine or innovative therapeutic strategies to treat this viral infection. The SARS-CoV infection that occurred in 2002, MERS and other more common infectious diseases such as HCV, led to the discovery of many RNA-based drugs. Among them, siRNAs and antisense LNAs have been demonstrated to have effective antiviral effects both in animal models and humans. Owing to the high genomic homology of SARS-CoV-2 and SARS-CoV (80-82%) the use of these molecules could be employed successfully also to target this emerging coronavirus. Trying to translate this approach to treat COVID-19, we analyzed the common structural features of viral 5’UTR regions that can be targeted by non-coding RNAs and we also identified miRNAs binding sites suitable for designing RNA-based drugs to be employed successfully against SARS-CoV-2.

Various symptoms have been reported to persist beyond acute phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which is referred as long coronavirus disease 19 (long COVID-19). Over 65 million individuals suffer from long COVID-19. However, the causes of long COVID-19 have been largely unknown. Since long COVID-19 symptoms are observed throughout the body, vascular endothelial dysfunction may be a strong candidate to induce long COVID-19. The angioten-sin-converting enzyme 2 (ACE2), the entry receptor of SARS-CoV-2, is ubiquitously expressed in endothelial cells. We previously found that the risk factors for athero-sclerotic cardiovascular disease (ASCVD) and the history of ASCVD can be the risk for severe COVID-19, suggesting a contribution of pre-existing endothelial dysfunction to severe COVID-19. Here, we show a significant association of endothelial dysfunction with development of long COVID-19 and show that biomarkers for endothelial dysfunction in patients with long COVID-19 are also crucial players in development of ASCVD. We consider the influence of long COVID-19 on development of chronic kidney disease (CKD) and ASCVD. Furthermore, we suggest the therapeutic interventions for long COVID-19 by considering endothelial dysfunction as the treatment tar-gets for long COVID-19. Such interventions may prevent pandemic of CKD and ASCVD in post COVID-19 era.

There are four types of influenza viruses: A, B, C, and D. Cattle are susceptible to influenza D infection and serve as a reservoir for this seven-segmented influenza virus. It is generally thought that bovines are not a host of other types of influenza viruses including type A. This long-standing viewpoint has been challenged by the recent highly pathogenic avian influenza (HPAI) H5N1 outbreaks in dairy cows in the United States. To date, HPAI H5N1 has spread into nine states affecting 49 dairy herds and causing clinical symptoms including reduced appetite, fever, and a sudden drop in milk production. This review article describes the current epidemiological landscape of HPAI H5N1 in U.S. dairy cows and the recent interspecies transmission events of HPAI H5N1 in other mammals reported in other countries. The review also discusses adaptation mutations of HPAI H5N1 to mammalian hosts and vaccination strategies. Finally, it summarizes some immediate questions that need to be addressed towards a better understanding of infection biology, transmission, and immune response of HPAI H5N1 in bovines.

Water is essential for all life on earth. It is a major component that makes up living organisms and plays a vital role in multiple biological processes. It provides a medium for chemical and enzymatic reactions in the cell and is a major player for osmoregulation and maintenance of cell turgidity. Despite this, many organisms, called anhydrobiotes, are capable of surviving under extremely dehydrated conditions. Less is known about how anhydrobiotes adapt and survive under the desiccation stress. Studies have shown that morphological and physiological changes occur in anhydrobiotes in response to desiccation stress. Certain disaccharides and proteins, including heat shock proteins, intrinsically disordered proteins, and hydrophilins, play important roles in the desiccation tolerance of the anhydrobiotes. In this review, we summarize the recent findings of the desiccation tolerance in the budding yeasts Saccharomyces cerevisiae. We also propose that the yeast under desiccation could be used as a model to study neurodegenerative disorders.

An asymmetric [3+2] cycloaddition of azomethine ylides generated from the corresponding imino esters to trans-β-nitrostyrene catalyzed by chiral aziridine-containing phosphines and phosphine oxides is described. Of the sixteen stereoisomers that could be formed as a result of the title reac-tion, three were formed, two of which were obtained in an enantiomerically enriched or pure form, and one in a racemic form. One of the products underwent epimerization under basic reaction conditions.

This paper presents the results of a study on the nature, pace and directions of socio-economic and spatial changes in the Warsaw Metropolitan Area (WMA) related to the suburbanization processes taking place there. In the course of research, data on municipalities from the Local Data Bank were used, and that made it possible to trace the changes occurring in 2002-2021. The selection of ana-lytical characteristics took into account a wide range of socio-economic diversities of the area related to changes in population, construction activity and housing stock, land use and spatial policy, economic and associated with investment attractiveness. The components of diversification were used to develop the typological classes of municipalities. The final result of the study is the identification of the specificity of suburbanization in the OMW which is reflected in the social, economic and spatial aspects of the functioning of municipalities.

The main source of electricity worldwide stems from fossil fuels, contributing to air pollution, global warming, and associated adverse effects. This study explores wind energy as a potential alternative. Nevertheless, the variable nature of wind introduces uncertainty in its reliability. Thus, it is necessary to identify an appropriate machine learning model capable of reliably forecasting wind speed under various environmental conditions. This research compares the effectiveness of artificial neural networks (ANN) and convolutional neural networks (CNN) in predicting wind speed across three locations in South Africa, characterised by different weather patterns. Empirical results show that CNN outperforms ANN in accurately forecasting wind speed under different weather conditions. This superiority is likely due to the inherent architectural attributes of CNNs, including feature extraction capabilities, spatial hierarchy learning, and resilience to spatial variability. These results could be useful to decision-makers in the energy sector.

This research delves into a detailed exploration of the thermal decomposition behavior of bio-based polymers, specifically Thermoplastic Starch (TPS) and Poly(lactic acid) (PLA). The study focuses on the impact of three different heating rates under a nitrogen atmosphere, providing comprehensive insights into the thermal stability of these eco-friendly polymers. In particular, the TPS kinetic model is examined, encompassing the decomposition of three distinct fractions. In contrast, PLA exhibits a simplified kinetic behavior requiring only a fraction described by a zero-order model. The kinetic study involves a systematic investigation into the individual contributions of key components within TPS, including starch, glycerin, and polyvinyl alcohol (PVA). This nuanced analysis contributes to a comprehensive understanding of the thermal degradation process of TPS and PLA, enabling the optimization of processing conditions and the prediction of material behavior across varying thermal environments. Furthermore, the incorporation of different starch sources and calcium carbonate additives in TPS enhances our understanding of the polymer's thermal stability, offering insights into potential applications in diverse industries.

The aviation industry has proposed multiple solutions to reduce fuel consumption, air pollution and noise at airports, one of which involves deploying electric trucks for aircraft towing between the stand and the runway. However, the introduction of tow trucks results in increased surface traffic, posing challenges from the perspective of Air Traffic Controller's (ATCO). Various solutions involving automated planning and execution have been proposed, but many are constrained by their inability to manage multiple active runways simultaneously, and their failure to account for tow truck battery state-of-charge during assignments. This paper presents a novel system for taxi operations that employs autonomous tow trucks to enhance ground operations and address deficiencies in existing approaches. The system focuses on identifying conflict-free solutions that minimize taxi-related delays and route length while maximising the efficient use of the tow trucks. The algorithm operates at a strategic level and uses a centralised approach. It has the capacity to cater for multiple active runways and considers factors such as tow truck battery state-of-charge and availability of charging stations. Furthermore, the proposed algorithm is capable of scheduling and routing tow trucks for aircraft taxiing without generating traffic conflicts.

In recent years, machine learning (ML) techniques have emerged as an efficient and effective technology within the petroleum industry. This paper focuses on the current application of ML in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits of climate-change mitigation strategy. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure-Volume-Temperature (PVT) properties estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. In this comprehensive review, we catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML building process, and analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry.

The early detection of small cracks in large structures is a crucial requirement for the implementation of structural health monitoring (SHM) system with a low transducer density. This work presents the results of the analysis of scattering of a submillimeter notch type defect with semielliptical shape with a groove at constant width 100µm in a 4.1mm thick aluminum plate. The defect is investigated with an ultrasonic guided wave (UGW) A0 mode at 550 kHz to cover long range in a thick plate. The mode selection is obtained by interdigital transducers designed to operate with 5mm central wavelength. The novel contribution is the assessment of the scattering amplitudes at different orientations of the defect when the distance is large as 35 cm in pulse-echo mode or 70 cm in pitch-catch mode. The system model is based on Finite Element Model (FEM) simulations and the results are in good agreement with experimental tests. The detection of the defect is obtained by comparing the scattered signals from the defect with base line signals of the pristine laminate. The analysis of scattering features and the efficient excitation with a probing UGW show the potential implementation in real structural health monitoring (SHM) systems.

Chronic lymphocytic leukemia (CLL) is a distinct category of lymphoproliferative disorder characterized by the clonal expansion of mature B cells, followed by their accumulation in primary and secondary lymphoid organs. Cluster of differentiation (CD) markers such as CD20, CD22, CD23, CD5, and CD81 serve as reliable prognostic indicators and disease progression factors in CLL. The human leukocyte antigen (HLA) system is a critical genetic variable in many diseases, with well-documented associations between HLA and various cancers. This study aims to investigate, for the first time, potential connections between HLA typing and CD marker expression in CLL. Despite being one of the most prevalent neoplasms in developed regions, there is a need for biomarkers that can improve survival predictions and identify possible genetic determinants. Methods: This study included 66 CLL patients and 100 controls, with all samples analyzed using biochemical methods, flow cytometry, and cytomorphology. Next-generation sequencing (NGS) was performed for HLA typing. Results: The results indicate that several CD markers are statistically associated with different HLA alleles. Specifically, CD20 was associated with HLA-DRB1*11:04:01; CD45 with HLA-C*07:01:01; CD79b with HLA-DPA1*02:01:02; CD23 with HLA-B*39:01:01; CD43 with HLA-DRB1*15:01:01; CD22 with HLA-B*49:01:01, HLA-C*07:01:01, HLA-DPB1*02:01:02, and HLA-DRB1*07:01:01; and CD81 with HLA-DPB1*04:02:01, HLA-DQA1*01:04:01, and HLA-DQB1*05:03:01.Conclusion: In conclusion, this research demonstrates significant statistical links between HLA genes and immunophenotypic markers in CLL patients, shedding new light on the immunological context of CLL.

Geographic information systems (GIS) are critical for mapping the geographical distribution of illness prevalence, disease transmission patterns, and spatially modeling environmental elements of disease occurrence. This study was purposively to map and cluster five diseases such as cholera, diabetes, malaria, asthma and hypertension using GIS techniques in Southern Malawi. Getis-Ord Gi* statistic was used to determine spatial distribution of diseases. Data were acquired from Malawi Demographic Health Survey (DHS) and assessed geographically using spa-tial-statistical analytical techniques, including the kernel and hotspot models. These models were developed to investigate and exhibit the dissimilar patterns of the selected diseases, as well as to depict places of high concentration, and they may be useful in understanding local patterns of diseases and their geographical correlations. The study found that asthma is high in Mangochi, Balaka, Blantyre, Thyolo, Mulanje, Chikwawa and Nsanje districts. It was also revealed that hypertension patients were highly concentrated in Blantyre, Thyolo, Chikwawa, Zomba, Mangochi and Nsanje Districts. Mangochi, Phalombe, Nsanje and Chikwawa are high risk areas of Malaria and diabetes is highest in Blantyre. Cholera is high in Mangochi, Balaka, Blantyre, Thyolo, Mu-lanje, Chikwawa and Nsanje districts.

Leptospirosis is a zoonosis with global public health impact, particularly in poor socio-economic settings of tropical regions. Transmitted through urine-contaminated water or soil from rodents, dogs, and livestock, leptospirosis causes over a million clinical cases annually. Risk factors include outdoor activities, livestock production, and substandard housing that foster high densities of animal reservoirs. This One Health study in southern Chile examined Leptospira serological evi-dence of exposure people from urban slums, semi-rural, and farm settings, using the Extreme Gradient Boosting algorithm to identify key influencing factors. In urban slums, age, shrub terrain, distance to Leptospira-positive households, and neighborhood housing density were contributing factors. Human exposure in semi-rural communities was linked to environmental factors (tree, shrub, and lower vegetation terrain) and animal variables (Leptospira-positive dogs and rodents, and proximity to Leptospira-positive households). On farms, dog counts, animal Leptospira preva-lence, and proximity to Leptospira-contaminated water samples were significant drivers. The study underscores that disease dynamics vary across landscapes, with distinct drivers in each community setting. This case study demonstrates how the integration of machine learning with comprehensive cross-sectional epidemiological and geospatial data provides valuable insights into leptospirosis eco-epidemiology. These insights are crucial for informing targeted public health strategies and for generating hypotheses for future research.

Plants are subjected to abiotic stresses throughout their developmental period. Abiotic stresses include drought, salt, heat, cold, heavy metals, nutritional element and oxidative stresses. Improving plant response to various environmental stresses is critical for plant survival and perpetuation. The WRKY transcription factors have special structure (WRKY structural domains), which enable WRKY transcription factors to have different transcriptional regulatory functions. The WRKY transcription factors can not only regulate abiotic stresses response and plant growth and development by regulating phytohormone signalling pathways, but also promote or suppress the expression of downstream genes by binding to the W-box [TGACCA/TGACCT] in the promoters of their target genes. In addition, WRKY transcription factors not only interact with other families of transcription factors to regulate plant defence responses in abiotic stresses, but also self-regulate by recognizing and binding to W-boxes in their own target genes to regulate their defence responses to abiotic stresses. However, in recent years, research reviews on the regulatory roles of WRKY transcription factors in higher plants are scarce and shallow. In this review, we focus on the structure and classification of WRKY transcription factors, as well as the identification of their downstream target genes and molecular mechanisms involved in the response to abiotic stresses, which can improve the tolerance ability of plants in abiotic stress and we also look forward to their future research directions, with a view to providing theoretical support for the genetic improvement of crop abiotic stresses tolerance.

Mannosylerythritol lipids (MELs), one of the most promising biosurfactants (BS), are glycolipids produced by yeasts or fungi, with great environmental performance and a high compatibility with the human body. MELs besides working as typical surfactants, can form diverse structures when in the critical aggregation concentration (CAC), reducing the surface tension of water and other solutions, and being stable over a wide range of conditions. Among others, MELs present anti-microbial, antitumor, antioxidant and anti-inflammatory activity, skin and hair repair capacity, which opens possibilities for their use in applications from cosmetics and pharmaceutics to bio-remediation and agriculture. However, their market share is still low when compared to other glycolipids, due to their less developed production process and higher production cost. This re-view gathers information on the potential applications of MELs mentioned on the literature since 1993. Furthermore, it is also explored the current strategies being developed to enhance the market presence of MELs, in parallel with the ones developed for rhamnolipids and sophorolipids.

The demand for a more sustainable agriculture, coupled with the need for optimized crop productivity, has driven the use of microorganisms for the biocontrol of diseases and pests, as well as for growth promotion. The use of plant growth-promoting rhizobacteria (PGPR) and plant growth-promoting fungi (PGPF) in Brazilian production systems has become increasingly common, with the number of microbial inoculant registrations in the country growing every year. PGPR and PGPF occupy various niches in the rhizosphere, playing a crucial role in soil nutrient cycling, as well as influencing various plant physiological processes. In this review, we address the main mechanisms used by these microbial agents in growth promotion and the use of strain co-inoculation as a strategy to enhance the efficacy of these products. Subsequently, we conduct an analysis of the available inoculants in Brazil, detailing the microorganisms available for the main Brazilian crops, and the prospects of this market regarding research and development of new products for the coming years based on the current challenges faced.

The purpose of this study was to identify candidate novel species performing beneficial ecological functions from soil. The hypothesis was that novel culturable species with antibiotic production potential could be readily found from soil in the Nature Canyon at Los Angeles Pierce College. 137 bacterial isolates from soil were grown on nutrient agar, ISP-6 medium, or LB agar and partial 16S was ascertained using the Sanger platform for regions V1-V4. One of the potentially novel isolates, Pseudomonas sp. 21-C3-ER, was isolated on nutrient agar from fallow soil in an agricultural nature preserve in Los Angeles. The soil conditions were 3.7% moisture, clay texture by touch, 13.37% organic matter, pH=6.63, TDS=638, 150 ppm nitrogen, 114 ppm phosphate, and 42 ppm potash. Whole genome sequencing was performed on the DNBseq platform and species identification was performed with TRUEBacID; the closest relative was Pseudomonas brassicacearum subsp. neoaurantiaca which was 92.05% similar by genomic evidence. 197 virulence factors were identified including flagellar biosynthesis, motor proteins, Beta-lactamase, Fosfomycin, copper, and sulfur drug resistance. Gram stain of strain 21-C3-ER revealed gram negative rods. Rods were visualized with scanning electron microscopy, revealing individuals approximately 1 µm in length. Secondary metabolite analysis using antiSMASH showed a variety of post-translationally modified proteins and a promysalin analog. The qualitative test for pyoverdine siderophore production using UV light was positive. Metabolic analysis using RAST indicated genes were present for pyoverdine production, antimicrobial resistance, and ammonification. The nitrate reduction test was positive. Denitrifying bacteria may prevent leaching of nitrates from soil, since ammonia has a higher affinity to soil and organic matter than nitrate. We propose Pseudomonas fernandeno as a novel species. The type stain is 21-C3-ER.

A positive fluid balance may evolve to fluid overload and associate with organ dysfunctions, weaning difficulties, and increased mortality in ICU patients. We explored whether individualized fluid management, assessing fluid-responsiveness via passive leg-raising maneuver (PLR) before spontaneous breathing trial (SBT) associated with less extubation failure in ventilated patients with high fluid balance, admitted to the ICU after liver transplantation (LT). We recruited 15 LT patients in 2023. Postoperative fluid balance was +4476 [3697,5722] mL. PLRs were conducted at ICU admission (T1) and pre-SBT (T2). Cardiac index (CI) changes were recorded before and after SBT (T3). Seven patients were fluid responsive at T1, and 12 at T2. No significant differences occurred in hemodynamic, respiratory, and perfusion parameters between fluid-responsive and fluid-unresponsive patients at any time. Fluid-responsive patients at T1 and T2 increased their CI during SBT from 3.1 [2.8,3.7] to 3.7 [3.4,4.1] mL/min/m2 (p= 0.045). All fluid-responsive patients at T2 were extubated after SBT and consolidated extubation. Two out of three of fluid-unresponsive patients experienced weaning difficulties. We concluded that fluid-responsive patients post-LT may start weaning earlier and achieve successful extubation despite high postoperative fluid balance. This highlights the profound impact of personalized assessment of cardiovascular state on critical surgical patients.

This research explores the development of thermoplastic vulcanizate (TPV) blends derived from natural rubber (NR) and ethylene-butene copolymer (EBC) using a specific blend ratio and melt mixing technique. A comprehensive full factorial design of experiments (DOE) methodology is employed to optimize the processing parameters. TPVs are produced through dynamic vulcanization, combining rubber crosslinking and melt blending within a thermoplastic matrix under high temperatures and shear. The physico-mechanical properties of these TPVs are then analyzed. The objective is to enhance the mechanical performance by assessing the influence of blend ratio, mixing temperature, rotor speed, and mixing time on crucial properties, including tensile strength, elongation at break, compression set, tear strength, and hardness. Analysis of variance (ANOVA) identifies the optimal processing conditions that significantly improve material performance. Validation is achieved through atomic force microscopy (AFM), confirming the phase-separated structure and thus a success of dynamic vulcanization. Rubber process analyzer (RPA) and dynamic mechanical analyzer (DMA) assessments provide insights into the viscoelastic behavior and dynamic mechanical responses. Deconvolution analysis of temperature-dependent tanδ peaks reveals intricate microstructural interactions influencing the glass transition temperature (Tg). The optimized TPVs exhibit enhanced stiffness and effective energy dissipation capabilities across a wide temperature range, making them suitable for applications demanding thermal and mechanical load resistance. This study underscores the pivotal role of precise processing control in tailoring the properties of NR/EBC TPVs for specialized industrial uses. It highlights the indispensable contribution of the DOE methodology to TPV optimization, advancing material science and engineering, particularly for industries requiring robust and flexible materials.

Androgenetic alopecia is a genetic disorder caused by an excessive response to androgens. This alopecia is the most common cause of progressive hair loss in men and causes low self-esteem in patients. In this study we used various methods, including quantitative PCR, flow cytometry, ELISA, and immunocytochemistry, to evaluate the biological effects of germinated hemp seeds extract and exosomes derived from calli of germinated hemp seeds. This research reveals pre-ventive functions of two biomaterials (GE2000: 2000 ug/mL germinated hemp seeds extract; E40: 40 ng/mL exosomes derived from calli of germinated hemp seeds) for androgenetic alopecia through three biochemical categories: genetic modulation in hair follicle dermal papilla stem cells (HFDPSCs), cellular differentiation, and modulation of immune systems. Despite exposure to dihydrotestosterone (DT), two materials upregulated alopecia preventing genes including Wnt, β-catenin, and TCF in HFDPSCs. Conversely, alopecia activating genes (STAT1, 5α-reductase type 1, and IL-15R) were suppressed. Moreover, two materials suppressed alopecia genes (NKG2DL, IL2-Rβ, JAK1, and STAT1) in CD8+ T cells. Notably, E40 was effected more in-tensely in these biochemical categories than those of GE2000. Consequently, E40 and GE2000 ef-fectively prevented DT stress. Additionally, two materials activated mechanisms for hair for-mation through the biological activities of three categories.