The concerning rise in emergence and prevalence of resistant strains to drugs of M. tuberculosis has ,the prompted researchers to look for new and effective treatments. With this motive, biscoumarins were identified as the lead molecules on a whole-cell-based screening of several less explored low molecular weight bioactive compounds against M. tuberculosis strains. Among the screened biscoumarins, the highest dock score derivatives were synthesized (a-h) using a programmable microwave synthesizer for better yields and reaction control. The synthesized derivatives were evaluated against H37Rv, H37Ra, M. smegmatis, an MDR surrogate model, and other bacterial strains for the structure-activity response. Assessment of the synthesized library against mycobacterial strains led to the identification of compounds (f and d) as lead anti-tuberculosis agents. Compounds (f and d) exhibited less toxicity against human cell lines. At the same time, it displayed enjoyable activity wherein MIC concentrations were observed to be 16- and 32 µg/mL against the susceptible H37Rv, and H37Ra strains of M. tuberculosis and MIC value of 128 µg/mL for M. smegmatis, respectively. For mechanistic insights and identification of drug binding targets, molecular docking and dynamic simulations were employed for a panel of 16 mycobacterial enzymes essential for mycobacterial growth and survival. These in silico studies revealed the DprE1 enzyme as a druggable target for the anti-tuberculosis activity of the selected biscoumarins derivatives. Further investigation is underway in our laboratory, leading to its development as an anti-tuberculosis drug (animal model studies).

Background: Pathologic complete response (pCR) of breast cancer after neoadjuvant chemotherapy (NAC) is highly related to molecular subtypes. Patients who achieved tumor pCR after NAC have a better prognosis. However, despite of better prognosis, pCR patients have a potential for recurrence. There is little evidence of risk factors of recurrence in patients with pCR. We aim to analyze factors associated with tumor recurrence in patients who achieved pCR. Methods: This study retrospectively reviewed the data of patients diagnosed with breast cancer who achieved pCR after receiving NAC between January 2009 and December 2018 in Samsung Medical Center. pCR was defined as no residual invasive cancer in the breast and axillary nodes even if there is residual ductal carcinoma in situ (ypT0 or ypTis with ypN0). Breast cancers are classified into 4 subtypes based on hormone receptors (HR) and human epithelial growth factor receptor 2 (HER2) status. Patients who had bilateral breast cancer, inflammatory breast cancer, distant metastasis, unknown subtype, and histologically unique case were excluded from the study. Results: In total 483 patients were included in this study except for patients who corresponded to the exclusion criteria. The median follow-up duration was 59.0 months (range, 0.5-153.3 months). Breast cancer recurred in 4.1% of patients (20 of 483). There was a significant difference in clinical T (p = .004) and clinical N (p = .034) stage in the Kaplan-Meier curve for disease-free survival. Molecular subtypes (p = .573), Ki67 (p = 1.000), and breast surgery type (p = .574) were not associated with tumor recurrence in patients who achieved pCR after NAC. In the clinical T stage and clinical N stage, there was a significant difference between recurrence and no-recurrence groups (clinical T stage; p = .045, clinical N stage; p = .002). Univariable Cox regression revealed statistical significance in the clinical T stage (p = .049) and clinical N stage (p = .010), while multivariable Cox regression demonstrated non-significance in the clinical T stage (p = .320) and clinical N stage (p = .073). Conclusion: Results in this study showed that clinical T, clinical N stage, and molecular subtypes were not statistically significant predictors of recurrence in patients who achieved pCR after NAC. It is hypothesized that a multicenter-based study would lead to the identification of factors as predictors of recurrence after achieving pCR.

The following system of equations {x_1 \cdot x_1=x_2, x_2 \cdot x_2=x_3, 2^{2^{x_1}}=x_3, x_4 \cdot x_5=x_2, x_6 \cdot x_7=x_2} has exactly one solution in ({\mathbb N}\{0,1})^7, namely (2,4,16,2,2,2,2). Hypothesis 1 states that if a system of equations S \subseteq {x_i \cdot x_j=x_k: i,j,k \in {1,...,7}} \cup {2^{2^{x_j}}=x_k: j,k \in {1,...,7}} has at most five equations and at most finitely many solutions in ({\mathbb N}\{0,1})^7, then each such solution (x_1,...,x_7) satisfies x_1,...,x_7 \leq 16. Hypothesis 1 implies that there are infinitely many composite numbers of the form 2^{2^{n}}+1. Hypotheses 2 and 3 are of similar kind. Hypothesis 2 implies that if the equation x!+1=y^2 has at most finitely many solutions in positive integers x and y, then each such solution (x,y) belongs to the set {(4,5),(5,11),(7,71)}. Hypothesis 3 implies that if the equation x(x+1)=y! has at most finitely many solutions in positive integers x and y, then each such solution (x,y) belongs to the set {(1,2),(2,3)}.

Increased screen time during the COVID-19 pandemic and the accelerated incorporation of technology into daily practices have directly contributed to the increased prevalence of eye disease. The human eye is not built for continual technological use and must adjust to exposure to digital screens. Overexposure to technology can result in eye strain and an increased risk of eye diseases such as myopia with complications later in life, including retinal tears, cataracts, and macular degeneration. As vision and ocular diseases become more prevalent, the study of eye diseases, including their causes and treatments, is especially relevant. There is a need to prevent ocular diseases before they become a burden to individuals, their families, and their communities by better understanding the pathophysiology of ocular diseases. Several ocular diseases are thought to be caused by imbalances in reactive oxygen species (ROS) and autophagy. ROS refers to a class of highly reactive oxygen-containing molecules that can undergo damaging reactions with other molecules in the cell. Autophagy is a cellular process of self-eating whereby damaged, harmful, or dead material in cells is broken down to maintain cellular homeostasis. This paper discusses the role of ROS and autophagy in the pathogenesis of computer vision syndrome (CVS), digital eye strain (DES), myopia, cataracts, glaucoma, diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve crush injury (OCN), optic nerve gliomas, and retinoblastoma. Lifestyle changes such as limiting screen time, ensuring adequate exposure to evening sunlight, and using blue light protection measures or protective eyewear are important in the prevention of eye disease. Nutrition also contributes significantly to eye health. A balanced diet rich in vitamins and antioxidants may help to prevent ROS and autophagy imbalance-induced eye disease. Medical and surgical treatments become necessary when preventative measures fail. This paper also addresses how government measures to decrease the onset of eye disease, including targeted programs to increase access to fresh vegetables and fruits in food deserts, are a critical macro-level avenue to reduce eye disease prevalence in the US, which according to the National Eye Institute, costs $139 billion yearly.

The Internet of Medical Things (IoMT) is playing a pivotal role in the healthcare sector by allowing faster and more informed hospital care, personalized treatment, and medical solutions. A very effective and trustworthy solution for resource-constrained medical devices is provided by Physical Unclonable Functions (PUF) - based identity and authentication systems, however they are not yet entirely reliable. This paper proposes VXorPUF, a Vedic Principles - Based Hybrid XOR Arbiter PUF. Modeling attacks were performed on the proposed architecture and an accuracy of 49.80 % was achieved. Uniqueness, Reliability and Randomness were the figures of merit used to evaluate PUF.

Psychomotor developmental delay in infants includes failure to acquire abilities such as sitting, walking, grasping objects and communication at the ages when most infants have acquired these abilities. Known risk factors include a large number of aspects of family environment, socioeconomic position, problems in pregnancy and birth, and maternal health. It is clinically useful to be able to screen for developmental delay so that healthcare interventions can be considered. The present research used machine learning (random forest) to create an algorithm predicting psychomotor delay in 9-month-old infants using information ascertainable at birth and in early infancy. The dataset was the UK longitudinal Millennium Cohort study. Fifty-two predictors measuring socioeconomic indicators, paternal, family and social support for the mother, beliefs about good parenting, maternal health, pregnancy and birth were included in the initial algorithm. Feature reduction showed that of the 52 variables, birthweight, family income and parents’ ages had the highest feature importance scores and could alone correctly predict developmental delay with over 99% sensitivity and 100% specificity. The relationships between delay and some of the predictors, particularly income, were nonlinear and complex. The results suggest that the risk of psychomotor developmental delay can be identified in early infancy using machine learning, and that the best predictors are factors present prior to birth. Surprisingly, the most important factors included in the present study did not include illnesses during pregnancy such as eclampsia and infections.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed.

Keywords: Grey slender loris, Mysore slender loris, Malabar slender loris, Phylogenetics

This article analyzes 6 probability distributions from the Generalized Pareto family, with 3, 4 and 5 parameters, with main purpose to identify other distributions from this family with applicability in flood frequency analysis compared to the distribution already used in the literature from this family such as Generalized Pareto Type II and Wakeby. This analysis is part of a larger and more complex research carried out in the Faculty of Hydrotechnics regarding the elaboration of a norm for flood frequency analysis using the linear moments method. In Romania, the standard method of parameter estimation is the method of ordinary moments, thus the transition from this method to the method of linear moments is desired. All the necessary elements for the distributions use are presented like, the probability density functions, the complementary cumulative distribution functions, the quantile functions, the exact and approximate relations for estimating parameters, for both methods of parameters estimation. All these elements are necessary for a proper transition between the two methods, especially since the use of the method of ordinary moments is done by choosing the skewness of the observed data depending on the origin of the maximum flows. A flood frequency analysis case study, using annual maximum and annual exceedance series, was carried out for the Prigor river, to numerically present the analyzed distributions. The performance of this distributions is evaluated using relative mean error, relative absolute error and linear moments diagram.

Existing one-shot neural architecture search (NAS) methods have to conduct a search over a giant super-net, which leads to the huge computational cost. To reduce such cost, in this paper, we propose a method, called FTSO, to divide the whole architecture search into two sub-steps. Specifically, in the first step, we only search for the topology, and in the second step, we search for the operators. FTSO not only reduces NAS’s search time from days to 0.68 seconds, but also significantly improves the found architecture's accuracy. Our extensive experiments on ImageNet show that within 18 seconds, FTSO can achieve a 76.4% testing accuracy, 1.5% higher than the SOTA, PC-DARTS. In addition, FTSO can reach a 97.77% testing accuracy, 0.27% higher than the SOTA, with nearly 100% (99.8%) search time saved, when searching on CIFAR10.

Disease control programs need to identify breeding sites of mosquitoes which transmit malaria and other diseases to target interventions and identify environmental risk factors. Increasing availability of very high resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, we identified land cover types associated with malaria vector breeding sites in West Africa. Drone images from two malaria endemic regions in Burkina Faso and Côte d’Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region of interest-based and deep learning methods to classify these habitat types from very high resolution natural color imagery. Analysis methods achieved a dice coefficient ranging between 0.68 and 0.88 for different vector habitat types; however, this classifier consistently identified the presence of specific habitat types of interest. This establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.

Acute appendicitis is the most common surgical emergency worldwide with peak incidence in the age range of 10 – 19 years, and the standard treatment procedure is the surgical removal of the appendix to avoid complications such as its rupture, peritonitis, and sepsis. The COVID-19 pandemic required the global population to stay indoors to stop the spread of virus, which led to a decline in the number of patients visiting the hospital for non- COVID -19 related emergencies. This is a case of a 9-year-old girl who tested positive for COVID -19 and developed acute appendicitis and benefited from individualised classical homeopathy. Further scientific investigation is necessary to establish the relevance of individualised classical homeopathy in treating serious acute conditions such as acute appendicitis.

Background: Malaria remains a serious public health concern worldwide, particularly in tropical and subtropical regions, and the leading causes of mortality and morbidity in children especially among those aged under five in developing countries. Malaria can be fatal if not promptly and accurately diagnosed, especially in children.This study was carried out to determine the level of malaria infection and its associated risk factors among febrile children.Methods: Blood samples were collected and analyzed from two- hundred (200) systematically selected febrile children aged 1-10 years old. assay of samples collected were carried out using standard methods.data were collected using a pretested structured questionnaire. Results: Results obtained from study subjects shows that, of the eighty-seven (87) infected subjects, 34 (39.1%) were aged 1-5 years, while 52 (59.8%) were within the range of 6 years and above.severity of infection showed 48 (55.2%) with scanty parasitic infection while 25 (28.7%) showed moderate infection, and 13 (14.9%) showed heavy infection.The association of age and level of parasitemia showed that 34 (58.8% ) subjects below 5 years, recorded scanty malaria compared to 14.7% with severe malaria within the age range.Considering gender, of the 33 infected female subjects, 45.5% had scanty malaria, 36.4% showed moderate malaria, while 18.2% had severe malaria, compared to the males subjects.Conclusion: This study reveals the prevalence of malaria infection with some level of severity among children at our study location.The need for prompt diagnosis and improved access to all malaria interventions becomes relevant,with a special focus on the high risk group.

Cancer is a major concern for people, and accurately predicting the probability of cancer incidence and mortality is an important research topic. With the development of big data and artificial intelligence technology, a new machine learning model has emerged. Using 72,591 pieces of data, including age, case count, population size, race, gender, site of onset, and year of discovery, we built a machine learning model. Through calculations, we found that the decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 62.11%, 61.68%, 54.53%, 55.72%, and 63.10%, respectively. With the help of this model, scientists and policymakers can accurately predict future cancer incidence and mortality rates through databases, allowing them to make relevant policies (such as timely allocating doctors and medical resources) to better serve the people.

The research paper proposes an algorithm to find congruence criteria between two convex polygons in Euclidean Geometry. It begins with a review of triangles, then extends to quadrilaterals and eventually gener- alizes the case to n-sided polygons. It attempts to prove said algorithm using a method of induction and a case-by-case analysis. It also states a corollary to said algorithm

The challenging issues of Computer Network and Databases are not only the intrusion detection but also the reduction of false positive and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior which are assumed to be potential intrusions or treat. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behavior are hard to predict as there is no precise boundary differentiat-ing one from another. Here rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach based on rough set theory and intuitionistic fuzzy set theory is pro-posed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of softness properties both rough and fuzzy set theory to deal with uncer-tainty in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. The experimental results with a real world dataset and a syn-thetic dataset show that the proposed algorithm has normal true positive rates of 91.989% and 96.99% and attack true positive rates of 91.289% and 96.29% respectively

Considering the dynamic stall effects in engineering calculations is essential for correcting the aerodynamic loads acting on wind turbines, both during power production and stand-still cases, and impacts significantly the turbine aeroelastic stability. The employed dynamic stall model needs to be accurate and robust for a wide range of airfoils and range of angle of attack. The present studies are intended to demonstrate the performance of a recently implemented "IAG dynamic stall" model in a wind turbine design tool Bladed. The model is transformed from the indicial type of formulation into a state-space representation. The new model is validated against measurement data and other dynamic stall models in Bladed for various flow conditions and airfoils. It is demonstrated that the new model is able to reproduce the measured dynamic polar accurately without airfoil specific parameter calibration and has a superior performance compared to other models in Bladed.

The characteristics of the Sentinel-2 mission with a decametric resolution and frequent acquisitions allow to improve the identification of crops. The majority of the studies on crop classification using RS were targeted at herbaceous and gramineous crop classes while fewer results were obtained on woody crops which present a strong variability in management practices that make their identification difficult. Thus, this study aimed to propose a rapid, accurate, and cost-effective analytical approach for the delineation of fruit orchards (OC), vineyards (VY), and olive groves (OL) in the Mediterranean (Southern France) considering two locations. A classification based on phenology metrics (PM) de-rived from temporal Sentinel-2 time series was developed to perform the classification. The PM were computed by fitting a double logistic model on temporal profiles of vegeta-tion indices to delineate OC, VY, and a DC class gathering all remaining surfaces. The generated PM were introduced in a random forest (RF) algorithm to identify woody crops across the two sites. The method was tested on different vegetation indices, the best results being obtained with the leaf area index (LAI). To delineate OL in the DC class, the tem-poral features of the green chlorophyll vegetation index (GCVI) were found to be the most appropriated with a typical drop of the signal during the mid-season (DOY 150-250). As a final result, we obtained an overall accuracy ranging from 89-96% and Kappa of 0.86-0.95 by considering each study site and year (2016-2021), separately. This accuracy is much better than applying the RF algorithm on the LAI times series, which led to a Kappa rang-ing between 0.3 and 0.52 and demonstrates the interest of using phenological traits rather than the raw time series of the RS data. The method can be well reproduced from one year to another. Moreover, it is possible to apply the classification model of a given year to an-other, keeping good accuracy. This is an interesting feature to reduce the burden of col-lecting ground truth information. On the contrary, the use of a classification model cali-brated in one site and applied to another led to a strong degradation of the classification accuracy. Woody crop phenology is dependent on site climatic conditions as well as the cultivar and management practices that can differ from one site to another.

There is a serious concern regarding framing rules and guidelines to regulate the content on OTT platforms that needs urgent attention. In the Current Scenario, the use of vulgar language in the public domain on OTT platforms needs to take seriously. Some OTT platforms heavenly use foul language, which is open to children that cause a huge psychological impact on them. The use of vulgar language, including profanity and bad words, has to be regulated strictly. Web series uses obscene, abusive words and foul language with excessive usage. This review paper highlights the excessive use of swear words, profane language, and vulgar expletives on OTT platforms.

In this article, we analyze the thermodynamics, thermal fluctuations and quasi-normal modes of a torus-like charge AdS black hole. The parameters of a torus-like charged AdS black hole are its mass, electric charge, and cosmological constant. We used a torus-like charge AdS black hole to evaluate thermodynamics through thermal fluctuations and found that it significantly affected the corrected thermodynamic potentials. Black holes of larger radii are more stable and significant. The effect of thermal fluctuation has been investigated on the other thermodynamic potentials such as the Hemohltz free energy, Gibbs free energy, enthalpy, and specific heat, using the concept of corrected entropy. Finally, we use null geodesics to deal with quasi-normal modes. The null geodesics provide the photon sphere angular velocity and Lyapunov exponent, which are the real and imaginary components of the quasi-normal modes in the eikonal limit. With inceasing charge and photon radius, angular velocity reaches its maximum, similar to Lyapunov's exponent.

Much evidence suggests a correlation between degeneration and mitochondrial impairment. Typical cases of degeneration can also be observed in physiological phenomena (aging) as well as in neurological neurodegenerative diseases and cancer. All these pathologies have as a common denominator the dyshomeostasis of mitochondrial bioenergy. Even neurodegenerative diseases show a bioenergetics imbalance in their pathogenesis or progression. Huntington's chorea and Parkinson's disease are both neurodegenerative diseases, but while Huntington's disease is a genetic, and progressive disease with early manifestation and severe penetrance, Parkinson's disease is a pathology with a multifactorial aspect. Indeed, there are different types of Parkinson/Parkinsonism. Many forms are early onset diseases linked to gene mutation, others can appear in young adults and senescent only post-injury, and a final group is idiopathic. Huntington's was defined as a hyperkinetic disorder, while Parkinson's is a hypokinetic disorder; but in the middle, there are a lot of similarities as well as neuronal excitability, the loss of striatal function, psychiatric comorbidity, etc. In the review, we would embrace the theories that both diseases start and develop in light of mitochondrial dysfunction. These dysfunctions act on energy metabolism and reduce the vitality of neurons in many different brain areas.

Nonequilibrium work switching simulations and Jarzynski’s equation are a reliable method to compute free energy differences, ΔAlow→high, between two levels of theory, such as a pure molecular mechanical (MM) and a quantum mechanical/molecular mechanical (QM/MM) description of a system of interest. Despite the inherent parallelism, the computational cost of this approach can quickly get very high. This is particularly true for systems where the core region, the part of the system to be described at different levels of theory, is embedded in an environment, such as explicit solvent water. We find that even for relatively simple solute–water systems, switching lengths of at least 5 ps are necessary to compute ΔAlow→high reliably. In this study, we investigate two approaches towards an affordable protocol, with emphasis on keeping the switching length well below 5 ps. Inserting a hybrid charge intermediate state with modified partial charges which resembles the charge distribution of the desired high level makes it possible to obtain reliable calculations with 2 ps switches. Attempts using step-wise linear switching paths, on the other hand, did not lead to improvement, i.e., faster convergence for all systems. To understand these findings, we analyzed the solutes’ properties as a function of partial charges used, the number of waters in direct contact with the solute, and studied the time needed for water molecules to reorient themselves upon a change in the solute’s charge distribution.

Spodoptera frugiperda is a new invasive and highly polyphagous pest that attacks corn in Indonesia. The availability of abundant plant species allows pests to switch to other host plants to maintain their population. This research aims to examine the development, reproduction, nutritional indices, and life table of S. frugiperda in several plant species. The plants tested were corn, rice, broccoli, oil palm, and baby corn as controls. Ten individual insects were used and repeated five times for each plant species. The test results show that different types of plant feed affect the development time, imago life span, fecundity, and fertility of S. frugiperda. The types of plant feed, that were baby corn fruit and broccoli had higher net reproduction value (R0), intrinsic growth rate (r), gross reproduction rate (GRR), shorter mean generation period (T), and population doubling time (DT) than in corn and rice leaves. On oil palm leaf feed no population parameters can be determined because no larvae developed into adults and had the lowest nutritional indices parameters, so that could not be exploited as a host plant. Also, the nutritional indices of several feed plant species tested provided information that broccoli could be a suitable host when there was no corn in the field.

Rhododendron is well-known for the colorful corolla. Molecular marker systems have the potentials to elucidate genetic diversity as well as to assess the genetic fidelity in rhododendrons. In the present study, the reverse transcription domains of long terminal repeat retrotransposons were cloned from rhododendrons and used to develop inter-retrotransposon amplified polymorphism (IRAP) marker system. Subsequently, 198 polymorphic loci were generated from the IRAP and inter-simple sequence repeat (ISSR) markers, of which 119 were derived from the IRAP markers. It was justified that in rhododendrons, IRAP markers were superior to the ISSRs in some polymorphic parameters such as the average number of polymorphic loci (14.88 versus 13.17). In comparison with the single one, the combination of IRAPs and ISSRs systems was more discriminative for detecting 46 rhododendron accessions. Further, IRAP markers demonstrated more efficiency in genetic fidelity detection of in vitro R. bailiens, an endangered species just recorded in Guizhzhou Province, China. The available evidences revealed the distinct properties of IRAP and ISSR markers in the rhododendron-associated applications, and highlighted the availability of highly informative ISSR and IRAP markers in the genetic diversity evaluation and the genetic fidelity assessment of rhododendrons, which may facilitate the preservation and genetic breeding in rhododendron plants.

This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise.

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.

A new low-pass filter (LPF) circuit with independent adjustment of various, incl. digitally controlled, pole frequency resistors, pole quality factor and transmission coefficient. The proposed low-pass filter is based on the use of the properties of a multi-differential operational amplifier that performs the functions of a signal adder. The peculiarity of the filter is that it has three inputs, with respect to which different transmission coefficients are implemented, incl. inverting (-1) and non-inverting (+1). To check these properties of a low-pass filter in the Micro-Cap environment, computer simulation of a specific circuit was performed on a multi-differential operational amplifier AD 8130. Mathematical expressions are given for the main parameters of the proposed low-pass filter, which allow parametric synthesis of elements of a specific circuit under given restrictions on the used element base.

The Influenza A Virus (IAV) represents a positive-sense, single-stranded RNA-based virus that infects mammals mainly via the respiratory system, although other bodily systems are also infected and undergo various extents of inflammatory pathogenesis. There are two well-known strains of IAV that cause life-threatening disease in mammals; H1N1 and H5N1, and the first strain caused the 1918 IAV H1N1 pandemic that claimed between 30 and 50 million human lives. Due to the significant ability of IAV to evade important immune recognition, the virus was observed to favor the onset of secondary microbial infections (i.e. bacterial or fungal), as the overall performance of the immune system became transiently weakened during the viral infection. During the IAV H1N1 pandemic, many patients died as a result of bacterial pneumonia, as pathogenic bacteria, such as Streptococcus pneumoniae and Haemophilus influenzae, gained a wider opportunity to colonize and infect vital areas of the lower respiratory tract, and such a phenomenon led to the excessive, prophylactic usage of antibiotics due to the increased levels of panic, which in turn favored the natural selection of bacteria with genes that became resistant to such antibiotics. Antibiotics might be required for usage solely when bacteria are known to be colonizing vital areas of the human body, and this aspect is tricky, as colonization is asymptomatic and screening is consequently rare. Recently, new variants of the avian IAV H5N1 strain were transmitted from live, infected birds to mammals, including humans in some isolated cases, and given that there have already been several zoonotic spillover events overall since the beginning of 2023, we are rapidly approaching the time when a zoonotic spillover into humans will mark the first epidemic outbreak of the avian flu in humans. A lethality rate of 60% was projected by the World Health Organization, as the virus was shown to favor the development of life-threatening hyper-inflammatory responses at the levels of alveolar tissues constituted by Type II pneumocytes. There are hints that novel variants of H5N1 are capable of infecting the intestinal layer, as recently, two dolphins died as a result of ingesting infected birds within the area of the British Isles. IAV is known to suppress the production and transmission of Type I Interferons by expressing various non-structural proteins (NSPs), such as NSP1, which was found to be also packaged into exosomes and transmitted to neighboring uninfected cells, thereby preventing them from responding to the virus in the first place. A more pronounced rate of innate immune evasion would probably be observed in H5N1 IAV infection than in the infection caused by recent variants of H1N1 IAV. The H5N1 strain of IAV was also found to secrete a higher concentration of NSP1 than SARS-CoV-2, indicating the existence of an association to the greater mortality rate of H5N1 IAV infection. A direct, prophylactic stimulation of the interferon system using a reduced oral or nasal dosage of recombinant anti-inflammatory and anti-viral interferon glycoproteins may represent the most viable approach to prevent an emergence of a life-threatening H5N1 IAV pandemic. A similar non-invasive approach could be developed for an Marburg Virus (MARV) and a Nipah Virus (NiV) infection of humans, as risks of the emergence of a Marburg epidemic and also of a Nipah epidemic may be substantial at this stage as well. Clinical testing of clinical approaches as such could be of critical importance at the moment. Animals could also benefit from related clinical approaches.

This article develops applications of the generalized method of lines to numerical solutions of the time-independent, incompressible Navier-Stokes system in fluid mechanics. We recall that for such a method, the domain of the partial differential equation in question is discretized in lines (or more generally in curves), and the concerning solutions are written on these lines as functions of the boundary conditions and the domain boundary shape.

A probabilistic and parsimony-based view of available genetics data is presented to argue that Hardy and Higgins’ amyloid cascade hypothesis is valid but is commonly misinterpreted to support, incorrectly, the primacy of the amyloid beta peptide (Aβ) in driving Alzheimer’s disease pathogenesis. Similar means are used to argue that the pathological effects of familial Alzheimer’s disease mutations in the genes PSEN1 and PSEN2 are not exerted directly via changes in APP cleavage to produce different ratios of Ab length. Instead, increase in the activity of the βCTF (C99) fragment of APP is likely the critical pathogenic determinant altered by mutations in the APP gene. This model is consistent with the regulation of APP mRNA translation via its 5’ iron responsive element (IRE). Familial Alzheimer’s disease (fAD) mutations in PSEN1 and PSEN2 likely affect the stability of presenilin holoprotein and/or γ-secretase multimers with consequences for γ-secretase activity and other important cellular functions. All fAD mutations in APP, PSEN1, and PSEN2 likely find unity of pathological mechanism in their actions on endolysosomal acidification and mitochondrial function, with detrimental effects on iron homeostasis and promotion of “pseudo-hypoxia” being of central importance. Aβ production is enhanced and distorted by oxidative stress and accumulates due to decreased lysosomal function. It may act as a disease-associated molecular pattern (DAMP) enhancing oxidative stress-driven neuroinflammation during the cognitive phase of the disease. We also discuss fascinating, but largely ignored, data on presenilin biology that may be important in understanding presenilins’ central role in familial Alzheimer’s disease.

I demonstrate that Bell's theorem is based on circular reasoning and thus a fundamentally flawed argument. It unjustifiably assumes the additivity of expectation values for dispersion-free states of contextual hidden variable theories for non-commuting observables involved in Bell-test experiments, which is tautologous to assuming the bounds of ±2 on the Bell-CHSH sum of expectation values. Its premises thus assume in a different guise the bounds of ±2 it sets out to prove. Once this oversight is ameliorated from Bell's argument, the bounds on the Bell-CHSH sum of expectation values work out to be ±2√2 instead of ±2, thereby mitigating the conclusion of Bell's theorem. Consequently, what is ruled out by the Bell-test experiments is not local realism but the additivity of expectation values, which does not hold for non-commuting observables in any hidden variable theories to begin with.

Mobile software applications (apps) have transformed how individuals oversee and maintain their own health. One way that girls can monitor their menstrual cycles is through the increasingly widespread use of mobile menstrual tracking apps. This study aims to examine menstrual symptom tracking for adolescents in English and Chinese apps, exploring menstrual literacy, cross-cultural differences, and framing, or presentation, of symptoms. The mixed methods content analysis involved 15 popular free menstrual tracking apps in English (n = 8) and Chinese (n = 7). Quantitative analysis of qualitative data was conducted through manual coding of content and automatically analyzing sentiment, or emotional tone, using a computational approach. We found that: (1) Menstrual literacy on symptom management or treatment was generally insufficient, (2) There were more available emotional than physical symptoms in English than Chinese apps, (3) Symptoms were framed more negatively than positively, somewhat more in Chinese than English apps. Findings emphasize the importance of improving information in digital menstrual trackers. Our findings further reflect cultural differences in emotional expression and negative attitudes toward menstruation. Since adolescence is a critical developmental stage that requires ample support, digital menstrual trackers can uniquely shape attitudes and experiences, ultimately, empowering teenagers to better manage their menstrual health.

COVID-19, the fear it engendered, and the policy measures to manage its spread have disproportionately impacted the wellbeing of children and adolescents (CAs). We present an intensive critical realist case study of the impact of COVID-19 on the health and wellbeing of CAs in Rwanda, seeing it as much a social and political crisis as a medical and public health one. To do this, we carried out interviews with a purposive sample of 25 leaders with a working knowledge of children and young people; they were more likely than the CAs themselves to observe changes across the CA population within their remit and more likely to be looking for general explanations rather than individual experiences. The findings show that CAs' responses to the changes wrought on their lives by Covid-19 were conditioned by their age, gender, social class and if they lived in urban or rural areas. However, Covid19 has not just revealed the structural weakness of the Rwandan health system but of education, social protection, child protection, employment, family, and financial systems. The pathway to (adverse) impacts of COVID-19 on CAs is conditioned by these institutions and their interactions together with structural socioeconomic inequalities both within Rwanda and globally.

Build a special identical equation, use its calculation characters to prove and search for solution of any odd converging to 1 equation through (*3+1)/2^k operation, change the operation to (*3+2^m-1)/2^k, and get a solution for this equation, which is exactly same with that got from calculating directly. Then give a specific example to verify. Thus prove the Collatz Conjecture is true. Furthermore, analysis the sequences produced by iteration calculation during the procedure of searching for solution, build a weight function model, prove it decrease progressively to 0, build a complement weight function model, prove it increase to its convergence state. Build a (*3+2^m-1)/2^k odd tree, prove if odd in (*3+2^m-1)/2^k long huge odd sequence can not converge, the sequence must outstep the boundary of the tree after infinite steps of (*3+2^m-1)/2^k operation.

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lesson preparation, evaluation, and new ways to teach complex concepts. However, ChatGPT poses different threats to the traditional education and research system, including the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This study explores potential opportunities and threats that ChatGPT poses to overall education from the perspective of students and educators. Furthermore, for programming learning, we explore how ChatGPT helps students improve their programming skills. To demonstrate this, we conducted different coding-related experiments with ChatGPT, including code generation from problem descriptions, pseudocode generation of algorithms from texts, and code correction. We also verified the generated codes with an online judge system to evaluate their accuracy.

There have been significant collaborative efforts over the past three years to develop therapies against COVID-19. During this journey, there has also been a lot of focus on understanding at-risk groups of patients who either have pre-existing conditions or have developed concomitant health conditions due to the impact of COVID-19 on immune system. There was a high incidence of COVID-19 induced pulmonary fibrosis (PF) observed in patients. PF can cause significant morbidity, long-term disability, and lead to death in the long run. Additionally, being a progressive disease, PF can also impact the patient for a long time after COVID infection and affect the overall quality of life. Although current therapies are being used as the mainstay for treating PF, there is no therapy specifically for COVID-induced PF. As observed in the treatment of other diseases, nanomedicine can show a significant promise in overcoming the limitations of current anti-PF therapies. In this review, we summarize the efforts reported by various groups to develop nanomedicine therapeutics to treat COVID-induced PF.

Marangoni flow in foam fractionation in the lamellar film for the interior and exterior of a micro-foam was investigated. The three-dimensional node-film-Plateau Border system was modeled using computational fluid dynamics. The importance of the surfactant concentration of the foam fractionation column and air-liquid interface mobility on the Marangoni velocity in the film was emphasized. The study found that an increase in surfactant concentration in the reflux column significantly increases the Marangoni velocities. Additionally, a mobile interface results in a higher Marangoni flow, while a rigid interface leads to less intensive flow. The behavior of the Marangoni flow in both interior and exterior foam was explored, revealing that the flow in exterior foam has different behavior due to the presence of the wall, which reduces the Marangoni velocity compared to interior films.

Obstructive Sleep Apnea (OSA) is nowadays one of the respiratory pathologies with a higher in-cidence globally in developed countries. This situation led to an increase in the demand for medical appointments and diagnostic studies related to that condition, especially those based on poly-somnographies and cardiorespiratory polygraphies. These studies are limited in resources, causing long waiting lists with the subsequent impact on the patients’ health. Furthermore, it is necessary to mention that OSA’s symptomatology is not very specific, and it is typically present in the general population (excessive sleepiness, snore, etc.). In this regard, this paper proposes a novel intelligent clinical decision support system for the diagnosis of OSA which could be used to help medical teams, both in primary care settings and in units specialized in respiratory pathologies. The aim of the proposed system is to help discriminate the patients suspected of suffering from the pathology from those who are not. To this end, two types of information sets of heterogeneous nature are consid-ered. The first one encompasses objective data, related to the patient's health profile with infor-mation usually available in electronic health records. The second type comprises subjective data, referred to the symptomatology reported by the patient in a previous interview. To process the first group of information, a Machine Learning classification algorithm is used, Bagged Trees in this case. For processing the second information set, related with the symptomatology of the patient, a col-lection of expert systems based on fuzzy inferential systems arranged in cascade are employed. As a result, the system is able to determine two risk indicators related to the patient's risk of suffering from OSA: the Statistical Risk and the Symbolic Risk respectively. Subsequently, by interpreting both risk indicators mentioned it will be possible to determine the severity of the patients’ health, proposing a preliminary evaluation on their condition. For the initial tests of the system, a software artifact has been built using a dataset with 4,978 selected patients, suspected of suffering from OSA, from the Álvaro Cunqueiro Hospital in Vigo. The results obtained are promising, demonstrating the potential usefulness of this type of tools in medical diagnosis. Once the system has been validated with new data from clinical environments, it is considered as possible to obtain a relevant improvement in the quality of the healthcare services, and a reduction in the associated costs.

Accurate measurement of low-level thoron gas and high-accuracy calibration of thoron measurement devices are essential for assessing and preventing thoron radiological risks. This study aimed to develop a thoron gas measurement technique using an airflow-through scintillation cell for both low-level measurement and high-accuracy calibration. To achieve this, a compartment model was developed to estimate the influence of progeny deposition and accumulation on the wall of the scintillation cell to prevent overestimation of thoron. A self-developed scintillation cell was utilized to implement and validate this technique. The lower detection limit and measurement uncertainty were then evaluated to assess the feasibility of the technique for low-level measurement and high-accuracy calibration. The results showed that the compartment model effectively addressed the influence of the progeny deposition. The measurement technique achieved a lower detection limit below 100 Bq m-3 even with the coexistence of 100 Bq m-3 of radon and attained a measurement uncertainty (k = 2) below 10% when the concentration of thoron exceeded 1,000 Bq m-3. In summary, this study developed a reliable and practical thoron gas measurement technique using an airflow-through scintillation cell with consideration for progeny deposition, and is expected to contribute to the assessment and prevention of thoron radiological risk.

In rural spaces, new activities are developed to add to the traditional ones in the context of multifunctionality, considering the protection and enhancement of natural and cultural heritage, as in Nature Parks. They incorporate new recreational and tourist functions, understood as an instrument for local development. This research aims to study the relationships between tourism and local development in three Natural Parks (Sierra de Aracena and Picos de Aroche, Sierra Norte de Sevilla and Sierra de Hornachuelos) in Andalusia (Spain). The research uses a mixed methodology consisting of, on the one hand, selective interviews with stakeholders focused on tourism and local development processes and, on the other, secondary data to analyze the repercussions on local development. The results show that (1) prevalence among the informants of the economic dimension in local development above the environmental and socio-cultural dimensions; (2) influence of declarations of protection and implementation of development policies on the growth of the tourist offer; (3) uneven tourism development in the different municipalities and Natural Parks; and (4) limited effects of tourism on local development.

We show how it is possible to define and study black holes of arbitrary shapes in the framework of Newtonian mechanics.

A new circuit of a low-pass filter (LPF) is considered, which is switched on at the input of analog-to-digital converters to limit the spectrum of input signals. The peculiarity of the proposed low-pass filter is the independent adjustment of the pole frequency, the quality factor of the pole and the transmission coefficient by different resistors. Computer simulation performed in the Micro-Cap environment confirms these properties of the LPF. The basic mathematical expressions are obtained, which make it possible to carry out the parametric synthesis of the LPF circuit. It is shown that the sensitivity of the LPF transmission coefficient to changes in the parameters of the frequency-setting elements of the circuit is close to zero and depends only on the ratio of the resistances of the two resistors in the feedback circuit.

Non-pneumatic tires have gained a lot of attention due to their advantages, including not having to worry about getting a flat tire. In this type of tire, metamaterial cells replace the pneumatic part of the tire. In general, metamaterials are formed by stacking cells together. These cells are obtained by various methods including topology optimization. Therefore, in this research, to achieve a metamaterial cell suitable for a non-pneumatic tire with the objective function of increasing compressive strength and bending fatigue life, optimization for three types of geometry including (1) square plane, (2) rectangular plane, and (3) entire circumference of tire and three types of material including polylactic acid (PLA), thermoplastic polyurethane (TPU) and void have been done. Optimization has been implemented by using MATLAB code in 2D mode. The cubic cell is obtained from extruding the optimized response of the square plane. From the optimized response of the rectangular plane, the cylindrical cell and from extruding the optimized response of the entire circumference of tire to the tire cross-section size, the main geometry of the tire has been calculated. Finally, to check the quality of 3D printing in producing cells and how they are connected, the optimal cell fabricated by the fused deposition modeling (FDM) method has been observed using a field-emission scanning electron microscopy (FE-SEM). The results show that in the optimization of the square plane, the sample with the minimum remaining weight constraint equal to 40% and in the optimization of the rectangular plane and the entire circumference of tire, the sample with the minimum remaining weight constraint equal to 60% was selected as the optimal sample. From checking the quality of 3D printing, it has been concluded that PLA and TPU materials are completely connected.

Imaginary dimensions in physics require an imaginary set of base Planck units and some negative parameter $c_n$ corresponding to the speed of light in vacuum $c$. The second, negative fine-structure constant $\alpha_2^{-1} \approx -140.178$ present in Fresnel coefficients for the normal incidence of electromagnetic radiation on monolayer graphene leads to the imaginary Planck units. Furthermore, it sets $c_n \approx -3.06 \times 10^8~\text{[m/s]}$. It follows that electric charges are the same in real and imaginary dimensions. Neutron stars and white dwarfs, objects emitting perfect black-body radiation, need energy exceeding their mass-energy equivalence ratios. Complex energies are defined in terms of real and imaginary Planck units. Their imaginary parts, inaccessible for direct observation, store the excess of these energies. It follows that black holes are fundamentally uncharged, charged micro neutron stars and white dwarfs with masses lower than $5.7275 \times 10^{-10}~[\text{kg}]$ are unphysical, and the radii of white dwarfs' cores are limited to $R_{\text{WD}} < 6.7933~G M_{\text{WD}}/c^2$. It is conjectured that the maximum atomic number $Z=238$. A black-body object is in the equilibrium of complex energies of masses, charges, and wavelengths if its radius $R_\text{eq} \approx 2.7665~G M_{\text{BBO}}/c^2$, which corrects the value of the photon sphere radius $R_{\text{ps}}=3G M/c^2$, taking into account the value(s) of the fine-structure constant(s), which is otherwise neglected in general relativity.

Molten LiCl-KCl salt and liquid cadmium are proposed as the electrolyte and the reactive cathode for the electrorefining of spent nuclear fuels. Corrosion testing of existing alloys is mandatory to ensure the service life of the electrorefiner vessel and electrode assemblies. Haynes C276, Inconel 600, AISI 316L stainless steel, and 42CrMo low alloy steel were exposed to LiCl-KCl melt at 500°C for 500 h in an argon atmosphere. All alloys suffered from dissolution attacks with the presence of oxide islands or porous oxide layer on the surface. AISI 316L, T91 steel and tungsten specimens were also submitted to corrosion tests in liquid cadmium at 500°C for 120 h. Corrosion of AISI 316L and T91 stainless steel were predominated by chemical oxidation with additional severe Ni dealloying occurred on AISI 316L. Tungsten only suffered from dissolution attack in comparison.

Biocontainment techniques for genetically modified yeasts (GMYs) are pivotal due to the importance of these organisms for biotechnological processes and also due to the design of new yeast strains by using synthetic biology tools and technologies. Due to the large genetic modifications that many yeast strains display, it is highly desirable to avoid the leakage of GMY cells into natural environments and, consequently, the spread of synthetic genes and circuits by horizontal or vertical gene transfer mechanisms within the microorganisms. Moreover, it is also desirable to avoid that patented yeast gene technologies spread outside the production facility. In this review, it was evaluated the different biocontainment technologies currently available for GMYs. Interestingly, uniplex-type biocontainment approaches (UTBAs), which relies on nutrient auxotrophies induced by gene mutation or deletion, or the expression of simple kill switches apparatus, are still the major biocontainment approaches still in use with GMY. While bacteria like Escherichia coli account for advanced biocontainment technologies based on synthetic biology and multiplex-type biocontainment approaches (MTBAs), GMYs are distant from this scenario due to many reasons. Thus, a comparison of different UTBAs and MTBAs applied for GMY and genetically engineered microorganisms (GEMs) was made, indicating the major advances of biocontainment techniques for GMYs.

Reinventing approved therapeutic proteins for a new dose, a new formulation, a new route of administration, an improved safety profile, a new indication, or a new conjugate with a drug or a radioactive source is a creative approach to benefit from the billions spent on developing new therapeutic proteins. These new opportunities were created only recently with the arrival of AI/ML tools and high throughput screening technologies. Furthermore, the complex nature of proteins offers mining opportunities that are not possible with chemical drugs; bringing in newer therapies without spending billions makes this path highly lucrative financially while serving the dire needs of humanity. This paper analyses several practical reinventing approaches and suggests regulatory strategies to reduce development costs significantly. This should enable the entry of hundreds of new therapies at affordable costs.

Design has often been interpreted as a practice of creating novel objects. However, the relationships between objects analysed upstream the process of creation have been under-studied. Here it is presented a model for enhancing the design practice by fostering a deep work on the relationships of mundane objects. The Three-dimensional Narratives (TdN) model comprises a stepwise five phases work package that facilitates a varied range of analyses to improve the creativity and innovation of any project led by any type of participants. One particular feature of the TdN model is the appropriation of concepts coming from non-design fields such physics and biology to both developing the work of the model and to improve the comprehension and outcomes of the enrolled participants. During three Case Studies with children, youngsters, and older participants the TdN model was validated, and its usability was successfully assessed. The TdN model shows that there is a need for a deeper design practice and that it is relevant to improve the lexicon of designers in order to facilitate a broader and design practice, irrespectively of the aim of the proposed project and nuances of the audience.

We propose a new statistical analysis of the Acoustic Emissions (AE) produced in a series of triaxial deformation experiments leading to fractures and failure of two different rocks, namely, Darley Dale Sandstone (DDS) and AG Granite (AG). By means of q-statistical formalism, we are able to characterize the pre-failure processes in both types of rocks. In particular, we study AE inter-event time and AE inter-event distance distributions. Both of them can be reproduced with q-exponential curves, showing universal features which are observed here for the first time and could be important in order to understand more in detail rock fractures dynamics.

Phagosomal escape and intracellular survival, often accompanied by Small Colony Variants (SCVs) formation, are typical features of infections caused by S. aureus. The survival in macro-phages favours S. aureus dissemination and complicates treatment. RAW 264.7 murine macro-phages infected with S. aureus USA300 and treated with erythromycin and 20mM carnosine, alone and in combination, were used as experimental model. SCVs were isolated from all treat-ment conditions, but only those undergoing the pressure of combined erythromycin and carnosine for 48 hours were stable for at least six passages on blood agar. Nucleic acid extraction was car-ried out for S. aureus USA300 wild-type and stable SCVs. Whole Genome Sequencing (WGS) was performed using Illumina DNA Prep and Illumina MiSeq, and quantitative reverse transcription PCR was performed. WGS analysis did not yield mutations pointing to differences between S. au-reus USA300 and stable SCVs, therefore the focus was shifted to evaluating gene expression vari-ations. Genes such as zur, mntR, uhpt, fur, sdrE were shown to be significantly up-regulated in SCVs compared to S. aureus USA300 wild-type, suggesting a global change that allows adapta-tion to intracellular persistence, including protection from inflammatory response and evasion of the immune system.

Communication is a key method of expressing one's thoughts and opinions. Amongst many modalities, speech and writing are the most powerful and common forms of human communication. Analysing what and how people think has inherently been an interesting and progressive research domain. This includes bimodal sentiment analysis which is an emerging area in natural language processing (NLP) and has received a great deal of attention in recent years in a variety of areas including social opinion mining, health care, banking, and so on. At present, there are limited studies on bimodal conversational sentiment analysis as it proves to be a challenging area given the complex nature of the way humans express sentiment cues across various modalities. To address this gap, a comparison of the performance of multiple data modality models has been conducted on the MELD dataset, a widely-used dataset for benchmarking sentiment analysis within the research community. Our work then demonstrates the results of combining acoustic and linguistic representations. Lastly, our proposed neural network-based ensemble learning technique is employed over six transformer and deep learning-based models, achieving a State-Of-The-Art (SOTA) accuracy.

Background and Objectives: A novel synthetic self-assembling peptide, PuraStat, has been introduced as a hemostatic agent. This case series aimed to evaluate the clinical efficacy of PuraStat for gastrointestinal bleeding during emergency endoscopy. Cases: Twenty-five patients with gastrointestinal bleeding who had undergone emergency endoscopy with PuraStat between August 2021 and December 2022 were retrospectively examined. Six patients were receiving antithrombotic agent, and ten patients with refractory gastrointestinal bleeding had undergone at least one endoscopic hemostatic procedure. The breakdown of bleeding was gastroduodenal ulcer/erosion in 12 cases, bleeding after gastroduodenal or colorectal endoscopic resection in 4 cases, rectal ulcer in 2 cases, postoperative anastomotic ulcer in 2 cases, and gastric cancer, diffuse antral vascular ectasia, small intestinal ulcer, colonic diverticular bleeding, and radiation proctitis in each case. The method of hemostasis was only PuraStat application in six cases, and hemostasis in combination with high-frequency hemostatic forceps, hemostatic clip, argon plasma coagulation, and hemostatic agents (i.e. thrombin) in the remaining cases. Rebleeding was observed in three cases. Hemostatic efficiency was observed in 23 cases (92%). Conclusions: PuraStat has the expected hemostatic effect on gastrointestinal bleeding during emergency endoscopy. The use of PuraStat should be considered in emergency endoscopic hemostasis of gastrointestinal bleeding.

Acetic acid bacteria (AAB) form a bacterial film on the surface of alcoholic solutions and ferment ethanol to acetic acid while also producing bioactive compounds. To discover functional AAB for industrial use, we isolated and selected strains from farm-produced vinegars using a CaCO3-containing medium. The seven strains belonged to Acetobacter cerevisiae and A. pasteurianus. Physiological properties were determined, including ethanol tolerance at up to 12% (v/v). Acidification at a growth temperature of 40 ℃ was seen for GHA 7, GYA 23, JGB 21-17, and GHA 20 strains, which may be useful in vinegar production where thermotolerant AAB are required. The seven AAB isolates had strong antibacterial activity against Staphylococcus aureus. Antioxidant activity was 2- and 4-fold higher, respectively, for A cerevisiae and A. pasteurianus than for the control, 1% acetic acid. We also observed 91.3% inhibition of angiotensin-converting enzyme activity for the KSO 5 strain, higher than that for the positive control, 0.1% captopril (76.9%). All strains showed complete inhibition of α-glucosidase, except JGB 21-17 and GHA 7 with 98.3% inhibition. Our work suggests usefulness of the strains selected as seed strains for highly efficient production of functional vinegar and illustrates identification of useful functional characteristics on a scientific basis.

This paper explores the challenges and opportunities of balancing cultural preservation and tourism investment in historic Syrian cities in the post-war period. The Syrian conflict has caused significant damage to the country's cultural heritage, including its ancient cities, which are UNESCO World Heritage sites. Key issues raised include what challenges local governments to face, the impact of the war on the preservation of historic sites, how tourism can be used as a tool for economic development, and how to ensure that cultural heritage is preserved while also allowing for tourism investment. To gain an in-depth understanding of the old Syrian cities, extensive research has been conducted, including a review of existing literature and interviews with key stakeholders. The researcher found that rebuilding historic Syrian cities is a complex task that requires balancing cultural preservation with tourism investment. It will require collaboration between government officials, preservationists, developers, and local communities to ensure that these cities are restored in a way that respects their history and culture while also promoting economic growth through tourism.

Traditional grain vinegar is fermented using multiple acetic acid bacteria (AAB) at various temperatures. A single AAB showed high acid–producing ability at 30 °C with a 5% alcohol concentration and an initial pH adjusted to 4.0. Multiple AAB were similar to single AAB, but the optimal initial pH was 3.0. Acid production ability according to the type of AAB was higher in multiple AAB than that for single AAB. That is, using multiple AAB helped to increase the titratable acidity of traditional grain vinegar. In addition, increasing the titratable acidity and content of volatile flavor compounds was advantageous when two, rather than four, AAB types were mixed and used. Titratable acidity was high at medium temperatures (30 °C), but volatile flavor compounds increased at low temperatures (20 °C) under multiple AAB. A 16S rDNA–based microbiome taxonomic profiling analysis identified differences in beta diversity due to multiple AAB and fermentation temperatures. In particular, beta diversity analysis revealed a specific pattern when a mixture of Acetobacter ascedens GV–8 and Acetobacter pasteurianus GV–22 were fermented at low temperature (20 °C). Therefore, we propose the application of multiple AAB with acidic and flavor–producing properties in traditional grain vinegar.

Abstract: The overuse of antibiotic prophylaxis during the COVID-19 pandemic would have led to the devel-opment of antibiotic-resistant bacterial strains, thereby increasing the epidemiological burden of antimicrobi-al resistance. The aim of this study was to determine the prevalence and antibiotic resistance pattern of bacte-ria isolated in 02 referral health facilities in Yaoundé before and during the COVID-19 pandemic era. We conducted a retrospective study over a period of 03 years (from 1 January 2019 to 31 December 2021) in the bacteriology units of the Central and General Hospitals of Yaoundé, Cameroon. The review of the services' registers was done to record bacterial isolates (Streptococcus, Staphylococcus, Neisseria meningitidis and Entero-bacteriaceae) as well as their susceptibility to selected, specific and highly potent antibiotics: Cefixime, Azythromycin and Erythromycin. The relationship between each group of resistant bacteria and the antibiotic of interest was determined by simple linear regression; the comparison of the prevalences of before (2019) and during 2 consecutive years of the COVID-19 pandemic onset (2020 and 2021) was done by the Chi2 test of in-dependence. In all, 426 bacterial strains were included. It appeared that the highest number of bacteria iso-lates and lowest rate of bacterial resistance were recorded during the pre-COVID 2019 period in 2019 (160 isolates vs. 58.8% resistance rate). Conversely, lower bacteria stains but greater resistance burden were rec-orded during the pandemic era (2020 and 2021) with the lowest bacteria amount and peak of bacteria re-sistance registered in 2020, the year of COVID-19 onset (120 isolates vs. 70% resistance in 2020 and 146 iso-lates vs.58.9% resistance in 2021). In contrast to almost all others groups of bacteria where the resistance burden was quite constant over years, the Enterobacteriaceae exhibited greater resistance rate during the pandemic period [60%(48/80) in 2019 to 86.9%(60/69) in 2020 and 64.5%(61/95) in 2021)]. Concerning antibiotics, unlike Erythromycin, Azythromycin related resitance increased during the pandemic period and the resistance to Cefixim tends to decrease the year of the pandemic onset (2020) and re-increase one year therafter. A significant association was found between resistant Enterobacteriaceae strains and Cefixime (R= 0.7; P-value= 0.0001) and also, between resistant Staphylococcus strains and Erythromycin (R= 0.8; P-value= 0.0001). It could be that the more frequent use of these antibiotics has increased resistance rate especially in Enterobacteriaceae. Anticromicrobial resistance should be closely monitor during and after COVID pandemic era.

When the theoretical foundations of Einstein’s quantum theory of radiation are compared with Heisenberg’s and Feynman’s theories the equations of nonrelativistic quantum mechanics are found to violate the conservation laws. The deficiencies are corrected by introducing Hamilton’s principle S = ∫ Ldt and applying the methods of quantum field theory to derive relativistic equations of motion. The calculus of variations is used to prove that the wave function equals twice the allowable action minimum. Einstein’s version of quantum mechanical foundations is thereby determined to be correct one.

Background: Bibliometric analysis provides insight into knowledge gaps of a specific field. We want to know what part of medical care has been the subject of research in a group of Mexican physicians. The Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado (ISSSTE) cares for a wide spectrum of disease and it provides a unique vision of what specialists has investigated about health. Methods: Papers belonging to “ISSSTE” affiliation were harvested and downloaded to .csv Excel files from the Scopus database including most bibliometric variables. VOSviewer, biblioshiny and bibliometric were used to conduct the bibliometric. Results: 2,063 papers were found and re-trieved; internal medicine had the greatest number of papers with 831; nine institutions were listed claiming “ISSSTE” as their mother affiliation; original papers represent 82% of the total and 52% of them were written in Spanish. Research production in Mexico City and the most productive center is Centro Médico Nacional 20 de Noviembre. Discussion: We identified main institutions, prolific authors, top-cited researchers and their affiliations, however, our paper is a call to action for the medical community in Latin America to join our efforts in building a solid group of researchers for the future of science.

Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

Alfalfa (Medicago sativa L.) is the most cultivated forage legume around the world. Under a variety of growing conditions, forage yield in alfalfa is stymied by biotic and abiotic stresses including heat, salt, drought, and disease. Given the sessile nature of plants, they use strategies such as differential gene expression to respond to environmental cues. Transcription factors control the expression of genes that contribute to or enable tolerance and survival during periods of stress. Basic-leucine zipper (bZIP) transcription factors have been demonstrated to play a critical role in regulating plant growth and development as well as mediate the responses to abiotic stress in several species, including Arabidopsis thaliana, Oryza sativa, Lotus japonicus, and Medicago truncatula. However, there is little information about bZIP transcription factors in cultivated alfalfa. In the present study, 237 bZIP genes were identified in alfalfa from publicly available sequencing data. Multiple sequence alignments showed the presence of intact bZIP motifs in the identified sequences. Based on previous phylogenetic analyses in Arabidopsis thaliana, alfalfa bZIPs were similarly divided and fell into 10 groups. The physicochemical properties, motif analysis, and phylogenetic study of the alfalfa bZIPs revealed high specificity within groups. The differential expression of alfalfa bZIPs in a suite of tissues indicates that particular bZIP genes are specifically expressed at different developmental stages in alfalfa. Similarly, expression analysis in response to ABA, cold, drought, and salt stresses, indicates that a subset of bZIP genes are also differentially expressed and likely play a role in abiotic stress signaling and/or tolerance. These expression patterns were further verified by qRT-PCR. However, further functional characterization of bZIP transcription factors in alfalfa will help illuminate the role they play in stress tolerance mechanisms in legumes and facilitate the molecular breeding of stress tolerance in alfalfa.

This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Finally, in the last section we present a concerning numerical example and the respective software.

The use of Nano crossbar-based switching lattice implementation of Boolean functions has been proposed as an alternative to traditional CMOS-based implementations [1] in digital circuits. As Moore’s law is expected to come to an end soon [2], the use of crossbar-based switching lattice implementation may be a solution to meet the demands of future electronic designs. In recent years, various methods and tools have been proposed for implementing boolean functions with crossbar struc- tures. In this work, a method for verifying crossbar-based lattice has been proposed and implemented. This kind of verification will be necessary for any design that utilizes this crossbar-based implementation of boolean logic.

This article presents a review of current advances and future prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

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

Multicellular organisms organize themselves according to two principles; first, the equilibrium between the driving force of unstable state cells and apoptosis or senescence; and second, the mutual equilibrium between the functional requirements of different types of cells. For the first principle, we deduce that stem cells at different levels have their own growth limits, which are determined by two factors: the ability of the stem cells to enter an unstable state (specifically, the ability of cells to divide and differentiate); and the rate of cellular senescence, loss of function, and finally, apoptosis. The superposition of stem cell limits at different levels over each other can amplify small differences at the genetic level. Moreover, the growth limits of different organs are different, which lead to different individuals having completely different appearances. The second principle is that the sum of the specific output functions of an organ must be equal to the overall demand for the function to be provided by the organ. If negative entropy input can meet this equilibrium, defects in certain genes will cause these defective individuals to become larger or develop other morphology.

We exploit the properties of complex time to obtain an analytical relationship based on considerations of causality between the two conserved quantities of a system’s Hamiltonian and its Entropy Production. In natural units, when complexified, the one is simply the Wick-rotated complex-conjugate of the other. A Hilbert transform relation is constructed in the formalism of Quantitative Geometrical Thermodynamics which enables system irreversibility to be handled analytically within a framework that unifies both the microscopic and macroscopic scales, and which also unifies the treatment of both reversibility and irreversibility as complementary parts of a single physical description. In particular the thermodynamics of two unitary entities are considered: the alpha particle which is absolutely stable (that is, trivially reversible with zero entropy production), and a black hole whose absolute disequilibrium (unconditional irreversibility) is characterized by a non-zero entropy production for which we show an alternate derivation confirming our previous one (Universe 7, 2021, 325). The thermodynamics of a canonical decaying harmonic oscillator are also considered. In this treatment the complexification of time also enables a meaningful physical interpretation of both “imaginary time” and “imaginary energy”.

At the beginning of the 21st century, it became obvious that radical changes had taken place in the concept of living matter and, in particular, in the concept of the organization of intracellular space. The accumulated data testify to the essential importance of phase transitions of biopolymers (first of all, intrinsically disordered proteins and RNA) in the spatiotemporal organization of the intracellular space. Of particular interest is the stress-induced reorganization of the intracellular space. Examples of organelles formed in response to stress are nuclear A-bodies and nuclear stress granules. The formation of these organelles is based on LLPS of intrinsically disordered proteins and non-coding RNA. Despite the overlapping composition and similar mechanism of formation, these organelles have different functional activities and physical properties. In this review, we will focus our attention on these MLOs and describe their functions, structure, and mechanism of formation.

Objective: Physical inactivity is linked to chronic illnesses and disabilities among workers, especially those in high demanding jobs like teachers. Despite the global prominence of sedentary behaviour research, studies drawing the relationships between physical inactivity and multimorbidity among working teacher populations in low-and middle-countries remain untapped. This study assessed the sedentariness and health status of primary school teachers in Cape Coast Metropolis in Ghana. Method: This cross-sectional survey employed 1109 primary school teachers from the Cape Coast Metropolis in the Central Region of Ghana, using convenient sampling method. Results: Generally, the teachers were highly sedentary and reported poor health status. Other results showed no difference in sedentariness across gender, t (1107) = -0.32, p > .05. However, female teachers suffer more pain and discomfort, [t (1103.51) = - 3.07, p < .05, anxiety and depression, [t (1099.56) = - 2.85, p < .000,)], and poor health status [t (1107) = 2.14, p < .05, than their male counterparts. Also, pain and discomfort, anxiety and depression, health status and years of work significantly predicted sedentariness among the teachers, F (4, 1104) = 5.966, p=.00, R = .145, R2 = .029, adjusted R2 = .018. Conclusion: The findings suggest that individualized or personalized interventions are urgently needed to promote regular physical activity to improve the health status and reduce associated complications on the health and well-being, especially among the female primary school teachers. Given the health risks of sedentary lifestyle, behavioral interventions at the person-level (i.e., individualized- routine weekly physical activity programmes) and built environmental restructuring (e.g., creation of walkways to encourage regular walking) could be done to physical activity behavior among teachers within the Cape Coast Metropolis, and perhaps beyond.

Dynamic variables in the non-equilibrium systems of life are determined by catalytic activities. These relate to the expression of the genome. The extent to which such a variable depends on the catalytic activity defined by a gene has become more and more important in view of the possibilities to modulate gene expression or intervene with enzyme function through the use of medicinal drugs. With all the complexity of cellular systems biology, there are still some very simple principles that guide the control of variables such as fluxes, concentrations, and half-times. Using time-unit invariance we here derive a multitude of laws governing the sums of the control coefficients that quantify the control of multiple variables by all the catalytic activities. We show that the sum of the control coefficients of any dynamic variable over all catalytic activities is determined by the control of the same property by time. When the variable is at a maximum, minimum or steady, this limits the sums to simple integers like 0, -1, 1, and -2, depending on the variable under consideration. Some of the implications for biological control are discussed as is the dependence of these results on the precise definition of control.

The development of bacterial resistance to conventional treatments (chiefly antibiotics) has led to the complication of wound infections, thus rendering the search for novel alternative infection-fighting techniques even more urgent. Historically, medicinal plants have been widely used to obtain natural health products, thus, they constitute sources for very promising alternative antibacterial agents against multidrug-resistant (MDR) bacteria. Thus, this study aimed to evaluate the in vitro antibacterial activity of different seed extracts of Silybum marianum against antibiotic-resistant wound infection–causing bacteria (Staphylococcus aureus strain NBRC 100910, Stenotrophomonas maltophilia strain IAM 12423, Klebsiella pneumoniae strain DSM 30104, and Escherichia coli strain NBRC 102203). The most effective extract was selected to investigate its antioxidant potency, assess its effects on the bacterial cell ultrastructure using transmission electron microscopy (TEM), and screen its bioactive components using gas chromatography–mass spectroscopy (GC–MS) analysis. The results indicated that most antibiotics were ineffective against the bacterial isolates; hence, the bacterial isolates were considered to be MDR bacteria. Ethanol extract exhibited the highest inhibition activity against gram-positive and -negative bacteria alongside high antioxidant and antibacterial activities, as revealed by the denaturation and deformation of the ultrastructure of methicillin-resistant S. aureus and S. maltophilia observed via TEM. The GC–MS analysis of ethanol extract revealed the presence of diverse active components with numerous biological activities. Therefore, S. marianum seeds might be promising antioxidant and antibacterial agents against MDR wound infection–causing bacterial strains.

In the mammalian isocortex CD44, a cell surface receptor for extracellular matrix molecules, is present in pial-based and fibrous astrocytes of white matter, but not the protoplasmic astrocytes. In the hominid isocortex, CD44+ astrocytes comprise the subpial “interlaminar” astrocytes, sending long processes into the cortex. The hippocampus also contains similar astrocytes. We have examined all levels of the human CNS and found CD44+ astrocytes in every region. Astrocytes in white matter and astrocytes that interact with large blood vessels, but not capillaries in gray matter, are CD44+, the latter extending long processes into the parenchyma. Motor neurons in the brainstem and spinal cord, such as oculomotor, facial, hypoglossal, and in the anterior horn of the spinal cord, are surrounded by CD44+ processes, contrasting with neurons in the cortex, basal ganglia and thalamus. We found CD44+ processes that intercalate between ependymal cells to reach the ventricle and that show a location-specific presence. We also found a CD44+ astrocyte in the molecular layer of the cerebellar cortex. Protoplasmic astrocytes, which do not normally contain CD44, acquire it in pathologies like hypoxia and seizures.

Since 2012, the northeast of Tunisia has been experiencing an emergence of dog rabies. Published data revealed that the disease is widespread in the north of Tunisia. Even with studies on rabies, lacking knowledge on the associated risk factors was highlighted. Therefore, we conducted a case-control study on dog rabies in northeast Tunisia to identify potential risk factors for dog rabies occurrence. The cases group (n=77) included positive dog rabies confirmed at the referral laboratory using Fluorescent Antibody Test (FAT) in 2014 and 2018. The controls group (n=77) involved all negative cases received at the laboratory and 15-days quarantine dogs that received a certificate of absence of rabies after the observation period. Univariate and multivariate logistic regression was conducted to explore the risk factors (age, sex, breed, confinement, vaccination status, presence of bites, owned or not and origin of dogs, sector, presence of rabies cases, and slaughterhouses, etc.…) related to the occurrence of rabies. The final logistic regression model revealed that unconfined dogs were almost twice as likely to be affected as confined dogs (OR=1.9; 95% CI: 1.17 to 3.27). The odds of rabies occurrence were 25 times higher in areas where rabies cases have been reported between 3 months and 1 year compared to the uninfected areas (25.7; 95% CI: 3.02-219.14). Similarly, the risk of rabies is significantly higher in dogs born in the home to the owner bitch (OR=2.41; 95% CI: 1.14-5.13). Living in areas with rabies cases in the last three months increases by 2.8 times the risk of getting rabies (2.8; 95% CI: 1.16-6.77). However, vaccination reduces by 1.6 times the risk of contracting rabies (0.6; 95% CI: 0.38-0.97). Our findings provide reliable data on dog rabies that will be very useful for decision-makers and could contribute to improving the ongoing national control program of rabies.

Spatial variation of indoor and outdoor PM2.5 within three states for a 5-year period is studied using regulatory and low-cost PurpleAir monitors. Most of these data were collected in an earlier study1 investigating the relative contribution of indoor-generated and outdoor-infiltrated particles to indoor exposures. About 260 regulatory monitors and ~10,000 outdoor and ~4,000 indoor PurpleAir monitors are included. Daily mean PM2.5 concentrations, correlations, and coefficients of divergence (COD) are calculated for pairs of monitors at distances ranging from 0 (collocated) to 200 km. We use a transparent and reproducible open algorithm that avoids use of the proprietary algorithms provided by the manufacturer of the sensors in PurpleAir PA-I and PA-II monitors. The algorithm is available on the PurpleAir API website under the name “PM2.5_alt”. This algorithm is validated using several hundred pairs of regulatory and PurpleAir monitors separated by up to 0.5 km. The PM2.5 spatial variation outdoors is homogeneous with high correlations to at least 10 km, as shown by the COD index under 0.2. There is also a steady improvement in outdoor PM2.5 concentrations with increasing distance from the regulatory monitors. The spatial variation of indoor PM2.5 indoors is not homogeneous even at distances <100 m. We document substantial differences between Federal Reference Methods (FRM) and Federal Equivalent Methods (FEM).

Due to the health crisis caused by SARS-CoV-2, the creation of a new vaccine platform based on mRNA was implemented. Globally, around 13.32 billion COVID-19 vaccine doses of diverse platforms have been given, and up to this date, 69.7% of the total population received at least one injection of a COVID-19 vaccine. Although these vaccines prevent hospitalization and severe forms of the disease, increasing evidence has shown they do not produce sterilizing immunity, allowing people to suffer frequent re-infections. Recent research has also raised concerns that mRNA vaccines could induce immune tolerance, which, added to that caused by the virus itself, could complicate the clinical course of a COVID-19 infection. Furthermore, recent investigations have found high IgG4 levels in people who were administered two or more injections of mRNA vaccines. It has been suggested that an increase in IgG4 levels could have a protecting role by preventing immune over-activation, similar to that occurring during successful allergen-specific immunotherapy by inhibiting IgE-induced effects. Altogether, evidence suggests that the reported increase in the IgG4 levels detected after repeated vaccination with the mRNA vaccines is not a protective mechanism; rather, it may be a part of the immune tolerance mechanism to the spike protein that could promote unopposed SARS-CoV2 infection and replication by suppressing natural antiviral responses. IgG4-induced suppression of the immune system due to repeated vaccination can also cause autoimmune diseases, promotes cancer growth, and autoimmune myocarditis in susceptible individuals.

Hexokinases (HKs) convert hexose sugars to hexose-6-phosphate, thus trapping them inside cells to meet the synthetic and energetic demands. HKs participate in various standard and altered physiological processes, including cancer, primarily through the reprogramming of cellular metabolism. Four canonical HKs have been identified with different expression patterns across tissues. HKs 1-3 play a role in glucose utilization, whereas HK 4 (glucokinase, GCK) also acts as a glucose sensor. Recently, a novel 5^th HK, hexokinase domain containing 1 (HKDC1), has been identified, which plays a role in whole-body glucose utilization and insulin sensitivity. Beyond the metabolic functions, HKDC1 is differentially expressed in many forms of human cancer. This review focuses on the role of HKs, particularly HKDC1, in metabolic reprogramming and cancer progression.

Toxoplasma gondii (T. gondii) is one of the most common parasites that infect humans. The parasite exists in approximately 40 million people in the United States. Latent infections are frequently associated with tissue cysts of T. gondii in the skeletal muscle and brain tissue that can lead to mental disorders, congenital disorders, and vision dysfunction. Furthermore, self-directed violence, impulsivity, and aggression are associated with T. gondii infection. Dopamine is associated with human behavior including pleasure, aggression, memory, and substance use disorder; however, the result of the studies on the association of T. gondii infection and drugs of abuse are not well understood. The frequency of substance use may be associated with substance-induced modification of dopamine-receptor densities and basal dopaminergic activity. Likewise, T. gondii can directly or indirectly influence dopaminergic activity in infected cells. Based on this hypothesis, the current paper aimed to systematically review all published literature on the association between drugs of abuse and T. gondii infection. Systematic review of controlled studies on T. gondii infection and substance use effects in adults were searched on the electronic databases. Studies have shown that individuals infected with T. gondii display increased risky behavior, such as excessive alcohol consumption. Furthermore, it was observed that T. gondii seropositive subjects had a reduced likelihood of self-reported substance use compared to T. gondii seronegative subjects. This study confirms that SUD is a potential risk factor for behavioral and psychiatric complications associated with T. gondii infection. Ultimately T. gondii infection in the context of drug dependence and the development of SUD remain to be elucidated. Therefore, it is necessary to conduct further research characterizing the mechanisms associated with dopamine metabolism of drug dependence and withdrawal in the context of a T. gondii infection, evaluating the role of inflammation, and identifying potential drug-and-sex specific underpinnings of these associations.

ChatGPT, a groundbreaking natural language processing technology released just three months ago, has attracted significant attention due to its remarkable capabilities. This AI milestone has urged researchers, industry, decision-makers, and governments to examine this technology, including its implications, threats, and benefits. Despite the short period since its release, several researchers have examined ChatGPT from different perspectives. This paper presents a comprehensive review of ChatGPT, highlighting its technical novelties compared to previous models and analyzing existing research from various perspectives. We followed a rigorous methodology to conduct a critical review of existing research on ChatGPT and developed a taxonomy for the different areas of study. Additionally, we identify future challenges and research trends associated with ChatGPT. Our paper is the first critical review of ChatGPT literature, providing valuable insights for practitioners and policymakers. This paper serves as a reference for researchers seeking to advance research on ChatGPT, including its applications and development.

ABSTRACT: Psychological vulnerability (PV) and low mental health literacy correlate negatively with higher education students' positive mental health and health behaviours. Levels of mental health literacy limit self-help, adaptative coping strategies and academic success. However, few studies examine the significant differences between students' PV, mental health literacy, positive mental health (PMH) and modifiable behaviours variables. Identifying modifiable vulnerability factors is essential when seeking and providing professional help. Method: A cross-sectional descriptive study was conducted to investigate the differences between modifiable behaviours and PV, mental health literacy, and PMH in higher education students. A sample of 3,600 undergraduate students was recruited, mostly female (78.8%), with an average age of 23 years (SD = 6.68). Results: The majority were women, did not exercise, were dissatisfied with sleep quality time per night (62.1% sleep less than 7 hours per night) and did not engage in leisure activities. Also, most respondents had PV and low literacy levels, particularly at F1, and 67.9% scored in the flourishing group. Results showed significant differences (p<0.001) between gender, age, sleep, exercise, diet, leisure activities and PV, and PMH and literacy. Conclusions: The results identified modifiable vulnerability factors that underpin the design of intervention-targeted programmes to promote literacy and PMH in higher education campuses in Portugal to foster students’ well-being and self-help strategies.

Heavy metal contamination is listed among the most alarming threats to the environment and human health. The detrimental effects of heavy metals in the natural environment span from a reduction of biodiversity to toxic effects on marine life - through microplastic born heavy metals -, to impairment of microbial activity in the soil, and to detrimental effects on animal reproduction. A host of different chemical and biological technologies have been proposed to alleviate environmental contamination by heavy metals. Relatively less attention has been paid to the microbial precipitation of heavy metals, as a side mechanism of the most general process of microbially induced calcite precipitation (MICP). This process is currently receiving a great deal of interest from both a theoretical and practical standpoint, because of its possible practical applications in concrete healing and soil consolidation, and its importance in the more general framework of microbial induced mineral precipitation. In this study, we analyse the ability of the marine bacteria Vibrio harveyi in co-precipitating CaCO3 minerals, together with Cd, Cr, Pb, and Zn added in form of nitrates, from solutions containing CaCl2. The precipitated carbonatic minerals were a function of the different heavy metals present in the solution. The process of co-precipitation appears to be rather effective and fast, as the concentrations of the 4 heavy metals were reduced in 2 days by 97.2%, on average, in the solutions.

Recent studies have been able to detect α-synuclein (αSyn) seeding in formaldehyde-fixed paraffin-embedded (FFPE) tissues from patients with synucleinopathies using seed amplification assays (SAAs), but with relatively low sensitivity due to limited protein extraction efficiency. With the aim of introducing an alternative option to frozen tissues, we developed a streamlined protein extraction protocol for evaluating disease-specific seeding in FFPE human brain. We evaluated the protein extraction efficiency of different tissue preparations, deparaffinizations, and protein extraction buffers using formaldehyde-fixed and FFPE tissue of a single Lewy body disease (LBD) subject. Alternatively, we incorporated heat-induced antigen-retrieval and dissociation using a commercially available kit. Our novel protein extraction protocol has been optimized to work with 10 sections of 4.5-µm-thickness or 2-mm-diameter micro-punch of FFPE tissue that can be used to seed SAAs. We demonstrated that extracted proteins from FFPE still preserve seeding potential and further show disease-specific seeding in LBD and multiple system atrophy. To the best of our knowledge, our study is the first to recapitulate disease-specific αSyn seeding behavior in FFPE human brain, with structural validation. Our findings open new perspectives in re-evaluating archived human brain tissue, extending the disease-specific seeding assays to larger cohorts to facilitate molecular subtyping of synucleinopathies.

In the history of civil aircraft transportation, ice formation has been identifies as a key factor in the safety of flight. Anti-icing and deicing system have emerged through the years with the aim to prevent or to eliminate ice formation on wing airfoil, control surface and probes. Modern flying machine demand more efficiency in order to reduce the carbon footprint and increase the sustainability of flight transport. In order to achieve this goal the need to have efficient aircraft with efficient and low power consuming system is fundamental. This paper proposes a new model for ice accretion using computational fluid dynamics (CFD). This model permits to simulate the shape of the ice formed over a profile varying boundary conditions (i.e.- speed, liquid water content and so on). The proposed model takes into account also the amount of heat transferred between water and the surrounding environment and includes the effects of air turbulence on the ice formation process. The CFD simulations have been validated with NASA experimental outcome and show good agreement. The proposed model can be also used to investigate the effects of various parameters such as air speed, Liquid Water Content and air temperature on the ice formation process. The results evidences that the proposed model can accurately predict ice formation process and is suitable to optimize the design of anti-icing or deicing system for aircraft and helicopters. This approach is not limited to aerospace but can also be exported to other applications such as transportation, wind turbine, energy management and infrastructure.

The cavovarus deformity is characterized by a pathologic longitudinal arch elevation caused by a severe hindfoot varus and forefoot equinus deformity. This disorder includes multiple anomalies and therapies and affects 25% of the population. Cavus feet usually indicate neurologic illness, which affects sensory and motor nerves. Clinical and radiological evaluations are needed to establish each patient's optimum therapy. Failure to identify a neurologic illness may lead to inappropriate surgical treatment, recurrence, and reconstructive failure. In progressive anomalies, surgical procedures must be performed quickly in a phased protocol to prevent the malalignment from worsening. Soft tissue surgeries, osteotomies, and arthrodesis are surgical options for patients. To aid and lead practitioners in selecting the most appropriate therapy for their patients daily, we have included the most common therapeutic options currently available in this article.

Federated Distillation (FD) has recently attracted increasing attention for its efficiency in aggregating multiple diverse local models trained from statistically heterogeneous data of distributed clients. Existing FD methods generally treat these models equally by merely computing the average of their output soft predictions for some given input distillation sample, which does not take the diversity across all local models into account, thus leading to degraded performance of the aggregated model, especially when some local models learn little knowledge about the sample. In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models. Specifically, we employ a domain discriminator for each client, which is trained to identify the correlation factor between the sample and the corresponding domain. Then, to facilitate the training of the domain discriminator while saving communication costs, we propose sharing its partial parameters with the classification model. Extensive experiments on various datasets and settings show that the proposed method can improve the model accuracy by up to $6.02\%$ compared to state-of-the-art baselines.

Cancers have been interpreted either as somatic evolution of cheater cells that escape replication regulation or alternatively as non-healing wounds. Both the interpretations have substantial support as well as glaring anomalies but the two have not been put together to make a coherent synthesis. We argue here that mechanisms and pathways to escape the normal regulation of cell proliferation do not need to evolve de novo. Mechanisms to override the normal regulation have already evolved for wound healing and tissue regeneration. Almost all of the hallmarks of cancer are also seen in the wound healing process. This suggests that cancer develops not by any de novo gain of function but by exaptation of pre-evolved wound healing functions. Somatic evolution that makes the wound healing triggers constitutive is not mutation limited but selection limited and the selective forces are dependent on the tissue microenvironment. Some mechanisms for such selection have been suggested. Many more need to be investigated. A series of mechanisms have evolved to minimize the risk of cancers which may fail in an altered lifestyle context. We support our synthesis with multiple lines of evidence and also make differential testable predictions. This evolutionary perspective suggests novel lines of research and also has translatable implications for cancer prevention.

COVID-19 has harmful impact on health. It is especially important for endurance athletes (EAs). Sleep and psychology influence sport performance. Aims of this study were: (1) investigation of the consequences of mild COVID-19 on sleep and psychology and (2) assessment of the conse-quences of the infection on cardiopulmonary exercise test (CPET) results. 49 EAs (males= 43; 87.76%, females= 6; 12.24%, age= 39.9±7.8 years, height= 178.4±6.8 cm, weight= 76.3±10.4 kg; BMI= 24.0±2.6 kg·m−2) underwent maximal cycling or running CPET pre- and post- COVID-19 and completed a survey. Exercise performance was deteriorated after COVID-19 (maximal oxy-gen uptake; VO2max= 47.81±7.81 vs 44.97±7.00 ml·kg·min−1 respectively pre- and post- infection; p<0.001). Waking up at night affected heart rate (HR) at the respiratory compensation point (RCP) (p=0.028). Sleep time influenced pulmonary ventilation (p=0.013), breathing frequency (p=0.010), and blood lactate concentration (Lac) (p=0.013) at RCP. Maximal power/speed (p=0.046) and HR (p=0.070) linked with the quality of sleep. Stress management and relaxation techniques linked with VO2max (p=0.046), maximal power/speed (p=0.033), and maximal Lac (p=0.045). Cardiorespiratory fitness was deteriorated after mild COVID-19 and was correlated with sleep and mental health. Medical Professionals should encourage EAs to maintain proper mental health and sleep after COVID-19 infection to facilitate recovery.

The oral cavity is thought to be one of the portals for SARS-CoV-2 entry. Because there is limited evidence of active oral infection by SARS-CoV-2 viruses, we assessed the capacity of SARS-CoV-2 to infect and replicate in oral epithelial cells. Oral gingival epithelial cells (hTERT TIGKs), salivary gland epithelial cells (A-253), and oral buccal epithelial cells (TR146), which occupy different regions of the oral cavity, were challenged with replication competent SARS-CoV-2 viruses and with pseudo-typed viruses expressing SARS-CoV-2 spike proteins. All oral epithelial cells expressing undetectable or low levels of human angiotensin-converting enzyme 2 (hACE2) but high levels of the alternative receptor CD147 were susceptible to SARS-CoV-2 infection. Viral dynamics in hTERT TIGKs were different from those in A-253 and TR146 cells. For example, levels of viral transcripts were sustained in hTERT TIGKs but were significantly decreased in A-253 and TR146 cells at day 3 after infection. Analysis of oral epithelial cells infected by replication competent SARS-CoV-2 viruses expressing GFP showed that the signals of GFP and SARS-CoV-2 mRNAs were not evenly distributed. Taken together, our results demonstrated oral epithelial cells were susceptible to SARS-CoV-2 viruses despite of low or undetectable levels of hACE2, suggesting that alternative receptors contribute to SARS-CoV-2 infection and may be considered for development of future vaccines and therapeutics.

Artificial Intelligence (AI) is used in our day-to-day life. AI for journalism is a reality. Just like other vital aspects of our life, AI has also entered the world of journalism. Many reputed news organizations have adopted AI in journalism to perform various newsroom tasks. In today’s digital world, several technologies are powering journalism. One such technology that is transforming the journalism field is artificial intelligence. Accuracy is a core value of journalism. With AI and machine learning systems, there’s a statistical element of uncertainty which means it’s impossible to guarantee 100 percent accuracy. AI tools such as OpenAI’s ChatGPT, Microsoft’s Bing chatbot, and Google’s Bard have been subject to debate now. AI tools have the potential to assist in a variety of tasks, such as scraping PDF files, writing code, and translating languages. AI can be a helpful tool for journalism students and media researchers. A main concern among computational journalists is that AI sometimes hallucinates data. This review paper highlights how artificial intelligence can help journalists.

Water is an essential commodity to sustain life. The condition of water in Tiruchirappalli was measured using different Physicochemical parameters like Temperature, pH, TDS, Total Solids, Salinity, Total Hardness, and Electrical Conductivity. Water samples were collected from different places in Tiruchirappalli city, Tamil Nadu. Water samples were examined by different chemical methods. According to the results, Thiruvarambur-1 showed comparatively higher results in the case of every parameter compared to other sampling stations. Obtained results were further interpreted using statistical tools. Considering ECs as a principal component for the regression and correlation analysis with other parameters significant correlation was found. A strong correlation was observed between ECs and TDS, Total Hardness, Turbidity, and Salinity.

Carbapenems have long been considered as treatment of choice for Gram-Negative Bacteria (GNB) infections, and today the clinical utility of this class is threatened by the emergence and spread of resistance favored by its increasing use. According to the WHO, Acinetobacter baumannii, nosocomial infection agent, tops the list of priority antibiotic-resistant pathogens, considered to be the most risky for humans. This study sought to determine the prevalence of Acinetobacter baumannii strains producing carbapenemases in four health facilities in the Center and Littoral regions of Cameroon and the associated risk factors. An analytical cross-sectional study was conducted over a six-month period from January to June 2022. All Acinetobacter baumanii or suspected strains isolated from pathological samples at the bacteriology laboratory of different health facilities were systematically collected and underwent a re-identification. After successfully subculturing and growing on nutrient agar, re-identification and antimicrobial susceptibility Testing (AST) were performed using the VITEK 2 System and the Kirby-Bauer method according to the guidelines of the Clinical and Laboratory Standards Institute (CLSI). Detection and phenotypic characterization of carbapenemases was performed according to adequate standard procedures. A total of 168/226 clinical isolates of Acinetobacter baumannii were confirmed after re-identification of which 52.69% came from male patients, and 55.09% from participants aged between 10-39 years old and 46.71% from pus samples. A very high resistance rates to all families of antibiotics was noted except to colistin (10.2%). About 40.12% of these strains produced carbapenemases with 62.69% of class B and 37.31% of class A. In addition, frequent hospitalisation was significantly associated to the production of carbapenemase among Acinetobacter baumanii (Ajusted-OR=16.53, P-value<0.0001). This study highlighted the emergence of carbapenemase-producing Acinetobacter baumannii which is increasingly growing. Continuous drug-resistant monitoring and preventive measures could help to prevent and curb the dissemination of A.baumanii resistance genes, especially in hospital settings.

Purpose Prospective life cycle assessment (pLCA) is a key method for providing environmental decision support for future technologies. The specific technology under study is often modeled by the practitioner (the so-called foreground system), whereas data for supply chains are sourced from life cycle inventory databases (the so-called background system). Within pLCA, it is paramount to consider possible future developments for both foreground and background systems to avoid a temporal mismatch between the specific technology under study and the wider economy, which could lead to incorrect conclusions. Therefore, and given the importance of future-oriented environmental decision support, efforts to develop prospective life cycle inventory (pLCI) databases have recently increased. However, there is still much to be improved to realize the widespread use of these databases and to have a stronger real-world effect on how future technology is designed. Methods This paper summarizes recent developments and structures the information chain behind pLCI databases. We then discuss conditions for the broad application of pLCI databases, and we propose priorities and ways forward to overcome existing challenges to foster the use of pLCI databases. Results The conditions are scientific integrity, usefulness to LCA practitioners, accessibility, usability, interpretability, and a continuous improvement process in which core stakeholders coordinate efforts to streamline the generation, sharing, and use of pLCI databases. Conclusions Priority should be given to installing a continuous process to improve the coverage and consistency of pLCI databases, to improve related guidance and documentation, and to remove practical barriers relating to the access and usability of pLCI databases in LCA software.

The article presents a new scalable iterative method for linear programming called the “apex method”. The key feature of this method is constructing a path close to optimal on the surface of the feasible region from a certain starting point to the exact solution of linear programming problem. The optimal path refers to a path of minimum length according to the Euclidean metric. The apex method is based on the predictor-corrector framework and proceeds in two stages: quest (predictor) and target (corrector). The quest stage calculates a rough initial approximation of linear programming problem. The target stage refines the initial approximation with a given precision. The main operation used in the apex method is an operation that calculates the pseudoprojection, which is a generalization of the metric projection to a convex closed set. This operation is used both in the quest stage and in the target stage. A parallel algorithm using a Fejér mapping to compute the pseudoprojection is presented. An analytical estimation of the parallelism degree of this algorithm is obtained. Also, an algorithm implementing the target stage is given. The convergence of this algorithm is proven. An experimental study of the scalability of the apex method on a cluster computing system is described. The results of applying the apex method to solve problems from the Netlib-LP repository are presented.

The splitting method developed in a previous study for linear, multi – component, convection-diffusion mass transfer equations is extended to non-linear, multi-component, convection-diffusion mass transfer equations. The diffusion term is non-linear (the diffusion coefficients depend on chemical species concentrations). Theoretical results about the splitting error are presented, as well as numerical simulations for mixtures with n ≤ 5. The numerical experiments show second-order accuracy and stability for the present splitting algorithm.

In a completely randomized design (CRD), 400 day-old Ross 308 broilers were randomly allotted to 5 diets with 0, 5, 10, 15 and 20% MSC, each with 8 replicates of 10. Weekly feed intake (FI), body weight gain (BWG) and feed conversion efficiency (FCE) were calculated whilst haemato-biochemistry was measured at d42. Overall, FI was linearly (P &lt; 0.05) and quadratically (P &lt; 0.01) decreased by MSC, of which the optimum inclusion was 15%, as BWG was linearly (P &lt; 0.001) and quadratically (P &lt; 0.05) decreased and FCE linearly decreased (P &lt; 0.01) by MSC, of which the optimum dietary inclusion was 10%. Also, MSC linearly and quadratically decreased slaughter weight (P &lt; 0.001 and P &lt; 0.05, respectively), hot carcass weight (P &lt; 0.001 and P &lt; 0.05, respectively) and cold carcass weight (P &lt; 0.001 and P &lt; 0.05, respectively). Similarly, it linearly decreased white blood cells (P &lt; 0.01) and lymphocytes (P &lt; 0.05) as it linearly (P &lt; 0.001) and quadratically (P &lt; 0.01) decreased symmetric dimethylarginine (SDMA) and linearly (P &lt; 0.001) increased serum cholesterol. In conclusion, up to 10% MSC can be incorporated into broiler diets in replacement of SBM without adverse effects.

As the echoes of ChatGPT's remarkable success continue to permeate the AI community, its formidable successor, GPT-4, has emerged, showing off a wealth of novel features. In this concise paper, we elucidate the capabilities of GPT-4 and conduct a comparative analysis with its predecessor, ChatGPT, offering insights into their relative strengths and advancements in the rapidly evolving field of generative AI. We also present a comprehensive summary of the major performance results reported by OpenAI on GPT-4 across various Natural Language Processing (NLP) tasks. We place great emphasis on the innovative advancements offered by GPT-4 in comparison to its predecessors. Our focus is on highlighting its remarkable performance while also mentioning its limitations. The purpose of this paper is to deliver a succinct understanding of the new features and performance benchmarks of GPT-4.

We introduce two symmetric concepts to physics: “distance” (space and time in one) and “wavematter” (electromagnetic wave packet and matter in one). We claim that physics has chosen the wrong concept of time: It is not aware that the time of a moving object flows in a direction other than my time. We provide 15 proofs for our claim by solving 15 mysteries of physics graphically! For example, we prove that length contraction and time dilation are actually geometrical effects in a 4D manifold that we call “Euclidean spacetime” (ES). We prove that the discrepancy in calculating the Hubble constant stems from a systematic error in the redshift measurement. We prove that what I deem wave, deems itself matter, which solves wave–particle duality. We even untangle quantum entanglement without the issue of non-locality. We finally understand why alternative theories of Euclidean relativity have failed: They are all based on four dimensions of space rather than distance. A huge amount of energy was injected into ES at a point that we take as origin. Ever since has this energy been moving radially away at the speed of light. We live in the 3D hypersurface of an expanding 4D hypersphere. Hyperspherical coordinates have the great advantage that they reduce all that is ever happening to just one formula. So, it is the Theory of Everything in these coordinates: “Energy is covering radial distance which, divided by Euclidean time, is equal to the speed of light.” Acceleration and force like gravitation emerge from a rotation of objects in ES and a subsequent projection to 3D space. Matching the symmetry simplifies physics.

White adipose tissue (WAT) represents an endocrinologically and immunologically active tissue that’s primary role is energy storage and homeostasis. Breast WAT is involved in the secretion of hormones and proinflammatory molecules that are associated with breast cancer development and progression. The role of adiposity and systemic inflammation in immune responses and resistance to anti-cancer treatment in breast cancer (BC) patients is still not clear. This review aims to evaluate the emerging evidence on the crosstalk between adiposity and the immune-tumour microenvironment in BC, its progression and treatment resistance. Adiposity, and by extension subclinical inflammation, are associated with metabolic dysfunction and changes in the immune-tumour microenvironment in BC. In oestrogen receptor positive (ER+) breast tumours, it is proposed that these changes are mediated via a paracrine interaction between macrophages and preadipocytes leading to elevated aromatase expression and secretion of pro-inflammatory cytokines and adipokines in the breast tissue in patients who are obese or overweight. In HER2+ breast tumours, WAT inflammation has been shown to be associated with resistance to trastuzumab mediated via MAPK or PI3K pathways. Furthermore, adipose tissue in patients with obesity is associated with upregulation of immune checkpoints on T-cells that is partially mediated via immunomodulatory effects of leptin and has been paradoxically associated with improved responses to immunotherapy in several cancers. In conclusion, evidence suggests that body composition and metabolic status are associated with patient outcomes. To optimise patient stratification and personalisation of treatment, prospective studies are required to evaluate the role of body composition and metabolic parameters in metabolic immune reprogramming with and without immunotherapy in patients with BC.

The gravitational constant is an unchanging quantity included in the law of universal gravitation. The value of this constant is known much less accurately than other fundamental physical constants, which is due to the experimental difficulties of measuring it. Here we report a simple method for the theoretical calculation of the gravitational constant using the elementary charge, speed of light, Z boson mass, and relativistic mechanics.

The increasing attendance of paediatric emergency departments becomes a serious health issue. To reduce an elevated burden of medical errors, inevitably caused by high level of stress exerted on emergency physicians, we propose potential areas for improvement in regular paediatric emergency departments. In effort to guarantee demanded quality of care to all incoming patients, the workflow in paediatric emergency departments should be sufficiently optimized. The key component remains implementing one of the validated paediatric triage systems upon patient's arrival at emergency department and fast-tracking patients with low level of risk according to the triage system. To ensure the patient's safety, emergency physicians should follow issued guidelines. Cognitive aids, such as well-designed checklists, posters or flow charts, generally improve physicians' adherence to guidelines and should be therefore available in every paediatric emergency department. To sharpen diagnostic accuracy, the use of ultrasound in paediatric emergency department, according to ultrasound protocols, should be targeted to answer specific clinical questions. Combining all mentioned improvements might reduce number of errors linked with overcrowding. The review serves not only as a blueprint for modernizing paediatric emergency departments, but also as a bin of useful literature which can be suitable in the paediatric emergency field.

These three syndromes - long covid (LC), postural orthostatic tachycardia syndrome (POTS), and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) - have many symptoms in common. The common denominator remains elusive. The blood brain barrier (BBB) has been a barrier not only to microbes and toxins but also to understanding pathogenetic links. There are several areas within the brain that have no BBB. These are known as circumventricular organs (CVOs) and their location relative to CNS nuclei that direct autonomic and neuroendocrine functions is provocative in the quest for pathogenesis. In addition the majority afflicted with LC and ME/CFS appear to be those with two MTHFR polymorphisms, present in over 50% of Americans. These polymorphisms elevate homocysteine. When homocysteine is combined with CVOs, the fog of POTS and its paradox are lifted. POTS may represent the intersection of LC and ME/CFS in those with the MTHFR gene (hypermethylation or 677TT). The gut microbiomes of LC and ME/CFS, deficient in butyrates, GABA, and diversity, are then linked with MTHFR genotype 677TT. Reactivation of neurotropic EBV and VZV, due to loss of surveillance by CD4+/CD8+ T cells, is seen as secondary. The oxidative stress generated by homocysteine, loss of glutathione, low fiber diet, and persistent chronic inflammation exhaust available mitochondria and, assisted by BKN and estrogen, exacerbate all the elements of these post viral fatigue syndromes.

Breaching and liquefaction are two types of coastal flow slides proposed in the literature. While their final results are similar, liquefaction and breaching failure are two different processes, with breaching failure characterized by a different progression and sand movement mechanisms. In densely packed sand, breaching causes delayed sand grain discharge and negative excess pore pressures. Liquefaction, when a mass of soil suddenly behaves like a liquid, flow out over overly gentle slopes. Flow slides typically go unnoticed until the slope fails above ground because they start below the water. Without modern technology, diving equipment, risk assessment, and probabilistic and sensitivity assessments, flow slides inherently challenging. The authors developed a new sensitivity index to detect the susceptibility of coastal flow slides. It was established a sophisticated hybrid model that accommodates flow slides in sync with unpredictable variables employed in this new sensitivity index. Innovative hybrid model has three distinctive models. The Hybrid Hydrodynamic Model addresses wave, wind, current, climate change, and sediment transfer. The Monte Carlo Simulation analyses sensitivity, and the Bayesian Network calculates joint probability of coastal flow slide parameters of this new index that includes environmental characteristics, including climate change. With the assistance of these three models, researchers aim at a) presenting a methodology for coupling coastal flow slide projections with outcomes; b) distinguishing two flow slides; c) examining variables influencing flow slides. The use of such a hybrid model and risk index offers a robust and computationally efficient approach to evaluating the critical angle slope for breaching failure.