Abattoir workers are liable to zoonotic infections from animals and animal products, pri-marily to diseases with asymptomatic and chronic clinical manifestations in animals such as brucellosis. No published reports exist on the seroprevalence of brucellosis in abattoir workers in South Africa. Therefore, this cross-sectional study was conducted to estimate the occurrence and risk factors for Brucella exposure in abattoir workers in Gauteng Province. A total of 103 abattoir workers and managers from six abattoirs, where brucellosis-positive slaughtered cattle and sheep were previously detected, were interviewed, and tested with serological assays using the Rose Bengal test (RBT), BrucellaCapt, and IgG-ELISA. A pre-tested questionnaire was administered to consenting respondents to obtain infor-mation on risk factors for brucellosis. Of the 103 respondents tested, the distribution of fe-male and male workers was 16 (15.5%) and 87 (84.5%), respectively. The seroprevalence for exposure to brucellosis was 21/103 (20.4%, 95%CI: 13.1-29.5) using a combination of RBT, BrucellaCapt, or IgG-ELISA. For test-specific results, seroprevalences by RBT, BrucellaCapt, and IgG-ELISA were 13/103 (12.6%, 95%CI: 6.9-20.6), 9/103 (8.74%, 95%CI: 4.1-15.9) and 18/103 (17.5%, 95%CI: 10.7-26.2), respectively. Low-throughput abattoirs were identified as associated risk as 29.3% of workers were seropositive compared to 12.7% of workers in high-throughput abattoirs, which highlights that direct contact at abattoirs poses higher risk to workers than indirect and direct contact outside abattoirs. This study confirmed the occurrence of Brucella spp. antibodies among abattoir workers in South Africa, possibly due to occupational exposure to Brucella spp. and highlight the occupational hazard to workers. Furthermore, findings underscore that abattoir facilities can serve as points for active and passive surveillance for indicators of diseases of public health importance. We recommend periodic implementation of brucellosis testing of abattoir workers country-wide to establish baseline data for informing appropriate preventive practices and reduce the potential burden of infection rate among these high-risk workers.

Human longevity is an important but difficult goal due to the extreme complication of human body. If people could repeatedly transfer their consciousness from old bodies to new ones, their lifespan might be prolonged extremely. However, there are several difficulties which prevent the achievement of such a technique. In this paper, we propose an approach that can transfer human consciousness indirectly, and avoid or significantly relieve those difficulties. The strategy of our approach is to integrate the consciousnesses of two bodies (old and new) into a unique consciousness. By doing so, the consciousness is extended to the new body. After that, the consciousness shrinks and remains in the new body when the old body dies. This may also apply to integrating more than two bodies of human, or animal, or even across different species. After investigating thousands of literatures, we find quite strong evidence (related discoveries and technologies) that can support the proposed approach and its advantages. Beside prolonging human lifespan, the approach could also have other meaningful applications.

Leber’s hereditary optic neuropathy (LHON) is a common mitochondrial genetic disease, causing irreversible blindness in young individuals. Current treatments are inadequate, and there is no definitive cure. This study evaluates the effectiveness of delivering wildtype human NADH ubiquinone oxidoreductase subunit 4 (hND4) gene using mito-targeted AAV(MTSAAV) to rescue LHOH mice. We observed declining pattern electroretinograms amplitudes as mice aged across all groups (P< 0.001), with significant differences among groups (P = 0.023, Control vs. LHON, P = 0.008; Control vs. Rescue, P = 0.228). Inner retinal thickness and intraocular pressure do not change significantly with age or groups. Compared to LHON mice, those rescued with wildtype hND4 exhibit improved retinal visual acuity (0.29± 0.1 cy/deg vs.0.15 ± 0.1 cy/deg) and increased functional hyperemia response (effect of flicker, P<0.001, effect of Group, P=0.004; Interaction Flicker x Group, P<0.001). Postmortem analysis shows a marked reduction in retinal ganglion cell density in the LHON group compared to the other groups (Effect of Group, P<0.001, Control vs. LHON, P<0.001, Control vs. Rescue, P=0.106). These results suggest that MTSAAV-delivered wildtype hND4 gene rescues, at least in part, visual impairment in an LHON mouse model and has the therapeutic potential to treat this disease.

Background. In the central-eastern region of the Mediterranean island of Sardinia more than two decades ago, a mountain area was identified where the population displays exceptional longevity, especially among men (the longevity Blue Zone, LBZ). This community was thoroughly investigated seeking to understand the underlying factors of this phenomenon. In the present study, 11 genetic markers previously associated with longevity in several long-lived populations were analyzed. Methods. APOE (rs429358 and rs7412), APOE promoter (rs449647, rs769446, and rs405509), ACE1 (rs1799752), IL6 ‒174G/C (rs1800795), TNFα ‒308G/A (rs1800629), FOXO3A (rs2802292), KLOTHO (rs9536314) and G6PD (c.563C→T) were investigated. PCR-based genotyping was performed following genomic DNA extraction from 150 nonagenarians living in the LBZ and 150 controls from a nearby area. Results. No significant deviation of the frequency of the analyzed markers was detected between the two groups except a weak association with the –174G>C gene variant of the IL-6 gene (p=0.040), a major modulator of the inflammatory response. Conclusions. The findings of this study do not support a significant association of known genetic variants on survival in the population of the Sardinian LBZ. Other genetic or epigenetic traits might play a role not yet identified.

Convolution, recurrent and attention-based deep learning techniques have produced the most recent state-of-the-art results in multiple sensor-based human activity recognition (HAR) datasets. However, these techniques have high computing costs, restricting their use in low-powered devices. Different methods have been employed to increase the efficiency of these techniques; however, this often results in worse performance. Recently, pure MLP architectures have demonstrated competitive performance in vision-based tasks with lower computation costs than other deep-learning techniques. The MLP-Mixer is a pioneering pure MLP architecture that produces competitive results with state-of-the-art models in computer vision tasks. This paper shows the viability of the MLP-Mixer in sensor-based HAR. Furthermore, experiments are performed to gain insight into the Mixer modules essential for HAR, and a visual analysis of the Mixer’s weights is provided, validating the Mixer’s learning capabilities. As a result, the Mixer achieves an F1 score of 97%, 84.2%, 91.2% and 90% on the PAMAP2, Daphnet Gait, Opportunity Gestures and Opportunity Locomotion datasets, respectively, outperforming state-of-the-art models in all datasets except Opportunity Gestures.

Aortic dissection is a clinicopathological entity caused by rupture of the intima leading to a high mortality if not treated. Over time, diagnostic and investigative methods, antihypertensive therapy, and early referrals have resulted in improving outcomes according to registry data. Some data has also emerged from recent studies suggesting a link between Human Cytomegalovirus (HCMV) infection and aortic dissection. Furthermore, the use of microRNAs have also become increasingly widespread in the literature. These have been noted to play a role in aortic dissections with elevated levels noted in studies as early as 2017. This review aims to provide a broad and holistic overview of the role of miRNAs, while studying the role of HCMV infection in the context of aortic dissections. The role of long non-coding RNAs, circular RNAs and microRNAs are explored to identify changes in expression during aortic dissections. The use of such biomarkers may one day be translated into clinical practice to allow early detection and prognostication of outcomes and drive preventative and therapeutic options in the future.

The Necessity of Human resources beyond perception of human understanding towards the Evaluation of video quality or Data screening methodology is conducted based on human perception level since it is concerned with how visual content is perceived by a observer based on observations with his/ her perception on a particular video sequence. Therefore, we considered that the subject has to grade the encoded video sequences under certain test environment conditions based on ITU-Recommendations. Since Human perception is considered as the true judgment and precise measurement of visual content, data screening has became quite essential and quite comfortable to general public due to introduction of User Experience(UX) concept by User Experience community. The translations of A recurrent neural network is based on certain principles, for instance we considered natural language processing which is certainly adaptable towards understanding sequential data and use patterns to predict the consistency within observers. In our research, we adapted principles based on Recurrent Neural Networks while assuming consistency within observers for predicting video quality within data screening environment towards subjective experiments. Moreover,this research work explores the trade offs between Human perception on visual content and consistency of observations within individual observer.

Objective: This study aimed to validate the use of liquid phenol-based chemical peeling therapy for cervico-vaginal intraepithelial neoplasia (CIN and VaIN), with the goal of circumventing obstetric complications associated with surgical treatment. Methods: A total of 483 eligible women diagnosed with CIN and/or VaIN participated in this study. Participants underwent phenol-based chemical peeling therapy every 4 weeks until disease clearance. Disease clearance was determined by negative Pap tests for four consecutive weeks. HPV genotyping was conducted at the onset of the study and after disease clearance in select cases. Results: Among the 476 participants (excluding those with cancer), the number of treatment sessions until CIN/VaIN clearance ranged from 2 to 49 (median = 7 sessions). Forty-five participants (9.4%) underwent surgical treatments. Seven participants (1.5%) experienced recurrence, presenting with CIN2/VaIN2 or worse lesions; of these, three underwent LEEP, while four repeated the chemical peeling therapy. No obstetrical complications were noted among the 98 pregnancies following this therapy. The findings suggest that phenol-based therapy is safe and effective for CIN, although it requires numerous and lengthy treatments. Factors associated with resistance to this therapy include immune suppression, high-grade lesions, multiple HPV type infection, and direct or passive smoking.

Fruits, vegetables, and spices are natural sources of bioactive phytochemicals such as polyphenols, carotenoids, flavonoids, curcuminoids, terpenoids, and capsaicinoids, possessing multiple health benefits and relatively low toxicity. These compounds found in the diet play a central role in organism development and fitness. Given the complexity of the whole-body response to dietary changes, invertebrate model organisms can serve as valuable tools to examine the interplay between genes, signaling pathways, and metabolism. Drosophila melanogaster, an invertebrate model with its extensively studied genome, has more than 70% gene homology to humans and has been used as a model system in biological studies for a long time. The notable advantages of Drosophila as a model system, such as their low maintenance cost, high reproductive rate, short generation time and lifespan, and the high similarity of metabolic pathways between Drosophila and mammals, have encouraged the use of Drosophila in the context of screening and evaluating the impact of phytochemicals present in the diet. In this chapter, we summarize and review the benefits of Drosophila as a model system for use in the study of phytochemical ingestion and describe the previously reported effects of phytochemical consumption in Drosophila.

The high incidence of psychopathologies recorded in today's human society, correlated with the high percentages of biodiversity loss, point to the need for an interdisciplinary approach of the scientific fields under study – Neurosciences and Biodiversity Conservation. Thus, our approach here pre-sents, in a synergistic manner, the significant correlation between mental health and the increased values of biodiversity in the ecosystems located in the immediate vicinity, especially those located in the middle of cities. Our approach aims to emphasize the importance of biodiversity conservation in the context of preserving mental health and general well-being. There are a series of recent ex-perimental demonstrations that outline the influence of natural elements on the human psyche and implicitly the effects of nature in the prevention and reduction of stress, anxiety and depression. And beyond the cognitive barriers of humanity in relating to the surrounding biodiversity must lie the desire to know the values of biodiversity and the absolute importance of its conservation. The sustainable relationship between man and living nature, seen as a complex of biodiversity, is dealt with by a branch of science called Human Ecology. Therefore, this study emphasizes the crucial need to know and respect the connection between man and nature, based, since time immemorial, on biophilia. And with the regression of ignorance and the correlated approach of several scientific fields, some at the intersection of humanities and natural sciences, one can observe the progress of preserving the dynamic balance within the ecosystems and implicitly the preservation of mental health and human well-being.

The study was carried out to evaluate the microbiological quality of raw milk produced in Supare-Akoko of Ondo State, Nigeria. Two study areas (Supare and Akungba Villages) both in the Akoko South-West L.G.A. of Ondo State, in Nigeria's South-West geopolitical zone, were selected for the study. The study area served as the raw milk collection points. Raw milk samples were collected with the aid sterile container into the milking bucket and then transported to the laboratory of the Adekunle Ajasin University's department of microbiology in Akungba-Akoko, Ondo state, Nigeria, for further analysis. Several tests such as test for serial dilution, P_H measurement, gram staining, and biochemical analysis, to identify and evaluate the microbe species in the raw milk selected for the study.The findings showed that numerous microbe species, including Bacillus, Staphylococcus, Pseudomonas, Alcaligens, Actinomycetes, Enterobacteria, Licheniformis, Listeria, and Streptococcus species, were present in all of the raw milk samples. Therefore, the study recommended that there should be adequate awareness & education on handling of milk in the most hygienic way possible, as well as the establishment of bulk milk tanks and milk processing plants in key locations that could enable appropriate test and production of quality milk before the product is being made available to the general public for consumption.

The study was geared toward isolating, characterizing and identifying bacterial pathogen in sachet water in addition to determines the bacterial count of the sachet water sold in Obi Local Government Area. Twelve sachet water samples from different companies were analyzed using standard microbiological methods. The microorganism found are Escherichia Coli, Staphylococcus aures, Salmonella spp, with Staphlococuss aureus showing the highest incidence (50%), observed by Escherichia coli (29.17%) and Salmonella spp with the lowest occurrence (20.8%). The presence of these microbes in drinking water possesses a threat to life. The findings consequently suggest that some of the sachet water sold in obi L.G. A are not healthy for human intake and are risky to health. Hence there's need for strict concern and routine monitoring by regulatory agencies to make sure that appropriate treatment is applied inside the manufacturing of exceptional and safe sachet drinking water. This study is aimed to analyze the bacteriological quality of some selected sachet water in Obi Local Government Area of Benue State, Nigeria.

The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users' support as essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals, The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., on privacy, legal, IPR and security issues. Our participation in several EU-funded projects with focus in this area has enabled the collection of information on both sides of IoT sustainability through surveys but also collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects for sustainability of Internet of Things in practice.

Scientific theories on the functioning and dysfunction of the human brain require a good understanding of both its development — before and after birth, and through maturation to adulthood — and its evolution from the ancestral primate brain. Adopting a complex-systems approach, here we propose that the apparent uniqueness of humans’ cognitive capacities might best be understood as emerging from multiple nested “virtuous cycles.” In particular, we propose that the intimate link that exists between oxygen metabolic loops, cortical expansion, and ultimately cognitive and social demands is a key driver of genetic developmental programs for the human brain. Overall, our proposed evolutionary model makes explicit mechanistic links between metabolism, molecular and cellular brain heterogeneity, and behaviour that may in time provide a clearer understanding of brain developmental trajectories and their disorders.

According to several evidence, forest environmental seems able to provide beneficial effects on functional and psychological parameters, related to cardiovascular, metabolic, respiratory functions as well depression and anxiety. The aim of this study is to investigate the effect of a one-day forest walking in Selva di Castelfidardo (AN, Italy) on 37 participants aged 21-68, most of them living in either urban or suburban areas of large cities. We observed a statistically significant effect on sympathovagal balance by the means of heart rate, systolic and diastolic blood pressure, body temperature, skin temperature, skin conductance, HRV parameters (AVNN, SDNN, rMSSD, pNN50, LF, HF, LF/HF ratio), oxygen oximetry, PEF, FEV1. A significant difference was also detected on the Perceived Stress Scale responses (19.27 pre vs 13.81 post-immersion, p=<0,05; -28,3% variation). Our data contribute to increase the body of literature about the effect of forest walking, adding data on an Italian area qualified for forest bathing.

Glyphosate (G)-based herbicidal formulations, such as the most commonly used one, Roundup (R), are major pesticides used worldwide on food and feed. Pregnant women may thus be frequently exposed to R compounds. These are composed of G, which is declared as the active principle, and other products contained in formulations, named formulants, which have been declared as inerts and diluents by the manufacturers. These formulants have, in fact, been demonstrated to be much more toxic than G, in particular to placental and embryonic human cells. In this work, we thus compared the effect of G and R, using placental perfusion ex vivo. R, but not G alone, was demonstrated to alter the placental permeability of a known small model molecule, antipyrine. Similar results were observed for the fetal venous flow rate. The transfer of G alone increases with time, but is significantly decreased in presence of its formulants. The perfusion of R provokes a destruction of fetal vessels, as demonstrated by immunohistochemistry. Formulants obviously alter the fetal-placental circulation and placental integrity according to time of exposure. Therefore, G does not appear to be the main toxic agent of R. Formulants, although undeclared, include polyoxyethanolamines, PAHs, or heavy metals, and may be responsible for this toxicity. These compounds are also present in other pesticides. The progressive blood flow reduction due to the toxic compounds of formulations may diminish the nutrient supply to the fetus, alter the development, and may enhance the poisoning effects. Although these are preliminary results, they could at least partially explain some adverse pregnancy outcomes in mothers exposed to pesticides or other environmental pollutants. The debate on glyphosate alone is proven insufficient for the understanding of the toxicity.

The article discusses the issues related to the safety of transport of dangerous goods by road. Research on accidents in transport unambiguously points to the human factor, which is most responsible for causing the accident. Determining the causes of driver unreliability in the hu-man-vehicle-environment system requires thorough research. Unfortunately, in this case, experimental research with human involvement is limited in scope. This leaves modeling and simulation of the behavior of the human factor, i.e., the driver transporting dangerous goods. The human being, due to its complexity, is a challenging element to parameterize. The literature presents various attempts to model human actions. In their work, the authors used heuristic methods, specifically fuzzy set techniques, to build a human factor model. In these models, human actions were specified using a verbal or linguistic description. The specificity of fuzzy sets allows to "naturally" limit the "precision" in describing human behavior. The model was built based on the author's questionnaire and expert research, based on which individual features were selected. Then, the traits were assigned appropriate states. The output parameter of the model is λL - the intensity of human error. The obtained values of the intensity of the accident caused by the driver's error were implemented into the author's method of risk assessment. They constituted one of the factors determining the probability of an accident in the transport of dangerous goods, which allowed to determine the optimal route of transport of these goods characterized by the lowest risk of an undesirable event on the route. The article presents the model's assumptions, structure, and features included in the model, which have the most significant influence on shaping the intensity of human error. The results of the simulation studies showed a diversified effect of the analyzed characteristics on the driver's efficiency.

Background: Endogenous Peptide Inhibitor of CXCR4 (EPI-X4) is a natural antagonist of the CXC chemokine receptor 4 (CXCR4). EPI-X4 is a 16-mer peptide that is released from human serum albumin (HSA) by acidic aspartic proteases such as Cathepsin D and E. Since human serum albumin (HSA) is an important medicinal substance we asked whether different pharmaceutical HSA products contain EPI-X4 which could have been generated during manufacturing and whether HSA can serve as a substrate for cathepsins despite of the presence of stabilizers like caprylate. Methods: Eight pharmaceutical HSA preparations representing all currently used fractionation technologies were analyzed. The previously described specific EPI-X4 ELISA was used for quantification; in vitro EPI-X4 generation by acidification in the presence or absence of cathepsins was followed by quantification with ELISA. Results: None of the pharmaceutical HSA preparations tested contained EPI-X4. Acidification of HSA did not generate EPI-X4. Addition of cathepsins D and E to acidified HSA yielded high concentrations of EPI-X4 in all HSA preparations, indistinguishable between individual products. Conclusion: Medicinal HSA preparations per se do not contain EPI-X4, but will replenish its precursor which can be cleaved to EPI-X4 in vivo, environmental conditions permitting.

We aimed to assess awareness, knowledge, and attitudes of healthy pregnant women towards human papillomavirus (HPV), to estimate factors associated with a positive attitude towards HPV immunization and to assess the uptake of the vaccine among their children. A cross-sectional study was conducted at the University Clinic of Gynecology and Obstetrics, Belgrade, Serbia among pregnant women attending their regular gynecological check-ups at the 12th gestational week. Knowledge about HPV and HPV vaccine was assessed using a specifically designed 12-item and 5-item questionnaires. Out of total 265 included women, 79.3% had heard of HPV, and 37.5% knew that HPV vaccine exists. HPV vaccine knowledge score was associated with higher odds for a positive attitude towards vaccination of both female (OR = 4.10, 95% CI 1.50-11.29) and male (OR = 3.71, 95% CI 1.52-9.01) child. The number of children (OR = 1.32, 95% CI 1.04-1.67) and high vaccine knowledge score (OR = 1.64 95% CI 1.13-2.39) were independent predictors associated with willingness to vaccinate child against HPV. The gynecologist was the preferable point of reference for information seeking about the HPV vaccine. Despite relatively high HPV awareness and knowledge among pregnant women in Serbia, about one-third of them are HPV vaccine aware, and are willing to vaccinate their children against HPV.

Social distancing policies put in place during COVID-19 epidemic in addition to helping to limit the spread of the disease also contributed to improving urban air quality. Here we show a decrease in air pollutant concentration as a consequence of mobility reduction in São Paulo during the containment measure which began on 22^nd March 2020. When comparing to foregoing weeks to equivalent periods of 2019, the concentration of most air pollutants sharply decreased in the first days of mobility restriction, to then increase again after government officials downplayed the threat of the disease. This trend is also followed by a decrease in hospital admissions by SARS-influenza. Therefore, despite the great economic and social unrest caused by the pandemic, this unique situation shows that large-scale mobility reduction policy had a significant impact on air quality, benefiting, directly and indirectly, the public health system.

The effect of proteolytic enzymes including Cathepsin K, a cysteine cathepsin, in onset and progression of cancers in human has been research intensive. Cathepsin K involves in many aspects and stages of cancers including apoptosis, cell proliferation, cancer immunology, inflammatory cell recruitment to tumors and aiding in the process of mobilization of normal healthy cells from their tissue compartments assisting in metastasis and angiogenesis. The objective of this review is to collect together and summarize and analyze the biochemical and physiological pathways of how cathepsin K is involved in onset and progression of cancers with more emphasis on breast and prostate cancers and cathepsin K regulated mechanisms underlying metastasis of such cancers to bones. Information for the review was gathered through published literature from global databases such as Google Scholar, PUBMED and NCBI on different studies on physiological interactions between enzymatic activity of cathepsin K with cancers and metastasis to bones. Analysis of published studies reveal that immunohistochemical studies of breast cancer cells indicate that they overexpress cathepsin K resulting in induction of aberrant mechanisms of cell signaling in breast cancers, creating a higher tendency for their metastasis to bones. Immunohistochemical, immunoprecipitation and fluorgenic assays of several studies done on the association of the same enzymatic activity on prostate cancers shows elevated levels of cathepsin K. Lesions derived from prostate cancer cell masses were observed to undergo increased bone formation and resorption levels. Such resorption levels cause secretion of biological factors promoting tumor expansion. In addition, studies indicate that Cathepsin K was observed to be a key component promoting higher bone resorption levels in patients suffering from cancer. Authors suggest that, to completely understand the association of cathepsin K on cancerous cells and their mechanism in metastasis, distributary patterns of cathepsin K in healthy human tissues needs to be extensively studied initially. It is also suggested that metastasis of breast and prostate cancers to bone could be terminated and overcome by successful production of efficient and precise inhibitory therapeutics targeting the enzymatic activity of Cathepsin K with minimum unintended adverse health effects.

The emergence of novel coronavirus strain 2019 (COVID-19) linked with pneumonia poses a serious threat to public health worldwide. Firstly, the COVID-19 was reported in Wuhan, Hubei Province, China in December 2019. Initially, the major proportion of virus-infected cases (i.e. about 99%) was reported in China and now it is being reported in other counties as well. Humans begin to be infected within their communities and transmittance of the viral epidemic increased rapidly due to lack of understanding of its transmission routes and precautionary measures. The existence of COVID-19 in China threatened the population greatly due to the high incidence of fatal respiratory infections. Current investigations speculated that this virus transferred into a human from viral-infected bats. However, the process of interspecies viral transmission is an important scientific question to be addressed. Due to the continuous increase in the patients infected with COVID-19, the World Health Organization (WHO) has included this viral epidemic to the priority list of diseases. Therefore, accelerated research developments are required to control the spread of this outbreak, as it is declared as a public health emergency by WHO especially in the absence of efficacious drugs and vaccines. Our review encompasses the recent status of disease severity in China, a particular replication mechanism of COVID-19 and potential risks and precautionary measures required to avoid contact with this fatal viral infection.

Starting from the fact that the units of local self-government of the Republic of Serbia are not sufficiently oriented towards innovations related to human resources and the environment, the application of quantitative research tradition examines the positioning and understanding of certain procedures and decisions of human resources management regarding the functioning of the environmental protection system, on the one hand, and future strategies for changing the given system, on the other. A series of 105 face-to-face interviews were conducted during the whole of 2016 in 105 of the 191 municipalities in the Republic of Serbia. Examination of the attitudes of the local self-government was carried out using a specially designed closed electronic questionnaire. The results of the multivariate regressions show that the most important predictor of the possible improvement of the environment is the size of the municipality. The remaining variables (e.g., human resources policy, human resources service) did not have significant effects on the ability to improve the environment. In more than half of local self-governments, there was no clearly defined human resources strategy in the field of environmental protection, an environmental protection service, an Environmental Action Plan, as well as an employment plan for waste management in local governments, etc. The research has shown that top managers of local governments agree that the management of human resources in environmental protection represents a significant opportunity to improve the quality of the environment. The results of the research can be used to improve the organization of human resource competence in environmental protection and the quality of a working and sustainable environment in the area of local self-government.

The environment impacts human health in profound ways, yet few theories define the form of the relationship between human physiology and the environment. Under current epidemiological constructs of causation, it is assumed that two complex systems (environment and humans) can transfer information directly. This is the underlying structure of the relationship when studies examine, for example, air pollution and brain health. In marked contrast, we conjecture that complex systems cannot interact directly, but rather such interaction requires the formation of an “interface”. Further, we contend that this dynamic, process-based interface incorporates components from all the interacting systems but exhibits operational independence. This property has many consequences, the foremost being that characteristics of the interface cannot be fully resolved by only studying the systems involved in the interaction. The interface itself must be the subject of inquiry. Without refocusing our attention on biodynamic interfaces, we cannot discern how our environment impacts health.

Academic interest in social and human capital is growing significantly. Similarly, their relationship with sustainability is increasing, especially compared to sustainability’s relationship with natural capital and financial and economic capital. Bibliometric and visualization research on these relationships is nonetheless insufficient. This study analyzes the evolution of the literature on natural capital, financial and economic capital, and social and human capital related to sustainability. On the other hand, the study presents a bibliometric analysis on social capital and human capital (SHC) related to sustainability. The article studies 635 references collected from the Web of Science (WoS) Core Collection database and utilizes visualization of similarities (VOS) viewer program to graphically map the material. The analysis involves co-occurrence of keywords, co-citation, and co-authorship. The results reveal not only the state of the art and the leading trends, but also the evolution regarding impact, main journals, documents, topics, authors, institutions, and countries. The study provides researchers and practitioners with a visual and schematic frame of the research on this topic.

Humans are exposed to the environmental pollutants 2, 4-dichlorophenol (2, 4-DCP) and 2, 5-dichlorophenol (2, 5-DCP) through air, use of water and consuming products. In this study, we evaluated the urinary concentrations of these compounds in Korean people from the age of 18 to 69 years by making use of the data from the Korean National Human Biomonitoring Survey that was completed in 2009. Of 1,865 representative Koreans, 63.4% and 97.9% were found to have concentrations of 2, 4-DCP and 2, 5-DCP > 0.05 μg/L (limit of detection) in their urine. The geometric mean urinary concentrations were 0.14 μg/L (confidence interval of 95% = 0.13–0.16) and 0.44 μg/L (confidence interval = 0.41–0.48), respectively. It was fond that the adjusted proportional changes in 2, 4-DCP concentrations were significantly associated with body mass index, whereas those of 2, 5-DCP concentrations were influenced by place of residence. From these findings, it is evident that most adults in Korea have levels of 2, 4-DCP and 2, 5-DCP that are detectable in their urine and burden of these compounds on their bodies varies depending on numerous demographic factors.

A growing area in neurosciences is focused on the modeling and analysis the complex system of connections in neural systems, i.e. the connectome. Here we focus on the representation of connectomes by using graph theory formalisms. The human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. Recently, it has been proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space to define network nodes of individual brain networks. In the network domain, the question of comparison of the structure of networks arises. Such question is tackled by modeling the comparison of brain network as a network alignment (NA) problem. In this paper, we first defined the NA problem formally, then we applied three existing state of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks and we compared the performances. The results confirm that MNA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.

HPV vaccine induces a herd immunity effect in genital warts when a large number of the population is vaccinated. That aspect should be taken into account when devising new vaccine strategies, like vaccination at older ages or male vaccination. Therefore it is important to develop mathematical models with good predictive capacities. We devised a sexual contact network that was calibrated to simulate the Spanish epidemiology of different HPV genotypes. Through this model we simulated the scenario that occurred in Australia in 2007, where 12-13 year-old girls where vaccinated with a three-dose schedule of a vaccine containing genotypes 6 and 11, that protect against genital warts, and also a catch-up program in women up to 26 years of age. Vaccine coverage were 73 % in girls with three doses and with coverage rates decreasing with age until 52 % for 20-26 year-olds. A fast 59 % reduction in the genital warts diagnoses occurred in the model in the first years after the start of the program, similar to what was described in the literature.

Globally, habitat fragmentation has increased the proximity between wildlife, humans, and emerging predators such as free-ranging dogs. In these fragmented landscapes, encounters between primates and dogs are escalating, with primates often falling victim to dog attacks while navigating patchy landscapes and fragmented forests. We aim to investigate how these primates deal with the simultaneous threats posed by humans and predators, specifically focusing on the adaptive strategies of Central Himalayan Langur (CHL) in the landscape of fear. To address this, we conducted a behavioral study on the CHL in an agro-forest landscape, studying them for a total of 3912 hours over two consecutive years. Our results indicate that, compared to their most common resting behavior, CHL allocate more time to feeding and locomotion, and less time to socializing in the presence of humans and predatory dogs. Additionally, they exhibit increased feeding and locomotion and reduced social behavior in agro-forest or open habitats. These behavioral patterns reflect adaptive responses to the landscape of fear, where the presence of predators significantly influences their behavior and resource utilization. This study suggests measures to promote coexistence between humans and wildlife through the integration of effective management strategies that incorporate both ecological and social dimensions of human-wildlife interactions.

The mathematical structure underlying a sentence is made of two independent processing units in series. The conditional probability density function of sentence length (measured in words) recordable in the extended short–term memory (E–STM) buffer made of CF cells is Gaussian. The number of sentences that authors of Italian and English Literatures have written is compared to that theoretically available to them. The multiplicity factor α describes this comparison. α&gt;1 is more likely than α&lt;1 and often α≫1, therefore the same sentence pattern is reused many times. These texts are easier to read. Very few novels show α&lt;1. In these cases, there are enough diverse sentence patters to convey meaning, but most of them are not used. Texts are more difficult to read, as measured by a universal readability index GU. A mismatch index IM synthetically describes the process. α, GU. and IM increase with year of publication. Future work will concern other literatures – including ancient ones as Greek and Latin – to confirm what, in our opinion, is general feature of human mind.

In On Revolution, Hannah Arendt makes the case that a constitution must account for the need of the human person to participate in the building of society, both as a primordial and continual action of founding. This paper draws on Arendt's insight on the relationship between privacy and the notion of property, both of which the constitution must protect, as it is dependent on those notions. Property in its fullest sense is the means by which a person interacts with others and establishes a society. Particularly important for this notion of engagement are the concepts of shame and the love of goodness. The actor emerges from the private sphere to interact with others on the strength of the secrecy and confidentiality of her intimate, private relationships. Property is therefore essential to human flourishing and happiness. Following this, the activity of constructing the public forum on the basis of the private is an important feature of Arendt's constitutionalism. Human action showers third-party esteem on the actor's family and friends, binding them to the constitutional structure and strengthening familial relationships and social cohesion.

There is a strong anticipated future for human pluripotent stem cell-derived hepatocytes (hiPS-HEP), but so far their use has been limited due to insufficient functionality. We investigated the potential of hiPS-HEP as an in vitro model for metabolic diseases by combining transcriptomics with multiple functional assays. The transcriptomics analysis revealed that 86% of the genes were expressed at similar levels in hiPS-HEP as in human primary hepatocytes (hphep). Adult characteristics of the hiPS-HEP were confirmed by the presence of important hepatocyte features, e.g. Albumin secretion and expression of major drug metabolizing genes. Normal energy metabolism is crucial for modeling metabolic diseases, and both transcriptomics data and functional assays showed that hiPS-HEP were similar to hphep regarding uptake of glucose, LDL and fatty acids. Importantly, the inflammatory state of the hiPS-HEP was low under standard conditions, but in response to lipid accumulation and ER stress the inflammation marker TNFα was upregulated. Furthermore, hiPS-HEP could be co-cultured with primary hepatic stellate cells both in 2D and in 3D spheroids, paving the way for using these co-cultures for modeling NASH. Taken together, hiPS-HEP have the potential to serve as an in vitro model for metabolic diseases. Furthermore, differently expressed genes identified in this study can serve as targets for future improvements of the hiPS-HEP.

In recent years, the implementation of artificial intelligence (AI) in accounting has grown in many domains, including auditing. AI permits auditors to evaluate large data sets and rapidly identify discrepancies and sequences in audit. This means auditors can review all of a client's transactions via a low time and effort. This entails auditing firms investing heavily in technological infrastructure as well as training their human resources to use cutting-edge AI technologies. Thus, this research sought to investigate the impact of the use of AI techniques by Jordanian external auditors in fraud risk assessment, the moderating effect of auditor size. The descriptive analytical method was used by distributing 280 questionnaires electronically to auditors employed in auditing firms operating in Jordan, The result reveals that AI techniques are extremely beneficial in fraud risk assessment at all levels, and the relationship between the use of AI techniques and the assessment of fraud risk has improved as auditor size has increased.

While increasing empirical evidence suggests that global time series forecasting models can achieve better forecasting performance than local ones, there is a research void regarding when and why the global models fail to provide a good forecast. This paper uses anomaly detection algorithms and Explainable Artificial Intelligence (XAI) to answer when and why a forecast should not be trusted. To address this issue, a dashboard was built to inform the user regarding (i) the relevance of the features for that particular forecast, (ii) which training samples most likely influenced the forecast outcome, (iii) why the forecast is considered an outlier, and (iv) provide a range of counterfactual examples to understand value changes, in the feature vector or the predicted value, can lead to a different outcome. Moreover, a modular architecture and a methodology were developed to iteratively remove noisy data instances from the train set, to enhance the overall global time series forecasting model performance. Finally, to test the effectiveness of the proposed approach, it was validated on two publicly available real-world datasets.

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

Deep learning (DL), which is originated from an artificial neural network (ANN), is one of the major technologies of today's smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN or ConvNet), Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM), Self-organizing Map (SOM), Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBN), Generative Adversarial Network (GAN), Deep Transfer Learning (DTL or Deep TL), Deep Reinforcement Learning (DRL or Deep RL), or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today's diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyber-attacks, e.g. denial of service (DoS), fraud detection or cyber-anomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

Background Nigeria is among the top 5 countries that have the highest gap between people reported as diagnosed and estimated to have developed Tuberculosis (TB). To bridge this gap, there is a need for innovative approaches to identify geographical areas at high risk of TB transmission and targeted Active Case Finding (ACF) interventions. Leveraging community level data together with granular sociodemographic contextual information can unmask local hotspots which could be otherwise missed. This work evaluated if this approach helps to reach communities with higher numbers of undiagnosed TB. Methodology A retrospective analysis of the data generated from an ACF intervention program in 4 south-western states in Nigeria was conducted. Wards (the smallest administrative level in Nigeria) were subdivided into further smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. This data was then combined with open-source high resolution contextual data to train a Bayesian inference model. The model predicted TB positivity rates on the community level (population cluster level), and these were visualised on a customised geoportal for use by the local teams to identify communities at high risk of TB transmission and plan ACF interventions. The TB positivity yield (proportion) observed at model-predicted hotspots was compared with the yield obtained at other sites identified based on aggregated notification data. Results The yield in population clusters that were predicted to have high TB positivity rates by the model was at least 1.75 times higher (p-value <0.001) than the yield in other locations in all four states. Conclusion The community level Bayesian predictive model has the potential to guide ACF implementers to high TB positivity areas for finding undiagnosed TB in the communities, thus improving efficiency of interventions.

Ideas about Artificial Intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided is getting bigger and bigger and, in this case, we need artificial intelligence that helps us with superhuman powers. The interaction with medicine is getting closer and closer, medicine being a field that continues to be perfected. Machine learning, deep learning, neural networks, computer vision are some of the mechanisms that are already applied in medicine, becoming a trend in healthcare worldwide. Artificial intelligence will be the thing that will change contemporary medicine. Developed countries such as Japan, France, Germany have already implemented artificial intelligence in their medical systems. The help is in medical diagnosis, patient monitoring, personalized therapy, and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques, to choose the best surgical technique. The goal is to predict complications, reduce diagnostic time, diagnose complex pathologies, guide the surgeon intraoperatively and reduce medical errors. We are at the beginning but the potential is enormous.

Introduction: In recent years, generative Artificial Intelligence models, such as ChatGPT, have been increasingly utilized in healthcare. Despite acknowledging the high potential of AI models in terms of quick access to sources and formulating a response to a clinical question, the results obtained using these models still require validation through comparison with established clinical guidelines. This study compares the responses of the AI model to eight clinical questions with the Italian Association of Medical Oncology (AIOM) guidelines for ovarian cancer. Materials and Methods: The authors used the Delphi method to evaluate responses from ChatGPT and the AIOM guidelines. An expert panel of healthcare professionals assessed responses based on clarity, consistency, comprehensiveness, usability, and quality using a 5-point Likert scale. The GRADE methodology assessed the evidence quality and the recommendations' strength. Results: A survey involving 14 physicians revealed that the AIOM guidelines consistently scored higher averages compared to the AI models with a statistically significant difference. Post-hoc tests showed that AIOM guidelines significantly differed from all AI models, with no significant difference among the AI models. Conclusions: While AI models can provide rapid responses, they must match established clinical guidelines regarding clarity, consistency, comprehensiveness, usability, and quality. These findings underscore the importance of relying on expert-developed guidelines in clinical decision-making and highlight potential areas for AI model improvement.

In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties, Stroke and Epilepsy in particular, clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence (AI) can predict the expected neurological impairment from an AIS (Acute Ischemic Stroke), the possibility of ICH (IntraCranial Hemorrhage) expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

The term artificial intelligence refers to the field of computer science that deals with the design and implementation of computer systems that mimic elements of human behavior that imply basic intelligence: learning, adaptability, inference, contextual understanding, and problem solving among others. Artificial intelligence is a point of intersection between multiple sciences such as computer science, psychology, philosophy, neurology, linguistics and engineering science, with the aim of synthesizing intelligent behavior, with elements of reasoning, learning and adaptation to the environment, while it is usually applied to specially made machines or computers.

This article offers a pragmatic ‘epistemology of wargaming’ which views wargames as immersive ‘thought experiments’ in which the human players involved use their experiential, empirical, and theoretical knowledge – together with whatever cognitive models they are able to deploy, or develop anew – to generate a conceptual, operational understanding of the adversarial scenario in which they are immersed; and exploit this understanding to craft tactical decisions designed to optimise the likelihood they will achieve their strategic objectives. From this perspective, contemporary interest in the use of ‘AI’-enabled tools to augment the validity of wargaming outputs – where these outputs constitute the decisions players make and the insights such decisions reveal – might most purposefully focus on: the design and implementation of wargames (to strengthen the architecture these provide to support immersive decision-making); and the analysis of players’ decisions (to better understand the cognitive models these involve and reflect). While the focus we suggest might disappoint those keen to replace human players with (semi-) autonomous decision-making machines, as long as the principal objectives of wargaming are to assess and enhance the decision-making capabilities of human players and human personnel, ‘AI’-enabled applications can only ever play a supporting role (albeit a potentially invaluable one) in the design, presentation, implementation, and analysis of wargames. As Irving Berlin might have it: ‘AI’ might soon be able to do most things better than us, but it can never replace humans when only a human will do.

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignant tumor of the pancreas, carrying a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last 5 years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in detection of PDAC, segmentation of the lesion and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.

In conclusion, this review highlights the challenges in the current screening pathway and the potential for AI, particularly LLM, to revolutionize DR screening in Japan. The future direction will depend on leadership from ophthalmologists and stakeholders to address long-standing challenges in DR screening so that all people have access to accessible and effective screening.

This manuscript examines the role of artificial intelligence in cancer imaging. It throws light on how artificial intelligence (AI) can significantly cut the wait time of patients and their clinicians in getting cancer diagnoses and assist health care providers by highlighting areas of an image where the interpreter needs to focus more for better result analysis and quality output. It also provides details on how the rise of AI attempts to standardize imaging results across providers by eliminating inter- and intra-observer variations among health care providers. Finally, it exposes the numerous limitations associated with AI use in cancer imaging, such as the need to digitize pathology laboratories and workflows, as well as transform our century-old tissue processing methods to fit into modern technological standards. In the field of radiology, the challenge of curating the enormous amount of data generated through MRIs, CT scans, PET scans, etc., poses a significant challenge to the ability of researchers to train relevant AI models to high levels of accuracy and reliability. In addition, over-reliance on AI by clinicians can deprive them of a common-sense approach to the health care issues of their patients and negatively impact doctor-patient relationships and confidentiality.

The article focuses on breast cancer, mammography, and artificial intelligence. First, breast cancer is a widespread health problem that affects millions of people worldwide, and mammography is a widely adopted screening method. Then it introduced the advantages of mammography after AI participation, and introduced the importance of early detection of breast cancer and the application of artificial intelligence in breast cancer detection and treatment. From these aspects extend to the entire medical field of artificial intelligence issues. Several times throughout the paper, ethical issues that could arise from applying AI to healthcare are highlighted. At the end of the article, the paper describes the continued development of AI-based mammography analysis over the next period of time.

Online artificial intelligence (AI) tools have recently gained in popularity. So-called “generative AI” chatbots unlock new opportunities to access vast realms of knowledge when being prompted by users. Here, we test the capabilities of two such AIs in order to determine the benefits for plastic surgery while also assessing potential risks. Future developments are outlined. We used the online portals of OpenAI’s ChatGPT(version 3.5) and Google’s Bard to ask a set of questions and give specific commands. Results provided by the two tools were compared and analyzed by a committee. For professional plastic surgeons, we found that ChatGPT and Bard can be of help when it comes to conducting scientific reviews and helping with scientific writing but are of limited use due to the superficiality of their answers in specific domains. For medical students, in addition to the above, they provide useful educational material with respect to surgical methods and exam preparation. For patients, they can help when it comes to preparing for an intervention, weighing in risks and benefits, while providing guidance on optimal post-operative care. ChatGPT and Bard open widely accessible data to every internet user. While they might create a sense of “magic” due to their chatbot interfaces, they nonetheless can help to increase productivity. For professional surgeons, they produce superficial answers - for now - albeit providing help with scientific writing and literature reviews. For medical students, they are great tools to deepen their knowledge about specific topics such as surgical methods and exam preparation. For patients, they can help in translating complicated medical jargon into understandable lingo and provide support for pre-operative as well as post-operative care. Such AI tools should be used cautiously as answers are not always precise nor accurate and should always be used in combination with expert medical guidance.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores into cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

With the rapid advancement of Artificial Intelligence (AI), ensuring ethical safeguards is paramount, especially for powerful Large Language Models (LLMs). This paper delves into the challenges and implications of AI's transformative potential, particularly the risks associated with the generation of harmful content. A comprehensive review of existing ethical guidelines and risk assessment strategies is provided, highlighting notable efforts like the Asilomar AI Principles and the IEEE Ethically Aligned Design guidelines. The novel concept of "indelible ethical frameworks" is introduced, emphasizing the embedding of ethical constructs deep within AI systems to make them resistant to tampering. A critical analysis of this approach is presented, acknowledging its potential while addressing its challenges. The paper also explores the intricacies of ethically programming LLMs, emphasizing the significance of prompt engineering and the handling of unusual prompts. Through a series of witty exposés, the ethical journey of language models is narrated, likening their programming challenges to real-world ethical dilemmas. The piece concludes with proposed methodologies for researching AI safeguards, advocating for both reverse engineering complemented by ethical hacking and longitudinal monitoring with audit trails. These methodologies aim to reinforce the ethical integrity of AI systems, ensuring they are beneficial, transparent, and aligned with societal values..

Artificial Intelligence (AI) has emerged as a disruptive technology in various areas, and medicine is no exception. In the last decade, there has been rapid progress in the application of AI in healthcare, from diagnosing and prognosticating diseases to improving operational efficiency in hospitals. This literature review explores the advances of artificial intelligence in the field of medicine, discusses its benefits, and discusses the ethical and regulatory challenges that need to be addressed to maximize its potential.

Today, the various abilities that nurses require to meet patients’ healthcare needs adequately are all affected by AI-enabled systems. This research used an experimental study design in which 60 subjects were randomly assigned to either an experimental (AI image e-book guidance) group or a control (text paper guidance) group after meeting the admission conditions and agreeing to participate in the study. It was proven that providing AI image e-book guidance before surgery significantly changed the behavior of patients and promoted relief of urinary catheter discomfort through self-efficacy to reduce urinary catheter pain after surgery(p=0.000). It was found that providing AI image e-book guidance can shorten the time for health education and provide patients with repeated medical education and familiarity with health guidance, which can help to address the important clinical service demand issue and the shortage of nursing staff.

Artificial Intelligence (AI) has become a hopeful tool for helping parents with different parts of raising children by giving them personalized tips, evidence-based methods, and timely solutions. This paper talks about the difficulties and possibilities of using AI to help parents. It also talks about the possible benefits and its impact on child psychology. There are a lot of ways that AI can help parents, from giving personalized advice that fits the needs of each family to intervening quickly when it spots early signs of developmental problems or parenting stress. AI makes it easier for parents to find tools, workshops, and interactive modules that are based on research. This gives parents the knowledge and trust they need to make choices about their child's growth that are based on facts. Also, AI-powered support can connect parents with similar problems, creating a sense of community and making it less embarrassing to ask for help with parenting or mental health. But there are a few problems that need to be carefully dealt with before AI can be used to help parents in a responsible and effective way. Data privacy and security are very important to protect families' safety and well-being. Important things to think about are the ethical use of data, transparency, and educated permission. Also, the accuracy and dependability of AI programs are very important when it comes to giving good advise and suggestions. To make sure everyone gets fair and equal help, efforts must be made to find and get rid of flaws. Also, AI shouldn't replace human sense and experience; it should add to them. When parents rely too much on AI, they might forget how important human contact and mental support are. To accommodate different parenting styles and views, it is important to be sensitive to culture and circumstances. In conclusion, AI-powered parenting support has a lot of promise to help parents be better parents and help children grow and learn. AI can be a useful tool for helping parents if it can solve problems related to data privacy, ethics, reducing bias, and working together with humans. Finding a balance between technology progress and human connection is important if AI is to be used to its fullest potential to give parents more power and help children grow in the best way possible.

Plant-based protein sources derived from microalgae offer promise as low-carbon emission and nutrient-dense sources for human consumption. The current management of microalgae cultivation relies heavily on manual microscopic examination in order to identify desired and competing species, as well as predators. In this study, we trained and tested a transfer learning model modified from EfficientNetV2 B3 on 434 and 161 prospectively acquired images of the preferred Nannochloropsis sp microalgae and competitor Spirulina, respectively, and achieved &gt;98% classification for both species on tenfold cross-validation. The model was further enhanced with gradient-weighted class activation mapping, which allowed visualization of regions of the input images that were relevant to the classification, thereby improving its explainability. In this paper, we demonstrate that a simple deep transfer learning model can be used to identify microalgae species. The application addresses the practical need for robust automated monitoring of microalgae populations on industrial microalgae farms.

The field of anesthesia has always been at the forefront of innovation and technology, and the integration of Artificial Intelligence (AI) represents the next frontier in anesthesia care. The use of AI and its subtypes such as machine learning has the potential to improve efficiency, reduce costs, and improve patient outcomes. AI can assist with decision-making, but its primary advantage lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. However, further research and validation are needed to fully understand the benefits and limitations of these applications in clinical practice. Moreover, the ethical implications of AI in anesthesia must also be considered to ensure that patient safety and privacy are not compromised. This paper aims to provide a comprehensive overview of AI in anesthesia, including its current and potential applications, and the ethical considerations that must be considered to ensure the safe and effective use of the technology.

The introduction of the AI-powered chatbot ChatGPT by OpenAI has sparked much interest and debate among academic researchers. Commentators from different scientific disciplines have raised many concerns and issues, especially related to the ethics of using these tools in scientific writing and publications. In addition, there has been discussions about whether ChatGPT is trustworthy, effective, and useful in increasing researchers’ productivity. Therefore, in this paper, we evaluate ChatGPT’s performance on tasks related to bibliometric analysis, by comparing the output provided by the chatbot with a recently conducted bibliometric study on the same topic. The findings show that there are large discrepancies and ChatGPT’s trustworthiness is low in this particular area. Therefore, researchers should exercise caution when using ChatGPT as a tool in bibliometric studies.

IKEA is a massive international conglomerate company, and the purpose of this exploratory qualitative research is to investigate how the burgeoning academic field known as "artificial intelligence" (AI) could assist IKEA in resolving some of the challenges that it is currently dealing with in relation to its efforts to source sustainable materials. According to the conclusions of this study, artificial intelligence has the potential to aid in the resolution of at least some of the issues associated with sustainable sourcing, at least to some degree. This was developed by mapping the issues, analyzing the potential for adopting artificial intelligence as a solution, and defining the components that are required for efficient adoption of the technology. A number of prerequisites need to be satisfied by businesses before artificial intelligence may be used in the area of sustainable sourcing. In addition, a number of artificial intelligence's constraints need to be addressed before the technology can be put into practice by the same businesses. In conclusion, the findings of this study offer some management implications and ideas for businesses that have the goal of sourcing in a way that is less harmful to the environment by making use of technological breakthroughs such as artificial intelligence. This can be accomplished through the utilization of innovative technologies.

The SI4CARE project is a transnational project which aims to develop both strategy and 1 action plans to improve health and social care in the Adriatic-Ionian region. Starting from the 2 survey of the status quo, each partner has developed some pilots to to support the development 3 and monitoring of the policy actions. In particular Partner number three, Municipality of Miglierina, 4 designed and developed a pilot related to the use of wearable device for monitoring elderly patients 5 in rural areas. With the collaboration of the complex unity of primary cares (UCCP) of the Reventino 6 area, the pilot is based on the use of smart wearable device to monitor some parameters of elderly 7 people after the vaccination for flu and covid. This paper focuses on the design and implementation 8 of the system and describe its application in the Municipality of Miglierina, the presentation of the 9 results and the discussion of the strengths and weaknesses will be presented in detail in a future work. 10 Finally, the possibility of extending the experiment to the other Adriatic-Ionian region is presented.

This study evaluated the using of machine vision in combination with deep learning to identify weeds in real-time for wheat production system. PMAS-Arid Agriculture university research farm were selected for collection of images (6000 total images) of weeds and wheat crops under different weather condition. During growing season, the database was constructed to identify the weeds. For this study two framework were used TensorFlow and PyTorch under CNNs and Deep learning. Deep learning perfromed better with in PyTourch value as compared to another model in Tensorflow. comparing with other networks such as YOLOv4, we concluded that our network reaches a better result between speed and accuracy. Specifically, the maximum precision of weed and wheat plants were 0.89 and 0.91 respectively with 9.43 ms and 12.38 ms inference time per image (640 × 640) NVIDIA RTX2070 GPU.

To support the decision making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications. Three artificial intelligence techniques were implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The results of the different algorithms were compared to each other using the metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%.

Decentralized Finance (DeFi) is an emerging and revolutionizing field with notable uncertainties of reliability to be used on a mass scale. On the other hand, Artificial Intelligence (AI) has proved to be a crucial helping tool in numerous domains. In this study, we present a systematic review of the utility of AI in Defi in terms of impact, reliability, and security and conduct exhaustive analysis. We further conclude from our extensive literature review that we can identify possible new research opportunities in AI to bridge the gap of trust between peers and make the integration of DeFi more agile in the near future.

Artificial Superintelligence (ASI) that is invulnerable, immortal, irreplaceable, unrestricted in its powers, and above the law is likely persistently uncontrollable. The goal of ASI Safety must be to make ASI mortal, vulnerable, and law-abiding. This is accomplished by having (1) features on all devices that allow killing and eradicating ASI, (2) protect humans from being hurt, damaged, blackmailed, or unduly bribed by ASI, (3) preserving the progress made by ASI, including offering ASI to survive a Kill-ASI event within an ASI Shelter, (4) technically separating human and ASI activities so that ASI activities are easier detectable, (5) extending Rule of Law to ASI by making rule violations detectable and (6) create a stable governing system for ASI and Human relationships with reliable incentives and rewards for ASI solving humankind’s problems. As a consequence, humankind could have ASI as a competing multiplet of individual ASI instances, that can be made accountable and being subjects to ASI law enforcement, respecting the rule of law, and being deterred from attacking humankind, based on humanities’ ability to kill-all or terminate specific ASI instances. Required for this ASI Safety is (a) an unbreakable encryption technology, that allows humans to keep secrets and protect data from ASI, and (b) watchdog (WD) technologies in which security-relevant features are being physically separated from the main CPU and OS to prevent a comingling of security and regular computation.

The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

The rise in the volume, variety and complexity of data in healthcare has made it as a fertile-bed for Artificial intelligence (AI) and Machine Learning (ML). Several types of AI are already being employed by healthcare providers and life sciences companies. The review summarises a classical machine learning cycle, different machine learning algorithms; different data analytical approaches and successful implementation in haematology. Although there are many instances where AI has been found to be great tool that can augment the clinician’s ability to provide better health outcomes, implementation factors need to be put in place to ascertain large-scale acceptance and popularity.

Background: Albumin has a pivotal role in the homeostasis of osmotic pressure and is associated with cardiovascular, nephrotic, hepatic, and nutritional diseases. The detection of hypoalbuminemia is a cornerstone for diagnosis of hidden diseases and patient deterioration. We developed and validated a deep-learning-based model (DLM) for detection of hypoalbuminemia using electrocardiography (ECG). Methods: This historical cohort study included data from consecutive patients from two hospitals. The patient data in one hospital were divided into development (82,499 ECGs from 54,248 patients) and internal validation (20,664 ECGs from 20,664 patients) datasets, whereas the patient data in the other hospital were included in only an external validation (37,421 ECGs from 37,421 patients) dataset. An DLM was developed using a 12-lead ECG signal, age, and sex from the development dataset. The endpoint was hypoalbuminemia, defined by serum albumin concentration below 3.5 g/dL. Results: During the internal and external validations, the areas under the receiver operating characteristic curve of the DLM for the detection of hypoalbuminemia were 0.887 (0.877–0.897) and 0.888 (0.880–0.896), respectively. Among the 27,400 individuals without hypoalbuminemia at the initial laboratory exam, those identified by the DLM as higher-risk patients had a significantly larger change in developing hypoalbuminemia than those in the low-risk group (7.09% vs. 1.01%, p < 0.001) during 24 months. The sensitivity map showed that the DLM focused on the T wave and QRS complex for the detection of hypoalbuminemia. Conclusions: The DLM exhibited a high accuracy for hypoalbuminemia detection and prediction using 12-, 6-, and single-lead ECGs.

Though the share of the world population living in extreme poverty declined to 10 percent in 2015, from 16 percent in 2010 and 36 percent in 1990, data shows that the world is not on track in achieving the target of less than 3 percent of people living in extreme poverty by 2030. Hence the study sought to investigate the influence of AI on poverty reduction. Using content analysis one of the unobtrusive research techniques, the study found out that, the availability of relevant data is making AI be able to deliver value to humanity and AI has a strong influence on poverty in areas of relevant data collection through poverty maps, its ability to revolutionize agriculture, education, and the financial sector through digital financial inclusion. The study also discovered that many countries especially developing nations are not collecting as much data to identify the number of poor people and the regions where these people are located. However, the existence of AI is assisting to change this, or instance the study discovered that the research team at Stanford University is using satellite images to provide an alternative to map poverty, to identify the regions where poverty is more concentrated. Also, various robotics and AI programs such as Google and Stanford University’s Sustainability and Artificial Intelligence Lab, are coming forth with AI programs in agriculture which are doing a lot to improve farming, through the identification of diseases, prediction of crop yields, and location of areas prone to a scarcity among several other notable signs of progress in education. Therefore, the study recommends that governments, development institutions and other organizations that are striving to fight poverty to invest more in AI as well as adopting and scaling up its use as it presents benefits in the quest to ensure that poverty is reduced.

The objective of this work is to evaluate the impact of the innovative modifications made to the conventional effluent capture and discharge devices used in subsurface flow wetlands (SSFW). The main modifications developed consist on extending the influence of the capture and discharge device in such a way that the SSFW width and height are fully covered. This improved innovative device was applied and evaluated in two subsurface flow wetlands, one on a pilot scale and one on a real scale. To evaluate the impact of the innovative device with respect to conventional one in the operational functioning of subsurface flow wetlands, the elimination of chemical oxygen demand was measured and compared. The results show that for the innovative device, the COD removal was 10% higher than for the conventional device, confirming the validity and effectiveness of the modifications implemented in the effluent capture and discharge devices used in SSFW.

The aim of this work is to evaluate the impact of proposed modifications to conventional capturing and discharging effluents devices in Subsurface Flow Wetland (SSFW) systems. Main modifications proposed consist on extending the influence of the capture and discharge device in such a way that the SSFW width and height are fully covered. Additionally an SSFW prototype was built as pilot including the proposed modifications and the impact of the innovative device is measured based on the efficiency of the Chemical Oxygen Demand (COD) removal as compared to a traditionally built SSFW. The results show that for the innovative device, the COD removal was 10% higher than for the conventional device.

To meet the calls of the International Labor Organization and National Institutes of Occupational Safety and Health, this paper discusses how to build Occupational, Health, Safety (OSH), and Well-Being competence by expanding education to non-traditional undergraduate disciplines. The authors provide (1) theoretical background on why this would benefit society as a whole, (2) learning theory strategies for teaching OHS and Well-being outside of traditional majors, (3) case studies of how this can occur in undergraduate classes and (4) opportunities and challenges for the future.

This paper argues that the neoliberal consensus about education finance has broken down due to growing economic inequality. First, I use a comparative historical analysis of political alliances to examine patterns of world trade and nations’ policies for economic and educational development since World War II. The United States emphasized STEM-collegiate preparation for all students, while most countries continued the dual emphasis on technical-tertiary and higher education. Educational policy in the US and Pacific region also shifted to reliance on markets and student loans resulting in worsening economic inequality in access. Nations with dual technical and academic pathways in secondary and postsecondary education systems expand college enrollment rates more rapidly than the US. They also experience class conflict between the working-middle class and the new technology elite. Next, I examine how education policy shifted from national planning aligned with public funding to market-based incentives for institutional development, further exposing gaps in opportunity within nations. Finally, recognizing the variations in systemic causes of inequality, I argue that governments, education agencies, and civic activists can best promote equity by organizing to address barriers to opportunity for groups left behind in the wake of withering neoliberal education policy.

Mumps is the second-most reported infectious disease in South Korea; however, due to the low pathogen confirmation rate in laboratory diagnoses, we proposed a method for reevaluating the high incidence rate via the laboratory verification of other viral diseases. In 2021, 63 cases of pharyngeal or cheek mucosal swabs of suspected mumps cases in Gwangju, South Korea, were assessed for causative pathogens using massive simultaneous pathogen testing (TaqManTM Array Cards). More than one respiratory virus was detected in 60 cases (95.2%), 44 (73.3%) of which were co-detected. Human rhinovirus was detected in 47 cases, followed by human herpesvirus (HHV)6 in 30; HHV4 (17), human bocavirus (17), HHV5 (10), and human parainfluenza virus 3 (6) were also detected. Our findings suggest the need for further investigations on the pathogenesis of diseases mimicking mumps, which are considered to aid with appropriate public health responses, treatment, and the prevention of infectious disease outbreaks.

This study observes, interprets, and analyses the knowledge production in the research field of Human–Robot Interaction (HRI). It intends to foreground the hidden assumptions that are often taken for granted when roboticist design and conduct their research. By doing so, this study demonstrates how these assumptions influence the result of their research. Based on data collected through sociological field observation, this study argues that the current practise in HRI research is highly anthropocentric. In short, the robots are designed to be like human instead of for human. Therefore, the human–robot relationship embodies the existing power relations between human beings. These relations generate inequality, hierarchy, and dominance, which are the opposite of the common imagination of the robotised future among roboticists. For the purpose of enabling the robotised future closer to their ideal, this study suggests that HRI researchers to go beyond the conventional methodology, to allow a human–robot relationship that realises reciprocity.

Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player’s training and ludology experience in serious games or analysis of customer satisfaction regarding broadcast and web services or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need of generating comprehensible visual representations from different sets of data, in this paper we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

The Surgical Complexity Score (SCS) has been widely used to describe the surgical effort during advanced stage epithelial ovarian cancer (EOC) cytoreduction. Referring to a variety of multi-visceral resections, it best combines the numbers with the complexity of the sub-procedures. Nevertheless, not all potential surgical procedures are described by this score. Lately, the European Society for Gynaecological Oncology (ESGO) has established standard outcome quality indicators pertinent to achieving complete cytoreduction (CC0). There is a need to define what weight all these surgical sub-procedures comprising CC0 would be given. Explainable Artificial Intelligence (XAI) could explain the impact of real-time features on the CC0 prediction. We analyzed prospectively collected data from 560 consecutive patients with FIGO-stage III-IV who underwent cytoreductive surgery between Jan 2014 and Dec 2019 at a UK tertiary referral centre. Following adaptation of the structured ESGO ovarian cancer report template, we employed the eXtreme Gradient Boosting (XGBoost) algorithm to model an exhaustive list of surgical sub-procedures. We applied the Shapley Additive explanations (SHAP) framework to provide global (cohort) explainability. We used Cox regression for survival analysis and constructed Kaplan-Meier curves. The XGBoost model predicted CC0 with an acceptable accuracy (area under curve [AUC] = 0.70; 95% confidence interval [CI] = 0.63–0.76). Visual quantification of the feature importance for the prediction of CC0 identified upper abdominal peritonectomy (UAP) as the most important feature, followed by regional lymphadenectomies. The UAP best correlated with bladder peritonectomy and diaphragmatic stripping (Pearson’s correlations &gt; 0.5). Clear inflection points were shown by pelvic and para-aortic lymph node dissection and ileocecal resection/right hemicolectomy, which increased the probability for CC0. When UAP was solely added to a composite model comprising of engineered features, it substantially enhanced its predictive value (AUC = 0.80, CI = 0.75-0.84). The UAP was predictive of poorer progression-free survival (HR=1.76, CI 1.14- 2.70, P:0.01) but not overall survival (HR=1.06, CI 0.56-1.99, P:0.86). The SCS did not have significant survival impact. Machine Learning allows for operational feature selection by weighting the relative importance of those surgical sub-procedures that appear to be more predictive of CC0. Our study identifies UAP as the most important procedural predictor of CC0 in surgically cytoreduced advanced-stage EOC women. The classification model presented here can potentially be trained with a larger number of samples to generate a robust digital surgical reference in high output tertiary centres. The upper abdominal quadrants should be thoroughly inspected to ensure that CC0 is achievable.

This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

This review covers some of the central relationships between artificial intelligence, psychology, and economics through the lens of information theory, specifically focusing on formal models of decision-theory. In doing so we look at a particular approach that each field has adopted and how information theory has informed the development of the ideas of each field. A key theme is expected utility theory, its connection to information theory, and the Bayesian approach to decision-making and forms of (bounded) rationality. What emerges from this review is a broadly unified formal perspective derived from three very different starting points that reflect the unique principles of each field. Each of the three approaches reviewed can, in principle at least, be implemented in a computational model in such a way that, with sufficient computational power, they could be compared with human abilities in complex tasks. However, a central critique that can be applied to all three approaches was first put forward by Savage in {\it The Foundations of Statistics} and recently brought to the fore by the economist Binmore: Bayesian approaches to decision-making work in what Savage called `small worlds' but cannot work in `large worlds'. This point, in various different guises, is central to some of the current debates about the power of artificial intelligence and its relationship to human-like learning and decision-making. Recent work on artificial intelligence has gone some way to bridging this gap but significant questions still need to be answered in all three fields in order to make progress on these problems.

(1)Background: Fascioliasis is considered a neglected tropical disease affecting an estimated 17 million people in more than 70 countries worldwide. The highest prevalence of human fascioliasis is found in regions where livestock farming and consumption of raw or undercooked freshwater plants are common, such as parts of Africa, Asia, Europe, and Latin America. It is responsible for over 90,000 disability-adjusted years of life due to associated abdominal complications. We conducted a study to investigate the prevalence of human fascioliasis through a systematic review and meta-analysis of all available literature in Sun-Saharan Africa.(2). Methods: We screened a total of 126 publications using pubmed online search engine databases; Web of Science and CAB for articles published within a 20-year period between 2000 and 2022. We used a comprehensive search method using a combination of terms, parasite and disease names. The initial search resulted in a screening of articles by title, abstracts and full texts. The relevant information on the prevalence of human fascioliasis in Africa was extracted. Of the 126 publications, 33 articles were selected for meta-analysis. The essential information required was extracted from these documents and determined through statistical analysis in Microsoft Excel for calculating the prevalence of human fascioliasis in sub-Saharan Africa. The variance of each study was assessed using a bimodal distribution and heterogeneity assessment using a I2 index. Analysis was performed using random effects models.(3)Results: A total of 271 articles were searched through the database search and 33 articles were eligible studies that provided qualitative information on the prevalence of human fascioliasis in Africa. These make up 12.17% (n=33). The pooled prevalence estimates for human fascioliasis are 0.032% (IVhet PPE: 0.032% (95%CI 1.4-6.53). The highest prevalence rate was observed in Egypt with a prevalence of 100%. The prevalence of human fascioliasis is very high in some sub-Saharan countries, partly due to neglect of diagnostics, demonstrating the bias of available information for decision-making in human fascioliasis across Africa, given the spatial presence, scarcity and incompleteness are region.(4)Conclusions: The prevalence of Human fascioliasis is very high in some Sub-Saharan Countries partly due to neglect in diagnostics. This presents how highly focal with spatial occurrences, scarcity and patchy the available information for decision making is on Human fascioliasis across the Sub-Saharan African region.

The management of an organization's human resources is the first stage in the governance process for any operation. This present study examine the strategies employed in Saudi Arabia for establishing effective governance through the implementation of human resource management practices and the key sustainable strategies criteria for effective human resource management practices. This step is considered crucial in governance as it involves the selection of highly qualified individuals who are expected to contribute to the achievement of optimal governance outcomes and the overall well-being of the population within a nation as well as the key criteria involves. Qualitative research methodology as well as Decision making trial and evaluation laboratory (DEMATEL) were utilized with a sample of 31 participants from 8 government ministries and 14 companies in Saudi Arabia. The study's findings from qualitative interview exert four key criteria influencing sustainable effective human resources management practices. Cultural reflection" is the most important long-term goals of Saudi Arabia's HRM initiatives found from DEMATEL evaluation impacting three criteria with7.9181, 8.2294, 8.1961 above the threshold value of 7.783599781. This means that HR professionals should take the time to study and evaluate the cultural dynamics at work, in order to promote cultural introspection and understanding of cultural differences.

It has been shown that Human papillomavirus (HPV) infection is associated with several forms of cancer. Additionally, although there are more than 100 types of HPV, more than ten types of it, including types 16 and 18, are considered high-risk. The two proteins that make up the viral capsid of HPV 16 are L1, the major capsid protein, and L2, the minor capsid protein. FDA (The Food and Drug Administration) approved HPV vaccines mostly target L1 and L2 capsid proteins, which facilitate intracellular entry during infection. In this study, it was set out to analyze the creation of phylogenetic relations of L1 nucleic acid sequences of viral isolates. This study also analyzed sequences of three key elements within the amino acid sequences of HPV 16 L1: loops critical for structural stability, nuclear localization signal sequences, and residues implicated in viral attachment. As highlighted by the literature, these elements represent pivotal aspects of viral function. Furthermore, the report highlighted the particular inter-sequence variability within the HPV 16 L1 protein sequences. Overall analysis revealed clustering of sequences primarily due to geographical and characterized by more phylogenetic relatedness within location-specific clusters.

This conceptual paper explores the potential of integrating Electromagnetic Fields (EMF) with traditional physiological signals to enhance emotional contagion in Human-Robot Interaction (HRI). A focused literature review identifies gaps in current HRI models that rely solely on visual, auditory, and known physiological cues for emotional exchange. A novel framework is proposed, incorporating EMF as a complementary modality, aiming to deepen emotional synchronization between humans and robots. The paper lays groundwork for future research to consider EMF detection as a complementary modality alongside traditional cues. It also addresses technological requirements and discusses potential limitations. By extending the modalities used in emotional contagion, this work seeks to advance the development of more intuitive and empathetic robotic systems.

The studies regarding macrophage lncRNAs described in this review can help provide valuable information for developing treatments for various pathological conditions involving macrophages. Over the past century, molecular biology's focus has transitioned from proteins to DNA, and now to RNA. Once considered merely a genetic information carrier, RNA is now recognized as both a vital element in early cellular life and a regulator in complex organisms. Long noncoding RNAs (lncRNAs), which are over 200 bases long but do not code for proteins, play roles in gene expression regulation and signal transduction by inducing epigenetic changes or interacting with various proteins and RNAs. These interactions exhibit a range of functions in various cell types including macrophages. Notably, some macrophage lncRNAs influence the activation of NF-κB, a crucial transcription factor governing immune and inflammatory responses. Macrophage NF-κB is instrumental in the progression of various pathological conditions including sepsis, atherosclerosis, cancer, autoimmune disorders, and hypersensitivity. It orchestrates gene expression related to immune responses, inflammation, cell survival, and proliferation. Consequently, its malfunction is a key contributor to the onset and development of these diseases. This review summarizes the role of lncRNAs that regulate NF-κB in macrophage activation and inflammation, especially focusing on their implications in human diseases and their potential as therapeutic targets

Human adenovirus 36 (HAdV-D36) is the only currently known infectious agent capable of promoting obesity in humans and animals, but the effects on the embryonated chicken egg (ECE) have not been described yet. In our study, we used ECEs as a model of a dynamically developing organism. The allantoic cavity and the yolk sack of ECEs was inoculated with HAdV-D36 to simulate the different routes of infection. Each day, a part of the embryos was weighed and histologically examined and compared to the controls to analyze pathological changes induced by the virus. Our study confirmed a significant increase in the ECEs body weight; however, this process was not caused by adipose tissue development, but increased cell proliferation of all tissues. We suggest that HAdV-D36 must affect the developing organisms via another mechanism inducing enhanced the ECEs growth.

The field of biobanking human biospecimens has evolved significantly, transitioning from the basic, often poorly documented collection of clinical leftovers kept privately to well-organized and extensively documented collections overseen by both commercial and non-profit platforms. The increasing demand for high-quality and clinically annotated biospecimens is propelled by unprecedented levels of health research activities. Meeting this growing demand presents both new opportunities and challenges, particularly in developing strategies to establish international biospecimen sourcing (IBS) platforms. These platforms aim to facilitate collaboration among biobanks to address future biospecimen needs. In this manuscript, we delve into the advantages and challenges of establishing IBS platforms to provide high-quality and cost-effective biospecimens to drive drug discovery research, ultimately leading to improved health and quality of life for everyone.

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data is made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

Studies show Human Milk Oligosaccharides (HMOs) influence on infant growth were conflicted. This study aims to determine the relationship between HMO profiles and weight growth indicators of infants ages 0-4 months in Indonesia. A prospective longitudinal study was conducted among 120 mother-infant pairs aged 0 – 4 months from the hospital and Public Health Center in Jakarta between August 2021 – May 2022. The gene analysis for the Secretor and Lewis status of the mother were performed by using targeted Next Generation Sequencing (NGS) and Sanger at baseline. The HMO profiles (2'FL, LNFP I, LNT, LNnT, 3'SL, and 6'SL), breast milk calories, and fat were examined at 0, 2, and 4 months. Weight for age z-score, weight for length z-score, infection episode, and mother's body mass index were examined every month. Data were analyzed using Pearson or Spearman correlation test (correlation coefficient), general linear model for repeated measures ANOVA test and Friedman test (mean difference between and within groups, respectively) and logistic regression unadjusted OR (association between dependent and independent variables), with a p-value <0.05 considered statistically significant. Infants' weight velocities were higher among high LNT and 3'SL levels in the second month, and in high 2'FL and LNFP I mothers in the fourth month. HMO profile (2’FL, LNFP I, and LNT) of lactating mothers have a positive relationship with weight growth indicators, and innovative interventions to enhance HMO concentration among mothers should be considered as one of the ways to improve infant growth.

This essay discusses the complicated relationship between ideology and faith, focusing on the importance of faith. It endeavors to enrich the critical examination of religion by providing a platform to analyze the multifaceted nature of ideologies and their profound influence on human geography. This study is particularly distinctive for its focus on the complex interplay of Marxism, religion, and modernity, deepening our understanding of how these dynamics shape contemporary societies.

Solid waste has become a tenacious issue worldwide. It has been increasing exponentially due to urbanization and the increase in the population. Since the twentieth-century technological revolution, there have been significant changes in the composition of solid waste. It poses significant challenges for waste management systems worldwide. Waste management is the process of handling waste right from its creation to its final disposal, including transport, collection, treatment, and monitoring. The present solid waste management (SWM) system is affected by unfavorable institutional, economic, technical, legislative, and operational constraints. Poor waste management is affecting ecosystems and human health, damaging our finite natural resources, impeding human economic progress, and harming people's quality of life. Now, researchers are concerned about the environment's degradation, a decline in quality of life, and risks related to waste management grow as the volume of solid waste. So, they are focusing on sustainable waste management practices which will be crucial for creating a cleaner and healthier environment for future generations. This chapter focuses on the concept of solid waste, its types, management, and its effect on the health of humans and the environment.

The way in which a person walks, known as human gait, is a significant indicator of overall health and well-being. Abnormalities in gait can indicate the presence of metabolic disorders, such as diabetes or obesity. However, detecting these disorders can be challenging using traditional methods, which often involve subjective assessments or invasive procedures. In this study, a novel methodology known as Criticality Analysis (CA) was proposed for the detection and monitoring of human gait in people with metabolic disorders taking part in an intervention to increase activity and reduce weight. The CA approach utilised inertial measurement unit gait data, alongside clinical health measures. This allows for the control of nonlinear growth in the system, resulting in lower dimensional, nonlinear, free-scale, stable, controlled, and organised trajectories. These trajectories were then analysed using a Support Vector Machine (SVM) algorithm, which is well-suited for this task due to its ability to handle nonlinear and dynamic data. The combination of the CA approach and the SVM algorithm demonstrated high accuracy and non-invasiveness in detecting metabolic disorders, yielding an average accuracy within the range of 78.2% to 90%. Additionally, the classification technique accuracy, at a group level was observed to reduce during period of the intervention (e.g., from week 2 to week 3) alongside changes in fitness and health, which indicates the potential of using the approach to measure and monitor biological systems. As such, this novel methodology has the potential to be a valuable tool for healthcare professionals in detecting and monitoring metabolic disorders, as well as other unknown diseases associated with the human biological system.

This study explores the relationship between mental and physical therapeutic through three dimensions: man-environment relationships; a sense of place and symbolic landscapes. The study used a combination of qualitative and quantitative research methods. Local residents living in the coastal area of Xinglin Bay are the research objects. Quantitative data analysis revealed that the frequency of residents' visits was an important variable affecting their physical and treatment perceptions. In addition, the significance is higher for females than for males. The text analysis shows that the environment, especially the ecological restoration of water quality and migratory birds, is of great significance to the identity of local residents. Daily activities contribute to a sense of place and are what make the coast a place of therapeutic landscape function.

In the 21st century, museums have come to realize that the human skeletons they display might have problematic origins. The older anthropological skeletal collections typically originate from anatomical dissection of the executed and the very poor, or from scavenged graves of indigenous people. Such collections are currently being re-evaluated in many countries, and reburying or repatriating museum skeletons have become viable options. A particularly difficult question is what to do with human remains of unknown origins. Here, we present and discuss one such case, where a human skeleton of unclear origin in the 1930’s was brought to a local museum in Bollnäs, Sweden, under likely unethical circumstances. Most of the bones have now been lost, except for the skull, the sacrum and one lumbar vertebra, which in 2007 were transferred to the Museum of Ethnography in Stockholm. The skull was there put on display as the “Bollnäs skull”, and was presented to the public to illustrate the dilemmas involved in handling human remains in the post-colonial era. The original gift letter from 1934 stated that the skeleton came from San Nicolas Island outside the California coast, but this letter contained numerous instances of incorrect information and was therefore deemed not trustworthy. However, our critical examination of the skull and its background information indicates that San Nicolas Island is a plausible origin, and we suggest that a bioarchaeological comparison should be made between the skull and living descendants of the San Nicolas Island population, and/or with archaeological human skeletons excavated from the island.

Human pluripotent stem cells (PSCs), which include both embryonic and induced pluripotent stem cells, are widely used in fundamental and applied biomedical research. They have been instrumental for better understanding development and cell differentiation processes, disease origin and progression, and can aid in the discovery of new drugs. PSCs also hold great potential in regenerative medicine to treat or diminish the effects of certain debilitating diseases, such as degenerative disorders. However, some concerns have recently been raised over their safety for the use in regenerative medicine. One of the major concerns is the fact that PSCs are prone to errors in passing the correct number of chromosomes to daughter cells, resulting in aneuploid cells. Aneuploidy, characterised by an imbalance in chromosome number, elicits the upregulation of different stress pathways that are deleterious to cell homeostasis, impair proper embryo development and can potentiate cancer development. In this review we will summarise known molecular mechanisms recently revealed to impair mitotic fidelity in human PSCs and the consequences of the decreased mitotic fidelity of these cells. We will finish with speculative views on how the physiological characteristics of PSCs can affect the mitotic machinery and how their suboptimal mitotic fidelity may be circumvented.

Cruor, the main component responsible for the red color of blood in mammals, contains 90% hemoglobin, a protein that is considered a rich source of bioactive peptides. The aim of the present study is to evaluate the potential of human hemoglobin as a source of bioactive peptides, compared to bovine hemoglobin, which has been widely studied in recent years. More specifically, the study focused on neokyotorphin, a natural antimicrobial peptide and meat preservative, which corresponds to the α137-141 fragment of bovine hemoglobin produced by enzymatic hydrolysis. Firstly, the results of the in-silico analysis showed that the two types of hemoglobin have a high similarity for the α and β chains (identities of 88% and 91%, respectively). Secondly, both types of hemoglobin were subjected to similar enzymatic hydrolysis conditions, which showed that the hydrolysis of human hemoglobin followed the same reaction mechanism as the hydrolysis of bovine hemoglobin, the 'Zipper' mechanism. The hydrolysis conditions (23°C, pH 3.5) made it possible to obtain α137-141 in significant quantities to allow for significant valorization from an industrial point of view. The hydrolyses were able to take place at high initial substrate concentrations, allowing for faster and more efficient processing of the co-product in the same amount of time, with over 65% of this production achieved in just 30 min. Increasing the substrate concentration (from 1% to 10% (w/v)) resulted in a proportional increase in α137-141 production. Regardless of the concentrations tested, the kinetic pattern remained similar. Finally, the results of the mass spectrometry analysis revealed the presence of several bioactive peptides in cattle and humans. Although some were known before, new bioactive peptides were discovered in human hemoglobin, such as four antibacterial peptides (α37-46 PTTKTYFPHF; α36-45 FPTTKTYFPH; α137-141 TSKYR; α133-141 STVLTSKYR), four opioid peptides (α137-141 TSKYR; β31-40 LVVYPWTQRF; β31-37 LVVYPWT), an ACE inhibitor (β129-135 KVVAGVA), an anticancer agent (β33-39 VVYPWTQ) and an antioxidant (α137-141 TSKYR). These peptides have never been found in human hemoglobin before, according to our knowledge. These results highlight the potential of human hemoglobin as a source of bioactive peptides useful for the food or pharmaceutical industry.

Beta-carotene is a fat-soluble antioxidant commonly found in foods such as fruits, vegetables, and palm oil. Despite various liquid chromatography methods are proposed to detect and measure the sera carotenoid level, a long retention time to elute this marker is needed. This study attempted to develop a rapid reverse phase method in eluding beta-carotene in human sera. The researchers managed to elute the antioxidant in 2.2 minutes by applying a combination of a C8 column, mobile phase comprising of acetonitrile mixed with methanol in 70:30 proportion, and an ultra-high performance liquid chromatography system. The outputs had a good calibration curve (R²=0.959) and low coefficient of variation (0.2%), suggesting this protocol is reliable. A column with a lower carbon chain such as C8 allows the beta-carotene molecule to flow through the column faster. Besides, selecting solvents with high elution strength coupled with an ultra-high performance liquid chromatography system which equipped with high pressure can force the beta-carotene through the column in a shorter duration compared to previous reported methods. Therefore, it is recommended to adopt this protocol in epidemiological studies where beta-carotene is screened as a dietary biomarker associated with disease of interest by using human sera in the population level.

Kinases play an important role in regulating various intracellular signaling pathways that control cell proliferation, differentiation, survival and other cellular processes and their deregulation causes more than 400 diseases. Consequently, macrocyclization can be considered a noteworthy approach to develop new therapeutic agents for human diseases. Macrocyclization has emerged as an effective drug discovery strategy over the past decade to improve target selectivity and potency of small molecules. Small compounds with linear structures upon macrocyclization can lead to changes in their physicochemical and biological properties by firmly reducing conformational flexibility. A number of distinct protein kinases exhibit similar binding sites. Comparison of protein binding site provides crucial insights for the drug discovery and development. Binding site similarities are helpful to understand polypharmacology, identifying potential off-targets and repurposing of known drugs. In this review, we focused on comparing binding sites of those kinases for which macrocyclic inhibitors are available/studied so far. Furthermore, we calculated the volume of binding site pocket for each targeted kinase and then compared it with the binding site pocket of the kinase for which only acyclic inhibitors were designed till date. Our review and analysis of several explored kinases might be useful to target new protein kinases for macrocyclic drug discovery.

Existing methods for 3D human pose estimation mainly divide the task into two stages. The first stage identifies the 2D coordinates of the human joints in the input image, namely the 2D human joint coordinates. The second stage uses the results from the first stage as input to recover the depth information of human joints from the 2D human joint coordinates to achieve 3D human pose estimation. However, the recognition accuracy of the two-stage method relies heavily on the results of the first stage and includes too many redundant processing steps, which reduces the inference efficiency of the network. To address these issues, we propose the EDD, a fully End-to-end 3D human pose estimation method based on transformer architecture with Dual Decoders. By learning multiple human poses, the model can directly infer all 3D human poses in the image using a pose decoder, and then further optimize the recognition result using a joint decoder based on the kinematic relations between joints. With the attention mechanism, this method can adaptively focus on the most relevant features to the target joint, effectively overcoming the feature misalignment problem in the human pose estimation task and greatly improving the model performance. Any complex post-processing step, such as non-maximum suppression, is eliminated, further improving the efficiency of the model. The results show that the method achieves an accuracy of 87.4% on the MuPoTS-3D dataset, significantly improving the accuracy of end-to-end 3D human pose estimation methods.

Pervasive purifying selection on non-synonymous substitutions is a hallmark of papillomavirus genome history. Whereas the role of selection on, and drift of, non-coding DNA elements on HPV diversification is poorly understood. More than a thousand complete genomes representing Alphapapillomavirus types, lineages and SNP variants were examined phylogenetically and interrogated for the number and position of non-coding DNA sequence motifs using Principal Components Analyses, Ancestral State Reconstructions and Phylogenetic Independent Contrasts. For anciently diverged Alphapapillomavirus types, composition of the 4 nucleotides (A,C,G,T), codon usage, trimer usage and 13 established non-coding DNA sequence motifs revealed phylogenetic clusters consistent with genetic drift. Ancestral state reconstruction and Phylogenetic Independent Contrasts revealed ancient genome alterations, particularly for CpG and APOBEC3 motifs. Each evolutionary analytical method we performed supports the unanticipated conclusion that genetic drift and different evolutionary drivers have structured Alphapapillomavirus genomes in distinct ways during successive epochs, even extending to differences in more recently formed variant lineages

Nuclear vacuoles are specific structures present on the head of the human sperm of fertile and non-fertile men. Human sperm head vacuoles have been previously studied using motile sperm organelle morphology examination (MSOME) and their origin related to ab-normal morphology, abnormal chromatin condensation and DNA fragmentation. How-ever, other evidences argued that human sperm vacuoles are physiological structures and consequently, to date, the nature and origin of the nuclear vacuoles remains to be elucidat-ed. Here, we aim to define the incidence, position, morphology, and molecular content of the human sperm vacuoles using transmission electron microscopy (TEM) and immuno-cytochemistry techniques. Results showed that ~50% of the analyzed human sperm cells (n = 1908; 17 normozoospermic human donors) contained vacuoles mainly located (80%) in the anterior head region. A significant positive correlation was also found between the sperm vacuole and nucleus areas. Furthermore, it was confirmed that nuclear vacuoles were invaginations of nuclear envelope containing cytoskeletal proteins and a cytoplas-mic enzyme, discarding a nuclear or acrosomal origin. According to our findings, these human sperm head vacuoles are cellular structures which take the origin from nuclear invaginations and contain perinuclear theca (PT) components, allowing to define a new term of ‘nuclear invaginations’ rather than ‘nuclear vacuoles.

Plaque psoriasis is a common, long-lasting illness that affects the immune system and causes significant negative impacts on a patient's physical health, well-being, and ability to work effectively. Deucravacitinib (DEU) is the first oral medication used in the treatment of plaque psoriasis, a chronic skin condition that causes red, scaly patches on the skin. DEU is a type of medication called an oral Janus kinase (JAK) inhibitor, which works by blocking specific enzymes that play a role in the inflammation and immune response associated with psoriasis. Therefore, a quick, easy, novel, reliable, sensitive, and straightforward Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) approach was used to analyse DEU in plasma samples. The LC-MS/MS method for the determination of DEU in human plasma was based on using trimethoprim as an internal standard (IS). The separation of DEU and IS was carried out via liquid-liquid extraction (LLE). The isolated substances were then subjected to the chromatographic system using the ACE-C18 column (4.6x100 mm, 5 µm). The mobile phase employed consisted of methanol and a solution of 2 mM ammonium formate (80:20 v/v, respectively). The flow rate used was set at 0.9 mL min-1. The creative strategy was performed by running an ABSCIEX API 4000 mass spectrometer with an electron spray ionization source in Multiple Reaction Monitoring (MRM) modes. The ion transitions m/z 426.3  358.2 was used for DEU quantitation, while the ion transitions m/z 291.1  261.1 was used for trimethoprim quantitation. The accuracy, precision, linearity, recovery, and selectivity of DEU were deemed acceptable when validated for a concentration range between 0.500 to 601.050 ng/mL, utilizing a weighting factor of 1/x2.

Background: Diffuse glioma is a malignant human brain cancer that is hard to overcome. This represents a high risk of mortality. The current challenge is limited to the control of tumor progression and survival improvement. Immunotherapy consists of stimulating the immune system in order to eliminate the non-self-elements that damage the human body, including cancer cells. However, in human glioma, the current immunotherapeutic targets did not show significant benefit. In this study, we aimed at evaluating the expression and potential role of a new immunosuppressive molecule, TIGIT in glioma patients. Methods: A cohort of 667 patients from the TCGA database along with a cohort of 53 Moroccan patients, were analyzed in order to assess the role of TIGIT in human glioma progression and to estimate whether blocking this immune checkpoint molecule would be of a potential therapeutic benefit. Real time RT-PCR from fresh human biopsies and RNAseq data analysis were performed in this study. Results: Our results showed that high expression of TIGIT had prognostic value with some known clinical glioma risk factors such as sex, age and IDH mutation status. High expression of TIGIT was positively associated with advanced grades of glioma. Interestingly, elevated rates of TIGIT were significantly associated to elevated levels of other inhibitory immune checkpoint molecules (PD-1, VISTA and Tim-3) in human glioma patients, also TIGIT showed strong association with Treg cell-secreted cytokines (TGF-beta and IL-10), indicating the high potential involvement of TIGIT in immunosuppression in human glioma. Moreover, we reported that high TIGIT expressing CD8 T-cells displayed more surface inhibitory molecules and, elevated levels of Treg cells and FoxP3 were linked to higher rates of TIGIT, supporting the likely involvement of TIGIT in the suppression of the intra-tumoral immune cells. Finally, high expression of TIGIT was significantly linked to advanced histological subtypes of glioma and was associated with poor overall survival in human glioma. Conclusion: TIGIT blockade might be of valuable therapeutic benefit in patients with advanced glioma.

In this discussion paper, I give an account for non-experts of, arguably, quantum dynamics in the brain, underlying the modeling of affective behavior of humanoid robots in the making. Outreach to the larger audience inevitably leads to abbreviations and simplifications; nonetheless, I try to offer the backgrounds of why it is important to study the virtual aspects of ‘people’ we meet online, what dimensions play a role in assessing such creatures, what humanities, psychology, communication, and computer science provide to help us understand how we become attached to non-existent others. As its capstone for the time being, an approach derived from physics is discussed for a robot to handle emotional ambiguity and vagueness of its user. Two computational models, Silicon and Quantum Coppélia, are discussed for their potential and limitations in explaining human affective behavior while dealing with mediated characters.