The objective was to compare the color match and color stability behavior of single and multishade resin-based composites (RBCs) used for direct restorations. This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Randomized clinical trials evaluating the shade performance of single-shade RBCs in direct restorations were included. A search of the scientific literature was performed in 5 databases (April 2024). The meta-analysis was performed using RevMan 5.4, calculating the risk difference (RD) and 95% confidence interval (CI) of the dichotomous outcome using a random effects model. Bias was assessed using the RoB 2.0 tool, and certainty of evidence was assessed using the GRADE tool. Four studies with 263 restorations analyzed were selected. The results showed comparable performance between single-shade RBCs and multishade RBCs in terms of color match and color stability over 12 months. Three studies had a low risk of bias with all expected results, and one study had some concerns. The certainty of evidence for color stability was considered low for all follow-up periods due to the small number of events and sample size. According to the United States Public Health Service Evaluation (USPHS) and the World Dental Federation (FDI), there is comparable clinical color performance between single-shade and multishade RBCs over 12 months.

Hypertension is a global public health challenge, with a high prevalence across various regions and significant implications for cardiovascular risk. This article reviews the effectiveness of herbal medicine as a potential therapeutic approach for hypertension, offering an alternative or complementary solution to conventional pharmacological treatments. It highlights several natural agents that have demonstrated efficacy in lowering systolic blood pressure (SBP) in clinical and meta-analytical studies. The methodology involved a systematic review of articles that discuss herbal therapeutics for hypertension, focusing on their efficacy compared to placebo and standard treatments. The findings suggest that certain nutraceuticals, such as garlic, hawthorn, and CoQ10, can significantly reduce SBP, presenting a viable option for managing hypertension. This review underscores the need for further research to validate these findings and optimize herbal treatment strategies, potentially expanding the use of these natural products in clinical practice. The article aims to increase awareness among medical professionals and patients about the benefits and applications of herbal medicines in treating hypertension.

Metabolites are at the end of the gene-transcript-protein-metabolism cascade. As such, metabo-lomics is the omic approach that offers the most direct correlation with phenotype. This allows that, where genomics, transcriptomics and proteomics fail to explain a trait, metabolomics might give an answer. Complex phenotypes, which are determined by the influence of multiple small effect alleles, are an example of these situations. Consequently, the interest in metabolomics has increased ex-ponentially in the last years. As a newer discipline, the metabolomics bioinformatics analysis pipe-lines are not as standardized as in the other omic approaches. In this review we synthesized the different steps that need to be carried out to obtain biological insight from the annotated metabolite abundance raw data. These steps were grouped in three different modules: preprocessing, statistical analyses and metabolic pathway enrichment. We included within each one of them the different state-of-the art procedures and tools that can be used depending on the characteristics of the study, providing details about the characteristics of each method has, as well as the issues the reader might encounter. Finally, we introduce genome scale metabolic modeling as a tool for obtaining pseu-do-metabolomic data in situations where its acquisition is difficult, being possible to analyze the resulting data with the modules of the described workflow.

The objective of the proposal is to find the optimal geometric shape of a photovoltaic panel that maximizes the collection of solar radiation. The optimal shape will be determined based on direct radiation, as it is the most relevant radiation for the photovoltaic process. To achieve this goal, we will use computer simulations to evaluate the performance of different geometric shapes under different conditions of solar radiation. Specifically, we will simulate the behavior of a photovoltaic panel with different shapes and orientations, and we will compare their energy yield over a given period of time. The simulations will be based on a mathematical model that takes into account the incidence angle of solar radiation on the panel, as well as other factors such as the reflection and absorption of radiation, and the temperature of the panel. The simulations will be carried out for different locations, with different solar radiation conditions, and for different periods of time, in order to obtain a comprehensive assessment of the performance of the different shapes. The results of the simulations will be analyzed to identify the optimal shape of the photovoltaic panel. This shape will be compared with the performance of flat panels, both static and with tracking systems, in order to evaluate the potential improvement in energy yield.

Nowadays, people like to travel to alleviate the stress and depression in their busy lives, and if they can do so at a low cost, it will be even more beneficial to them. The usual way for a traveler to book a ticket or to contact a travel agency is to book a ticket. It becomes more expensive for travelers. Due to the lack of vehicle management, travel agencies don’t get their appropriate profit. In this journal, we have provided proficient solutions for vehicle optimization to minimize travel costs. However, when travelers want to travel, they don’t find a proper tour guide, and it is sometimes expensive to arrange a proper tour guide. Moreover, travelers often don’t find a suitable package, and occasionally they do not know the place where they may want to visit. In this paper, we have discussed how we used effective methods to reduce vehicle costs for tour purposes. Further, we have developed a solution where bus seats are optimally occupied, and the package system and tour guide are included in it. We have also added a blog portal to our web-based software to introduce all tourist places and their information. In this journal, the proposed optimal vehicle usage system has been developed using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), the Tailwind Framework, React, and Node.js.

Aquatic macroinvertebrates play a crucial role in freshwater ecosystems, serving as reliable indicators of water quality and ecosystem health. To effectively monitor and assess these ecosystems, it is essential to employ appropriate sampling methods that capture the diversity and abundance of macroinvertebrate communities. The objective of this study was to provide a comprehensive overview and comparative analysis of different sampling methods for aquatic macroinvertebrates, highlighting their advantages, limitations, and applicability in ecological research was carried out. The results showed that the most commonly used methods include Kick-net Sampling, Surber Sampler, D-frame Net, Bottle Sampling, and Pitfall Traps. Researchers should select methods based on study goals, environmental conditions, and target organisms. Integrating multiple techniques enhances understanding of macroinvertebrate communities and their responses to environmental changes, contributing to improved assessments of water quality, ecosystem health, and conservation in freshwater ecosystems.

A frequent problem in Geotechnics is the soils with inadequate physical-mechanical properties before the efforts to which they will be subjected by the constructions, incurring cost overruns caused by their engineering improvement. The need to improve the engineering properties of soils is not recent, currently observing that the most common alternatives are binders such as cement and lime. The climate change observed in recent decades and the uncontrolled emission of greenhouse gases have motivated Geotechnical and Geoenvironmental researchers to seek mechanisms for soil reinforcement from a more sustainable and environmentally friendly approach by proposing the use of recycled and waste materials. An alternative is natural fibers, which can be obtained as waste from many agro-industrial processes, implying their high availability and low cost. Sawdust as a by-product of wood processing has a rough texture that can generate high friction between the fiber and the matrix of the soils, leading to a significant increase in its shearing strength and bearing capacity. This concept of improving the properties of soils using natural fibers distributed randomly is inspired by the natural phenomenon of grass and/or plants that when growing on a slope can effectively stabilize a said slope.

Nowadays, frameworks and models are critical to enabling organizations to identify their current sustainability integration into business and to follow up on these initiatives over time. In this context, the maturity models offer a structured way of analyzing how a supply chain meets specific sustainability requirements and which areas demand attention to reach maturity levels. This study proposes a five-level maturity model to help supply chains identify their level of engagement with sustainability practices combining three perspectives: intra and inter-organizational sustainability practices, triple-bottom-line approach and critical areas for sustainability. All the steps followed in constructing the maturity model were based on a literature review, and case studies supported its improvement, application, and testing. The proposed model presents many advantages, such as being used as a self-assessment tool, a roadmap for sustainability behaviour improvement, and a benchmarking tool to evaluate and compare standards and best practices among organizations and supply chains.

There is a strong need and demand from the United Nations, public institutions, and private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by the community volunteers. Results demonstrate that especially RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

This article addresses a challenge modeling novices face in model conceptualization: the recognition of behavior modes. Despite behavior modes being at the heart of system dynamics modeling, there is no unifying taxonomy for them, making them harder to learn for beginners. The article proposes criteria a taxonomy should satisfy, critically reviews previous taxonomies in the literature, and then introduces a new consistent taxonomy based on slope and curvature with mode names that refer only to visual cues and free of references to prior mathematical or domain-specific knowledge. Evidence from an exploratory experiment suggests that novices actually have difficulties classifying curves when using previous taxonomies. Links from these elementary modes to analogous terms in other taxonomies in diverse disciplines allow this taxonomy to be related to other ones. The article concludes by mentioning relevant limitations and future steps.

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

In this paper, we review devices used in silicon photonic switches. Devices in switches are divided into active and passive devices. Active devices consist of microring resonator, contra directional couplers, mach zhender switches. Passive devices consist of waveguide crossings and arrayed waveguide gratings. We also list the state of the art in devices in a comparison table.

This paper shows the proposal of the use of linked data for smart metering systems. The aim of linked data in smart grid applications such as smart metering systems is to give semantic knowledge of the energy transactions between all participators in the power markets. In combination with other technologies like blockchain, the linked data could improve decentralization and security in the world's energetic future.

Neither tumor growth nor regression is truly spontaneous, but both may under special circumstances be driven by similar events. We describe a sequence of processes that typically leads to tumor progression but may on occasion inadvertently result in regression. A possible procedure for reducing tumor mass through a controlled intervention is also outlined.

The Rasch model is one of the most prominent item response models. In this article, different item parameter estimation methods for the Rasch model are compared through a simulation study. The type of ability distribution, the number of items, and sample sizes were varied. It is shown that variants of joint maximum likelihood estimation and conditional likelihood estimation are competitive to marginal maximum likelihood estimation. However, efficiency losses of limited-information estimation methods are only modest. It can be concluded that in empirical studies using the Rasch model, the impact of the choice of an estimation method with respect to item parameters is almost negligible for most estimation methods. Interestingly, this sheds a somewhat more positive light on old-fashioned joint maximum likelihood and limited information estimation methods.

This paper presents a novel statistical model i.e. the Laplace-Rician distribution, for the characterisation of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterising SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed Laplace-Rician model is investigated for SAR images of several frequency bands and various scenes in comparison to state-of-the-art statistical models that include K, Weibull, and Lognormal. The results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes.

The inherent trace quantity of primary fatty acid amides found in biological systems presents challenges for analytical analysis and quantitation, requiring a highly sensitive detection system. The use of microfluidics provides a green sample preparation and analysis technique through small-volume fluidic flow through micron-sized channels embedded in a PDMS device. Microfluidics provides the potential of having a micro total analysis system where chromatographic separation, fluorescent tagging reactions, and detection are accomplished with no added sample handling. This study describes the development and optimization of a microfluidic-laser indued fluorescence (LIF) analysis and detection system that can be used for the detection of ultra-trace levels of fluorescently tagged primary fatty acid amines. A PDMS microfluidic device was designed and fabricated to incorporate droplet-based flow. Droplet microfluidics have enabled on-chip fluorescent tagging reactions to be performed quickly and efficiently, with no additional sample handling. An optimized LIF optical detection system provided fluorescently tagged primary fatty acid amine detection sub-fmol (436 amol) LODs. The use of this LIF detection provides unparalleled sensitivity, with detection limits several orders of magnitude lower than currently employed LC-MS techniques and might be easily adapted for use as a complementary quantification platform for parallel MS-based -omics studies.

The human proteome is composed from diverse and heterogeneous gene products/proteoforms. Previously, we have been discussing the main technical aspects in developing for inventory of human proteoforms that would be visually attractive, clear, and easy to search (Naryzhny S. J. Proteomics 2018, S1874-3919(18) 30220-3). Here, we present our first draft of the database of proteoforms that is based on this discussion. The database principles and structure are described. The database is called “2DE-pattern” as it contains multiple isoform-centric patterns of proteoforms separated according to 2DE principles.

Big Data warehouses are a new class of databases that largely use unstructured and volatile data for analytical purpose. Examples of this kind of data sources are those coming from the Web, such as social networks and blogs, or from sensor networks, where huge amounts of data may be available only for short intervals of time. In order to manage massive data sources, a strategy must be adopted to define multidimensional schemas in presence of fast-changing situations or even undefined business requirements. In the paper, we propose a design methodology that adopts agile and automatic approaches, in order to reduce the time necessary to integrate new data sources and to include new business requirements on the fly. The data are immediately available for analyses, since the underlying architecture is based on a virtual data warehouse that does not require the importing phase. Examples of application of the methodology are presented along the paper in order to show the validity of this approach compared to a traditional one.

Cooking is a very decentralized and private way of energy consumption in human activities. The existing investigation and statistical analysis can’t effectively calculate the proportion of population relying on clean cooking fuel and utilization technology in the region. Therefore, based on the big data of the earth, this paper adopts the combination of spatial analysis and statistical analysis to determine the survey sample area, and according to the economic conditions, topographic characteristics, national policies for new energy construction and living habits of the provinces and cities under investigation, the questionnaire survey is conducted for the research area to calculate the proportion of the regional population relying on clean cooking fuels and utilization technologies. Taking the south of the Yangtze River in China as an example, the paper effectively calculates that 88.25% of the population depends on clean cooking fuel and technology in this region, of which 89.81% are in urban areas and 79.87% are in rural areas. Analysis of the survey data shows that the proportion of the population using clean cooking fuels and technologies is related to factors such as economic development, income and resource endowment. There is a large urban–rural gap in terms of energy consumption tendency and structure in the south of the Yangtze River in China and cooking energy consumption in rural households also varies from region to region. The technical ideas and conclusions of the paper have high reference application value, which can help promote the upgrading of clean energy utilization and provide data basis for relevant decision and policy making.

The studied area (the Ionian Islands: Paxoi, Lefkas, Kefalonia and Zakynthos), is situated in the western ends of Ionian Basin (IB) in contact with the Apulian Platform (AP) and named as Apulian Platform Margins (APM). The proposed model is based on fieldwork, previously published data and balanced geologic cross-sections. Late Jurassic characterizes Ionian Basin to Early Eocene NNW–SSE extension followed by Middle Eocene to Middle Miocene compression (NNW–SSE directed). The space availability, the distance of Ionian Thrust from the Kefalonia strike-slip fault (KSSF) and the altitude between Apulian platform and Ionian basin that produced during extensional regime were the main factors for the produced structures due to inversion tectonic. In Zakynthos Island, the space availability (far from KFT), and the reactivation of normal bounding faults formed an open geometry anticline (Vrachionas anticline) and a foreland basin (Kalamaki thrust foreland basin). In Kefalonia Island, the space from FKT was limited and the tectonic inversion formed anticline geometries (Aenos Mountain), nappes (within the Aenos Mountain) and small foreland basins (Argostoli gulf), all within the margins (APM). In Lefkas Island, the lack of space, very close to KFT, lead to the movement of the Ionian basin over the margins, attempting to overthrust the Apulian platform. Because the obstacle between basin and platform was very large, the moving part of Ionian Basin strongly deformed producing nappes and anticlines in the external part of the Ionian basin, and a very narrow foreland basin (Ionian Thrust foreland basin).

While molecular methods have begun to transform ecology, most algal biodiversity is still studied using the classical approach of identifying microalgae by light microscopy directly in sample material or using cultures. In this study, we compare both approaches (light microscopy and metagenomics as a molecular approach) using freshwater ponds of the Eifel National Park in Germany as a case study. The ponds were found to be rich in desmids by light microscopy. A total of 299 species representing 81 genera were identified by light microscopy. While the molecular method does not currently allow species identification in most cases, we were able to identify 207 different algal genera. 157 genera were detected only by metagenomics, 50 genera with both methods and 31 genera were found by light microscopy, highlighting the need to continue using light microscopy in addition to a molecular approach. The metagenomics method has several advantages over the light microscopy method: 1) deeper assessment of alpha biodiversity 2) better abundance numbers 3) complete coverage of all living matter. The latter is also a significant improvement over metabarcoding, as universal PCR primers are not available.

Despite being extremely potent against malignancies, cisplatin (CIS) has limited practical applicability due to its adverse effects such as testicular damage. Consequently, it becomes necessary to reduce its toxicity. In this study, cilostazol, a selective phosphodiesterase-3 inhibitor that is frequently used in the treatment of intermittent claudication, was examined for its ability to abrogate CIS-induces testicular toxicity and its ameliorative effect was compared with two phosphodiesterase inhibitors, tadalafil and pentoxifylline. The study also focused on the possible mechanisms involved in the proposed protective effect. Ten groups of rats were included; Group (1): control, Groups (2-4): 5 mg/kg tadalafil or 75 mg/kg pentoxifylline or 20 mg/kg cilostazol respectively), Group (5):7 mg/kg CIS, Group (6&7): CIS + 5 mg/kg tadalafil or 75 mg/kg pentoxifylline, Group (8-10) CIS+ 5, 10 or 20 mg/kg cilostazol respectively. CIS treated rats showed a significant decrease in testicular function, serum testosterone and reduced glutathione levels, and significant elevation in malondialdehyde, total nitrite levels, tumor necrosis factor-alpha and nuclear factor-kappa β alongside caspase-3. These outcomes were confirmed by marked testicular architecture deterioration. Contrary, Cilostazol in a dose-dependent manner showed potential protection against testicular toxicity, reversion of the disrupted testicular function and improvement of the histological alterations through rebalancing of oxidative stress, inflammatory and apoptotic biomarkers. In addition, cilostazol exerted more pronounced protective effect in comparison to tadalafil and pentoxifylline. In conclusion, administrations of cilostazol might ameliorate cisplatin-induced testicular impairment through alteration of oxidative stress, inflammation, and apoptotic pathway, offering a promising treatment for cisplatin -intoxication in the testes.

Over the years, sustainability, impact on the environment, as well as the operation expenditure have been a major concern to the deployment of mobile cellular base stations worldwide. This is because the mobile cellular base stations are known to consume a high percentage of power within the mobile cellular network. Such energy consumption contributes to the emission of Greenhouse Gases (GHG) through the use of conventional diesel generating a set. As a result, the mobile cellular operators are faced with the dilemma of minimising the power consumption, GHG emission, and the operation cost, while improving the Quality of Service of the networks. In attempting to find a solution, this study presents the feasibility and simulation of a solar photovoltaic (PV) with battery hybrid power system (HPS) as a predominant source of power for a specific mobile cellular base station site situated in Soshanguve area of the city of Pretoria, South Africa. It also presents the technical development, showed the environmental advantage and cost benefits of using a solar PV-battery HPS to power a base station site of a 24 hrs daily load of 241.10 kWh/d and the peak load of 20.31 kW as compared to using the HPS of solar PV-diesel generating set-battery. The solar resource pattern for the city of Pretoria was collected from The National Aeronautics and Space Administration and modelled statistically. Thus, the statistical modelling done using solar radiation resource exposure characteristic patterns of Pretoria, South Africa, revealed an average annual daily solar radiation of 5.4645 Wh/m2/d and 0.605 clearness index. The simulation and the design were done using the Hybrid Optimization Model for Electric Renewables and Matlab/Simulink software. The simulation finding shows that the HPS of solar PV-battery combination has about 59.62 % saving on Net Present Cost, Levelized Cost of Energy, and 80.87% saving on Operating cost as against conventional BS powered with Gen Set-Battery.

Alfalfa, an important legume forage, is an ideal crop for sustainable agriculture and a potential bioenergy plant. Drought, one of the most common environmental stresses, substantially affects plants’ growth, development and productivity. MicroRNAs (miRNAs) are newly discovered gene expression regulators that have been linked to several plant stress responses. To elucidate the role of miRNAs in drought stress regulation of alfalfa, a high-throughput sequencing approach was used to analyze 12 small RNA libraries comprising of 4 samples, each with 3 biological replicates. We identified 348 known miRNAs, belonging to 80 miRNA families, from the 12 libraries and 281 novel miRNAs using Mireap software. 18 known miRNAs in roots and 12 known miRNAs in leaves were screened out as drought-responsive miRNAs. Except for miR319d and miR157a which were upregulated under drought stress, the expression pattern of drought-responsive miRNAs were different between roots and leaves in alfalfa. This is the first study discovering miR157a, miR1507, miR3512, miR3630, miR5213, miR5294, miR5368 and miR6173 are drought-responsive miRNAs. Target transcripts of drought-responsive miRNAs were computationally predicted. All 447 target genes for the known miRNAs were predicted using an online tool. This study provides a significant insight on understanding drought-responsive mechanisms of alfalfa.

The study assessed the impact of different diets on gastrointestinal pH, intestinal microbiota, digestive tract morphometry, and productive performance in guinea pigs. A total of 160 improved genotype guinea pigs were used, distributed into three groups with different diets: alfalfa (T1), alfalfa + concentrate (T2), and concentrate (T3), to which a mixture of probiotics combined with vitamins, minerals was added and amino acids in T2 and T3. Gastrointestinal pH showed significant differences between treatments in the various segments of the digestive tract. The intestinal microbiota varied according to the diet, with Escherichia coli being the predominant bacterium in weaned guinea pigs. Intestinal morphometry was significantly altered with the addition of a reinforced probiotic mixture, improving the length and density of intestinal villi. Productive parameters such as weight gain, feed consumption, and feed conversion were better in guinea pigs fed a mixed diet with 0.5% probiotic mixture. These results indicate that intestinal pH may influence microbial composition, which impacts productive efficiency and immune health, highlighting the importance of balanced nutrition and supplementation to optimize the health and performance of guinea pigs.

Keywords: Artificial Intelligence in Implantology; AI in Dental Implantology; Machine Learning in Dental Implantology; Deep Learning in Implant Dentistry; Robotics in Dental Implantology; AI-Based Implant Planning.

Bangladesh faces significant wheat grain deficiency compared to its demand due to the lengthy process (10-12 years) required to release a variety through a conventional breeding approach. Traditional breeding methods involve several years of various types of crosses between male and female parents and/or progenies, along with recurrent selection-crossing to obtain a homozygous line (F₁ to F₆ generations) with desired traits within almost 10 years. The double haploid (DH) method is also a time-saving, easier, and more reliable technique to achieve the desired traits in homozygous lines within 2-3 years. DH lines were produced by crossing two F₁ hybrids of spring wheat with maize. 0.1% solution of colchicine was applied to produce DH of F₂ progeny of wheat in the study. 80.8% (340) of the 421 pollinated florets from the two F₁ wheat hybrids were produced as green parthenocarpic caryopses (GPCs) (F₂). 35 of the 70 rescued embryos (or 20.6% of the GPCs) germinated. The germinated embryos regenerated 10 haploid green plants (14.3% of total embryos). After being applied colchicine, 8 of the regenerated haploid (H₁) plants made it. Out of the 8 plants that made it through, only one developed homozygous double haploid seed (DH₁ seed). These seeds were multiplied to produce DH₂, used in the preliminary yield in the next year. The results demonstrated the potential of this method to produce a homozygous line within a short period (2-3 years) by doubling the haploid line generated through pollination of spring bread wheat F₁ with maize.

Chronic conditions like type 2 diabetes mellitus (T2DM), arterial hypertension (HTN), and obesity are major global health issues, causing significant morbidity and mortality. In Latin America, these conditions contribute to millions of premature deaths annually, posing challenges exacerbated by limited healthcare access and poor primary care quality, particularly in Mexico. Complementary medicine presents a potential solution, offering minimally invasive techniques to improve physical, mental, and spiritual well-being. Effective treatment adherence, crucial for positive outcomes, is influenced by self-efficacy, the ability to manage one's behavior in response to medical conditions. However, treatment adherence rates remain low, indicating a pressing public health concern. This study aimed to explore how perception of complementary medicine and treatment adherence predict self-efficacy among individuals with chronic conditions in Mexico. Analyzing data from 113 participants revealed significant correlations between variables, with treatment adherence positively associated with self-efficacy and perception of holistic medicine negatively correlated with self-efficacy. Regression analysis showed that incorporating perception of complementary medicine and treatment adherence significantly explained self-efficacy levels, suggesting the potential of complementary therapies in holistic healthcare approaches. However, collinearity between variables highlights the need for further research to better understand these relationships and their implications for improving healthcare outcomes in Mexico. Integrating complementary therapies with conventional treatments could enhance overall well-being and treatment adherence, offering promising avenues for addressing chronic conditions in the region. These findings underscore the importance of comprehensive healthcare approaches that integrate both conventional and complementary modalities to optimize health outcomes and quality of life for individuals with chronic conditions in Mexico and beyond.

Familial pancreatic cancer (FPC) represents a significant yet underexplored area in pancreatic cancer research. Basic research efforts are notably limited, and when present, they are predominantly centered on BRCA1 and BRCA2 mutations due to the scarcity of other genetic variants associated with FPC, leading to a limited understanding of the broader genetic landscape of FPC. This review examines the current state of FPC research, focusing on the molecular mechanisms driving pancreatic ductal adenocarcinoma (PDAC) progression. It highlights the role of homologous recombination (HR) and its therapeutic exploitation via synthetic lethality with PARP inhibitors in BRCA1/2-deficient tumors. The review discusses various pre-clinical models of FPC, including conventional two-dimensional (2D) cell lines, patient-derived organoids (PDOs), patient-derived xenografts (PDXs), and genetically engineered mouse models (GEMMs) as well as new advancements in FPC research.

This research ventures into the critical public health challenge of childhood obesity by exploring the dynamic interplay between psychological well-being and Body Mass Index (BMI) throughout various developmental stages of childhood. It delves into how emotional regulation, attachment dynamics, and social relationships correlate with obesity from early childhood to adolescence. Highlighting key findings, such as the negative correlation between psychological resilience and higher BMI in young children, the impact of social relationships on obesity risk during pre-adolescence, and the link between adaptive emotional strategies and higher BMI in adolescents, this study brings to the fore the nuanced relationship between psychological factors and obesity. Utilizing the interdisciplinary process of bioinformatics, this research synergizes psychometric and biomedical data to unearth psychological markers critical for crafting targeted, age-appropriate interventions. This study advocates for a holistic healthcare approach, emphasizing the integration of psychological support within obesity prevention and management strategies, thereby underscoring the indispensable role of psychological factors in the fight against childhood obesity. The application of bioinformatics methods to analyze complex datasets demonstrates how collaboration across medical specialties can enrich our understanding and response to childhood obesity, contributing significantly to the development of comprehensive, bioinformatics-enhanced healthcare solutions.

The prognostic abilities of artificial intelligence and neuroscience in forensics and the criminal justice system stand as a reformatory paradigm for understanding any criminal conduct. While the use of artificial intelligence has been labeled transformational data analytical capabilities, neural predictive approaches also enable an intricate understanding of culpability and criminal propensities. The literature on the complex nature of neuroprediction and artificial intelligence, its ethical deliberations and its usability in curving recidivism are analyzed. This theoretical review elucidates the complex interplay, nuptial relationships and convergence of these relationships in the quest for justice. The consequences of not protecting individual rights in the criminal justice system are surveyed using grounded theory. The degree of acceptability and dependability of AI-generated evidence in legal proceedings are also reviewed. All these topics are yet to be contemplated under one roof to offer an argumentative view. The author expects to prompt readers and new commers to embrace more sociolegal and technological research before incorporating such research in the Indian Judiciary. The review focuses on the question of whether to blame such technology inclusion wholly or rather to prioritize the acquisition of bias-free pretrained datasets and processing models.

This paper presents a novel real time control strategy on four-wheel independent steering system for electric vehicle with stepper motors using neural networks combined with fuzzy logic. The control of the four wheels is based on a variation of Hopfield Neural Network (VHNN) method, in which the input is the error of each steering motor and the output is processed by a hyperbolic tangent function (HTF) feeding the fuzzy logic controller (FLC), which ultimately drives the stepper motor. The whole system is made up of the four aforementioned blocks where they work in sync and inseparable from each other with the common goal of driving all the steering stepper motors at the same time. Simulation results demonstrate that the proposed control method has strong robustness and practicability under different angles and speeds.

Stevia Rebaudiana Bertoni Morita II, a perennial plant native to Paraguay and Brazil, is also widely cultivated in the state of Colima, Mexico, for its use as a sweetener in food and beverages. The op-timization of soil parameters is crucial for maximizing biomass production and stevioside levels in stevia crops. This research presents the development and implementation of a monitoring system to track essential soil parameters, including pH, temperature, humidity, electrical conductivity, ni-trogen, phosphorus, and potassium. The system employs a wireless sensor network to collect qua-si-real-time data, which is transmitted and stored in a web-based platform. A Mamdani-type fuzzy logic model is utilized to process the collected data and provide to farmers an integrated assessment of soil quality. By comparing the quality data output of the fuzzy logic model with a line-ar-regression model, the system demonstrated acceptable performance, with a determination coef-ficient of 0.532 for random data and 0.906 for gathered measurements. The system enables farmers to gain insights into the soil quality of their stevia crops and empowers them to take preventive and corrective actions to improve the soil quality specifically for stevia crops.

Ethiopia has 74.3 million hectares of arable land and an irrigation potential of 3,088,395 hectares. Despite the country's vast irrigation potential, it hasn’t benefited much. A huge percentage of Ethiopia’s population is currently food insecure and a very significant portion lives under the national poverty line. Studies have confirmed the role of irrigation in alleviating poverty and promoting food security. Irrigation plays a huge role in poverty reduction, especially in developing countries like Ethiopia where agriculture is the backbone of the economy and the major source of employment. Irrigation does so by boosting farmer incomes, improving crop productivity, providing more employment and better pay rate, and contributing to the national GDP in the long run. Irrigation also promotes food security by contributing to all the dimensions including, food availability, access to food, food utilization, and stability. Given such importance of irrigation in poverty and hunger alleviation, it’s believed that the development of the sector massively contributes to the realization of sustainable development goals (SDG). Hence, this article voices the need for exploiting the huge irrigation potential and improving the irrigation sector, in order for the country to pull its population out of the misery of hunger and poverty.

Background: Pharmacologic and non-pharmacologic treatments for stroke are essential but could be costly or harmful whereas probiotic has been a promising alternative. This scoping review aimed to synthesize the in vitro and in vivo evidence of probiotics on stroke-related neurological, biochemical, and histochemical outcomes. Method: We searched in PubMed, Embase, and Cochrane Central Register of Clinical up to May 7, 2021, and screened by two independent reviewers. We included the use of probiotics, prebiotics, and symbiotics both in vitro and in vivo for the prevention or treatment of the stroke-related model. Result: Of 6,293 articles, 4,990 passed the initial screen, of which 36 theme-related full-texts were assessed and 13 were included in this review. Probiotics could ameliorate the neurological deficit and show their property as an anti-inflammation and anti-oxidative stress. Histopathologically decreased loss of cerebral volume and inhibition of neuronal apoptosis were found. Conclusion: There are potential cognitive benefits of probiotic supplementation, especially among animal models, on decreasing cerebral volume, increasing neurological score, and decreasing the inflammatory response. However, further investigation is needed to validate these conclusions in various populations.

The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use-cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times of its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset, by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

Kinetic parameters of SO2 adsorption on unburned carbons from lignite fly ash and activated carbons based on hard coal dust were determined. The model studies were performed using the linear and non-linear regression method for the following models: pseudo first and second-order, intraparticle diffusion, and chemisorption on a heterogeneous surface. The quality of the fitting of a given model to empirical data was assessed based on: R2, R, Δq, SSE, ARE, χ2, HYBRID, MPSD, EABS, and SNE. It was clearly shown that it is the linear regression that more accurately reflects the behaviour of the adsorption system, which is consistent with the first-order kinetic reaction – for activated carbons (SO2+Ar) or chemisorption on a heterogeneous surface – for unburned carbons (SO2+Ar and SO2+Ar+H2O(g)+O2) and activated carbons (SO2+Ar+H2O(g)+O2). Importantly, usually, each of the approaches (linear/non-linear) indicated a different mechanism of the studied phenomenon. A certain universality of the χ2 and HYBRID functions has been proved, the minimization of which repeatedly led to the lowest SNE values for the indicated models. Fitting data by any of the non-linear equations based on the R or R2 functions only, cannot be treated as evidence/prerequisite of the existence of a given adsorption mechanism.

In microwave imaging it is often of interest to inspect electrically large spatial regions. In these cases, data must be collected over a great deal of measurement points which entails long measurement time and/or costly, and often unfeasible, measurement configurations. In order to counteract such drawbacks, we have recently introduced a microwave imaging algorithm which looks for the scattering targets in terms of equivalent surface currents supported over a given reference plane. While this method is suited to detect shallowly buried targets, it allows to independently process each frequency data, hence the source and the receivers do not need to be synchronized. Moreover, spatial data can be reduced at large extent, without incurring in aliasing artefacts, by properly combining single-frequency reconstructions. In this paper, we validate such an approach by experimental measurements. In particular, the experimental test site consists of a sand box in open air where metallic plate targets are shallowly buried (few cm) under the air/soil interface. The investigated region is illuminated by a fixed transmitting horn antenna whereas the scattered field is collected over a planar measurement aperture at a fixed height from the air-sand interface. The transmitter and the receiver share only the working frequency information. Experimental results confirm the feasibility of the method.

In the framework of 2D circular membrane Micro-Electric-Mechanical-Systems (MEMS), a new non-linear second-order differential model with singularity in the steady-state case is presented in this paper. In particular, starting from the fact that the electric field magnitude is locally proportional to the curvature of the membrane, the problem is formalized in terms of the mean curvature. Then, a result of existence of at least one solution is achieved. Finally, two different approaches prove that the uniqueness of the solutions is not ensured.

Abstract: (1) Background: We determined the prevalence of hepatitis B virus (HBV) amongst people without human immunodeficiency virus (HIV) in rural and peri-urban areas in Botswana. (2) Methods: We screened hepatitis B surface antigen (HBsAg) from archived plasma samples of people without HIV (n=2135) randomly selected from the Botswana Combination Prevention Program (BCPP) (2013–2018). We sequenced 415 bp of the surface region using BigDye sequencing chemistry. (3) Results: The median age of participants was 31 (IQR: 24–46) and 64% (1360/2135) were female. HBsAg prevalence was 4.0% (86/2135) [95% CI: 3.3 – 4.9]) and ranged between 0%–9.2%. Older participants (>35 years) had increased odds of HBsAg positivity (OR: 1.94; 95% CI: [1.32–2.86]; p = 0.001. Thirteen samples were sequenced and 7 (53.8%) were genotype A, 3 (23.1%) were genotype D and genotype E each. Clinically significant mutations were identified in the surface region, but no classic drug resistance mutations were identified. (4) Conclusions: We report an HBV prevalence of 4.0% (95% CI 3.3 – 4.9) among people without HIV in rural and pe-ri-urban communities in Botswana with varying rates in different communities. A comprehen-sive national HBV program is required in Botswana to guide HBV prevention, testing and management.

Patients satisfaction with fast and high-quality services is the most important in the healthcare settings and specially in outpatient departments and private clinics. High quality services on health sector are indicated by continuous quality improvement (CQI). CQI is a progressive incremental process focused on safety of all participants, outcomes, systematic process, regulated and improved working environment at the later than earlier stages. Among the various suggested strategies, we adopted the patents feedback to improve the quality of services our clinic. All of the patient visited hospital were sent a massage on mobile and requested to provide their feedback on our services. Roughly around 5% of all visitors responded and filled up questionnaire. Majority of them were satisfied with the services in various department. However, they provided us some suggestions to identify the further gaps and improve the services in improving patients experience at our clinic. We considered their feedback, identified problems, redesigned the policies and implemented. After implementation of new strategies, we preliminary again evaluated the patients’ feedback on our services. Patients feedback explores that optimized methods of services for the has considerably increased the patients’ satisfaction. Taken together, our this shows that patients’ feedback is very important factor to improve patients’ experience at outpatient clinics using continuous quality improvement tools. For that reason, this study would serve as reference for public health stack holders, administrators and researchers.

The keyless systems have replaced the old fashion methods of inserting physical keys in the keyhole to, i.e., unlock the door, because they are inconvenient and easy to be exploited by the threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, Keyless systems are susceptible to being compromised by a thread actor who intercepts the transmitted signal and performs a reply attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system remotely. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.

Data augmentation techniques recently gained more adoption in speech processing, including speech emotion recognition. Although more data tends to be more effective, there may be a trade-off in which more data will not provide a better model. This paper reports experiments on investigating the effects of data augmentation in speech emotion recognition. The investigation aims at finding the most useful type of data augmentation and the number of data augmentations for speech emotion recognition. The experiments are conducted on the Japanese Twitter-based emotional speech corpus. The results show that for speaker-independent data, two data augmentations with glottal source extraction and silence removal exhibited the best performance among others, even with more data augmentation techniques. For the text-independent data (including speaker and text-independent), more data augmentations tend to improve speech emotion recognition performances. The results highlight the trade-off between the number of data augmentation and the performance of speech emotion recognition showing the necessity to choose a proper data augmentation technique for a specific application.

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform adaptive filtering techniques and algorithms normally used in adaptive control, especially when the plant is nonlinear. The deeper the controller the better the inverse function approximation, provided that the nonlinear plant have an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to parameter change of the nonlinear plant in that, under such change, the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. Settling times and rise times of the step response are shown to improve in the DL-based AIC system.

The purpose of this study was to explore the role of moral distress on physician burnout during COVID-19. Physicians in the US were interviewed between February and March 2021; 479 responded to our survey. Results indicated that moral distress was a key mediator in explaining the relationship between perceived organizational support, medical specialization, emotional labor, and coping on burnout. There was no support for increased burnout among female physicians, and contracting COVID-19 likewise did not play a role in burnout. Our findings suggest that physician burnout can be mitigated by increasing perceived organizational support; likewise, physicians who engaged in deep emotional labor and problem-focused coping tended to fare better when it came to feelings of moral distress and subsequent burnout.

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R≥1, where R is the sample reduction ratio of compressive sensing and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise, owing to the noise shaping property of sigma-delta modulation.

Cannabis has developed into a multi-billion dollar industry that relies on clonal propagation of elite genetics with desirable agronomic and chemical phenotypes. While the goal of clonal propagation is to produce genetically uniform plants, somatic mutations can accumulate during growth and compromise long-term genetic fidelity. Cryopreservation is a process in which tissues are stored at cryogenic temperatures, halting cell division and metabolic processes to facilitate high fidelity germplasm preservation. In this study, a series of experiments were conducted to optimize various stages of cryopreservation and develop a protocol for long-term germplasm storage of Cannabis sativa. The resulting protocol uses a standard vitrification procedure to cryopreserve nodal explants from in vitro shoots as follows: Nodes were cultured for 17 hours in a pre-culture solution (PCS), followed by a 20 minute treatment in a loading solution (LS), and a 60 minute incubation in plant vitrification solution 2 (PVS2). The nodes were then flash frozen in liquid nitrogen, re-warmed in an unloading solution at 40°C, and cultured on basal MS culture medium in the dark for 5 days followed by transfer to standard culture conditions. This protocol was tested across 13 genotypes to assess the genotypic variability. The protocol was successful across all 13 genotypes, but significant variation was observed in tissue survival (43.3-80%) and regrowth of shoots (26.7-66.7%). Plants grown from cryopreserved samples were morphologically and chemically similar to control plants for most major traits, but some differences were observed in the minor cannabinoid and terpene profiles. While further improvements are likely possible, this study provides a functional cryopreservation system that works across multiple commercial genotypes for long-term germplasm preservation.

Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disciplines by studying the effects of compressed measurements of a single sinusoid in moving-average (MA) colored noise on its frequency estimation accuracy. CCS techniques can recover the second-order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation-based frequency estimation of single tones in colored noise using higher-order lags. Acceptable accuracy is achieved for moderate compression ratios and for a sufficiently large number of available compressed signal samples. It is expected that the proposed method would be advantageous in applications involving resource-limited systems such as wireless sensor networks.

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, like sensor networks.

While the mortality rates of cancer are generally declining, pancreatic cancer persists to be an exception with a 5-year-survival rate of less than 7%. Late diagnosis and resistance to conventional therapies contribute to high mortality rates in spite of the remarkable recent advances in cancer management and research. Consequently, there is an urgent need to find new and unconventional therapeutic targets to improve prognosis and survival of pancreatic cancer patients. In this review, we discuss the transcriptional effects of the most widely used epigenetic inhibitors in pancreatic cancer focusing on Bromodomain and Extraterminal domain (BET) and Histone Deacetylase (HDAC) inhibitors, which are currently highly promising therapeutic options. We suggest that these inhibitors can be better utilized at lower doses which exploit their transcriptional modulatory effects on pancreatic cancer transcriptional programs directed by specific factors such as MYC and FOXA1, rather than simply based on their anti-proliferative effects. This approach can potentially help avoid the intolerable adverse events frequently elicited by the use of these treatments at higher doses. In particular, we underscore the crucial role of distal regulatory elements in mediating the specific effects of these epigenetic inhibitors and propose using them in a more selective and prudent manner.

Since chronic wasting disease (CWD) was first identified nearly 50 years ago in a captive mule deer herd in the Rocky Mountains of the United States, it has slowly spread across North America through the natural and anthropogenic movement of cervids and their carcasses. As the endemic areas have expanded, so has the need for rapid, sensitive, and cost effective diagnostic tests – especially those which take advantage of samples collected antemortem. Over the past two decades, strategies have evolved from the recognition of microscopic spongiform pathology and associated immunohistochemical staining of the misfolded prion protein to enzyme-linked immunoassays capable of detecting the abnormal prion conformer in postmortem samples. In a history that parallels the diagnosis of more conventional infectious agents, both qualitative and real-time amplification assays have recently been developed to detect minute quantities of misfolded prions in a range of biological and environmental samples. With these more sensitive and semi-quantitative approaches has come a greater understanding of the pathogenesis and epidemiology of this disease in the native host. Because the molecular pathogenesis of prion protein misfolding is broadly analogous to the misfolding of other pathogenic proteins, including Aβ and α-synuclein, efforts are currently underway to apply these in vitro amplification techniques towards the diagnosis of Alzheimer’s disease, Parkinson’s disease, and other proteinopathies. Chronic wasting disease – once a rare disease of Colorado mule deer – now represents one of the few naturally occurring protein misfolding disorders which might allow continued development and implementation of novel diagnostic strategies in an animal model.

This study investigates the use of Au-doped Pd anodic electrocatalysts on ATO support for the conversion of methane to methanol. The study involves cyclic voltammetry, in-situ Raman spectra, polarization curves, and FTIR analysis to determine the optimal composition of gold and palladium for enhancing the conversion process. The results demonstrate the potential for utilizing methane as a feedstock for producing sustainable energy sources. Pd75Au25/ATO electrode exhibited the highest OCP value, and Pd50Au50/ATO had the highest methanol production value at a potential of 0.05 V. Therefore, it can be concluded that an optimal composition of gold and palladium exists to enhance the conversion of methane to methanol. The findings contribute to the development of efficient and sustainable energy sources, highlighting the importance of exploring alternative ways to produce methanol.

Alzheimer’s disease is the most common neurodegenerative disease and affects persons of all races, ethnic groups, and sexes. The disease is characterized by neuronal loss leading to cognitive decline and memory loss. There is no cure and the effectiveness of existing treatments is limited and depends on the time of diagnosis. The long prodromal period, during which patients’ ability to live a normal life is not affected despite neuronal loss, often leads to a delayed diagnosis because it can be mistaken for normal aging of the brain. In order to make a substantial impact on AD patients, early diagnosis may provide a greater therapeutic window for future therapies to slow AD-associated neurodegeneration. Current gold standards for disease detection include magnetic resonance imaging and positron emission tomography scans, which visualize amyloid β and phosphorylated tau depositions and aggregates. Liquid biopsies, already an active field of research in precision oncology, are hypothesized to provide early disease detection through minimally or non-invasive sample collection techniques. Liquid biopsies in Alzheimer’s disease have been studied in cerebrospinal fluid, blood, ocular, oral, and olfactory fluids. However, most of the focus has been on blood and cerebrospinal fluid due to biomarker specificity and sensitivity attributed to the effects of the blood-brain barrier and inter-laboratory variation during sample collection. Many studies have identified amyloid β and phosphorylated tau levels as putative biomarkers, however, advances in next-generation sequencing-based liquid biopsy methods have led to significant interest in identifying nucleic acids species associated with Alzheimer’s disease from liquid tissues. Differences in cell-free RNAs and DNAs have been described as potential biomarkers for AD and hold the potential to affect disease diagnosis, treatment, and future research avenues.

Work context is essential to understand in relation to handle the stress at work that ultimately creates a feeling of satisfaction or dissatisfaction among health professionals. The current study was conducted to investigate the relationship of work context and work stress among sonographers (n=153) in Riyadh, Saudi Arabia. Additionally, the study provided a gender-based comparison of both variables among sonographers. Work context was measured by administering subscale of work context derived from Work Design Questionnaire. Whereas, work stress was measured by Job Stress Scale. In addition, relationship of lifestyle was explored with work context and work stress. Data was collected through survey research forms. Results revealed the significant relationship of work context and work stress (r=.251, p=.002). Among lifestyle variables, perceived good health (r= .214, p=.008) and sleep (r=.242. p=.003) were found positively related with satisfaction toward work. Whereas, the strong positive correlation was found between work context and frequency of physical activity (r=.255, p=.005). No significant difference was found among male and female sonographers. The findings of this study contributed to evaluating the working condition of sonographers in relation to work stress. Effective strategies for better working settings as well as strategies for achieving satisfaction in work will be discussed to enhance the performance of sonographers.

Image analysis is widely applied in plant science for phenotyping and monitoring botanic and agricultural species. Although a lot of software is available, tools integrating image analysis and statistical assessment of seedling growth in large groups of plants are limited or absent, and do not cover the needs of the researchers. In this study, we developed Morley, a free, open-source graphical user interface written in Python. Morley automates the following workflow: (1) group-wise analysis of a few thousand seedlings from multiple images; (2) recognition of seeds, shoots and roots in seedling images; (3) calculation of shoot and root lengths and surface areas, (4) evaluation of statistically significant differences between plant groups, (5) calculation of germination rates, (6) visualization and interpretation. Morley is designed for laboratory studies of biotic effects on seedling growth, when molecular mechanisms underlying morphometric changes are analyzed. Performance was tested using cultivars of T. aestivum, P. sativum on seedlings of up to 1 week old. Accuracy of the measured morphometric parameters was comparable with the ones obtained using ImageJ and manual measurements. Dose-dependent laboratory tests for germination affected by new bioactive compounds and fertilizers, assuming extraction of seedlings from a substrate and/or dissection are among the suggested applications.

Background: Areca nut and betel quid (ANBQ) chewing is a widespread carcinogenic habit. The BENIT (ClinicalTrials.gov - NCT02942745) is the first known randomized trial designed for ANBQ chewers. Methods: We compared intensive behavioral treatment intervention condition (IC) with control condition (CC) in the BENIT and included a 5-stage early stopping rule. We report the primary analysis at stage 3. English literate adults in Guam and Saipan who self-identified as ANBQ chewers with tobacco were enrolled between August 2016 and August 2020. IC participants (n=88) received five in-person sessions over 22 days and a brochure containing quitting advice. CC participants (n=88) received only the brochure. Participants were assessed at baseline and day-22 follow-up. Self-reported chewing status at day-22 was determined by a composite of two survey items with disparate wording and response options for cross-verification. Results: Cessation rates were 38.6% (IC) and 9.1% (CC). Proportional hazards regression revealed a p=0.0058, which met the Stage 3 criteria for significance and an estimated reduction in ANBQ chewing for IC compared to the CC of 71% (95% CI: 41%-88%). Conclusion: Robust self-reported intervention effects at day-22 suggests that intensive cessation programs such as BENIT should be further developed and implemented on a larger scale.

Understanding and addressing dehumanization in healthcare is crucial due to its pro-found impact on patient care, ethical implications on patient dignity and autonomy, and its potential to affect the psychological well-being of healthcare professionals. The primary aim of this study was to establish reliable and valid instruments measuring two different types of dehumanization, namely animalistic dehumanization (i.e., stripping one of their uniquely human characteristics) and mechanistic dehumanization (i.e., stripping one of their human nature characteristics) among healthcare professionals. In a cross-sectional validation study among healthcare professionals, we tested measures of both animalistic and mechanistic dehumanization, focusing on dehumanization of patients (hetero-dehumanization) and oneself (self-dehumanization), respectively. All measures were developed and validated based on a concept analysis, a literature review, and an appraisal of pre-existing scales. The research was conducted among 400 nurses and medical doctors employed in Greek public hospitals. Coefficient Validity Ratio re-sults showed that 100% of items were acceptable for both measures. The newly estab-lished and validated hetero-dehumanization scale encompassed two-factors (factor 1: animalistic dehumanization, factor 2: mechanistic dehumanization, Cronbach’s alpha was equal to 0.86 for each measure). The self-dehumanization scale was a mono-factorial measure mechanistic dehumanization (Cronbach’s alpha = 0.97). Two validated measures of (self and hetero-) animalistic and mechanistic dehumanization measures were developed for the assessment of dehumanization among health profes-sionals, which will form the basis for future research in this important scientific field.

This study aimed to investigate the prevalence of cardiovascular disease (CVD) risk factors among Algerian aircrew, a population exposed to unique environmental and occupational stressors. By comparing these rates to the general population, we sought to identify specific health challenges faced by aircrew. Methods: A cross-sectional study was conducted on 154 Algerian aircrew participants. Prevalence rates for current smoking, hypertension, diabetes, and dyslipidemia were compared between the aircrew and a general population control group. Results: Our findings revealed significantly higher rates of current smoking among aircrew compared to the general population. Additionally, smoking, dyslipidemia, hypertension, and diabetes emerged as the most prevalent risk factors within the aircrew population. Conclusions: Despite the rigorous selection process for aircrew recruitment, our study confirms findings from previous literature, highlighting the high rate of normal qualifying examinations. The substantial sample size and homogeneous nature of the aircrew population enhance the reliability and generalizability of our conclusions. These findings underscore the importance of targeted interventions to mitigate CVD risk factors within this specific population, given the unique environmental and occupational factors associated with aircrew.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1-measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data is expensive), and the complex relationships between anomalies not easily captured by traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model is deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

This retrospective study examines 248 test-bolus examinations preceding contrast-enhanced magnetic resonance angiography (CE-MRA) to extract clinically relevant data for critical limb ischemia (CLI) management. Methods involved a retrospective review of test-bolus exams, analysing 60 graphs for time-to-peak (TTP), full-width half-maximum (FWHM) time, and time to continual rise in signal intensity. These values were correlated with heart function parameters (ejection fraction, ASA-Classification, Lee-Index, and MET-Score). Results indicate a mean TTP of 31.2 ± 7.3s, showing a correlation between ejection fraction and ASA classification. Patients with atrial fibrillation exhibited prolonged TTP compared to those without. Despite population heterogeneity, findings facilitate risk stratification for limb-saving interventions in CLI. TTP emerges as a potential clinical cardiovascular parameter and a risk factor for vascular interventions. Given the variation in injection protocols across centres, this study underscores the importance of precise bolus arrival time documentation for future multicentre studies.

Neurofibromatosis type 2 (NF2) is a genetic syndrome characterized by the growth of benign tumors in the nervous system, particularly bilateral vestibular schwannomas, meningiomas, and ependymomas. This review consolidates the current knowledge on NF2 syndrome, emphasizing the molecular pathology associated with the mutations in the gene of the same name, the NF2 gene, and the subsequent dysfunction of its product, the Merlin protein. Merlin, a tumor suppressor, integrates multiple signaling pathways that regulate cell contact, proliferation, and motility, thereby influencing tumor growth. The loss of Merlin disrupts these pathways, leading to tumorigenesis. We discuss the roles of another two proteins potentially associated with NF2-deficiency as well as Merlin: Yes-associated protein 1 (YAP), which may promote tumor growth, and Raf kinase inhibitory protein (RKIP), which appears to suppress tumor development. Additionally, this review discusses the efficacy of various treatments, such as molecular therapies that target specific pathways or inhibit neomorphic protein-protein interaction caused by NF2-deficiency. This overview not only expands on the fundamental understanding of NF2 pathophysiology but also explores the potential of novel therapeutic targets that affect the clinical approach to NF2 syndrome.

As a crucial part of head protection equipment, research on the material and structural application of helmet liners has always been a hot topic in the field of helmet manufacturing. This paper aims to introduce the most common helmet liner materials, including traditional polystyrene, polyethylene, polypropylene, and others, as well as emerging materials such as anisotropic materials, and polymer nanocomposites. In addition, the design principles of helmet liner structure are discussed, including the evolution of multi-layer structure, integration of geometric irregular bubbles, etc., to enhance energy absorption capacity. Advanced manufacturing processes such as additive manufacturing technology are introduced to fabricate complex structures. Finally, the application of biomimetic structures in helmet liner design is explored. For example, the structure of biological tissue can be used to design a helmet liner structure with superior energy absorption performance. On this basis, the idea of bionic structures is extended to the combination of plant stalks and animal skeletal structures, and additive manufacturing technology is used to significantly reduce energy loss during elastic yield energy absorption, thus designing a reusable helmet. This provides a promising research direction for future helmet liner materials and structural applications.

Children with hearing loss have been found to have significantly more behavioral and emotional challenges than their typically hearing peers, though these outcomes are variable at the individual level. Working memory deficits have been found to relate to executive functioning and overall emotion regulation, leading to behavior challenges. Language development is essential for development of social relationships and communicating one’s needs - this may lead to distress when children cannot communicate effectively. Based on prior findings in children with hearing loss and their typically hearing peers, working memory and language skills were hypothesized to be related to parent and teacher report of socioemotional functioning. Participants were 35 children with hearing loss (66% female, M = 5.17 years old, SD = ±1.97) whose language, working memory, and socioemotional functioning were evaluated during the course of treatment and educational planning. Bivariate analyses indicated that working memory was related to a number of socioemotional domains (e.g., functional communication, atypicality, withdrawal), as were language scores (e.g., social skills, inattention). The direction of these associations was such that stronger working memory and language skills were related to more regulated socioemotional functioning. A call to action of the current study includes more education with regard to profiles and presentations of children with hearing loss, and an early focus on socioemotional learning to foster the development of regulatory skills.

In this work, a highly efficient, molybdenum disulfide (MoS2) based near infrared (NIR) heterojunction photodetector is fabricated on a Si substrate using a cost-effective and simple drop casting method. A non-stoichiometric and inhomogeneous MoS2 layer with a S/Mo ratio of 2.02 is detected using energy dispersive X-ray spectroscopy and field emission scanning electron microscope analysis. Raman shifts are noticed at 382.42 cm-1 and 407.97 cm-1, validating MoS2 thin film growth with a direct bandgap of 2.01 eV. The fabricated n-MoS2/p-Si photodetector is illuminated with a 785 nm laser at different intensities, and demonstrate the ability of the photodetector to work in both regions, the forward biased and reverse biased from above 1.5 V and less than -1.0 V. The highest responsivity, R is calculated to be 0.52 A/W while the detectivity D* is 4.08 x 10^10 Jones for an incident light intensity of 9.57 mW/cm2. The minimum rise and fall times are calculated as 1.77 ms and 1.31 ms for an incident laser power of 9.57 mW/cm^2 and 6.99 mW/cm^2 respectively at a direct current bias voltage of 10 V. The demonstrated results are promising for the low-cost fabrication of a thin MoS2 film for photonics and optoelectronic device applications.

Introduction: Contrast-enhanced spectral mammography (CESM) is a novel technique employed in breast cancer screening and detection. In this systematic review, we aim to compare the diagnostic performance of CESM with magnetic resonance imaging (MRI) in the context of breast cancer diagnosis. Material and Method: We have searched for paper in Pubmed and Scopus from July 2023 to February 2024, finding 256 suitable papers according to our selection criteria (perspective or retrospective studies, women diagnosed with breast cancer, comparison of Contrast-enhanced Spectral Mammography and Magnetic Resonance Imaging, sensitivity and specificity metrics provided). Every paper was then checked for quality using the QUADAS 2 assesement score. Sensibility, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and accuracy were recorded. Results: 15 studies were selected for analysis. CESM demonstrated comparable sensitivity to MRI, with values ranging from 36.4% to 100% for CESM and 0% to 100% for MRI. Specificity varied between the modalities, with CESM showing specificity ranges of 17% to 100% compared to 23% to 92.31% for MRI. Discussion and Conclusion: CESM has comparable diagnostic validity to MRI, and is a valuable tool in detecting breast cancer.

Demand for livestock products in East Africa is anticipated to triple by 2050. Therefore, sustainable intensification of livestock production systems for increased productivity is necessary in line with minimal negative environmental consequences. An agronomic field experiment was set up at the International Livestock Research Institute in Nairobi, Kenya, and the effects of organic and inorganic soil amendments on greenhouse gas emissions (particularly N2O) from a Humic Nitisol planted with Brachiaria brizantha cv. xaraes were evaluated between October 2018 and August 2019. The treatments comprised mineral NPK fertilizer, Lablab intercrop, FYM, FYM-BC, Bioslurry, and control. Fertilizer treatments were applied at a rate of 45 kg N ha-1 following each harvest. GHG emissions were measured using the static vented chamber technique. Treatment and season significantly influenced daily N2O emissions. The lowest (4.51±3.30 µg N m-2 h-1) and highest (27.16±3.61 µg N m-2 h-1) mean N2O emissions were recorded under NPK and Control treatments during the short rains and dry seasons, respectively. Cumulative N2O emissions and the corresponding yield-scaled emissions were similar across all the treatments but varied significantly (p < 0.001) between the wet and dry seasons. Cumulative N2O emissions were 0.31±1.49, 0.33±1.47, 0.33±1.74, and 0.37±1.74, 0.38±2.3 and 0.42±1.81 Kg N ha-1 under FYM-BC, Lablab, NPK fertilizer, and FYM, Bioslurry and control treatments respectively. The corresponding yield-scaled emissions were also higher during the wet (0.23±1.16 g N kg-1 DM) than in the dry seasons (0.16±0.50 g N kg-1 DM). Higher (-21.86±4.47 mg CH4-Ch-1) CH4 uptake was recorded under the control treatment whereas the lowest (-2.69±17.97 mg CH4-Ch-1) uptake was recorded under Bioslurry (P < 0.01). Treatment and season exhibited individual effects on daily CO2 emissions (P < 0.001), with a significant interaction effect (P < 0.001). The highest (157.5±28.76 mg CO2-C m-2h-1) and lowest (44.33±8.37 mg CO2-C m-2h-1) CO2 emissions were recorded under Control and FYM treatments during the October 2018-January 2019 and January-March 2019 HD. Since the experiment was newly established via ploughing a field which had been used as a permanent pasture during previous years, did not expect considerable yield differences between treatments. Yet, it is interesting to see first effects of fertilizer amendments, pointing to their potential as climate-smart forage intensification strategies. The study established that Manure + biochar is a better strategy for forage soil amendments in mitigating soil N2O emissions.

In this article, a generalization of the geometry of Grassmannian chain complex of free abelian groups generated by the projective configuration of points and Cathelineau's infinitesimal complex of polylogarithmic groups is proposed. Firstly, homomorphisms for weight n =2 up to weight n=6 will be introduced to connect sub-complexes of Grassmannian and Cathelineau. Lately, generalization of these morphisms will be shown for weight n=N. The associated diagrams will also be proven to be commutative and bi-complex.

Human papillomavirus (HPV) is associated with cellular changes of the cervix leading to cancer, which highlights the importance of vaccination in preventing HPV infections and subsequent cellular changes. Women undergoing treatment (LEEP) for high-grade cervical intraepithelial neoplasia (CIN2+) remain at risk for recurrence. This study assesses the effect of post-conization HPV vaccination on the viral status of women six months post-conization, aiming to evaluate the vaccine's effectiveness in preventing recurrence of CIN2+. A retrospective cohort study was conducted among women in Troms and Finnmark who underwent conization in 2022. Using the SymPathy database and the national vaccination register (SYSVAK), we analyzed the vaccination status and HPV test results of women born before 1991, who had not received the HPV vaccine prior to conization. Out of 419 women undergoing conization, 243 met the inclusion criteria. A significant association was found between post-conization HPV vaccination and a negative HPV test at six months follow-up (ARR = 12.1%, P = 0.039). Post-conization HPV vaccination significantly reduced the risk of a positive HPV test at the first follow-up, suggesting its potential in preventing recurrence of high-grade cellular changes. The retrospective design and the insufficient control of confounding variables underscore the necessity for further studies to confirm these findings.

This study aims to provide an updated overview of the systematic evaluation and management of Post-Traumatic trigeminal neuropathic pain (PTTN) resulting from dental procedures or trauma. PTTN arises following an injury to the trigeminal nerve, which is responsible for sensory and motor functions in the maxillofacial region. The etiology encompasses various dental procedures and craniofacial trauma, leading to a spectrum of symptoms, from tingling to severe pain. Diagnostic challenges stem from the absence of standardized criteria and the overlap with focal neuralgia, necessitating comprehensive evaluation. Misdiagnosis can result in prolonged suffering and unnecessary procedures. Successful management hinges on prompt diagnosis and interdisciplinary collaboration, with early intervention crucial in mitigating chronicity. While nerve recovery post-trauma is challenging, preventive measures through accurate evaluation and treatment are paramount.  Management for PTTN includes non-invasive and surgical interventions. Non-invasive intervention includes systematic and local pharmacological management. This review enhances uniformity in PTTN evaluation and treatment approaches despite standardized study limitations.

Diffusion Models (DMs) are probabilistic models that create realistic samples by simulating the diffusion process, gradually adding and removing noise from data. These models have gained popularity in domains such as image processing, speech synthesis, and natural language processing due to their ability to produce high-quality samples. As DMs are being adopted in various domains, existing literature reviews that often focus on specific areas like computer vision or medical imaging may not serve a broader audience across multiple fields. Therefore, this review presents a comprehensive overview of DMs, covering their theoretical foundations and algorithmic innovations. We highlight their applications in diverse areas such as media quality, authenticity, synthesis, image transformation, healthcare, and more. By consolidating current knowledge and identifying emerging trends, this review aims to facilitate a deeper understanding and broader adoption of DMs and provide guidelines for future researchers and practitioners across diverse disciplines.

Neurotensin (NT) is a peptide involved in digestion, neuromodulation, and cancer progression. NT and its receptors (NTR1 and SORT1 mainly) have been widely studied in oncology. Data show NT expression is under the control of sex steroid hormones, in particular estradiol. We focused on its involvement in three main female hormone-sensitive cancers: breast, ovarian, and endometrial cancer in a narrative review. NT, NTR1 and SORT1 are mostly expressed in these three cancers and their involvement in oncologic processes such as proliferation and invasion seems to match, as does their impact on prognosis for most. The development of NT receptor-targeted therapies, including theranostics and radioligand treatments, presents a promising avenue for personalized cancer treatment.

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

Background: breastfeeding is the optimal nourishment for infants and recommends that children commence breastfeeding within the first hour of birth and be exclusively breastfed for the initial 6 months of life. Our objective was to determine which factors related to mothers could influence the degree of exclusive breastfeeding during hospitalization, as well as to assess breastfeeding mothers' attitudes towards breastfeeding. Methods: A multicenter cross-sectional study was undertaken in the healthcare area of Santiago de Compostela, Spain. The reduced Iowa Infant Feeding Attitude Scale (IIFAS-s) was employed to gauge maternal attitudes toward feeding their baby. Results: 64 women were studied. The overall score of IIFAS-s (mean ± standard deviation) was 36.95 ± 5.17. No use of pacifier by the newborn was associated with a positive attitude for breastfeeding. Having previous children (ORa=6,40; IC95% 1,26 – 32,51) and previous experience with breastfeeding (ORa=6,70; IC95% 1,31 – 34,27) increase the likelihood of exclusive breastfeeding during admission. Conclusions: It is important to identify the factors that contribute to increasing the probability that mothers choose exclusive breastfeeding. We must guide our training efforts to these factors.

With huge varieties of fabrics, the first challenge for any performance evaluation is to categorize the vast types of the products into fewer, more homogeneous and thus akin groups. Classification or sorting is arguably the first step of any scientific investigation, and comparison of product quality is meaningful only when conducted within a group of comparable products. A new criterion termed fabric linear density λ is first proposed in this paper so that fabrics can in general be divided into 4 groups. The derivation and validation of this parameter are provided. The importance of fabric drape is almost self-evident, but there is still no effective ways to measure this fabric attribute. The Cusick Drapemeter suffers from its low repeatability and low sensitivity, and is hence not widely or frequently used. The PhabrOmeter, along with the fabric linear density λ, is proposed and demonstrated in this study as a much more efficient alternative for fabric drape test. By actually testing 40 various fabrics, the principle, procedure and results of this method is presented in this paper.

Corporate Sustainability has garnered increasing attention within the business community, as corporates communicate to influence its stakeholders for building sustainable relationships. There has been a surge in research exploring its connection to firm performance but, existing studies lack a cohesive and concentrated approach. This study employs scientometric and bibliometric methods to analyse scientific production which signals a scarcity of research from developing countries, indicating a need for greater investigation. Focussing on specific subfields of Corporate Sustainability and Firm Performance, we analyze and narrow down the scope of our study to a focussed sample. Employing use of structured methods and PRISMA Protocol, this scientifically rigorous study, points towards identification of research themes linking sustainability practices to firm performance for achieving business excellence. It goes beyond mapping of literature, thereby establishing linkages to Sustainable Development Goals (SDGs). Our analysis reveals clusters centered on Environmental Performance, Firm Performance, Corporate Sustainability Reporting, Corporate Social Performance and Green Supply Chain Management with significant citations related to various SDGs. We make use of time series analysis to determine the future trends of research in the field. This scientometric and bibliometric analysis study contributes to a more focused understanding of the linkages between corporate sustainability and firm performance, emphasizing the need for cohesive research approaches to address the complexities of sustainable business practices and their implications for financial performance and the economy.

This article presents an Internet of Things (IoT)-enabled system meticulously engineered for comprehensive environmental assessment, encompassing air quality, temperature, and humidity. Simultaneously, the devised system integrates cutting-edge MQ6 sensors, adept at detecting ambient gases, with a primary focus on monitoring Liquefied Petroleum Gas (LPG) and Butane gases concentrations in the atmosphere. This distinctive feature not only facilitates the identifi-cation of hazardous gases leakages, particularly common in household LPG usage, but also empowers preemptive measures. The environmental parameters, air quality, temperature, and humidity, are precisely gauged through the utilization of MQ135 and DHT11 sensors. The con-trolling and managing of the hardware component of the system (i.e., device) is assured by an Arduino UNO, that orchestrates the collection of sensor data, transmitting it seamlessly to both the ThingSpeak cloud and a Convolutional Neural Network (CNN) model. By interfacing with ThingSpeak, the device ensures the systematic storage and visualization of environmental data, streamlining monitoring and analysis processes. In the event of perilous gases detection, the system promptly triggers an alarm, concurrently dispatching a real-time notification to the device owner via the integrated If-This-Then-That (IFTTT) application programming interface (API). To further enrich environmental awareness, an embedded microphone captures audio recordings, subsequently processed by a CNN-based deep learning model. This model employs advanced techniques such as spectrogram generation, Convolution2D, and MaxPooling2D within a Se-quential Model architecture, to attain elevated levels of accuracy, furnishing insightful interpre-tations of ongoing activities within the environment.

This study innovatively assesses the Mun River flow components in Thailand, integrating Artificial Neural Networks (ANN) and Isotopic (18O) End-Member Mixing Analysis (IEMMA). It quantifies the contributions of the Upper Mun River (UMR) and Chi River (CR) to the overall flow, revealing a discrepancy in their estimated contributions. ANN predicts the UMR and CR contribute approximately 70.1% and 29.5% respectively, while IEMMA indicates a more pronounced disparity with 84% from UMR and 16% from CR. This divergence highlights the distinct perspectives of ANN, focusing on hydrological data patterns, and IEMMA, emphasizing isotopic signatures. Despite discrepancies, both methods validate UMR as a significant contributor to the overall flow, highlighting their utility in hydrological research. The findings underscore the complexity of river systems and advocate for an integrated approach of river flow analysis for a comprehensive understanding, crucial for effective water resource management and planning.

This study explores the impact of carbon fiber length and content on the rheological properties and performance of coatings used in lost foam casting. The investigation encompassed fiber lengths of 1 mm, 3 mm, and 6 mm, and fiber contents of 0.2%, 0.5%, and 0.8%. The effects on coating viscosity, shear stress, coating weight, and surface morphology were meticulously evaluated. The results demonstrate that incorporating carbon fibers significantly enhances coating viscosity and shear stress compared to fiber-free coatings, with more pronounced effects observed at higher fiber contents and longer fiber lengths. Nevertheless, excessive fiber content and length can lead to agglomeration, negatively impacting coating uniformity. Optimal fiber length and content were identified, striking a balance between improved rheological properties and coating performance. These findings provide critical insights for the development and industrial application of high-performance coatings in lost foam casting.

Pedagogical translanguaging (PTL) refers to the use of educational techniques which incorporate learners’ entire linguistic repertoire. Recent studies indicate that PTL is efficient in the teaching of morphological awareness to bilingual children. The question remains whether it can successfully be applied in the highly specific context of adult forced migrants' classrooms. This study describes a new protocol developed within the framework of PTL to teach derivational morphology to L2 French and Greek adults. We used questionnaires to assess quantitatively and qualitatively the feasibility of the protocol, teachers' and learners' attitudes, and learners' emotions in the PTL lesson. A total of 141 migrant learners (79 forced migrants) and 13 teachers were involved in 23 2-hour lessons. Teachers and learners gave overall high evaluations of the feasibility of the PTL protocol and of their pleasure to teach and learn using PTL tools. Learners’ ease to learn and learning benefits were positively influenced by their perception of the proximity between their L1 and L2. Learners reported higher positive emotions and lower negative emotions during the PTL lesson, while forced migrants showed globally higher hope and shame than voluntary migrants, as well as higher benefits from PTL on enjoyment. These findings suggest that the use of pedagogical translanguaging in migrants’ classrooms is feasible and develops positive attitudes and emotions, which are more pronounced in forced migrants.

The Internet of Things (IoT) aims to connect all objects and facilitate data interchange, with data accuracy being paramount. While IoT has seen extensive adoption in conventional sectors, its application in the petroleum industry, especially with downhole equipment, poses challenges due to the extreme high-temperature and high-pressure conditions, making accurate measurements elusive. This Paper we aim to enhance the accuracy of downhole pressure measurements in slim-hole drilling parameter monitoring, mitigate the temperature influences on strain gauges, and surmount the intrinsic limitations of the traditional BP neural network, such as sluggish convergence speeds, high sensitivity to initial weights and biases, unstable learning rates, and a tendency to fall into local minima. This study introduces a temperature compensation model using a chaotic-initiated adaptive whale optimization algorithm (C-I-WOA) to optimize a BP neural network, which is henceforth referred to as the C-I-WOA-BP model. Initially, we harnessed chaotic mapping techniques to refine the initialization process of the whale optimization algorithm (WOA) population, thereby bolstering the heterogeneity of the algorithm. Subsequently, we integrated an adaptive weight recalibration mechanism, refined the encompassing methodology of the WOA, and bolstered its capabilities for expansive global scouting and meticulous local refinement in the solution arena. Subsequently, the WOA was used to optimize the weight coefficients of the BP neural network. Finally, we conducted a detailed comparative analysis by juxtaposing the performance metrics of the BP, C-WOA-BP, I-WOA-BP, and C-I-WOA-BP networks. The experimental results indicate that the C-I-WOA-BP model demonstrates faster convergence speed, superior global search capabilities, and enhanced local exploitation potential compared to the other models. Additionally, it provides a more stable and accurate reduction in temperature-induced errors in drilling pressure parameter measurements.

Background: Extraintestinal pathogenic Escherichia coli (ExPEC) is the most common cause of urinary tract infections (UTIs). They are often multidrug-resistant (MDR), making them challenging to treat. Additionally, virulence mechanisms as biofilm formation are associated with persistent UTIs. Aims: To reveal a possible association between patients’ risk factors and UTIs caused by MDR or biofilm-forming ExPECs and characterize ExPECs causing asymptomatic bacteriuria, community- (CA), or hospital-acquired (HA) UTIs in hospitalized patients in Brazil. Methods: Bacterial DNA was extracted from the urine of 63 hospitalized patients and sequenced using short-read sequencing. Antibiotic susceptibility was evaluated using VITEK-2, and the biofilm-forming, adhesion, and invasion abilities were quantitatively assessed. Results: Antibiotic resistance rates were high, and the majority of UTIs were complicated CA-UTIs. Most MDR- and ESBL-producing E. coli isolates belonged to high-risk lineages and were associated with UTIs in patients with comorbidities and over 60 years of age. The mortality rate of patients infected with MDR-isolates was higher than of those infected with non-MDR isolates. Most isolates were biofilm-forming, but no association with patients’ risk factors was found. Conclusions: Complicated UTIs caused by MDR- and biofilm-forming bacteria are frequently found in hospitalized patients in Brazil suffering from a UTI and are associated with high-risk lineages.

Melt mixing is a convenient method to prepare polymer nanocomposites, but the extent of the dispersion of the solid filler reached is often limited, and may compromise the anticipated performance of these materials during service. Since the efficiency of extensional flows on dispersion is now well recognized, several mixers were designed with the aim of inducing both shear and extensional flow components. This work combines experimental and numerical data to better understand the kinetics of the dispersion of graphite nanoplates in a polypropylene melt, using a mixing device that consists of a series of stacked rings with equal outer diameter and alternating larger and smaller inner diameters, thus creating a series of converging/diverging flows. Numerical simulation of the flow using the opensource OpenFOAM software assuming both inelastic and viscoelastic responses predicted the velocity, streamlines, flow type and shear and normal stress fields for the mixer. Experimental and computed data were combined to determine the trade-off between the local degree of dispersion of the PP/GnP nanocomposite, measured as Area ratio, and the absolute average value of the hydrodynamic stresses multiplied by the local cumulative residence time. From considerations based on a theoretical approach to dispersion, the cohesive strength of the GnP agglomerates studied was estimated to be in the range 3 - 10kPa.

In the process of expanding the application of electronic licenses in the Chinese agricultural sector, challenges such as inconsistent information standards, poor interoperability, significant data barriers, and lack of regulatory basis are encountered. By examining online information and practical data of agricultural electronic licenses in the 31 provincial-level administrative units in mainland China, variations in the reasons for promoting the expanded application of electronic licenses in different regions were identified. It is believed that strategies should be proposed around key factors such as government capacity, economic development, social needs, and intergovernmental relations. Suggestions include enhancing the intensity of information aggregation, diversifying the management entities, advancing the intelligence of underlying technology, and improving the completeness of legal rules. New conceptual proposals, such as constructing a database for agricultural information collection, establishing a multi-entity management authorization mechanism, implementing intelligent applications of electronic licenses at the grassroots level, and developing specialized legal rules, require further validation.

Various cultivars of citrus fruits have unique construction, such as thick outer skin. These constructions generate gas diffusion resistance between atmosphere and fruit, which can limit a gas exchange of O2 and CO2. However, it has not been sufficiently investigated. In this study on 7 cultivars of citrus fruit, it was aimed firstly to investigate a gas diffusion resistance utilizing ethane efflux method and secondly to investigate internal gas concentration of O2 and CO2. As a result, a cultivar of citrus fruit with slimer outer skin thickness had lower resistance. For the internal gas, a high CO2 concentration in comparison with the atmosphere was observed even in the fruits with the minimum resistance, which no considerable difference among all cultivars was observed in regardless of gas diffusion resistance value. However, when the fruits stored at 25 °C for 2 weeks, it was tended to increase CO2 gas concentration and decrease O2 gas concentration with increase in the resistance value. Therefore, when respiration of citrus fruits is activated at ambient temperature, self-control system of internal gas concentration can be driven to suppress their respiration, which was induced by gas diffusion resistance generated from their construction.

The developmental phase of adolescence is characterized by a multitude of neurocognitive and psychosocial changes and is therefore considered one of the most critical developmental periods of life. Experimentation on the use of substances often begins in adolescence and so does the addiction process. Most research in human subjects shows that chronic cannabis abuse is the cause of the impairment of some cognitive functions, affecting the performance on divided attention, verbal memory and working memory. In this study, we wanted to investigate how the abuse of cannabis (chronic, occasional and absence use) can influence global cognitive functioning, also through executive functions. From the statistical analyzes of our study, it emerges that the group of subjects who use chronic cannabis (group 1) has a significant drop in working memory tasks compared to the group that does not use it (group 3). In addition, the goal of future studies by our group is to verify the permanent alteration of cognitive processes affected through revaluations with calendar follow-up (controlled).

(1) Background: This manuscript introduces the "Lines and Dots" (LAD) technique, a novel method for administering Botulinum Toxin Type A (BoNT-A) in treating horizontal forehead lines. This technique emphasizes precision in targeting nerve endplates, considering individual anatomical variations, and optimizing treatment efficacy. (2) Methods: The LAD technique focus on using the frontalis muscle contraction dynamics to identify the specific distribution of the supraorbital and supratrochlear nerves It involves creating a grid of predetermined lines and points on the forehead that align with nerve paths and motor endplate zones. (3) Results: By targeting injections along these precise lines and points, the LAD technique ensures more effective utilization of BoNT-A. This approach not only enhances the toxin's effect but also allows for a reduction in the overall dosage. The strategic placement of injections results in a more natural, symmetrical, and sustained therapeutic effect, ensuring an improved aesthetic outcome. (4) Conclusions: The LAD technique represents a significant advancement in the realm of cosmetic injectables. It offers a tailored, patient-specific approach, maximizing the effectiveness of BoNT-A injections for treating forehead lines while minimizing risks and enhancing patient satisfaction. This method underscores the importance of a deep anatomical understanding and precision in cosmetic procedures.

This article develops duality principles and related convex dual formulations suitable for the local optimization of non-convex primal formulations for a large class of models in physics and engineering. The results are based on standard tools of functional analysis, calculus of variations and duality theory. In particular, we develop applications to a Ginzburg-Landau type equation.

Molecular autism research is evolving towards a biopsychosocial framework that is more informed by autistic experiences. In this context, research aims are moving away from correcting external autistic behaviors and towards alleviating internal distress. Autism Spectrum Conditions (ASCs) are associated with high rates of depression, suicidality and other comorbid psychopathologies, but this relationship is poorly understood. Here, we integrate emerging characterizations of internal autistic experiences within a molecular framework to yield insight into the prevalence of psychopathology in ASC. We demonstrate that descriptions of social camouflaging and autistic burnout resonate closely with the accepted definitions for early life stress (ELS) and chronic adolescent stress (CAS). We propose that social camouflaging could be considered a distinct form of CAS that contributes to allostatic overload, culminating in a pathophysiological state that is experienced as autistic burnout. Autistic burnout is thought to contribute to psychopathology via psychological and physiological mechanisms, but these remain largely unexplored by molecular researchers. Building on converging fields in molecular neuroscience, we discuss the substantial evidence implicating mitochondrial dysfunction in ASC to propose a novel role for mitochondrial allostatic load in the relationship between autism and psychopathology. An interplay between mitochondrial, neuroimmune and neuroendocrine signaling is increasingly implicated in stress-related psychopathologies, and these molecular players are also associated with neurodevelopmental, neurophysiological and neurochemical aspects of ASC etiology. Together, this suggests an increased exposure and underlying molecular susceptibility to ELS that increases the risk of psychopathology in ASC. This article describes an integrative framework shaped by autistic experiences that highlights novel avenues for molecular research into mechanisms that directly affect the quality of life and well-being of autistic individuals. Moreover, this framework emphasizes the need for increased access to diagnoses, accommodations, and resources to improve mental health outcomes in autism.

Lead Acid Batteries (LABs) are used for starting, lighting, and igniting, as well as in air conditioning systems and to supply power to electric engines in transport vehicles (TVs). However, the application of LABs for TVs has faced a number of market challenges, mounted by the upcoming high energy density and long lifespan batteries, such as lithium ion. LABs, on the other hand, are inexpensive. The key research question is, how can the lifespan of LABs used in automotive industries be increased, while still ensuring a low cost solution? Thus, integrating LABs with the supercapacitor (known as an electric double layer capacitor—EDLC) is likely to outperform the competing alternative batteries for TVs. This paper proposes a multiple stage approach to hybrid lead acid batteries and a supercapacitor system for TVs that is capable of maintaining the battery state-of-charge (SOC) at statistically high limits, ranging between 90% and 95%. This SOC target will likely ensure that the lifespan of the hybrid battery system can be elongated (extended) more than its competitors. In this study, the multiple stage approach of concatenated converters has been designed in order to satisfy all energy storage requirements for different characteristics of LABs and the supercapacitor. The designed hybrid system has been simulated using Matrix Laboratory (MATLAB/Simulink (version R2016a, MathWorks, Natick, MA, USA)). The simulated results show that high transient currents from the direct current (DC) bus of LABs, caused by the regenerative braking or deceleration of the TVs, reduce the battery lifespan and induce mechanical stress. The supercapacitor reduces the stress on the LAB by absorbing high transient currents. This, in turn, keeps the LABs’ SOC between 90% and 96% and the voltage at 12 V. As indicated by the simulated results, the hybrid battery SOC is maintained at 90–96% and the terminal voltage is approximately 12 V.

Silver eel were sampled in 1981 and 1983 in River Brede, Denmark, with outlet to the North Sea. Yellow eel in River Køge-Lellinge, Denmark, with outlet to the Baltic Sea were sampled 1965-1968. Silver eel were aged by burning the otoliths. Silver male ages varied from 4 to 25 years, lengths 30.8 to 45.3 cm, and female silver eel varied from 7 to 25 years, lengths 42.3 to 77.3 cm. von Bertalanffy trajectories of length-at-age of male and female yellow eel were calculated in both rivers. Younger yellow eel had significantly higher annual growth rate compared to older age groups, and females grew significantly faster than males. Two models for natural mortality M were used to estimate number of glass eel needed to produce number of silver eel for each sex and silver age group. Annual silver eel production from River Brede Å was 49.2 kg ha-1, demanding 2,894 glass eel ha-1 and in River Køge-Lellinge, the silver eel production was 48.5 kg ha-1, demanding 5,570 glass eel ha-1. It was calculated that one (0.29 g) thousand glass eel contribute to 8.8 kg silver eel in River Køge-Lellinge, and 17.0 kg silver eel in River Brede. To fulfill the Danish EMP in rivers requires annual stocking of 33 tons or 9.4 million reared on-grown eel (3.5 g) to compensate for 183 tons lost silver eel.

Plant transcriptomes are an extremely dynamic entities shaped spatially and temporally by many intracellular and environmental cues. In this review, we first summarize the complexity and diversity of plant genomes and transcriptomes as a start point for the multitude of transcriptomic responses. Numerous alterations within various tissue and organ-specific transcriptomes as well as the most relevant transcriptomic responses associated with plant acclimation to selected abiotic and biotic stress conditions, from the current studies employing high-throughput transcriptomic analysis are widely discussed. Understanding changes within plant transcriptomes, revealed by in silico functional analysis, allows for the characterization of stress-affected genes and stress acclimatory mechanisms, as well as allows to perform plant metabolic engineering. The latter allow cultivars to produce more secondary metabolites in the future, which are often desirable substances in the biomedical industry. Accordingly, in this review special attention was also paid to characterize the potential of transcriptomic analyses of medicinal species, particularly to search for new cultivars. Extensive characterization of transcriptomic responses in stress would also result in the development of new cultivars that display physiological and molecular mechanisms that allow them to cope with adverse environmental conditions more adequately.

The Software-Defined Networking concept plays an important role in network management. The central controller, which is the main element of SDN, allows to provide traffic engineering and security solutions in single and multiple-layer networks based on optical transmission. In this work, we compare selected open-source implementations of SDN controllers. Throughput and latency measurements were analyzed using the CBench program. The simulation of a link failure and a controller failure were conducted using the API provided by the Mininet network simulator. For detecting security vulnerabilities, a dedicated program, called sdnpwn, was used. The work provides an overview of the selected controllers, indicating their strengths and weaknesses. Moreover, some implemention suggestions and recommendations are presented.

Background: Accidental caustic burns of the esophagus in children represent a significant global health challenge, often necessitating esophageal reconstruction. The aim of this study is to compare the efficacy and morbidity related to esophagus replacement with colonic and gastric tube transplants in a pediatric population followed for caustic stenosis. Method: This retrospective study was conducted at a tertiary pediatric surgery unit for children treated from January 1989 to December 2022. We compared colonic and gastric tube esophageal replacement. Short-term (within 30 days) and mid-term outcomes and complications were reviewed. Statistical evaluation was considered using a Chi-square test for categorical data analysis. Results: A total of 124 children with caustic esophageal burns were included. Among them, 23 (18.5%) had a gastric tube transplant for esophagus replacement and 101 (81.5%) a colonic transplant. During surgical intervention, we found a significantly higher risk of complications when using a colonic transplant (34%, p < 0.001). There was no significant statistical difference in postoperative short-term and mid-term complications between the two techniques. Twenty-six (26%) of the children required a reoperation, with a higher risk in the gastric tube transplant group (p < 0.001). Endoscopic dilatation after surgery was also performed on a higher number of children who had received a gastric tube transplant (p = 0.005). Overall, 97.6% recovered full normal oral feeding. Conclusions: We found that colonic and gastric tube replacement are both good options for pediatric esophageal replacement after a caustic injury and show effectiveness over time. Gastric tube transplants carried a slightly higher risk of reoperations and a higher number of dilatations post-surgery. However, our groups are not really comparable due to the much higher number of colonic transplants. Both surgical options have to be considered during surgery and the choice depends on the anatomy of the patient. Our future research will focus on assessing long-term quality of life and potential risk of neoplastic complications.

Passive and forced drinking harm was prevalent but less recognized in Chinese adolescents. We educated adolescents on such harm to reduce their intention to drink. Students (n=1244) from 7 secondary schools in Hong Kong participated in a video-based health talk on passive and forced drinking harm. Paired t-test was used to assess their change in knowledge of passive and forced drinking, health and social harm of drinking after the health talk. McNemar's chi-squared test and adjusted multivariable logistic regression (AOR) were used to assess their change in intention to drink and intention to quit. Students were less likely to drink (OR 0.29, 95% CI 0.19-0.42) and more likely to quit drinking (OR 3.50, 1.10-14.6) after the health talk. Increased knowledge of passive drinking was associated with less intention to drink (AOR 0.93, 0.90-0.97), increased knowledge of health harm (adjusted b 0.06, 0.05-0.08), and social harm of drinking (adjusted b 0.12, 0.10-0.16). Similar associations were observed in forced drinking (intention to drink: AOR 0.87, 0.79-0.96; health harm: adjusted b 0.16, 0.12-0.19; social harm: adjusted b 0.36, 0.28-0.43). We showed preliminary evidence that the health talk on passive and forced drinking reduced the intention to drink in adolescents.

Childhood diarrhea continues to be a major cause of under-five (U-5) mortality globally and in India. In this study, 1571 U-5 children residing in nine rural villages and four urban slums in Ujjain, India were included with the objective to use community participation and drug utilization research to improve diarrheal case management. The mean age was 2.08 years, with 297 (19%), children living in high diarrheal index households. Most mothers (70%) considered stale food, teething (62%), and hot weather (55%) as causes of diarrhea. Water, sanitation, and hygiene (WASH)-related characteristics revealed that most (93%) households had toilets, but only 23% of the children used them. The study identified ineffective household water treatment by filtration through cloth by most (93%) households and dumping of household waste on the streets (89%). The results revealed low community awareness of correct causes of diarrhea (poor hand hygiene, 21%; littering around the household, 15%) and of correct diarrhea treatment (oral rehydration solution (ORS) and zinc use, 29% and 11%, respectively) and a high antibiotic prescription rate by healthcare providers (83%). Based on the results of the present study, context-specific house-to-house interventions will be designed and implemented.

The aim of this article has been to evaluate the impact of a physical-sport education programme, based on the pedagogical model of Sport Education within the framework of quality Physical Education and approached from the field of social and emotional learning, on a set of psychological variables: subjective well-being (quality of life related to health, positive affect and negative affect); trait emotional intelligence and social anxiety. A total number of 113 Compulsory Secondary Education students were involved, aged 12 to 15 years old, 44 of whom belonged to the control group (CG) and 69 to the experimental group (EG). A quasiexperimental design of repeated pretest and posttest measures with the CG was used. The results obtained in this investigation revealed that the intervention programme caused significant improvements in the subjective well-being and the trait emotional intelligence for the EG. These findings reinforce the pedagogical efficiency of the programme with regards to the aim that has been set. Likewise, the findings also highlight the suitability and appropriateness in terms of innovative teaching proposals. In the same way, the results showed relevant empirical contributions in this given school context due to its psychological benefits and the encouragement of healthy living.

This study examines the impact of executives implicated in fraud on firms' investment decisions using AAERs in the US, aiming to address the underexplored aspect of rationalization within the fraud triangle. Executives implicated in fraud often display abnormal attitudes to justify accounting irregularities, prompting an investigation into how abnormal investment decisions are used for rationalizing fraud, given their critical role in a firm's long-term sustainability. Analysis of AAERs spanning from 1981 to 2013 reveals that implicated executives, particularly CEOs and CFOs, tend to make abnormal investment decisions, and that collusive fraud exacerbates this behavior. Notably, such executives lean towards overinvestment, particularly in R&amp;D expenditures, to hide or justify fraud; the duration of fraud amplifies its impact on investment decisions. By shedding light on the rationalization aspect of the fraud triangle, this research contributes valuable insights for investors, regulators, and academia emphasizing the significance of public disclosure of fraud to enhance transparency in capital markets and the importance of ethics-focused education in accounting to prevent corporate fraud.

Patches of Diopatra species from Brazilian sandy beaches were followed for ca. 50 years. Data were accessed from papers, gray literature, images and collections to verify time changes in the South Brazilian Bight (SBB) from 1974-2021. We modeled maximum density over time at 15 beaches, observing very high densities (&gt; 100 ind.m-2) in 1974 followed by a decrease (~ 10 ind.m-2) of three species of Diopatra until 1995 and a strong decline (1996-2002) when populations were almost regionally extinct (0-1 ind. m-2). A slight recovery (3-4 ind.m-2) occurred after 2006 for a single species, D. marinae, associated with warmer northern waters, suggesting a range shift. This pattern was associated with a) heatwaves linked to an El-Niño event (1988) and gradual SST surface warming of ca. 1 oC since 1974. The usage of Diopatra spp. as fishing bait could also be associated with such a reduction. After 2016, D. neapolitana, a likely alien species, was established in the SBB in high densities. Projections based on Species Distribution Modeling (SDM) suggest a potential of invasion in the same range of the known species of D. cuprea complex along the Brazilian coast despite that there are no signs of competition between both species.

Here we performed a comparative experimental analysis by EPR spectroscopy of the intensity of nitric oxide (NO) production and copper content in the injured and uninjured areas of the frontal lobes and the hippocampus of the brain of male Wistar rats, after modeling of combined brain and spinal cord injury. Brain and spinal cord injury were modelled by local destruction of the left precentral region of the brain using a stylet followed by hemorrhage injury at the level of the first lumbar vertebra of the spinal cord. We found a significant decrease in NO production 7 days after injury modeling in the injured and uninjured (contralateral) brain regions, but the copper content remained unchanged one week after injury modeling. Thus, combined brain and spinal cord injury is not accompanied by a radical change in the activity of the antioxidant system in the brain, which is also confirmed by the absence of changes in NO production and copper content in the hippocampus.