This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

In Cameroon, Aedes mosquitoes transmit various arboviruses, posing significant health risks. We aimed to characterize the Aedes virome in southwestern Cameroon and identify potential core viruses which might be associated with vector competence. A total of 398 Aedes mosquitoes were collected from four locations (Bafoussam, Buea, Edea and Yaounde). Aedes albopictus dominated all sites except for Bafoussam where Aedes africanus prevailed. Metagenomic analyses of mosquitoes grouped per species into 54 pools revealed notable differences in eukaryotic viromes between Ae. africanus and Ae. albopictus, with the former exhibiting greater richness and diversity. Thirty-seven eukaryotic virus species from 16 families were identified, including six novel viruses with near complete genome sequences. Seven viruses were further quantified in individual mosquitoes via qRT-PCR. Although none of them could be identified as core viruses, Guangzhou sobemo-like virus and Bafoussam mosquito solemovirus virus were highly prevalent regionally in Ae. albopictus and Ae. africanus, respectively. This study highlights the diverse eukaryotic virome of Aedes species in southwestern Cameroon. Despite their shared Genus, Aedes species exhibit limited viral sharing, with varying viral abundance and prevalence across locations. Ae. africanus, an understudied vector, harbors a rich and diverse virome, suggesting potential implications for arbovirus vector competence.

The lithium-ion batteries are widely used as a power source for portable devices, including cell phones. The useful life is about 2 years or 500 cycles, contributing to the generation of waste from electrical and electronic equipment (WEEE). Mining of lithium and cobalt damages the environment and is onerous; therefore, sustainable alternatives, such as obtaining these elements from secondary sources as recycling of lithium-ion batteries, are essential to provide the inputs used in the sector. However, the metallurgical route which will used to recovering them must be considered, due to this work aims for a more environmentally favorable process using DL-malic acid 1.5 M and instead of compared with sulfuric acid 2 M, heat pretreatment of 1 h and 3 h, and for all conditions, experiments were carried out with and without adding the oxidizing agent hydrogen peroxide. The best yields occurred in presence of H2O2 10 % v/v, and heat pretreatment of 1 h: 33.49 % Co and 4.63 % Li, and 29.78 % Co e 3.44 % Li were recovered by sulfuric acid and DL-malic acid, respectively.

Epithelial–mesenchymal transition (EMT) is defined as a cellular process during which epithelial cells acquire mesenchymal phenotypes and behavior following the downregulation of epithelial features. EMT and its reversed process, the mesenchymal-epithelial transition (MET), and the special form of EMT, the endothelial-mesenchymal transition (EndMT), have been thought as a universal dogma controlling developmental and pathological processes. More than half a century of EMT study has made it a large research field and a mainstream concept. However, discrepancies and disputes over EMT and EMT research have also grown over time. Particularly, neither the epithelial and mesenchymal states/properties nor their regulatory networks nor specific markers have been defined, rendering the EMT, MET and EndMT concepts groundless. Moreover, EMT and MET effects should not be a cause, but rather a consequence of or an accompanying effect during developmental and pathological processes. EMT and MET represented by the change in cell shapes or adhesiveness or symbolized by EMT factors are biased interpretation of the overall change in cellular property and regulatory networks during development and cancer progression. The true meaning of EMT effects in some developmental and pathological processes, such as fibrosis, needs re-evaluation. But the core EMT factors are actually a few components of the regulatory networks of neural stemness, which determines tumorigenicity and pluripotency. The EMT effects in cancer progression and neural crest formation are wrong attribution of the role of neural stemness during cancer progression and the cell-intrinsic property of neural crest cells to the unknown mesenchymal state. It is time to reassess the significance of EMT and its related concepts in scientific research.

Flavonoids are secondary plant metabolites involved in biological activity that are present in plentiful proportions in many plant origin foods and beverages. Owing to their chemical difference and qualities that make them beneficial for human health, they have received a lot of focus in research. This review therefore presents an account on the chemical structure of flavonoids, food sources and their metabolism with emphasis on bioavailability. And they also examine their antioxidant, anti-inflammatory, anticancer, cardiovascular protective, neuroprotective, and antimicrobial properties and reveal related molecular processes. In addition, it overviews the clinical data concerning their possible use in the prevention and treatment of chronic diseases. Some of the problems that attend current flavonoid research are also dealt with; such issues as variation in the bioactivity of flavonoids and scarcity of clinical studies. This review is designed to present current state of knowledge and to provoke further research concerning the further potential of flavonoids in enhancing human health.

Solanum indicum Linn, is commonly known as Indian Nightshade or Poison Berry belongs to the family Solanaceae. This review studied the botanical description, traditional uses of its seeds, its chemical composition, pharmacological properties and modern applications in medicinal system. The plant is rich in alkaloids, steroidal saponins, flavonoids and phenolic acids having diverse pharmacological activities and phytochemical profile. It is used in treatment of various respiratory disorders, inflammation, skin problems and gastrointestinal disease since ancient times. Scientific research in modern times also tested many of the traditional uses revealing anti-inflammatory, antioxidant, antimicrobial activities as well as some cytotoxic and even antidiabetic effects. These pharmacological activities show potential future as uses for organic drugs, natural health products, skincare formulations and agricultural advancements.

Background: Subclinical myocardial injury (SCMI) is associated with an increased risk of poor cardiovascular disease (CVD) outcomes. Understanding the underlying risk factors for SCMI is crucial for the prevention and management of CVD. We hypothesized that atherogenic dyslipidemia, a combination of high triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C), is associated with an increased risk of SCMI. Methods: This analysis from the third National Health and Nutrition Examination Survey (NHANES-III) included 7,093 participants (age 59.3±13.4 years, 52.8% women, and 49.4% White) free of CVD. Atherogenic dyslipidemia was defined as TG ≥150 mg/dL and HDL-C <40 mg/dL in men or <50 mg/dL in women. A validated electrocardiographic-based cardiac infarction injury score (CIIS) ≥10 was considered positive for SCMI. Multivariable logistic regression analysis was used to examine the association of different combinations of TG and HDL-C groups, including atherogenic dyslipidemia with SCMI. Results: About 22.5% (n=1,594) of participants had atherogenic dyslipidemia, and 26.3% (n=1862) had SCMI. Compared to participants with normal TG and normal HDL-C, those with atherogenic dyslipidemia had a higher prevalence of SCMI (31.2% vs. 23.9%, p-value <0.001). In a multivariable logistic regression model, atherogenic dyslipidemia was associated with the highest odds of SCMI followed by high TG/normal HDL-C, then low HDL-C/normal TG [OR (95% CI): 131 (1.14,1.52), 1.13 (0.97,1.33), and 1.01(0.86,1.20), respectively). Conclusions: Atherogenic dyslipidemia is associated with a higher risk of SCMI, which highlights the role of nontraditional risk factors in the development of subclinical CVD.

Peste des Petits Ruminants (PPR) is one of the major transboundary animal diseases that severely impact livestock production and trade worldwide. An investigation was carried into the seroprevalence and risk factors of PPR in small ruminants, camels, and wild ruminants in Bahrain. A total of 1240 serum samples were collected from 119 sheep flocks and 1224 serum samples from 125 goat herds. In addition, 100 sera from camels and 38 sera from wild ruminants, were collected. Serum samples were tested for the presence of PPR specific antibodies using a competitive ELISA. Flock management and health information of the flocks and herds were collected using a semi-structured questionnaire. The individual prevalence of PPR in sheep and goats was 26% and 25.5%, respectively. The flock or herd level prevalence of PPR was 22.7% and 29.6% in sheep and goats, respectively. None of the tested camels or wild ruminants were positive for PPR-specific antibodies. Sheep and goats from the Northern governorate were more likely to be seropositive to PPR than in other governorates. On the individual animal level, the univariate analysis suggested; age (&lt;12 months), sex (male), and health status (weak animals) as risk factors for PPR seropositivity. On the flock/herd level, the multivariable logistic regression analysis identified large flock/herd size and semi-intensive farming as risk factors for PPR seropositivity.

Background and Objectives: Wilson disease (WD) is a disorder of copper metabolism caused by genetic muta-tions in the ATP7B gene which lead to the accumulation of copper in the body. The aim of the present study is to investigate the severity of WD in patients in Romania. Materials and Methods: This study was conducted us-ing an online questionnaire consisting of 32 questions in a group of patients suffering from Wilson disease. Results: The study included 67 people aged 7 to 56 with WD. The most common symptoms of the participants were hepatic and neurological in 22 cases (32.8%). The most common neurological symptom in the assessed cases was muscle stiffness (49.5%), followed by tremor (45%), and speech disorders (40.5%). The Kay-ser-Fleischer ring was present in 50.7% of the participants. The most commonly used drug treatment in the participants was D-penicillamine (77.61%). Conclusions: The severity of WD is based on many aspects, the most important being early diagnosis and access to treatment throughout the year. The prognosis is favorable with early diagnosis and treatment, but it is crucial to diagnose individuals before they develop major symp-toms. Therefore, advances in WD screening can lead to an earlier diagnosis and better results.

A compartmental epidemiological model with seven groups is introduced herein, to account for the dissemination of diseases similar to the Coronavirus disease 2019 (COVID-19). In its simplified version, the model contains ten parameters, four of which relate to characteristics of the virus, whereas another four are transition probabilities between the groups; the last two parameters enable the empirical modelling of the effective transmissibility, associated in this study with the cumulative number of fatalities due to the disease within each country. The application of the model to the fatality data (the main input herein) of five countries (to be specific, of those which had suffered most fatalities by April 30, 2020) enabled the extraction of an estimate for the basic reproduction number $R_0$ for the COVID-19 disease: $R_0=4.91(33)$.

Background: Endometriosis is a female chronic inflammatory disease in which endometrial tissue develops outside the uterine cavity. It is a complex pathology, that significantly contributes to morbidity in premenopausal women, leading to chronic pain, infertility, and subfertility negatively impacting the physical, and emotional well-being and overall quality of life. The public health burden of endometriosis remains elusive and challenging to determine, and this uncertainty can lead to inadequate healthcare services and treatments. Objective: To estimate the incidence and prevalence of endometriosis in Italy using the hospital discharge records database. Population-based retrospective study. Nationwide between 2011 and 2020. Methods: From the National Hospital Discharge Database were selected all admissions with a diagnosis of endometriosis (ICD-9-CM, codes 617.x), supported by the presence of a procedure code of laparoscopy or any other surgical procedure allowing direct visualisation of the lesions. Main outcomes measures Incidence and prevalence of endometriosis were estimated for the entire 2010-2020 period and by individual year, analysing the time trend and variability in different geographical areas of Italy. Results: A total of 134,667,646 women aged 15–50 years with one or more hospitalisations for endometriosis in all Italian hospitals. The incidence of endometriosis in Italy during this period was 0.839 per 1000 women (CI95% 0.834-0.844), exhibiting a statistically significant decreasing trend over the years. A discernible north-south gradient was observed, with higher rates documented in the northern regions. The prevalence rate stood at 14.0 per 1000 during the same period, and a similar north-south geographical gradient was identifiable in the prevalence rates as well. Conclusions: The utilization of national-level hospital data enables the generation of incidence and prevalence data for endometriosis without variations in methods and definitions, facilitating the evaluation of temporal trends and regional comparisons. Understanding and quantifying this phenomenon is essential for appropriate healthcare planning in various Italian regions.

In this paper, we consider an epidemiological Susceptible-Infected-Susceptible (SIS) model in which the individuals cured from the infection return back into the group of people endangered by the infection. Delay differential equations were introduced to create more realistic models since many processes depend on past history. Many different mathematical methods, including the Susceptible-Infected-Recovered (SIR) model as well as derivatives thereof, can be used to prognose the spread of many various infectious diseases, including Covid-19.We use a nonlinear differential system with time delay as a mathematical formulation of this model. It is examined by the existence of a positive solution which is bounded by two functions $k_1, k_2\in C([t_0,\infty),(0,\infty))$ with constants $\lambda_1 > 0,\,\lambda_2 > 0$ where $0<\lambda_1\leq \lambda_2$. The conditions are put in place for existence of a positive solution that approaches zero in infinity, if $t\to\infty$. Infectious diseases accompany a person throughout their entire life, and in the history of mankind often appeared in frequent epidemics caused high mortality. The occurrence of infectious diseases in the population represents a serious health, social and economic problem. There is a lot of ambiguity concerning the time span and final spread of the epidemic thus making it very demanding for the governing bodies, healthcare institutions, and economic sector to precisely judge the future development.Several examples are shown at the end of this paper.

The COVID-19 pandemic caused by the SARS-CoV-2 virus has inflicted significant mortality and morbidity worldwide. Continuous virus mutations have led to the emergence of new variants. The Omicron BA.1 sub-lineage prevailed as the dominant variant globally at the beginning of 2022 but was subsequently replaced by BA.2 in numerous countries. Wastewater-based epidemiology (WBE) offers an efficient tool for capturing viral shedding from infected individuals, enabling early detection of potential pandemic outbreaks without relying solely on community cooperation and clinical testing resources. This study integrated RT-qPCR assays for detecting general SARS-CoV-2 and its variants levels in wastewater into a modified triple susceptible-infected-recovered-susceptible (SIRS) model. The emergence of the Omicron-BA.1 variant was observed, replacing the presence of its predecessor, the Delta variant. Comparative analysis between the wastewater data and the modified SIRS model effectively described the BA.1 and subsequent BA.2 waves, with the decline of the Delta variant aligning with its diminished presence below the detection threshold in wastewater. This study demonstrates the potential of WBE as a valuable tool for future pandemics. Furthermore, by analyzing the sensitivity of different variants to model parameters, we are able to deduce real-life values of cross-variant immunity probabilities, emphasizing the asymmetry in their strength.

Fractional differential mathematical model unfolding the dynamics of the COVID-19 pandemic in India is presented and explored in this paper. The purpose of this study is to estimate the future outbreak of disease and potential control strategies using mathematical models in India as a whole country as well as in some of the states of the country. This model is calibrated based on reported cases of infections over the month of April 2020 in India. We have used iterative fractional complex transform method to find approximate solutions of the model having modified Riemann Liouville fractional differential operator. We have also carried out a comparative analysis between actual and estimated cumulative cases graphically, moreover, most sensitive parameters for basic reproduction number$(R_0)$ are computed and their effect on transmission dynamics of COVID-19 pandemic is investigated in detail.

The Interdisciplinary Canadian Collaborative ME Research Network (ICanCME), established in 2019, aims to foster research in Canada and contribute to finding the causes and possible treatments for myalgic encephalomyelitis /chronic fatigue syndrome (ME/CFS), thereby reducing the impact of ME/CFS on the health of Canadians. The main objectives of this paper are to suggest standards for ME/CFS research for the collection of data from participants (Recommended Data Elements) and to consider other factors, such as design, language, cultural issues, equity, and diversity. Consensus of the relevant contents of this research guideline was reached during the ICanCME working group meetings and were based on existing guidelines. Members of the working group contributed to guideline development based on their respective expertise. The proposed research guidelines could improve research quality and advance knowledge in the field of ME/CFS, and ultimately benefit ME/CFS patients.

This paper explores various aspects of microbiology and immunology, focusing on the epidemiology and molecular characterisation of infectious diseases in the Caribbean and South America. Key areas of investigation include tuberculosis (TB), nosocomial infections, Salmonella infections, and bloodborne pathogens. A retrospective study in Jamaica underscores the high mortality rates in HIV-positive TB patients and the need for early HIV diagnosis and integrated care. Research in Trinidad and Tobago reveals the genetic diversity of Mycobacterium tuberculosis strains, highlighting high HIV-TB co-infection rates. The paper also examines the challenges posed by nosocomial infections, particularly those caused by antibiotic-resistant Gram-negative bacteria and methicillin-resistant Staphylococcus aureus (MRSA), and emphasises the importance of infection control. In addition, the study investigates Salmonella prevalence in poultry in Jamaica and the risks associated with foodborne infections. The paper further explores transfusion-transmitted infections and the development of IgM antibodies in egg whites for bacterial antigen detection. Lastly, it addresses immune responses in patients with immunodeficiency disorders, such as severe combined immunodeficiency (SCID) and chronic granulomatous disease (CGD), highlighting their susceptibility to a wide range of infections.

Coxsackievirus A6 (CV-A6) has emerged as the predominant causative agent of hand, foot, and mouth disease (HFMD) in young children. Since the declaration of coronavirus disease 2019 (COVID-19) as a global pandemic, the incidence of infectious diseases, including HFMD, has decreased markedly. When social mitigation was relaxed during the COVID-19 pandemic in 2022, re-emergence of HFMD was observed in Gwangju, South Korea, and seasonal characteristics of the disease appeared to have changed. To investigate the molecular characteristics of enterovirus (EV) associated with HFMD during 2022, 277 specimens were collected. Children aged under 5 years accounted for the majority of affected individuals. EV detection and genotyping were performed using real-time RT-PCR and nested RT-PCR followed by sequence analysis. EV detection rate was found to be 82.3%, and the main genotype identified was CV-A6. Sixteen CV-A6 samples were selected for whole genome sequencing. According to phylogenetic analysis, all CV-A6 strains from this study belonged to the sub-genotype D3 clade based on VP1 sequences. Analysis of 3D polymerase phylogeny showed that only the recombinant RF-A group was identified. In conclusion, circulating EV types should be continuously monitored to understand pathogen emergence and evolution during the post-pandemic era.

A U.S. government health agency conducted epidemiological studies to evaluate whether exposures to drinking water contaminated with volatile organic compounds at U.S. Marine Corps Base Camp Lejeune, North Carolina, were associated with increased health risks to children and adults. These health studies required knowledge of contaminant concentrations in drinking water—at monthly intervals—delivered to family housing, barracks, and other facilities within the study area. Because concentration data were limited or unavailable during much of the period of contamination (1950s–1985), the historical reconstruction process was used to quantify estimates of monthly mean contaminant-specific concentrations. This paper integrates many efforts, reports, and papers into a synthesis of the overall approach to, and results from, a drinking-water historical reconstruction study. Results show that at the Tarawa Terrace water treatment plant (WTP) reconstructed (simulated) tetrachloroethylene (PCE) concentrations reached a maximum monthly average value of 183 micrograms per liter (ug/L) compared to a one-time maximum measured value of 215 ug/L and exceeded the U.S. Environmental Protection Agency’s current maximum contaminant level (MCL) of 5 ug/L during the period November 1957–February 1987. At the Hadnot Point WTP, reconstructed trichloroethylene (TCE) concentrations reached a maximum monthly average value of 783 ug/L compared to a one-time maximum measured value of 1,400 ug/L during the period August 1953–December 1984. The Hadnot Point WTP also provided contaminated drinking water to the Holcomb Boulevard housing area continuously prior to June 1972, when the Holcomb Boulevard WTP came on line (maximum reconstructed TCE concentration of 32 ug/L) and intermittently during the period June 1972–February 1985 (maximum reconstructed TCE concentration of 66 ug/L). Applying the historical reconstruction process to quantify contaminant-specific monthly drinking-water concentrations is advantageous for epidemiological studies when compared to using the classical exposed versus unexposed approach.

We review and assess the classic SIR and SEIR epidemiological models regarding possible applications to the COVID-19 pandemic. In spite of numerous more complicated models, we show how the qualitative features of the solution to the SIR and SEIR models continue to provide valuable public health insights in some scenarios. Using estimated COVID-19 data as of this date, the SEIR model shows that if it were possible to reduce R₀ from 2.5 to 1.25 through social distancing and other measures, the maximum fraction of the population that would become infected at any particular time would drop from 17% to 4%, provided that all of the model assumptions are satisfied. Finally, we compare the classic SIR model with a recent stochastic model with favorable results. Since this comparison underscores the importance of underlying connectivity assumptions, we conclude with Monte-Carlo simulations with specific connectivity that reproduce the classical SIR model with standard incidence.

Qualitative comparative analysis (QCA) was developed by Charles Ragin in 1987 for the comparative analysis of small data sets. The method has become widespread in sociological and economic research. There are examples of successful use of QCA in the field of medicine and epidemiology. The purpose of this review is to describe the key stages of QCA with a discussion the application of this method to the analysis of medical and epidemiological data.

Background and Objectives: Metastases are the most common tumors of the spine. As an important increase in the annual incidence of spinal metastases (SM) has been observed in the last decade, the aim of this study was to describe the epidemiology and histopathological types of SM surgically treated in the neurosurgery clinics of a regional hospital in North-Eastern Romania over a period of 5 years, in order to define a certain tumor profile that would benefit from an early screening. Materials and Methods: We retrospectively evaluated 115 adult patients, searching for demographic data (gender and age of the patients), primary tumor characteristics (location and histological type), topography and histopathological type of the spinal metastasis, and the time interval between the diagnosis of the primary tumor and the surgery for the spinal metastasis. Results: The patients were elderly (average age = 58.96 years), with a male predominance (67.82%). Main location of SM was in thoracic region (44.34%), with multiple vertebral metastases in 30.43% patients. Only 33.04% of the patients had a known cancer at the time of admission. Primary tumor was located mainly in lung (47.82%), gastrointestinal tract (15.65%), breast (11.30%), prostate (10.43%) and kidney (9.56%). SM from lung cancer mostly expressed squamous cell carcinoma (19.13%), and those from the digestive system mostly exhibited a moderately/poor colorectal adenocarcinoma (8.69%). Conclusions: Our data suggest the need for close surveillance of patients diagnosed with lung cancer and colorectal cancer because these malignancies most frequently develop spinal metastases. Smoking prevention actions and screening programs for the detection and removal of precancerous colorectal lesions must be developed and expanded.

Lassa fever, commonly known as Lassa hemorrhagic fever (LHF), is one of the progressive illnesses invading a large population of two to three million individuals in West Africa. The infection transmitted through the rodents severely impacts the local population and the medical professionals in surrounding areas, which were also the primary target of LHF. In epidemic areas, Lassa fever causes a public health threat since it poses a significant morbidity and fatality Case rate (CFR) ≥ 50%). The disease is widespread in West Africa and has developed into one of the most common and life-threatening viral hemorrhagic fevers. Monitoring and preventing persistent disease outbreaks has been challenging in affected regions due to insufficient healthcare facilities, diagnostics labs, care centers, and low socioeconomic conditions. An absence of public awareness and the emergence of an ecological niche is advantageous for the survival and multiplication of the mouse (Mastomys natalensis) inhabiting the Lassa virus serving as the disease's natural host and reservoir. The current review focuses on early diagnosis and appropriate treatment, highlighting the immediate requirement of clinically approved vaccines for LHF, causing preventative and control actions more difficult in the present era.

Different nematodes affect canines, however, Dirofilaria immitis is the most prevalent filariae. D. immitis causes heartworm disease that can be fatal for dogs and potentially transmitted to humans. Climate change, animal migration, among other factors have changed the dynamics of vector-borne diseases, including filariasis. In the last four years, a sudden increase of dogs with microfilaremia was reported by different veterinary centers in Cali, the main city of Southwest Colombia. The objective of this study was to molecularly identify the etiologic agent of this filariasis outbreak reported in this area from 2018-2019, using a novel PCR–RFLP method. Those filariasis cases were initially detected by microscopic and serological tests. A total of 82 canine filariasis cases were molecularly analyzed, identifying 55 (67%) of them as Acantacheilonema reconditum. PCR-sequencing was performed in eight cases confirming this finding. The filariasis cases were statistically associated with male dogs who had clinical signs of anemia, with low levels of hemoglobin and hematocrit (p &lt;0.0001), and high levels of plasma proteins (p &lt;0.001). This emerging canine disease constitutes an important public health concern among clinicians and advises active surveillance to explore its zoonotic potential.

We previously proposed a public intervention framework concept that would allow people to resume personal and economic activities. We showed that intervention measures are used in a quantitative scale to reduce transmission probabilities and disease severity. In this article, we systematically examine the origin, assumptions, performance and limitations of epidemiological models from different views used in past review. We found that nearly all model assumptions fail to hold or are remote from reality; R0 does not exit or has no utility in guiding treatment options; personalized intervention measures are vitally important to COVID-19 due to its transmission characteristic; and current epidemiological models are unable to accurately assess the true benefits of personalized intervention measures. We suggest that poor performance of the models are attributed to flawed assumption that health/disease properties can be treated as transferable properties. The flaw creates a fiction that disease properties such as infection probabilities and death risks can be transferred from any vulnerable persons to anyone in the population and thus severely limit societal ability to fight the pandemic. We finally show that the benefits of personalized mitigation measures could be determined directly by using variable Ri values for infected persons (or nodes) together assessment of death rate and disability rate; the attempt of avoiding the disease by defeating all potential transmission probabilities is unrealistic; but mitigating disease severity for specific persons is more feasible and reliable. A most reliable strategy for reviving economy is using personalized protective measures and improving person health before effective vaccine is available.

Our study assessed the impact of using ovitraps with pyriproxyfen on mosquito populations and the feasibility of using human saliva samples to test for seroconversion to dengue virus (DENV). We used a quasi-experimental research design by forming the intervention (n=220) and the control (n=223) groups in neighboring Taguig City, Philippines, over four months. Socio-demographic data, entomological indices, and IgG antibodies against DENV were measured. Associations between the implementation of ovitraps dosed with pyriproxyfen and mosquito densities (percentage positive ovitraps and container indices) and DENV seroconversion were calculated post-intervention in Months 2, 3, and 4. Among participants recruited at baseline, 17 and 13 were seropositive for dengue (DENV) in the intervention and control groups, respectively. Both entomological indices were lower in the treated area than the control site at months two, three, and four post-interventions but not earlier. Dengue seroconversions rates did decrease in the treated population but not significantly so. In conclusion, the use of PPF-treated ovitraps impacted the mosquito population but not the seroconversion rates. Compliance to provide saliva samples and the ability to detect IgG antibodies within these samples were encouraging and suggested that further studies on a larger population and longer duration are warranted.

Total edentulism is the loss of all teeth for any cause by a multifactorial process that involves biological and patient-related factors. Studies on edentulism and risk factors in Mexico are limited, and the epidemiological surveillance data is scarce and controversial since official governmental reports are not statistically representative of the country. We estimate the distribution for edentulism according to sociodemographic and socioeconomic variables in adults from a low-income state in 2003 and its progress in Mexico. We analyzed data from the National Performance Evaluation Survey in Oaxaca, Mexico, and the annual reports of the Epidemiological Surveillance System of Oral Pathologies in 2009-2019 using X². Oaxacan patients older than 75 y.o (17.9%, p<0.05), those with lower schooling (11.2%), and diabetes (14.5%) presented the highest percentage of edentulism. We do not observe differences in edentulism between sex or residence (p>0.05). From 2009 to 2019, country data reports the lowest rate of edentulism in adults over 20 y.o (0.32%; 95% CI 0.18%-0.48%) and the most affected population over 79 y.o. (7.29%; 95% CI 5.2%-9.30%). As it is a cumulative phenomenon, it is necessary to establish better surveillance, prevention, and treatment programs to improve the oral health of older thus reducing edentulism.

The real-world outcomes of patients with metastatic prostate cancer (mPCa) are largely unexplored. We investigated the improvements in overall survival (OS) and cancer-specific survival (CSS) in patients with de novo mPCa in latest years. The USA SEER Research Data (2000-2017) were analyzed using the SEER*Stat software. The Kaplan-Meier method and Cox regression were used. Patients with de novo mPCa were allocated to 3 cohorts based on year of diagnosis: A (2000-2003), B (2004-2010), C (2011-2014). Maximum follow-up was fixed to 5 years. Overall, 26434 patients were included. Age, race and metastatic stage significantly affected OS and CSS. After adjustment for age and race, patients in cohort C showed 9% reduced risk of death (HR:0.91 [95% CI, 0.87-0.95], p&lt;0.001) and 8% reduced risk of cancer-specific death (HR:0.91 [95% CI, 0.87-0.95], p&lt;0.001) compared to those in cohort A. After adjustment for age, race and metastatic stage, patients in cohort C showed an improvement in OS and CSS compared to cohort B (HR:0.94 [95% CI, 0.91-0.97], p=0.001 and HR:0.89 [95% CI, 0.85-0.92], p&lt;0.001). Patients with M1c disease had a more pronounced improvement in OS and CSS compared with the other stages. No differences were found between cohort B and C. In conclusion, the prognosis of de novo mPCa remains poor with a median OS of 30 months and a median CSS of 35 months. Limited OS and CSS improvements were observed in latest years.

C. auris is a newly emerging yeast, raising public health concerns due to its outbreak potential, lack of protocols for decontamination and isolation of patients or contacts, increased resistance to common antifungals and associated high mortality. This research aimed to describe the challenges related to identifying the outbreak, limiting further contamination, and treating affected individuals. We retrospectively analysed all cases of C. auris detected between October 2022 and August 2023, but our investigation focused on a three-month-long outbreak in the department of cardio-vascular surgery and the related intensive care unit. Along with isolated cases in different wards, we identified 13 patients who became infected or colonized in the same area and time, even though the epidemiological link could be traced only in 10 patients, according to the epidemiological investigation. In conclusion, our study emphasizes the substantial challenge encountered in clinical practice when attempting to diagnose and limit the spread of an outbreak. Therefore, it is crucial to promptly apply contact precaution measures and appropriate environmental cleaning, from the first case detected positive.

Background: Flattening the curve refers to community isolation measures that keep the daily number of disease cases at a manageable level for medical providers. Efforts to completely contain the COVID – 19 have failed. As there is currently no vaccine or specific medication to treat COVID-19, the only way to flatten the curve is through collective action of mitigation efforts. Objectives: The study was carried out with aim to study the effect of mitigation efforts in reducing the overall COVID -19 attack rate and flattening the curve of COVID-19 in Egypt. Methods: Secondary data in the situation reports of WHO, Worldometer and Egyptian MoHP Report about the COVID-19 epidemic in Egypt were analyzed till June 27, 2020. Survival analysis was carried out to determine the cumulative proportional survival of COVID-19. The Susceptible Infected Recovered (SIR) model was used to study the epidemic dynamics of COVID-19 pandemic. Results: In Egypt in the fourth week after exposure β was 0.17 and γ = 0.07. The fraction of infectious individuals will grow exponentially as the exponential growth rate was 0.10, with an epidemic time of 7 days. R in 22 ^nd June was 0.80 (less than one person infected) where β=0.21 and γ =0.26. There was reduction in the actual overall attack rate at 22^nd June of 96 % of its value without intervention with a cumulative proportional survival of 0.89. There was flattening of the curve. Conclusion and Recommendation: Mitigation efforts of Egypt had succeeded in flattening the curve of COVID-19 Pandemic. Even though, strengthening all the mitigation efforts to keep the exposure probability, as well as the transmission rate as low as possible is mandatory for containment of the epidemic.

Introduction: Arterial hypertension and obesity are two major risk factors for cardiovascular morbidity and mortality closely linked. The objective of this study was to describe the epidemiological and clinical characteristics of hypertensive patients according to their level of obesity as proposed by the classification of the World Health Organization (WHO). Patients and Methods: Retrospective study (January 2022-April 2023) involving 230 hypertensive and obese adult patients whose mean age was 52.5 ± 12.05 years [Extremes: 24-85 years] followed on an outpatient basis and classified into 3 Classes according to their body mass index (BMI). Epidemiological, clinical parameters and cardiovascular risk stratification were analyzed. The relevance of the risk stratification method was analyzed by area under the curve (AUC). Statistical analysis was performed using SPPS version 26 software (SPSS Inc., Chicago, IL, USA). Results: Classes 1 (n=170; 73.9%), 2 (n=43; 18.7%) and 3 (n=17; 7.4%) were identified. There were significantly more women than men in Class 3 (9.7% versus 2.7%; p=0.001). The risk of being diabetic (OR=0.135;95%CI=0.034–0.541;p=0.005), of having angina pain (OR=0.084;95%CI=0.009–0.790;p=0.030) or developing chronic renal failure (OR=2.416;95%CI=1.471–85.268;p=0.020) was significantly higher in Class 3 than in Class 1. According to cardiovascular risk stratification, 152 patients (66.1%) were at high and very high risk. The level of cardiovascular risk of our patients was not related to the degree of obesity based on body mass index (p=0.32). The risk stratification method was relevant (AUC˃0.70;p˂0.001). Conclusion: Morbid obesity exposes hypertensives more to cardiovascular, renal and metabolic complications.

Background. We created agent-based model for short- and longterm forecasting of COVID-19 and for evaluation how the actions of the regulator affected the human and material resources of the healthcare system. Methods. The model was implemented in the AnyLogic software. It includes two state charts – social network and disease transmission. The COVID-19 Essential Supplies Forecasting Tool (COVID-ESFT, version 2.0) was used to determine healthcare resources needed. Results. Satisfactory results were obtained with long-term (up to 50 days) forecasting in the case of a monotonous change in total cases curve. However, if periods of relative stability are accompanied by sudden outbreaks, relatively satisfactory results were obtained with short-term forecasting, up to 10 days. Simulation of various scenarios showed that the most important place for the spread of infection are families. Wherein the maximum number of cases of COVID-19 is observed in the age group of 26-59 years. Due to a set of measures taken by government agencies, the number of cases in Karaganda city was 3.2 times less than was predicted in “no intervention” scenario. Economic effect is estimated at 40 %. Conclusion. Performed model is an attempt to consider as much as possible the peculiarities of the socio-demographic situation in the country. In the future, we will be prepared to some extent for challenges like those we have experienced in the past three years.

Canine leptospirosis is a zoonosis of epidemiological importance. Dogs are recognized as primary reservoirs of Leptospira interrogans serogroup Canicola and a source of infection to the environment through urine. This study aimed to determine the presence of antibodies against Leptospira in canines from 49 municipalities in the Department of Antioquia, Colombia. We performed a cross-sectional study of dogs included in a neutering control program. We collected 1335 sera samples, assayed by a microagglutination test (MAT), and performed PCR detection in 21 urine samples. We also surveyed 903 dog owners. We found a seroreactivity of 11.2% (150/1335) in Antioquia with titers 1:50. Municipalities with the highest number of cases were Belmira (46.1%), Turbo (34.5%), and Concepción (31.0%). L. santarosai was identified by phylogenetic analysis in one urine sample from the municipality of Granada. The most important factor associated with a positive result was the lack of vaccination against leptospirosis (PR 3.3, p < 0.014). Environmental factors such as water presence and bare soil around the household were also associated with Leptospira seroreactivity in the Department of Antioquia. We reviewed a national epidemiological surveillance database for human cases in those municipalities. We found a correlation between the high number of cases in canines and humans, especially in the Uraba. Serological and molecular results showed the circulation of Leptospira. Future public health efforts in the municipalities with the highest numbers of seroreactivity should be directed towards vaccination to prevent animal disease and decrease the probability of transmission of Leptospira. Dogs actively participate in the Leptospira cycle in Antioquia and encourage the implementation of vaccination protocols and coverage.

Disease transmission in wildlife is linked to ecosystem and human health, and host community structure can mediate pathogen spread. Here, we decompose the well-known susceptible-infected (SI) compartmental models of disease transmission for multi-host communities to explore the disease-biodiversity relationship. We examine the parameters involved in interspecific transmission and link them to the multi-host outbreak potential, R0. We partition R0 into its separate elements that can be associated with either the recipient or donating host, and show how this allows us to identify maintenance versus spillover hosts, allowing us to quantify separate stages of spillover into a novel, recipient host. We further suggest that the evolutionary relatedness between donating and recipient host may play a role in the probability a pathogen establishes in a novel species, as illustrated by the observed phylogenetic signal in the host-breadth of multi-host pathogens. We discuss how anthropogenic changes to the environment and wildlife communities, such as the reduction and fragmentation of wildlife habitat, might alter the mode and dynamics of disease transmission. This discussion contributes to the ongoing debate on the relationship between disease incidence and biodiversity. Through a synthesis of classic ecological theory, we illustrate how pathogens can affect host populations through phenomena such as in/direct symbioses, that can save a pathogen from extinction or suppress competing host species, but we highlight how host diversity can simultaneosuly influence the survival and success of the pathogen, suggesting a complex feedback between hosts and parasites in multi-host communities. Our review provides a guide to common derivations of interspecific transmission, and underscores the critical connections between community and disease-ecology.

Background and Objectives: Vision impairments and related blindness are major public health problems. Prevalence of eye disease and barriers to optimal care markedly vary among different geographic areas. In the Abruzzo region (Central Italy), an epidemiological surveillance on the state of ocular health in the population aged over 50 years was performed in 2019. Materials and Methods: Participants were sampled to be representative of the region inhabitants. Data were collected through a telephone interview and an eye examination. Prevalence of cataract, glaucoma, retinopathy, maculopathy was assessed. The Cohen’s kappa (k) was used to measure the agreement between presence of eye disease and awareness of the disease by the participants. Results: Overall, 983 people with mean age of 66.0±9.5 years were included in the study. The prevalence of cataract, glaucoma, maculopathy, and retinopathy was 52.6%, 5.3%, 5.6%, and 29.1%, respectively. Among the total of affected people, those aware of their condition were 21.8% (k=0.12, slight agreement) for cataract, 65.4% (k=0.78, substantial agreement) for glaucoma, 7.1% (k=0.10, slight agreement) for maculopathy, and 0% for retinopathy (k=-0.004, agreement lower than that expected by chance). Refractive defects were corrected in the vast majority of participants. Conclusion: In the Abruzzo region, about two third of citizens aged 50 years or over suffer from cataract, glaucoma, retinopathy or maculopathy, which are recognized as leading causes of blindness. Many people with eye disease do not know they have it. These data can be used by clinicians and policymakers to undertake clinical, political, and social actions.

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: ‘model organisms’ chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through ‘causal inference’), and the (past/future) value of unmeasured variables (through ‘classification/prediction’); and a range of modelling techniques to predict beyond the available data (through ‘extrapolation’), compare different hypothetical scenarios (through ‘simulation’), and estimate key features of dynamic processes (through ‘projection’). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: ‘selection-collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ – all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined. Selection-collider bias occurs when these two variables independently cause a third (the ‘collider’), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or ‘mediators’) fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at ‘mediation analysis’). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to ‘data-driven’ modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks.

Background: Hidradenitis Suppurativa (HS) is characterized by inflamed lesions that appear in apocrine rich flexural areas. Although studies have reported clinical and epidemiological data from western countries, data from the Middle East are scarce. The aim of this study is to characterize the differences in the clinical characteristics of patients with HS of Arab and Jewish ancestry and to review the clinical characteristics, the course of the disease, the comorbidities and the response to treatment. Methods: This is a retrospective study. We collected clinical and demographic data from patient files at the Rambam Healthcare Campus dermatology clinic – a tertiary hospital located at the north of Israel. between 2015-2018. Our results were compared to those of a previously published Israeli control group registered in clalit health services. Results: Of the 164 patients with HS, 96 (58.5%) were men and 68 (41.5%) were women. The average age at diagnosis was 27.5 years and the latency between the onset and diagnosis of the disease was 4 years. we found a higher adjusted prevalence of HS in Arab patients (56%) than in their Jewish counterparts (44%). Gender, smoking and obesity, as well as axilla and buttock lesions, were risk factors for severe HS, with no differences between ethnicities. No differences were documented in comorbidities and in response to Adalimumab, with a high overall response rate of 83%. Conclusions: Our findings revealed differences between Arab and Jewish patients with HS in terms of incidence and gender predominance, while no differences were documented in comorbidities and response to Adalimumab.

Drowning is a serious public health concern. Low middle income countries are mostly affected, as they carry 90% of the global drowning burden. The purpose of this epidemiological study is to provide a comprehensive overview of fatal drownings in South Africa between 2016 and 2021. The data used for the study was obtained from the South African Police Service. Descriptive statistics were used to summarise data. Statistical analysis included a t-test and chi-square test. The results indicate that the average fatal drownings per annum is 1477 in South Africa, with a drowning rate of 2.54 per 100 000 population from 2016 - 2021. The KwaZulu-Natal province had the highest incidence of drowning. The 0 – 4-year category has a high prevalence of drowning amongst all the age categories. More males drowned in South Africa compared to females. The study highlights key areas of concern these include age, sex, race, province, type of water body and time of day. This information is crucial to inform drowning prevention initiatives in South Africa.

The initial outbreak of COVID-19 was first reported in Wuhan (China) during the latter part of December 2019. Indonesia has the fourth-largest population globally and reported the country’s first case of the virus on 2nd March 2020. The World Health Organisation (WHO) in addition to several neighbouring provinces and educational institutions within the region began questioning the Indonesian government upon the initial case reported. The objective of this study was to describe the epidemiological characteristics of the COVID-19 outbreak in Indonesia during March 2020. The data were collected from Indonesian government databases and non-government organisations (NGOs). The data were analysed using Microsoft Office 2019 (Excel) and Adobe Illustrator 2017 software, was used in drawing the map depicting the distribution of COVID-19 in Indonesia. As at 31st March 2020, a total of 1,528 people in Indonesia have been infected by COVID-19, in addition to 136 mortalities (CFR of 8.9%). Jakarta, as the principal capital of Indonesia, quickly has become the epicentre of the virus since this period. Most patient cases were attributed to those aged between 31 and 70 years (72.64%), with male patients (64.93%) representing the highest incidence of cases compared to female patients (35.07%). The number of ventilating machines was 3,326, with hospital numbers at 859. The distribution of cases depicting COVID-19 was mainly seen in urban areas compared to rural areas. Males compared to females, are at a higher risk of contracting COVID-19, including those aged below 30, between 30 and 60 or above. Indonesia also has the highest case fatality rate (CFR) with respect to mortalities in Southeast Asia and has the second-highest CFR globally. Similarly, while the number of ventilator machines as at 31st March 2020 were sufficient in meeting the growing number of COVID-19 cases in the country, it is possible that the government may need to increase the number of ventilators if the cases continue to escalate.

Amid the covid-19 pandemic, other diseases, including viruses, are still acting to the detriment of their seasonality and risk factors for contagion. For this reason, it is interesting to know the degree of impact of other viruses, mainly respiratory, in which they have similar symptoms, in diagnoses for contamination by the new coronavirus based on epidemiological surveys, via epidemiological weeks, in Brazil. To what extent there may be a hypothesis of confusion of contaminated data, harming the health system, with regard to the need for intensive care units and control of viruses, and negatively or positively implying in the control or uncontrolling of viruses in general.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

This paper deals with the formulation of a Cobot kinematic model by developing both the direct and inverse kinematics and choosing the noa transformation matrix for defining the end-effector position and orientation with respect to the base frame. These algorithms have been validated via software in Matlab environment and by means of suitable experimental tests, which have been carried out by using a UR5e Cobot. Moreover, a specific industrial application regarding the robotized assembling of a multi-cylinder IC engine, has been designed in terms of layout and corresponding automatic cycle that is intended as a sequence of the robot elementary actions. In particular, the proposed robotized cell is composed by the UR5e Cobot along with its controller and a suitable electropneumatic circuit that includes a PLC with its user interface, which are both governed by means of a specific electronic board and a switch control box. The IC engine in scale of 1:5 and specific parts of the end-effector, which takes the role of vacuum gripper and torque limiting screwdriver, have been designed and manufactured by using a 3D printer.

Amidst the escalating demands for efficient Intelligent Transportation Systems (ITS) management, the significance of accurate Origin-Destination (OD) data prediction has emerged as a paramount concern, given its indispensable role in the ITS domain. This paper introduces a groundbreaking integrated model, creatively fusing the unique strengths of Graph Convolutional Network (GCN), Residual Neural Network (ResNet), and Long Short-Term Memory Network (LSTM) into a single architecture, which we name GraphResLSTM, to exploit road segment average speed data for OD prediction. Compared with conventional road segment traffic volume data, average speed data is more readily available, significantly reducing the data accessibility barrier for OD prediction. We underscore the critical role of selection of key road segments and employs a combination of Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify 41 representative road segments from the experimental road network. In the experimental phase, we simulate 86400000 seconds of traffic conditions, generating a total of 1440000 data records. Utilizing these simulated data, we design and conduct comparative experiments between models as well as data types. The results demonstrate that both the GraphResLSTM model and road segment average speed data outperform other models and data types notably in OD prediction tasks.

The studied solar pot is a recent invention, which gained a utility model protection in 2021 from the Hungarian Intellectual Property Office under patent number 5489. The device is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary.

Pulmonary Embolism (PE) is the obstruction of blood arteries in the lungs by a blood clot. The mortality risk for PE is approximately 30%. Detecting pulmonary embolism in the segmental arteries of the lung is more challenging than in the main arteries and is very prone to being missed. A computer-based approach was created in this work to automatically identify pulmonary embolism in the segmental arteries using computed tomography images. The system's infrastructure includes an enhanced Mask R-CNN deep neural network that has been trained with images containing PE. The network accurately identifies the location of the pulmonary embolism on the computed tomography picture and successfully extracts its borders. The investigation was conducted by generating a local dataset. The study's performance was evaluated using pulmonary embolisms identified manually by the expert radiologist. The sensitivity, specificity, accuracy, dice coefficient, and Jaccard index values for 2130 pictures were 96.4%, 93.5%, 96.1%, 0.96, and 0.90, respectively. The improved Mask R-CNN model has demonstrated superior performance when compared to the traditional Mask R-CNN and U-Net models. This high-performance technology is designed to identify pulmonary embolism and will aid professionals in assessing the size of the embolism.

Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the ocean subsurface temperature (OST) and ocean subsurface salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly(SSHA), sea surface wind (SSW), and geographical information (including longitude (LON) and latitude (LAT)). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating OST (OSS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of OST and OSS across different depths with Argo-derived data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model displays strong seasonal adaptability. In conclusion, this research introduces a novel artificial intelligence technique for estimating OST and OSS in the tropical Western Pacific Ocean.

Introduction: Suicide is the second leading cause of death among 15 to 19-years-olds in Europe. From the beginning of COVID-19 pandemic, literature confirms a significant increase in mental health disease in children and adolescents. At Bambino Gesù Pediatric Hospital emergency department (Rome, Italy) there was a dramatic increase of suicidal attempts, and particularly a rise of suicidal jumpers. Many of these patients presented vertebral fractures. Material and Methods: This retrospective study includes all suicidal jumpers with vertebral fractures treated at our Pediatric Hospital from April 1, 2017 to March 31, 2023. We collected and compared data from three years before to three years after pandemic spreading analyzing vertebral fractures. Results: From April 1 2019 to March 31, 2020 141 cases of suicide attempt arrived at Bambino Gesù Pediatric Hospital emergency department. Five of these patients were suicidal jumpers but none had a vertebral fracture. From April 1, 2020 to March 31, 2023, 362 cases of suicide attempts arrived in the same emergency department and 19 were suicidal jumpers; 12 reported vertebral fractures and were included in this study. Mean age was 14 years. Most common level of fracture was L1. Four patients presented with preoperative neurologic impairment and two cases recovered completely. There was a transforaminal sacral fracture, a spinopelvic dissociation and an Anderson-D’Alonso C2 type II fracture. Two cases with thoracolumbar vertebral fractures were conservatively treated. Seven patients were treated by percutaneous pedicle fixation to achieve an indirect decompression and to restore a better sagittal balance. Only three patients needed an open spinal surgery by posterior approach and in two cases a posterior decompression was performed. One case with cervical fractures was treated by Halo-Vest. Conclusions: Recent literature highlights concern about the consequences of COVID-19 pandemic on pediatric population mental health. This is the first report that shows a significant increase in vertebral body fractures due to suicide jumping attempt. The comparison between three years pre and post pandemic is sufficient to demonstrate a sharp increase of these cases. This could be a new epidemiological phenomenon that could persist or even increase over time because of COVID-19 pandemic.

(1) Background: This paper investigates the epidemiological risk related to temperature before and during COVID-19 in Italy; (2) Methods: Deaths in 2015-2019 and temperatures were correlated. Excess and COVID-19 deaths were examined to estimate the relationships with temperatures; (3) Results: Annual deaths were higher during the cold months (+45,000, SD = 4,700, S = 21). The scenario worsened during COVID-19. Mortality was higher during minimum temperature periods, although the curve moderately rose in the warmest months (r = -0.75, 95% CI = [-0.87; -0.56], S = 23). COVID-19 deaths showed a decreasing seasonality. Monthly excess deaths during COVID-19 were high (+4,200, IQR = [2,800; 8,000], S = 28) with a doubt of seasonality. COVID-19 mortality was correlated with regional latitude (r = 0.86, 95% CI = [0.68; 0.94], S = 20). Discrepancies between COVID-19 and excess deaths were found. The exposed population was subject to aging; (4) Conclusions: The epidemiological risk in Italy is seasonal and geographically dependent. Low and very high temperatures can lead to mortality peaks. Therefore, COVID-19 and any other epidemiological risk must be evaluated in light of this evidence. Health systems need to be strengthened during cold and extremely hot periods. Future research should investigate these phenomena at the causal level.

This article systematically studied the A-optimal designs of a high-order mixture central polynomial model, which is an advancement of the research results for the A-optimal design of the most recent third-order mixture central polynomial model and a more accurate response surface model. This article is based on the simplex center design, obtaining measurement values at various design points, and verifying them through the A-optimal equivalence theorem. We also compare the A-efficiency of designs through examples, which can be applied to the A-optimal precise design of the fourth-order mixture model.

Refractoriness to antiseizure medications is still a major concern in the pharmacotherapy of epilepsy. For this reason, we decided to evaluate the combination of levetiracetam and cannabidiol, administered at a subthreshold dose, to limit the possible adverse effects of this phytocannabinoid. We administered levetiracetam (300 mg/kg/day, via osmotic minipumps), cannabidiol (120 mg/kg/day, injected once a day subcutaneously), or their combination for one week in epileptic rats. Saline-treated epileptic rats were the control group. Animals were monitored with video electroencephalography the week before and after the treatment. No changes were found in the controls. Levetiracetam did not significantly reduce the total seizure number or the overall seizure duration. Still, the overall number of seizures (p&lt;0.001, Duncan’s new multiple range test) and their total duration (p&lt;0.01) increased in the week following treatment withdrawal. Cannabidiol did not change seizures when administered as a single drug. Instead, levetiracetam combined with cannabidiol resulted in a significant reduction in the overall number and duration of seizures (p&lt;0.05), when comparing values measured during treatment with both pre- and post-treatment values. These findings depended on changes in convulsive seizures, while non-convulsive seizures were stable. These results suggest cannabidiol determined a remarkable potentiation of levetiracetam antiseizure effects at a subthreshold dose.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

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

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

The SK model proposed by Sherrington and Kirkpatrick is an important mean field model for spin glasses. The dependence structure of Hamiltonians of the SK model is considered in this paper. We have investigated the covariance matrix of Hamiltonians of the SK model, and establish a relationship between the rank of the corresponding covariance matrix and the dimension of the space spanned by the Hamiltonians. Finally, we also give an upper bound for the rank of the covariance matrix, which might be the best.

Background: Hepatitis B virus (HBV), a lethal form of viral hapatitis, is an extra burden on the socioeconomic status of a country. Objectives: The population-based studies to quantify the incidence of hepatitis B. Data sources: in the North Waziristan, formally known as EX-FATA (Federal Administrated Tribal Area), Pakistan, are still rare. Study eligibility criteria: 943 individuals were selected on the basis of signs and symptoms to determine the epidemiology of HBV in the study population. Tests: Hepatitis B virus (HBV). Methods of data synthesis: Among these, 626 were male and 317 were female. All the HBV suspected individual were screened through ICT (Indirect Coombs Test) , ELISA (enzyme-linked immunosorbent assay) and RT-PCR (Re-verse transcription polymerase chain reaction). Results: The results obtained from the screening tests for HBV revealed that RT-PCR based results are more reliable and it indicated that HBV infection is more common in male 41 (6.54%) than female 15 (4.73%). The age range selected for the study was 1-75 years. Age wise analysis indicated that HBV is more prevalent among individuals of age group between 16 to 45 years. This age range also showed higher number of patients having either mild, moderate or elevated viral loads. The HBV genotype D was the most common, present in 62.5% patients. However, 16.07 % patients were having genotype U, 12.5 % genotype C, 8.92 % genotype A, and 7.14% had genotype B. Discussion: The questionnaire based investigation revealed that HBV was most prevalent among married individuals (64.6%), whereas least prevalent among individuals having HBV positive family member (1.4 %). The other risk factors contributed were history of dental procedures (57.8%), ear, nose, piercing, or tattooing and uses of un-disposable syringes (56.5%), transfusion of blood or plasma (42.9%), surgery (41.8%), frequent visits to barber shops (39.45%), and addiction of injectable drugs (38.1%). The current study is providing a clear picture of the alarming situation HBV transmission and emphasis on the need for more effective planing, strategies and policies for the control and prevention of HBV.

Background and objective K. pneumoniae appeared to be a significant problem due to its ability to accumulate antibiotic-resistance genes. After 2013, alarming colistin resistance rates among carbapenem-resistant K. pneumoniae have been reported in the Balkans. The study aims to perform an epidemiological, clinical, and genetic analysis of a local outbreak of COLr CR-Kp. Material and methods All carbapenem-resistant and colistin-resistant K. pneumoniae isolates observed among patients in the ICU unit of Military Medical Academy, Sofia from 1 January to 31 October 2023 were included. The results were analyzed according to the EUCAST criteria. All isolates were screened for blaVIM, blaIMP, blaKPC, blaNDM, and blaOXA-48. Genetic similarity was determined using the Dice coefficient as a similarity measure and the unweighted pair group method with arithmetic mean (UPGMA). mgrB genes and plasmid-mediated colistin resistance determinants (mcr-1, mcr-2, mcr-3, mcr-4, mcr-5) were investigated. Results There was a total of 379 MDR-K. pneumoniae isolates, 88% of which were carbapenem-resistant. Of them, there were 9 (2.7%) colistin-resistant isolates in six patients. A time and space cluster for five patients was found. Epidemiology typing showed that two isolates belonged to clone A (pts. 1, 5) and the rest to clone B (pts. 2-4) with 69% similarity. Clone A were coproducers of blaNDM-like and blaOXA-48-like and had mgrB-mediated colistin resistance (40%). Clone B isolates had only blaOXA-48-like and intact mgrB genes. All isolates were negative for mcr-1,-2,-3,-4,-5 genes. Conclusions The study describes a within-hospital spread of two clones of COLr CR-Kp with a 60% mortality rate. Clone A were coproducers of NDM-1 and OXA-48-like enzymes and had mgrB-mediated colistin resistance. Clone B isolates had only OXA-48-like enzymes and intact mgrB genes. No plasmid-mediated was found. The extremely high mortality rate and limited treatment options warrant strict measures to prevent outbreaks.

This study conducted an extensive analysis of campylobacteriosis surveillance in Portugal from 2009 to 2021, aiming to investigate demographic shifts, seasonal variations, and antimicrobial resistance (AMR) within Campylobacter isolates. Surveillance network and sentinel laboratory-based system data revealed a substantial under-notification of campylobacteriosis cases, suggesting an underestimated disease burden. A paradigm shift was observed in notification rates, with a notable prevalence among the pediatric population, particularly in children aged 1-4 years, diverging from European reports. However, the study also highlighted an emerging trend of Campylobacter infections in younger adults (15-44 years). The study unveiled a unique seasonal distribution of cases, defying typical summer peaks seen elsewhere. AMR analysis revealed extremely to very high-level resistance to ciprofloxacin and tetracycline, both in C. jejuni (93.7% and 79.2%, respectively) and in C. coli (96.5% and 93.2%, respectively), stable throughout the studied period (2013-2021). C. coli also exhibited significantly higher resistance to erythromycin, gentamicin, ampicillin, amoxicillin-clavulanic acid (decreased resistance), and ertapenem compared to C. jejuni (P&lt;0.001). In conclusion, the study highlights the need for enhanced surveillance efforts and emphasizes worrying AMR levels. The implementation of WGS-based surveillance is recommended to attain a more profound comprehension of disease patterns and the evolving landscape of AMR.

Objective: To evaluate the existing meta-analytic evidence of associations between exposure to ultra-processed foods, as defined by the Nova food classification system, and adverse health outcomes. Design: Systematic umbrella review of existing meta-analyses. Data Sources: MEDLINE, PsycINFO, Embase, and the Cochrane Database of Systematic Reviews, as well as manually searching reference lists from 2009 to June 2023. Inclusion Criteria: Systematic reviews and meta-analyses of cohort, case-control, and/or cross-sectional study designs that evaluated the associations between exposure to ultra-processed foods and adverse health outcomes in humans across the lifespan. Results: The search identified 45 unique pooled analyses, including 13 dose-response and 32 non-dose-response associations (n=9,888,373). Overall, direct associations were found between exposure to ultra-processed foods and 32 (71%) health outcome domains spanning cardiometabolic, gastrointestinal, and respiratory conditions, cancer, mental health, and mortality. Based on a pre-specified evidence classification criteria, convincing evidence (Class I) supported direct associations between greater ultra-processed food exposure and higher risks of incident cardiovascular disease-related mortality (RR: 1.50; 95%CIs: 1.37 to 1.63), type two diabetes (dose-response RR: 1.12; 95%CIs: 1.11 to 1.13), and depressive outcomes (HR: 1.21; 95%CIs: 1.16 to 1.28), as well as higher risks of prevalent anxiety outcomes (OR: 1.48; 95%CIs: 1.37 to 1.59) and combined common mental disorder outcomes (OR: 1.53; 95%CIs: 1.43 to 1.63). Highly suggestive (Class II) evidence indicated that greater exposure to ultra-processed foods was directly associated with higher risks of incident all-cause mortality (RR: 1.21; 95%CIs: 1.15 to 1.27), heart disease-related mortality (dose-response HR: 1.66; 95%CIs: 1.51 to 1.84), and type two diabetes (OR: 1.40; 95%CIs: 1.23 to 1.59), together with higher risks of prevalent obesity (OR: 1.55; 95%CIs: 1.36 to 1.77), adverse sleep-related outcomes (OR: 1.41; 95%CIs: 1.24 to 1.60), and wheezing (RR: 1.40; 95%CIs: 1.27 to 1.55). Out of the remaining 34 pooled analyses, 21 were graded as suggestive or weak strength (Class III-IV) and 13 were graded as no evidence (Class V). Using the GRADE framework, 22 pooled analyses were rated as "Low" quality, with 19 rated as "Very Low” and four rated as "Moderate" quality. Conclusions: Higher exposure to ultra-processed food was associated with a greater risk of adverse health outcomes, especially cardiometabolic, common mental disorder, and mortality outcomes. These findings provide a rationale to evaluate the effectiveness of using population-based measures to target and reduce dietary exposure to ultra-processed foods for improved human health. Systematic Review Registration: PROSPERO CRD42023412732.

The COVID-19 outbreak has severely affected the social and economic conditions across this globe. Little is known about the relationship of COVID-19 with countries’ economic and socio-demographic status. Publicly available data on COVID-19 test rate, attack rate, case fatality rate, and recovery rate were analyzed in relation to country’s economic status, population density, median age, and urban population ratio. We also conducted multinomial logistic regression analysis to predict the influence of countries’ social and economic factors on COVID-19. The results revealed that the median age had significant positive correlation with attack rate (r=0.2389, p=0.003), case fatality rate (r=0.3207, p=0.000) and recovery rate (r=0.4847, p=0.000). The urbanization has positive significant correlation with recovery rate (r=0.1957, p= 0.016). The multinomial logistic regression analysis revealed low-income countries are less likely to have an increased recovery rate (p=0.000) and attack rate (p=0.016) compare to high-income countries. The lower-middle-income and upper-middle-income countries are less likely to have an increased recovery rate (p=0.000 and p=0.001, respectively) compared to high-income countries. Based on the result of this study, these economic and socio-demographic factors should consider in designing appropriate preventive measures as a next step. The low and lower-middle-income countries should invest more in health care services to lower the case fatality rate and increase test and recovery rates as part of pandemic preparation like COVID-19. As the number of COVID-19 attacks, death and recovery rates are constantly changing; however, the intensive study is required to obtain a clear picture.

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

Geothermal energy, being a clean energy source, has immense potential, and accurate wellbore modeling is crucial for optimizing the drilling process and ensuring safety. This paper presents a novel geothermal wellbore model based on the drift-flux approach, tested under three different temperature and pressure well conditions. The proposed model integrates the conservation equations of mass, momentum, and energy, incorporating the gas-liquid two-phase flow drift-flux model and heat transfer model. Key features include handling heat transfer between the for-mation and the wellbore, addressing the slip relationship between gas and liquid phases, and accounting for wellbore friction. The nonlinear equations are discretized using the finite differ-ence method, and the highly nonlinear system is solved using the Newton-Raphson method. Numerical simulation, validation, and comparison with existing models demonstrate the en-hanced accuracy of this model. In our tests, the model achieved high accuracy in calculating bottom hole pressure and temperature, with Mean Relative Errors (MRE) significantly lower than those of other models. These results offer valuable insights for optimizing drilling parameters and ensuring drilling safety. Comparisons indicate that this approach significantly outperforms others in capturing the complex dynamics of geothermal wellbores, making it a superior tool for geo-thermal energy development.

Objectives: To analyze cumulative incidence and mortality by COVID-19 in the different types of nursing homes, in the first and second wave.Methods: Data source: Transparency portal of the Generalitat de Catalunya, official COVID-19 register of all Catalan nursing homes (from March to December 10, 2020). Variables: Affectation of disease and mortality by COVID-19 in geriatric centers in Catalonia, stratified by size of center and type of management. Univariate, bivariate and multivariate linear regression analysis, IBM^TMSPSS^® v.20.Results:N = 1,057 residences (63,682 beds, average = 60.48). 52% have <50 beds, 42.8% between 50-150 beds, and 5%> 150 beds. COVID-19: Cumulative incidence: 21,370 (confirmed + suspicious); mortality: 5,010 (confirmed + suspicious).233 residences did not register COVID-19 patients (22%) and 636 did not register COVID-19 deaths (60.2%).According to the type of supplier, the lowest incidence of COVID-19 was in non-accredited (36.2% without cases), then concerted / collaborators (19.3%) and own of the Generalitat de Catalunya (7.9 %, p = 0.000). The centers with the fewest deaths were the non-accredited (78% without deaths), followed by contracted / collaborators (56.8%) and those of the Generalitat de Catalunya (41.3%, p = 0.000).Depending on the size, no COVID-19 patients were registered: 168 centers <50 beds (30.5%); 64 of the 50-150 beds (14.2%); and 1 of the> 150 beds (1.9%) (p = 0.000). 395 of the <50 beds did not register deaths due to COVID-19 (71.8%), 232 of the 50-150 beds (51.3%), and 9 of the> 150 beds (17%) (p = 0.000).Discussion:Of the 1,057 centers analyzed, 22% did not register cases and in 60% there were no deaths from COVID-19.Residences without concert / collaboration with the Generalitat and with fewer beds showed a lower cumulative incidence and mortality due to COVID-19 during the first wave.

Dengue fever remains a persistent threat in Mexico, with cyclical outbreaks driven by the circulation of multiple dengue virus serotypes. The sudden emergence of DENV-3 in 2023 raised questions about its epidemiological significance and potential implications for disease severity. This study aimed to characterize dengue virus serotypes by age and their association with disease severity, considering sex, time, and the origin of clinical samples. A retrospective analysis was conducted on dengue laboratory results obtained from the epidemiological surveillance system in Mexico during 2023. Descriptive statistics summarized the prevalence of dengue virus serotypes, while bivariate analysis utilized an adjusted Chi-squared test to assess associations with demographic variables. Multivariate analysis using unconditional logistic regression evaluated the relationship between dengue virus serotypes and disease severity, adjusting for potential confounders. Additionally, co-infections were identified and analyzed. Results revealed DENV-3 as the most prevalent serotype (63.9%), with co-infection involving two serotypes detected in 0.75% of cases. DENV-2 exhibited a higher median age compared to other serotypes. Pregnant women in their last trimester faced elevated risks of hospitalization and mortality. Multivariate analysis indicated a stronger association between DENV-2 and hospitalization. However, co-infection did not significantly influence disease severity. The unexpected predominance of DENV-3 underscores the dynamic nature of dengue virus circulation in Mexico. Despite its prevalence, DENV-2 demonstrated a stronger association with hospitalization, highlighting the importance of serotype-specific surveillance and management strategies.

Bovine tuberculosis (BTB) is endemic in Ethiopian cattle. BTB is caused by Mycobacterium bovis (M. bovis) and has economic and public health significance. which has significant impact on the health of livestock and human. It has been significantly a cause for great economic loss in animal production. Associated risk factors contributed to the prevalence of the disease in cattle and its transmission. Moreover, the majority of cattle owners lack awareness about the disease and its public health significance. The presence of multiple hosts including wild animals, inefficient diagnostic techniques, absence of defined national controls and eradication programs could impede the control of bovine TB. Awareness rising about the disease, its transmission andzoonotic implication however, in Ethiopia Bovine Tuberculosis in Human-Livestock-Wildlife Interface is not well studied in the country and there were no studies concerning the burden of the disease between human ,animal and wild life which is of great importance for reduction and control measures. This paper aims to review the potential health and economic impact of bovine tuberculosis control in order to safeguard human and animal population in Ethiopia

Abstract – Objective: Ovarian cancer, although not possessing a high incidence, is still the most common cancer-related deaths among women diagnosed with a gynecologic malignancy. The present study aims to highlight the epidemiology, risk factors of this disease and the significance of development of improved early detection strategies. Materials and Methods: This study was conducted using current published English studies by searching PubMed and Google Scholar. The search strategy included the keywords “ovarian cancer”, “diagnosis”, “risk factors”, “screening”, “epidemiology”. Studies on incidence and mortality were also considered. Case reports were excluded.Results: The highest incidence and mortality rates are observed in Central and Eastern Europe, while rates are relatively low in Asia and Africa. These rates are highest among the white population (14.3 per 100,000) and lowest among blacks (10 per 100,000) and Asians (9.7 per 100,000). The risk factors for this disease includes a family history, hormonal factors, nutrition and diet and physical activity, with some of them playing protective roles in reducing risk of ovarian cancers. There are no reliable screening methods for ovarian cancers. The most common diagnosis methods include a transvaginal ultrasound and a blood test to detect CA125 markers.Conclusions: The mortality rate of ovarian cancer is gradually increasing; thus, preventative measures are required to reduce lifetime risk of ovarian cancers and improve mortality rate.

World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV- 2). The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as, deep learning, in (i) rapid disease detection from x-ray/computerized tomography (CT)/ high-resolution computed tomography (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) identification of the epicenter in each country/state and forecasting the disease from social networking data, (iv) prediction of drug-protein interactions for repurposing the drugs, and (v) socio-economic impact and prediction of future relapses, has attracted much attention. In the present manuscript, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real time polymerase chain reaction (qRT-PCR) and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, possibility of future relapses and socio-economic impact are also discussed. We inspect how the virus transmits depending on different factors, such as, population density and mobility among others. We depict how AI-based mobile app for contact tracing and surveys can prevent the transmission. A modified deep learning technique can assess affinity of the most probable drugs to treat COVID-19. Here a few effective antiviral drugs, such as, Geneticin, Avermectin B1, and Ancriviroc among others, have been reported with their appropriate validation from previous investigations.

Background: Burns are a common and severe medical emergency requiring immediate specialized care to minimize damage and prevent complications. Burn severity depends on depth, extent, and location, with more complex care needed for burns on critical areas or extensive burns. Nursing is essential in burn management, providing immediate care, adapting treatments, managing pain, preventing infections, and offering emotional support for recovery. Methods: Retrospective, observational study that analyzed burn cases treated at the Hospital Complex of Cáceres (Spain) 2018-2022. Inclusion criteria were based on ICD-10 codes for burns, excluding severe cases not treated in this service. Data were analyzed using descriptive statistics, Student's t-tests, Chi-square tests, and ANOVA. Results: 220 patients, with a mean age of 47 years and 60.9% male. Most burns (95.5%) affected the external body surface, with a mean hospital stay of 7.86 days. Medical treatment was provided to 75.5% of patients, and 24.5% required surgical intervention. Significant differences in treatment procedures were observed according to age, skin thickness, and burn degree. Older patients had more procedures and longer hospital stays. Excision and transfer procedures were more common in full-thickness and severe burns. Conclusions: The findings align with previous research on burn demographics and treatment approaches. Treatment differences by age and burn severity highlight the need for tailored interventions. The study underscores the importance of comprehensive burn management, including psychological support for improved long-term outcomes. Further research could explore the impact of socio-economic factors on burn incidence and treatment.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

This study addresses the implementation of the International Financial Reporting Standard 9 (IFRS 9) in the European Union as of January 1, 2018, replacing the International Accounting Standard 39 (IAS 39) to introduce a new model for recognizing Loan Loss Provisions (LLP) based on Expected Credit Loss (ECL). This model responds to criticisms of the former Incurred Credit Loss (ICL) system for its inability to reflect credit losses in a timely manner, potentially exacerbating the effects of financial crises (Bushman &amp; Williams, 2015; Gebhardt &amp; Novotny-Farkas, 2011). The study focuses on the effects of adopting the ECL model on the level of Loan Loss Allowances (LLA) in loans, own equity, and the Common Equity Tier 1 (CET1) ratio across 13 Portuguese commercial banks. A mean comparison test is used to evaluate scenarios before and after the application of the ECL model, highlighting the importance of regulator actions and the adequacy of loss recognition policies, including the effects of Regulation (EU) 2017/2395 of December 12. The results obtained demonstrate significant negative impacts on the net values of loans, own equity, and the CET1 ratio upon adopting the IFRS 9 ECL model, due to the widespread increase in LLAs.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

SARS-CoV-2 is constantly evolving leading to new variants. We analysed data from 4,400 SARS-CoV-2-positive samples in order to continue variant surveillance in Italy to evaluate their epidemiological and relative impact on public health in the period April-December 2021. The main circulating strain (76.2%) was Delta followed by Alpha (13.3%), Omicron (5.3%) and Gamma variants (2.9%). B.1.1 lineages, Eta, Beta, Iota, Mu and Kappa variants represented around 1% of cases. Overall, 48.2% of subjects were not vaccinated with a lower median age compared to vaccinated subjects (47 vs. 61 years). An increasing number of infections in vaccinated subjects was observed overtime, with the highest proportion in November (85.2%). Variants correlated with clinical status; the largest proportion of symptomatic patients (59.6%) was observed among Delta variant, while subjects harboring Gamma variant showed the highest proportion of asymptomatics (21.6%), albeit also of deaths (5.4%). The Omicron variant was only found in vac-cinated subjects, of which 47% were hospitalized. Diffusivity and pathogenicity associated with the different SARS-CoV-2 variants are likely to have relevant public health implications, both at national and international level. Our study pro-vides data on the rapid changes in the epidemiological landscape of SARS-CoV-2 variants in Italy.

Statistical data on officially registered cases of health care-associated infections (HCAIs) in Ukraine in the period 2009-2019 have been analysed. On average, 5089±756 cases of HCAIs were registered annually. Odessa region of Ukraine is the leading country in the number of reported cases. The majority of HCAIs cases involve surgical and therapeutic invasive interventions and perinatal HCAIs. On average, 78.0±5.8 % of HCAIs cases involved adults. The estimated minimum number of HCAIs in Ukraine was expected to be about 1 million per year. Official statistics on registered cases of HCAIs in Ukraine do not reflect reality, so the system of registration and investigation of HCAIs in Ukraine needs to be reformed.

Assessing the quality of corn seeds necessitates evaluating their content of water, fat, protein, and starch. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using both the original spectra and the extracted characteristic wavelengths, four content detection models were constructed: PLSR, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Least Squares Support Vector Machine (LSSVM). The experimental results demonstrate that the DT-CARS-LSSVM model exhibits exceptional accuracy and stability in predicting these four key components, with determination coefficients of 0.9877, 0.9344, 0.9827, and 0.9592, respectively. This study not only provides a scientific basis for evaluating the quality of corn seeds but also opens up new avenues for the development of non-invasive detection technology in related fields.

This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and utilizing publicly available data. Specifically, the study focuses on designing and proposing a digital twin data model for buildings, which are the most applicable among the basic digital twin data. To achieve this, the first section provides essential background information, introduces concepts and requirements related to digital twin basic data, and offers a brief overview of CityGML. The second section explains the methodology and data used in this study. The third section presents the main findings: the selection of public data (buildings) for constructing digital twin basic data, the mapping process using CityGML, and the creation of UML diagrams. The fourth section discusses these findings. Finally, the conclusion and suggestions for future research are provided. This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for energy domain, and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.

We propose a pricing formula for a defaultable zero-coupon bond with imperfect information under a regime-switching model using a structural form of credit risk modelling. While the value of the firm’s equity is observed continuously, we assume that the total value of the firm is only observed at discrete times, such as the dates of the release of the firm’s annual reports, or quarterly reports. This uncertainty about the true value of the firm results in credit spreads that do not approach zero as the debt approaches maturity, which is a problem with many structural models. The firm’s value is typically defined as its equity and debt; however, we consider the asset-to-equity ratio, an accounting ratio used to examine a firm’s financial well-being. A dependent regime-switching process models the components of this ratio, and the Markov chain represents the states of the economy. The main contribution is to study this problem when the dynamics of a firm’s assets have different parameters with regime changes.

Extracorporeal membrane oxygenation (ECMO) is increasingly used in intensive care units; however, ECMO can modify drug pharmacokinetics (PK), leading to underexposure and associated treatment failure. The aim of the present study was to determine the effect of ECMO on the PK of dexmedetomidine (DEX). In vivo rat models were developed, both of which involved a bolus injection of DEX and concentration monitoring. In the in vivo model experiment, rats which underwent ECMO were compared with a control group. PK analysis of in vivo data (noncompartmental analysis and nonlinear mixed-effects modelling) was carried out to determine the effect of ECMO. In vivo PK exploration showed that ECMO induced a decrease in DEX clearance. Because the effect of ECMO on the PK of DEX is not clinically significant, normal DEX dosing for critically ill patients should be effective for patients undergoing ECMO.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML task; image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.

The traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the network incorporates the Squeezed-and-Excitation (SE) attention mechanism to improve. Moreover, the YOLOv5 algorithm’s loss function has been optimized by substituting it with EIoU_loss, which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model , trained with this enhanced network, achieves a 99.4% mAP on a custom dataset, which is 6.3% higher than the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements.

CAMx-LPiG (Comprehensive Air Quality Model with Extensions – Linear Plume in Grid) is an online hybrid model, based on the Chemistry and Transport Model (CTM) CAMx, that includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions, called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work the model is presented and evaluated against measured NO2 concentrations for the city of Milan for the month of January 2017. The model is able to introduce road traffic induced gradient in NO2 concentration at sub-grid resolution. Moreover, CAMx-LPiG is shown to reduce the bias compared to CAMx stand-alone simulations.

To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

The Asiatic apple leafminer, Phyllonorycter ringoniella (Matsumura), is a significant secondary pest of apple trees in Northeast Asia. To better understand its population dynamics, a population model based on temperature-developmental relationships was constructed. This model includes three sub-models: spring emergence, immature stage transition, and adult oviposition. Field data were collected from sex-pheromone baited traps in apple orchards in Andong, Korea, dur-ing 2015 and 2016 to validate the model. Simulations under six pesticide-natural enemy scenari-os showed that the population size of each generation was best simulated when weighted mor-tality factors for pesticides and natural enemies were applied. Using daily temperature inputs, the model demonstrated that P. ringoniella typically undergoes five generations per year, with peak times predicted within a seven-day margin of field data. Sensitivity analyses revealed that population size was influenced by total fecundity and the larval stage model, but peak times remained consistent despite parameter changes. Higher temperatures led to earlier adult peak dates, especially in summer generations. This model serves as a fundamental tool for estimating population dynamics and abundance changes of P. ringoniella and can guide the timings of pesti-cide application. Further validation is necessary to test the model's efficacy in controlling pests in apple orchards.

Segment Anything Model 2 (SAM 2) is a state-of-the-art development by Meta AI Research, designed to address the limitations of its predecessor, SAM, particularly in the realm of video segmentation. SAM 2 employs a transformer-based architecture enhanced with streaming memory, enabling real-time processing for both images and videos. This advancement is important given the exponential growth of multimedia content and the subsequent demand for efficient video analysis. Utilizing the SA-V dataset, SAM 2 excels in handling the intricate spatio-temporal dynamics inherent in video data, ensuring accurate and efficient segmentation. Key features of SAM 2 include its ability to provide real-time segmentation with minimal user interaction, maintaining robust performance even in dynamic and cluttered visual environments. This study provides a comprehensive overview of SAM 2, detailing its architecture, functionality, and diverse applications. It further explores the model's potential in improving practical implementations across various domains, emphasizing its significance in advancing real-time video analysis.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

The harrowing second wave of COVID-19 in India has led to much discussion over the quality and timeliness of reporting of deaths attributed to the pandemic. In this brief report, we aim to present the existing evidence, as well as the broader complexities surrounding the mortality burden of COVID-19 in India. This article sheds light on the following epidemiological issues: (1) general and India-specific challenges to COVID-19 death reporting, (2) latest COVID-19 mortality estimates in India as of May 16, 2021, (3) the apparent scale of uncaptured COVID-19 deaths, and (4) the role of disaggregated historic mortality trends in quantification of excess deaths attributed to COVID-19. We conclude with a set of high-level policy recommendations for improving the vital surveillance system and tracking of causes of death in India. We encourage direct efforts to integrate health data and indirect strategies for cross-validation of registered deaths. Such system-wide advances would drastically aid epidemiological research efforts and strengthen India’s position to overcome future public health crises.

The European Network on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (EUROMENE) was established after a successful grant application to the European Cooperation is Science and Technology (COST). This network aimed to assess the existing knowledge and/or experience on health care delivery for people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in the European countries and worldwide, and to enhance coordinated research and health care provision in this field. The EUROMENE proposal, was based on the establishment of interrelated working groups (WGs), where the participants contributed with specific knowledge and viewpoints according to their specialties and/or areas of interest. In this paper we outline the work of a multidisciplinary team of researchers, including epidemiologists, clinicians, statisticians, biomedical scientist and heath economists, who set out their recommendations to guide data acquisition for ME/CFS research, aiming to standardise data collection and improve epidemiological research.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

The social fear conditioning (SFC) paradigm, which is an animal model of social anxiety disorder, has been extensively used to investigate the cellular and molecular mechanisms underlying the psychopathology of this disorder. Studies using the SFC paradigm have suggested that the lateral septum plays a pivotal role in regulating social fear. While several lines of evidence have indicated the involvement of various neuronal mechanisms within the LS in modulating social fear, the role played by LS astrocytes in the tripartite synapses in regulating social fear expression is not yet fully understood. Over the years, astrocytes have emerged as key players in regulating synaptic transmission and a plethora of animal behaviors. Given their dynamic morphological and molecular response to various stimuli, studying astrocyte morphology remains an excellent tool for understanding their role in brain function and disease. By applying immunofluorescent-immunohistochemical methods, here we describe a protocol to characterize changes in the morphology of astrocytes in response to social fear acquisition using confocal microscopy.

In this work model of mirror elements equilibrium of the micromechanical components is developed, the behavior analysis of the mirror element of micromechanical mirrors in case changing of control voltages of electrostatic actuators is carried out, an expression for determining the maximum value of deflection voltage at which the snap-down effect will take the following form is obtained in case of the influence of the coefficient of the electrostatic rigidity of electrostatic actuators. The developed equilibrium model of mirror elements and the obtained results of modeling can be used at design of micromechanical mirrors with internal suspensions.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

1. Background Septic shock is a rare and serious infectious disease. Patients surviving the early phase are at high risk of multiple amputations of limbs. They are potential sources of serious disability. 2. Methods The objective was to describe the functional prognosis and quality of life of the four-limb amputee patients following septic shock. This is a descriptive, retrospective and mono-centric study. The patients included between 2010 and 2019 had septic shock after 18 years, responsible for amputation of the four limbs. We studied the mean age of septic shock, the causative organisms, the infectious door enter, the different levels of amputation, the fitting process, the resumption of walking, the Functional Independence Measure (FIM) and the SF-12 Quality of Life Questionnaire. 3. Results We suceed to include 15 patients.. 53.3% women and 46.7% men with an average age of 46.1 years. 80% presented bilateral trans-tibial amputations, all were able to return walking even over short distances, within an average of 8.4 months. 40% did not require a technical walking aid, 33.3% had no walking perimeter limitation. 47% had a higher mental and social quality of life score than the general population. The younger the patients, the better their physical quality of life. After two years of follow-up, the younger the patients, the better their functional autonomy. Patients with better functional autonomy had a better physical quality of life. 4. Conclusion The number of amputations due to spetic shock is likely to increase in the years to come throughout the world. Our study prove that quadri-amputee patient can regain a fully acceptable functional and quality of life being monitored in a specialized health center with multidisciplinary health professional to help him.

Background: Sleeping Sickness, Human African Trypanosomiasis (HAT) is a vector- borne disease caused by Trypanosoma brucei (T.b). Sleeping sickness in Ethiopia was reported in 1967 for the first time. Recently in Southern parts of Ethiopia, in August 2022, five (5) cases of sleeping sickness (T. b. rhodesiense) were confirmed. Following this outbreak, the current investigation was aimed to identify the entomological and epidemiological drivers for the reemergence of HAT outbreak and recommend appropriate interventions. Methods: A cross sectional study design with descriptive data analysis was used. Tsetse fly collection and blood samples from cattle animals were taken. NGU and bio-conical traps were used to determine the distribution (density and abundance) of the vector. About 10μl of blood was collected from the marginal ear vein of 301 cattle animals using the heparinized microhematocrit capillary. The parasite detection was carried out through vector dissection under binocular stereo-microscope (magnification of 60X) and microscopic examination from serum of Animals using the Buffy coat method. Results: A total of 329 tsetse flies were captured and identified to Glosina (G.) pallidipes 259 (60.4%) and Glossina fuscipes 70 (16.3%). 188 (51.1%) of tsetse flies were collected from Dembagofa with 94 apparent density. Among all captured Tsetse flies, 39 (11.8%) of Tsetse were fed with high female apparent density in each ecological variation: wood land (51), Bush land (20) and grassland (11). Overall, the apparent density of tsetse fly was high in Wood land (93): G. pallidipes (76.5) and Bush land (36.5). Among all examined cattles for the presence of parasite, 9 cattles were detected positive with an overall prevalence of 3%. T. congolense 6 (2%) and T. vivax 1 (0.3%) with 2 (0.7%) suspected brucei. The parasite prevalence Trypanosoma was 4 (4.6%) in poor body condition (Bcs) cattle. The animals in the age range 5 - 9 years were infected high with 7 (5.3%) prevalence. Conclusion: The current study revealed that there are high-risk factors that predispose the community to Human African Trypanosomiasis (HAT) due to the presence of two different species of Tsetse flies and many animal reservoirs. The transmissions of Human African trypanosomiasis (HAT) are related to environmental, Vector, and human factors. Further geographically expanded investigation should be conducted throughout the country.

Between October 2021 and April 2022, 317 outbreaks caused by highly pathogenic avian influen-za (HPAI) H5N1 viruses were notified in poultry farms in the northeastern Italian Regions. The complete genomes of 214 strains were used to estimate the genetic network based on the virus similarity. An exponential random graph model (ERGM) was used to assess the effect of at-risk contacts, same owners, in-bound/out-bound risk windows overlap, genetic differences, geograph-ic distances, same species and poultry company, on the probability of observing a link within the genetic network, which can be interpreted as the potential propagation of the epidemic via lateral spread or a common source of infection. The variables same poultry company (Est.=0.548, C.I.=[0.179;0.918]) and risk windows overlap (Est.=0.339, C.I.=[0.309;0.368]) were associated with a higher probability of link formation, while the genetic differences (Est.=-0.563, C.I.=[-0.640;-0.486]) and geographic distances (Est.=-0.058, C.I.=[-0.078;-0.038]) indicated a re-duced probability. The integration of epidemiological data with genomic analyses allows moni-toring the epidemic evolution and helps explain the dynamics of lateral spreads suggesting the potential diffusion routes. The 2021-2022 epidemic stresses the need to further strengthen the bi-osecurity measures, and to encourage the reorganisation of the poultry production sector to mini-mize the impact of future epidemics.

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, leading to increased economic and social benefits. Currently, some simple AI and hybrid models has issues to deal with and struggle with multivariate dependencies, long-term dependencies, and non-linear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while Lasso regression serves as the meta-learner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in forecasting accuracy, with a substantial reduction in Mean Absolute Percentage Error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, suggesting potential operational efficiency benefits for the power industry.

This study presents a unique model for assessing the dependability of continuous parts of combined systems in open-pit mining through the application of fuzzy logic. Continuous sub-systems as part of the combined system of coal exploitation in surface mines have the basic function of ensuring safe operation, high capacity with high reliability and low costs. These subsystems are usually part of the thermal power plant's coal supply system and ensure stable fuel supply. The model integrates various independent partial indicators of dependability into an expert system specifically designed for evaluating these systems. It deconstructs the complex parameter of system dependability into distinct partial indicators: reliability, maintainability, and logistical support. These indicators are then integrated using fuzzy composition (max-min composition). Historical data from 2018 to 2023 is utilized alongside the fuzzy model to provide a retrospective analysis of system dependability, serving to validate the model's effectiveness. What sets this model apart from conventional approaches is its consideration of practical dependability indicators, thereby obviating the need for extensive long-term monitoring and data collection to portray the system's status accurately over time. This model serves as a valuable tool for assisting decision-makers in open-pit mining operations, facilitating planning, exploitation control, and the selection of maintenance strategies to ensure consistent production and cost reduction. Designed for quick assessment, the model relies on expert judgments and assessments to determine system dependability efficiently.

Background: In response to inflammation, the absorption of nutritional iron is restricted. Multiple inflammation-related signaling pathways can lead to ferroptosis, an iron-dependent mechanism of programmed cell death. Since the pathophysiological significance of the presence and uptake of iron in chronic inflammation is still unknown, we tested the effect of a low iron diet on the clinical course of arthritis in the mouse model of collagen-induced arthritis (CIA). Methods: Six- to eight-week-old male DBA/1 mice were fed either a normal or a low iron diet starting three weeks before arthritis induction. From day four after the collagen-booster made on day 25, the development of arthritis was regularly monitored until the end of the experiment (day 34), using a standard clinical arthritis score. The complete blood count was determined as well as Fe2+, Fe3+, oxidized and reduced glutathione (GSH and GSSG) and malondialdehyde (MDA) in whole paw tissue by ELISA. Quantitative PCR was performed from whole paw tissue for glutathione peroxidase 4 and other key regulator genes of iron metabolism and ferroptosis. We used nonparametric tests to com-pare markers of iron metabolism and ferroptosis as well as nonlinear regression models for longitu-dinal data for signs of arthritis. Results: Mice fed a low iron diet showed a significantly less severe course of arthritis compared to mice fed a normal iron diet (p