The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk level in different counties have been determined and analyzed. Using Pearson Correlation, K-means clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

More than 15,000 prescriptions and over the counter drugs are available according to the US Food and Drug Administration website. Moreover, several herbal medicines and dietary supplements are readily available to add to the list of possible drugs, which can potentially cause adverse drug interactions. These are a pressing concern for all as they can interfere with many dental procedures. Additionally, the number of geriatric patients seen in routine dental practice has increased over time. This implies that there are more patients with multiple diseases and health conditions like hypertension, diabetes, problems associated with the cardiovascular, renal and gastrointestinal systems, arthritis, osteoporosis, etc. All these require patients to be on certain medications. Furthermore, advancement in the dental field has led to more complex dental procedures (implants, grafts) being carried out in a general dental practice. These advanced and slightly more invasive treatments require the use of certain drugs before, during and after the treatment like local anesthetics, vasoconstrictors, anxiolytics, analgesics and antibiotics. All of these can possibly interact with medications a patient is already taking and can also interfere with the current dental treatment and create complications. This article aims to provide necessary information about commonly encountered systemic diseases and associated treating medications, their mechanism of action, possible complications and their management. The classes of drugs discussed include anti-platelet agents, vitamin K antagonists, novel oral anticoagulants, bisphosphonates, disease-modifying anti-rheumatic drugs and oral contraceptives.

Concept Knowledge (C-K) theory has been used in engineering and science-based research for more than a decade. Design of an Information Technology (IT) artefact is mostly pragmatic in nature. Design Science Research (DSR) methodology applied and studied in many Information Systems (IS) research. Many sub design decisions involved through the design of an IT artefact from a concept (idea) to a working prototype. A DSR artefact is based on a combination of decisions made during several sub-design stages. Artefacts are built based on the selection of elements in each sub-design space. Recording the design decisions on each sub-design space would be beneficial for future researchers. By knowing the design decisions on each sub-design space, researchers would be able to try different combinations of the design. C-K theory provides the ability to capture the design processes’ several sub-design spaces. In this paper, we discuss the DSR research methodology by looking at the stages proposed in the literature, and the application of C-K theory in an IT-based DSR. This paper also proposed a C-K theory-based protocol called Concept Tree for tracking and reporting artefact design steps. The application of C-K theory in DSR is exhibited using the implementation of the Concept Tree for a prototype design IT artefact.

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

A Pogorelov polytope is a combinatorial simple 3-polytope realizable in the Lobachevsky (hyperbolic) space as a bounded right-angled polytope. It has no 3- and 4-gons and may have any prescribed numbers of k-gons, k ≥ 7. Any polytope with only 5-, 6- and at most one 7-gon is Pogorelov. For any other prescribed numbers of k-gons, k ≥ 7, we give an explicit construction of a Pogorelov and a non-Pogorelov polytopes. Any Pogorelov polytope different from Löbel polytopes can be constructed from the 5- or the 6-barrel by cuttings off pairs of adjacent edges and connected sums with the 5-barrel along a 5-gon with the intermediate polytopes being Pogorelov. For fullerenes there is a stronger result. Any fullerene different from the 5-barrel and the (5, 0)-nanotubes can be constructed by only cuttings off adjacent edges from the 6-barrel with all the intermediate polytopes having 5-, 6- and at most one additional 7-gon adjacent to a 5-gon. This result can not be literally extended to the latter class of polytopes. We prove that it becomes valid if we additionally allow connected sums with the 5-barrel and 3 new operations, which are compositions of cuttings off adjacent edges. We generalize this result to the case when the 7-gon may be isolated from 5-gons.

While lidar-based forest inventory methods have been widely demonstrated, prediction of tree diameters with lidar is not well understood. The performance metrics typically used in studies for prediction of diameters can be difficult to interpret and may not support comparative inferences between sampling designs or study areas. We evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions using two indices which are easier to interpret and compare. The indices are based on the coefficient of determination (R²), and root mean square deviation (RMSD). These indices facilitate comparisons with alternative (non-lidar) inventory strategies, and with other project areas. We evaluate k nearest neighbors (k-NN) dbh density (relative frequency by dbh class) prediction strategies with lidar for 190 training plots distribute across the 800 km² Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. Amongst the examined strategies we found Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

Molecularly imprinted polymers (MIPs) were synthesized and applied for the selective extraction of oblongifolin C (OC) from fruit extracts of Garcinia yunnanensis Hu. A series of experiments and computational approaches were employed to improve the efficiency of screening for optimal MIP systems in the study. The molar ratio (1:4) was chosen at last based on the comparison of the binding energy of the complexes between the template (OC) and functional monomers using density functional theory (DFT) at the RI-PBE-D3-gCP/def2-TZVP level of theory. The binding characterization and the molecular recognition mechanism of MIPs were further explained using the molecular modeling method and NMR and IR spectra data. The reusability of this approach was demonstrated in over 20 batch rebinding experiments. 140.5 mg of OC (>95% purity) was obtained from the 5 g extracts with 2 g of MIPs with the best binding properties through gradient elution program from 35% to 70% methanol-water solution. At the same time, another structural analog, 46.5 mg of guttiferone K (GK) (>88% purity), was also obtained by the gradient elution procedure. Our results showed that the structural analogs could be separated from the crude extracts by the molecularly imprinted solid-phase extraction (MISPE) using a gradient elution procedure for the first time.

The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

Studies carried out have revealed that every day around three thousand and twelve people lose their lives in the world due to traffic accidents and poor air quality. Large cities, with their millions of inhabitants and vehicles, face many problems relating to vehicular traffic. In 2015 the speed limit was modified on several roads in the city of São Paulo-Brazil. However, in 2017 the speed limits were increased again, but not on all previous routes. This study analyzed the impact of this change on the number of accidents and pollutant concentrations, over a period of ten years, comparing the periods before and after the implementation of the measure, using real data collected and provided by the authorities of the city and the state transit and environmental companies, on more than forty routes and two nearby air quality stations. The results show a clear reduction in the number of accidents without victims on the roads of the city of São Paulo starting in 2010. However, the restrictive measures imposed by government officials may have contributed to the decrease in the number of accidents, but the number of fatalities has not changed much. Air pollution has not improved substantially with speed changes, as new speed increases have been linked to new episodes of congestion. The average number of fatalities due to accidents has been increasing since 2010 and accidents are becoming more serious. The application of a general linear statistical model (GLM) estimated the impact of the speed reduction policy in terms of the number of injuries avoided per month: 43.4 and 14.1 on other roads and on Pinheiros marginal, respectively. The results highlight the need for a constant data collection by the authorities in cities with high vehicle traffic. The important temporal time trend in terms of reduction of injuries but not in terms of fatalities and air quality, shows the need to apply joint public policies, not only speed reduction, but also the use of new technologies and raising drivers’ awareness of the problem.

COVID-19 infection in high-risk populations is fatal and has a poor prognosis, necessitating a test to determine the protectiveness of immune response. Antibody testing is necessary to determine the body's immune response to COVID-19 infection and also vaccination strategies. Among the various methods available, chemiluminescent immunoassay (CLIA) test is more widely used and accessible to determine antibody levels. This study aimed to determine the protection level of S-RBD SARS-CoV-2 IgG using CLIA compared to Surrogate Virus Neutralization Test (SVNT). The population of this study comprised all healthcare professionals who experienced S-RBD SARS-CoV-2 IgG antibody level examinations. S-RBD SARS-CoV-2 IgG antibody levels were examined using CLIA and SVNT. The cut-off was determined using Receiver Operating Characteristic (ROC) curve and AUC (Area Under the Curve) measurements were evaluated. The result showed a strong positive correlation between S-RBD SARS-CoV-2 IgG CLIA and SVNT with a value of r=0.933 and p <0.001. The value ≥37.29 BAU/mL was determined as the cut-off based on SVNT 30% inhibition level with sensitivity, specificity, positive, and negative predictive values of 96.5%, 90.9%, 96.5%, and 90.9%, respectively. A titer of antibodies greater than or equal to 37.29 BAU/mL with CLIA showed the presence of protective antibodies compared to SVNT.

Drawing from literature and Geert Hofstede’s cultural dimensions, this study delves into the impact of cultural factors on family structures, roles, and relationships. Ghana and Latvia have different cultural and social orientations that influence family dynamics. Through a comparative lens, this paper will contribute to understanding how a country’s culture influences family dynamics, highlighting the influence of Power Dynamics of parenting style and Individualism vs. Collectivism on interaction levels within the family.

The appropriate concentration of vitamin D in the athlete’s body is essential for its proper functioning, especially in terms of muscular health, by enhancing recovery and oxygen consumption. Soccer players are required to perform frequent sprints and change of directions during training and competition. The aim of the present study was to establish whether the level of vitamin D (measured by 25-hydroxyvitamin D concentrations- 25(OH)D) in blood serum has any influence on speed (s) and change of direction (COD, s) performance. Material and methods: Twenty male soccer players from the top league in Poland participated in the study. All subjects' were evaluated for the serum concentration of 25(OH)D at the beginning of the preseason. . The linear sprint test was performed at 5m and 30m and COD (time and deficit), was evaluated at 30m at the beginning and after 6 weeks of preparatory period. Results: The results revealed that 50% of soccer players had an insufficient level or a deficit of 25(OH)D (≤ 30 ng/ml), with 20% having a significant deficiency of 30 ng/ml).There were no statistically significant correlations between the training effect for the variables ∆5m (before - after) and ∆30m (before - after). Statistically significant positive correlations were found between the training effect for the variables ∆COD (before - after) (R=0.62/p=0.003) and ∆deficit (before-after) (R=0.47/p=0.039). With a higher concentration of vitamin D in the blood, the training effect for COD and the deficit increased, which was visible in the faster performance of the COD test. No statistically significant differences were found for the mean values of the variables ∆5m (before - after) and ∆30m (before - after) in the group of subjects whose 25(OH)D concentration was 30mg /ml. Significant differences were noticed for the variables ∆COD (m=0.60 [s]) and ∆deficit (m=0.56[s]) in the group of subjects whose 25(OH)D concentration was 30mg /ml. The training effect was significantly higher for the group of subjects whose 25(OH)D concentration was above 30 mg/ml, which was observed by the faster performance of the COD test in this group of subjects. Conclusions: The serum concentration of vitamin D affects training adaptations of muscles. A lower concentration of Vitamin D seems to have a detrimental effect on the change of direction performance. Soccer players whose blood serum 25(OH)D concentrations were >30mg/ml achieved better results in the applied fitness tests.

A relativistic positioning system is a set of four emitters broadcasting their proper times by means of light signals. The four emitter times received at an event constitute the emission coordinates of the event. Covariant quantities associated with relativistic positioning systems are analysed relatively to an observer in Minkowski space-time by splitting them in their relative space-like and time-like components. The location of a user in inertial coordinates from a standard set of emission data (emitted times and satellite trajectories) is solved in the underlying 3+1 formalism. The analytical location solution obtained by Kleusberg for the GPS system is recovered and interpreted in a Minkowskian context.

. Protein cysteine S-glycosylation is a relatively rare and less well characterized post-translational modification (PTM). Creating reliable model proteins that carry this modification is challenging. The lack of available model or natural S-glycosylated proteins significantly hampers the development of MS-based methodologies for detecting protein cysteine S-glycosylation in real-world proteomic studies. There is also limited MS-sequencing data describing easier to create synthetic S-glycopeptides. Here, we present the results of an in-depth manual analysis of automatically annotated CID/HCD spectra for model S-glucopeptides. The CID spectra show a long series of y/b-fragment ions with retained S-glucosylation, regardless of the dominant m/z signals corresponding to neutral loss of 1,2-anhydroglucose from the precursor ions. In addition, the spectra show signals manifesting glucosyl transfer from the cysteine position onto Lys, Arg side chains, and a peptide N-terminus. Other spectral evidence indicates that the N-glucosylated initial products of transfer are converted into N-furanosylated (i.e., glycated) structures due to Amadori rearrangement. We discuss the peculiar transfer of the glucose oxocarbenium ion (Glc+) to positively charged guanidinium residue (ArgH+) and propose a mechanism for the gas-phase Amadori rearrangement involving a hydride ion shift.

Recent studies have shown that the endogenous cannabinoid system (ECS) of the brain is essentially involved in the pathogenesis of Parkinson's disease (PD), influencing its symptoms by regulating the level of endogenous cannabinoids and altering the activation of cannabinoid receptors (CBR). Therefore, modulation of ECS with new drugs developed for this purpose may prove to be a promising strategy in the treatment of PD. However, fine regulation of ECS is quite a challenge due to the functional diversity of CBR in the basal ganglia. Our review analyses the effects of ECS modulators on experimental PD models and in patients with PD, as well as presents the outlooks for the development of new cannabinoid drugs for the treatment of motor and non-motor symptoms in PD.

Objective: To explore motivation to exercise, support, and self-management needs among people with Parkinson’s (PwP), family-members and physiotherapists. Methods: Purposeful and maximum difference sampling methods (age, sex, geographical setting, and disease severity) were employed. PwP and family-members were recruited through physiotherapy services and local support groups. Semi-structured interviews with 12 PwP, focus groups with four family-members, and five physiotherapists were conducted. Interview guides were informed by patient-public input, and a recent systematic review. Interviews were recorded, transcribed, and analysed using thematic analysis. Results: Four common themes emerged: 1) The value of an intrinsic connection with exercise: but there are challenges. A greater connection to exercise led to long-term compliance. 2) Tailoring exercise is essential. Preferred exercises and environments were mixed, with differences emerging between sexes. 3) Physiotherapists’ aim to only maintain physical function led to frustration. Limited self-management opportunities, stigma and dehumanisation were discussed. 4) Non-motor symptoms, stigma, fear, and determination. Apathy, pain, and low mood were discussed. Conclusion: Exercise provided physical, emotional, and social rewards. Supports are necessary; however, challenges arise when PwP’s motivation are mismatched to family-members’ and physiotherapists’ goals. Co-created goals, tailored to their preferences, and exercise plans with supported self-management are recommended.

Defining integrals over volume element in curved spacetime together with the mass and energy densities within that region of spacetime is apparently equivalent to the given energy and mass of that body or particle. With this in mind equations involving energies and mass can be rewritten in terms of their densities and the integral over the volume element. The idea find its place in the Hamiltonian formulations, and can be carried over into quantum mechanical frameworks systematically accounting for the effect of spacetime volume structures and gravity on dynamics of quantum systems. This article briefly explores this concept and runs through various frameworks and equations relevant to this idea, particularly the schrodinger-Newton equation and discusses the cosmological constant as a vacuum energy density.

Ecuador is an important center of biodiversity of the plant family Amaryllidaceae, specially the subfamily Amaryllidoideae, known as a source of important bioactive molecules. The aim of this study was to assess the chemical and biological potential of four different species of Amaryllidoideae collected in Ecuador: Urceolina formosa, Urceolina ruthiana, Clinanthus incarnatus, and Stenomesson aurantiacum. Sixteen alkaloids were identified in the bulb extract of these species by GC-MS, which the extract of S. aurantiacum exhibiting the greatest structural diversity. The potential of these three genera against Alzheimer’s disease was evaluated by measuring their AChE and BuChE inhibitory activity, revealing that C. incarnatus and U. formosa (from Sucumbíos province) showed the best results with IC50 values of 1.73 ± 0.25 and 30.56 ± 1.56 g·ml-1, respectively. Molecular dynamic assays were conducted to elucidate the possible interactions that occurs among 2-hydroxyanhydrolycorine and AChE enzyme, suggesting some similarity with those observed for galanthamine. This study enhances our understanding of the biodiversity of Amaryllidoideae species from Ecuador, highlighting their potential as a source of chemical compounds with pharmaceutical potential.

Alzheimer's Disease (AD) presents a significant challenge in the wake of its multifaceted nature, characterized by cognitive decline, memory impairment, and neuroinflammation. Despite extensive research, effective pharmacological interventions remain elusive, prompting exploration into non-pharmacological approaches. Microcurrent therapy (MC), utilizing imperceptible currents, has emerged as a potent clinical protocol. While previous studies have focused on its therapeutic effects, this study investigates its impact on neuronal damage and neuroinflammation in an AD mouse model, specifically addressing potential side effects. Utilizing 5xFAD transgenic mice, we examined the effects of MC therapy on neuronal integrity and inflammation. Our findings suggest that MC therapy attenuates memory impairment and reduces neurodegeneration, as evidenced by improved performance in memory tests and preservation of neuronal structure. Additionally, MC therapy significantly decreases amyloid-beta (Aβ) plaque deposition and inhibits apoptosis, indicating its potential to mitigate AD pathology. Our study discovered that MC therapy effectively reduces glial activation by inhibiting the TLR4-MyD88-NFκB pathway, consequently leading to decreased levels of inflammatory factors TNF-α, IL-1β, and IL-6, thus implicating TLR4 in neurodegenerative disease-related neuroinflammation. Furthermore, while our study did not observe significant adverse effects, further clinical trial into potential side effects and neuroinflammatory responses associated with MC therapy is warranted.

In Mexico viticulture represents the second source of employment in the agricultural area after the fruit and vegetable sector. In developed countries, remote sensing is widely used for vineyard monitoring, however, this tool is barely used in developing countries of Iberoamerica. In this re-search, our overall objective is to characterize two vineyards in the state of Queretaro (Mexico) using Sentinel-2 and meteorological data, specifically spectral and thermal indices. Results showed that spectral indices obtained from Sentinel-2 bands have characterized adequately the phenolog-ical evolution of the different varieties of the vineyards. The Modified Soil-Adjusted Vegetation Index (MSAVI) was adequately used to discriminate the first stages of vineyards, while the Nor-malized Difference Vegetation Index (NDVI) was useful to monitor vineyards during the rest stages of vineyards. Thermal indices have shown that the best grape varieties were those that can adapt to both cooler and warmer temperatures, have a reasonable ripening period, and can produce wines with balanced acidity and flavors. In conclusion, the combination of meteorological (including thermal indices) and remote sensing data (NDVI and MSAVI) provide information for choosing the suitable grape variety for this region.

Entropy as a mathematical concept is the first of its own kind, and second to none. Barely one or two papers in literature have investigated fractal dimensions of very basic entropies, such as Shannon, Tsallis, and Rényi entropic expressions. This was a thoughtful inspiration to delve into a step forward towards a unified information- theoretic fractal theory. The storytelling of this current paper starts by introducing my third entropy formula, namely Ismail’s entropy, , as a novel generalization to Shannonian entropy with a visionary link to both long- and short-range interactions, (LRIs), (SRIs) respectively. Clearly, this would generate a fourfold unification with statistical mechanics as well as contemporary physics. Hence, this adds more credibility and weight for this study. The fractal dimension of , is identified in this paper. As an essential part of this work, it was influential to spotlight the importance of fractal geometry to space industry as well as education, which is crucial to develop our societies. Thus, some potential fractal applications to CubeSat Technologies and Education are highlighted. So, will the deep search for more ground-breaking research has come to an end. Certainly, not. This was another inspiration to propose new open problems to widen research gates to the scientific community for more research pathways. Consequently, the paper ends with closing remarks combined with some challenging open problems and the next phase of research.

Vectorial Boolean functions and codes are closely related, and each corresponding domain gives been to each other. On the one hand, various requirements of binary linear codes are needed for their theatrical interests but, more importantly, for their practical applicants (such as few-weight codes or minimal codes for secret sharing, locally recoverable codes for storage, etc.). On the other hand, various criteria and tables have been introduced to analyse the security of S-boxes that are related to vectorial Boolean functions, such as the Differential Distribution Table (DDT), the Boomerang Connectivity Table (BCT), and the Differential-Linear Connectivity Table (DLCT). In previous years, two new tables have been proposed for which the literature was pretty abundant: the $c$-DDT to extend the DDT and the $c$-BCT to extend the BCT. In the same vein, we propose extended concepts to study further the security of vectorial Boolean functions, especially the $c$-Walsh transform, the $c$-autocorrelation, and the $c$-differential-linear uniformity, and its accompanying table, the $c$-Differential-Linear Connectivity Table ($c$-DLCT). We study these properties of the novel functions at their optimal level concerning these concepts and describe the $c$-DLCT of the crucial inverse vectorial (Boolean) function case. Finally, the derived functions could lead to new families of binary minimal codes, as the recent achievements on minimal codes from low differential uniformity. We draw new avenues for future research toward linear code designs.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel coronavirus responsible for the coronavirus disease 2019 (COVID-19) pandemic, represents a serious threat to public health. The spike (S) glycoprotein of SARS-CoV-2 mediates viral entry into host cells and is heavily glycosylated. In this study, we systemically analyzed the roles of 22 putative N-linked glycans in SARS-CoV-2 S protein expression, membrane fusion, viral entry, and stability. Using α-glycosidase inhibitors, castanospermine and NB-DNJ, we confirmed that disrupting the N-linked glycosylation process blocked the maturation of the S protein, leading to impairment of S protein-mediated membrane fusion. Single substitution of each of the 22 N-linked glycosylation sites with glutamine revealed that 9 out of the 22 N-linked glycosylation sites were critical for S protein folding and maturation, resulting in reduced S protein-mediated cell-cell fusion and viral entry. Of note, N1074Q mutation markedly affected S protein stability and induced significant receptor-independent syncytium (RIS) formation in HEK293T/hACE2-KO cells, and removal of the furin-cleavage site partially compensated instability of N1074Q mutation. Although the corresponding mutation in the SARS-CoV S protein (N1056Q) did not induce RIS in HEK293T cells, the mutants N669Q and N1080Q exhibited increased fusogenic activity and induced syncytia formation in HEK293T cells. Therefore, N-glycans on the SARS-CoV and SARS-CoV-2 S2 subunits are of great importance in maintaining the prefusion state of the S protein. The study revealed the critical roles of N-glycans in S protein maturation and stability, which have implications for the design of vaccines and anti-viral strategies.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of corona-virus disease 19 (COVID-19) and employs angiotensin-converting enzyme 2 (ACE2) as the recep-tor. Although the expression of ACE2 is crucial for cellular entry, we found that the interaction of ACE2 with the Spike (S) protein in the same cells led to its downregulation through degradation in the lysosomal compartment via the endocytic pathway. Interestingly, the ability of the S protein from previous variants of concern (VOCs) to downregulate ACE2 was variant-dependent and correlated with disease severity. The S protein from the Omicron variant, associated with milder disease, exhibited a lower capacity to downregulate ACE2 than that of the Delta variant, which is linked to a higher risk of hospitalization. Chimeric studies between the S proteins from the Delta and Omicron variants revealed that both the receptor-binding domain (RBD) and S2 subunit played crucial roles in the reduced ACE2 downregulation activity observed in the Omicron var-iant. In contrast, three mutations (L452R/P681R/D950N) located in the RBD, S1/S2 cleavage site, and HR1 domain were identified as essential for the higher ACE2 downregulation activity ob-served in the Delta variant than that in the other VOCs. Our results suggested that dysregulation of the renin-angiotensin system due to the ACE2 downregulation activity of the S protein of SARS-CoV-2 may play a key role in the pathogenesis of COVID-19.

Coagulase-Negative Staphylococci (CoNS) are commensal on human body surfaces and for years, they were not considered a cause of bloodstream infection and were often regardered as contamination. However, the association of CoNS with nosocomial infection is increasingly recognized. The insertion of cannulas and intravascular catheters represents the first source of CoNS entry into the bloodstream, causing bacteremia and sepsis. They owe their pathogenic power to their ability to produce biofilms on surfaces, such as medical devices. In this study, we evaluate the adhesive capacity of CoNS isolated from blood cultures by comparing spectrophotometric phenotypic assay with genotypic analysis based on the evidence of the ica operon. We retrospectively reviewed the database of CoNS isolated from blood cultures, that were considered to be responsible for 361 bloodstream infections. Among them, we selected 89 ones who had full records. Our data show that S. epidermidis was the predominant species isolated, and with greater adhesive capacities, especially those with the complete operon. Knowing the adhesive capabilities of a microorganism responsible for sepsis can be useful in implementing appropriate corrective and preventive measures, as conventional antibiotic therapy cannot effectively eradicate biofilms.

Staphylococcus aureus are commensal bacteria present on skin and mucosae in both humans and animals, but they can also be found in food, water and in various environments. They are also considered a major pathogen having a great morbidity leading to a wide range of infections. They are included in the list of most virulent and antimicrobic resistant bacterial pathogens (ESKAPE, Escherichia coli, Staphylococcus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterococcus faecalis and faecium)) by the Center for Disease Control and Prevention (CDC), and S. aureus is part of the worldwide threat represented by the presence of antimicrobial resistance (AMR). The outcomes of an epidemiological investigation on 26,430 S. aureus isolates from a global public database (NPDIB; NCBI Pathogen Detection Isolate Browser) support the evidence of significant differences in the distribution of isolates and ARGs (Antimicrobial Resistance Genes) clusters among geographical areas of origin and among sources of the isolates. Moreover, the ARGs cluster patterns suggest an association between the isolates from human and animal populations. This outcome and the large diffusion of the pathogens among human and animal populations support the importance of gaining information on the epidemiology of these infections with a One Health approach.

Recently, we demonstrated the validity of anomalous numbers law, known as Newcomb-Benford´s law, at the mammalian neuromuscular transmission, considering different extracellular calcium concentrations . The present work continues to examine how alterations in extracellular solution modulate the first digit law in the context of the spontaneous release of acetylcholine from the neuromuscular junction. We investigated if the intervals of miniature potentials collected at the neuromuscular junction obey the law in a hyperkalemic environment. The analysis showed that the interval between the miniature potentials at high potassium concentrations follows Newcomb-Benford´s law. Also, our data allowed us to uncover a conformity fluctuation as a function of the number of intervals of the miniature potentials. Finally, we discuss the biophysical implications of the present findings.

Polymyositis is a rare condition with an unknown etiology occurring more frequently in adult women. There is a lack of evidence on the coexistence of PM and CMV infection in a patient with Hashimoto Thyroiditis hypothyroidism. However, the increasing incidence of CMV infection and autoimmune diseases overlapping points out a relationship, while the association direction remains unclear. Case outline: A 32-year-old woman recently treated for HT hypothyroidism was admitted to the hospital two weeks after being treated for common flu by the family doctor, complaining about a worsening condition with muscle pain, weakness, frequent falls, and fatigue. The first tests showed a normalized thyroid function, with elevated values of troponin and serum creatinine kinase (KC). The immunological tests revealed the presence of a high titer of CMV IgG antibodies and raised levels of CMV DNA. Pelvis MRI images demonstrated markedly elevated signals on the STIR sequences in the pelvis, thighs, and calves, indicating active and severe multifocal myositis. The diagnosis of PM was confirmed with the muscle biopsy on day 7 of hospitalization. The patient showed significant improvements within two weeks after the medical therapy and physiotherapy.

The humoral response of the COVID-19 vaccine varies from person to person. It largely depends on prior SARS-CoV-2 infection, obtaining an adequate immune response, and leaving a trace of changing antibody concentration over time. We retrospectively analyzed five clinical cases from selected patients and employees of the oncology hospital. All mild COVID-19 convalescents received the BNT162b2-Comirnaty mRNA vaccine three or four times. The levels of SARS-CoV-2 IgM- and IgG-specific antibodies, as well as S-RBD antibodies, were analyzed for two years. The concentration of antibodies was assessed in the laboratory using the chemiluminescent immunoassay CLIA, MAGLUMI. Results: (1) Active autoimmune disease stabilized the level of IgG-specific antibodies after systemic mRNA vaccination for at least six months. (2) Post-vaccination IgG and S-RBD levels decreased when vaccination was performed within three months of onset. (3) The booster dose administered only increased the S-RBD antibody levels. Declining IgG-specific antibodies were observed. (4) The S-RBD IgG levels were not correlated with the SARS-CoV-2 IgG levels in the vaccinated convalescents. (5) Subsequent reinfection with SARS-CoV-2 after vaccination three times released a more significant specific antibody response. Based on the collected data, we suggest that monitoring S-RBD antibodies is sensitive but not equivalent to a specific humoral response for SARS-CoV-2 IgG. We suggested that administering at least three doses of the mRNA vaccine should serve as the basis for immunization. The three-month interval may be the best alternative to an immunization schedule for non-immunocompromised people.

The relationship between Parkinson's disease (PD), the second most common neurodegenerative disease after Alzheimer's disease, and palmitoylation, a post-translational lipid modification, is not well understood. In this study, we have analyzed the differential palmitome present in the cerebral cortex of PD patients compared to controls (n=4 per group) to better understand the role of protein palmitoylation in PD and the pathways altered in this disease. Data-mining of the cortical palmitome from PD patients and controls has allowed to: i) detect a set of 150 proteins with altered palmitoylation in PD subjects in comparison with controls, ii) describe the biological pathways and targets predicted to be altered by these palmitoylation changes, and iii) depict the overlap between the differential palmitome identified in our study with protein interactomes of the PD-linked proteins α-synuclein, LRRK2, DJ-1, PINK1, GBA and UCHL1. In summary, we have partially characterized the altered palmitome in the cortex of PD patients which is predicted to impact cytoskeleton, mitochondrial and fibrinogen functions, as well as cell survival. Our study points out that protein palmitoylation could have a role in the pathophysiology of PD, and that comprehensive palmitoyl-proteomics offers a powerful approach for elucidating novel cellular pathways modulated in this neurodegenerative disease.

From the hospital wastewater, a novel bacteriophage was isolated and characterized. According to characterization properties, this bacteriophage belongs to the Siphoviridae family, the maximum bacteriophage titer was recorded at 37°C and a pH of 7.2, had a 44,789 bp linear double-strand DNA genome, and within the genome sequence, there are 61 genes, all of which are encoded into proteins. Although this bacteriophage does not have any virulence factors or antimicrobial resistance genes and had specific lytic activity against some antimicrobial resistance S. aureus clinical isolates.

Abstract The present work was designed to investigate antiviral potential of novel mono and oligomeric Chitosan derivatives through in-silico approaches to find potent broad-spectrum anti-viral and promising drug candidates against SARS-CoV-2 and augmentation of their mode of action. Chitosan biopolymer and its derivatives were screened virtually against spike glycoprotein and human ACE2 receptor of nCoV-19. HTCC a polymeric Chitosan has been reported to interact with the corona viral Spike (S) protein and blocks its interaction with ACE2 receptor. Better biocompatibility, structural correlations, variation in degree of decetylation and molecular weight of modified Chitosan derivatives are the key attributes for enhancement of anti-viral activity. The Chitosan derivatives constructively interact with viral S protein. N-Carboxy methyl Chitosan (NCMC) among the Chitosan derivatives displayed efficient binding affinity. NCMC, when compared to mHTCC, a monomeric Chitosan for its interaction with the S protein, RBD site and ACE2 receptor, displayed efficient binding affinity with binding energy of -7.9, -6.3 and -7.4; -6.2, -4.8 and -5.5 Kcal/mol respectively. The interaction of S protein with ACE2 and ligand mHTCC-S protein complex and NCMC-S protein complex with ACE2 was calculated through flexible docking resulted in efficient reduction of binding energy from -901.2kJ/mol to -765.06kJ/mol and-814.72kJ/mol respectively, elucidated that the binding affinity of the viral S protein for its receptor ACE2 decreases in the presence of NCMC/mHTCC. The computational study envisages the antiviral efficiency of NCMC, mHTCC and biocompatible Chitosan derivatives for the first time,as preventive intervention against Covid-19.

Critical care of neonates involves substantial usage of antibiotics and exposure to multidrug resistant (MDR) nosocomial pathogens. These pathogens are often exposed to sub-MIC doses of antibiotics which might result in a range of physiological effects. Therefore, to understand the outcome of sub-inhibitory dosage of antibiotics on Staphylococcus populations, nasal swab specimens were collected from 34 neonates admitted to the Sick Newborn Care Unit between 2017-2018, a total of 41 non-repetitive isolates were included in this study. Staphylococcus haemolyticus was the prevalent species (58.54%) with high non-susceptibility to cefotaxime (CTX) (79.16%), gentamicin (87.50%), and meropenem (54.17%). Biofilm forming abilities of S. haemolyticus isolates in the presence of sub-optimal CTX (30μg/mL), the predominantly prescribed β-lactam antibiotic, were then determined by crystal violet assays and extracellular DNA (eDNA) quantitation. CTX was found to significantly enhance biofilm production among the non-susceptible isolates (p-valueWilcoxin test- 0.000008) with increase in eDNA levels (p-valueWilcoxin test- 0.000004). Additionally, no changes in non-susceptibility were observed among populations of two MDR isolates, JNM56C1 and JNM60C2 after >500 generations of growth in the absence of antibiotic selection in vitro. These findings demonstrate that sub-MIC concentration of CTX induces biofilm formation and short-term non-exposure to antibiotics does not alter non-susceptibility among S. haemolyticus isolates.

This single-blind and cross-sectional study evaluated the role of Rho-kinase (ROCK) as a biomarker of the cardiovascular remodelling process assessed by echocardiography in competitive long-distance runners (LDR) during the training period before a marathon race. Thirty-six healthy male LDR (37.0±5.3 years; 174.0±7.0 height; BMI: 23.8±2.8; VO2-peak: 56.5±7.3 mL·kg-1·min-1) were separated into two groups according to previous training level: high-training (HT, n=16) ≥100 km·week-1 and low-training (LT, n=20) ≥70 and <100 km·week-1. Also, twenty-one healthy nonactive subjects were included as a control group (CTR). A transthoracic echocardiography was performed and ROCK activity levels in circulating leukocytes were measured at rest (48-hr without exercising) the week before the race. HT group showed higher left ventricular mass index (LVMi) and left atrial volume index (LAVi) than other groups (p<0.05, for both), also higher levels of ROCK activity were found in LDR (HT=6.17±1.41 vs CTR=1.64±0.66 (p<0.01); vs LT=2.74±0.84; (p<0.05)). In LDR a direct correlation between ROCK activity levels and LVMi (r=0.83; p<0.001), and LAVi (r=0.70; p<0.001) were found. In conclusion, in male competitive long-distance runners, the load of exercise implicated in marathon training is associated with ROCK activity levels and the left cardiac remodelling process assessed by echocardiography.

The most common form of mixed dementia (MixD) is constituted by abnormal protein deposits associated with Alzheimer´s disease (AD) that coexist with vascular disease. Although olfactory dysfunction is considered a clinical sign of AD-related de-mentias, little is known about the impact of this sensorial impairment in MixD at molecular level. To address this gap in knowledge, we have assessed olfactory bulb (OB) proteome-wide expression in MixD subjects (n=6) respect to neurologically intact controls (n=7). Around 9% of the quantified proteins were differentially expressed, pinpointing aberrant proteostasis involved in synaptic transmission, nucleoside monophosphate and carbohydrate metabolisms and neuron projection regeneration. In addition, net-work-driven proteomics revealed a modulation in cell-survival related pathways such as ERK, AKT and PDK1-PKC axis. Part of the differential OB protein set was not specific of MixD, being also deregulated across different tauopathies, synucleinopathies and tardopathies. However, the comparative functional analysis of OB proteome data between MixD and pure AD pathologies deciphered commonalities and differences between both related phenotypes. Finally, olfactory proteomics allowed to propose serum Prolow-density lipoprotein receptor-related protein 1 (LRP1) as a candidate marker to differentiate AD from MixD phe-notypes.

Since December 2019, the worldwide spread of COVID-19 has brought the majority of the world to a standstill, affecting daily lives as well as economy. Under these conditions, it is imperative to develop a cure as soon as possible. On account of some of the adverse side effects of the existing conventional drugs, researchers all around the world are screening natural antiviral phytochemicals as potential therapeutic agents against COVID-19. This paper aims to review interactions of some specific phytochemicals with the receptor binding domain (RBD) of the Spike glycoprotein of SARS-CoV-2 and suggest their possible therapeutic applications. Literature search was done based on the wide array of in-silico studies conducted using broad spectrum phytochemicals against SARS-CoV-2 and other viruses. We shortlisted 26 such phytochemicals specifically targeting the S protein and its interactions with host receptors. To validate the previously published results, we also conducted molecular docking using the AutoDockVina application and identified 6 high potential phytochemicals for therapeutic use based on their binding energies. Besides this, availability of these compounds, their mode of action, toxicity data and cost-effectiveness were also taken into consideration. Our review specifically identifies 6 phytochemicals that can be used as potential treatments for COVID-19 based on their availability, toxicology results and low costs of production. However, all these compounds need to be further validated by wet lab experiments and should be approved for clinical use only after appropriate trials.

Glyceraldehyde 3–phosphate dehydrogenase (GAPDH) is a key glycolytic enzyme, which is crucial for the breakdown of glucose to provide cellular energy. Over the past decade, GAPDH has been reported to be one of the most prominent cellular targets of post-translational modifications (PTMs), which divert GAPDH towards different non-glycolytic functions. Hence, it is termed a moonlighting protein. During metabolic and oxidative stress, GAPDH is a target of different oxidative PTMs (oxPTM), e.g. sulfenylation, S-thiolation, nitrosylation and sulfhydration. These modifications alter the enzyme’s conformation, subcellular localization and regulatory interactions with downstream partners, which impact its glycolytic and non-glycolytic functions. In this review, we discuss the redox regulation of GAPDH by different redox writers, which introduce the oxPTM code on GAPDH to instruct a redox response; the GAPDH readers, which decipher the oxPTM code through regulatory interactions and coordinate cellular response via the formation of multi-enzyme signaling complexes; and the redox erasers, which are the reducing systems that regenerate the GAPDH catalytic activity. Human pathologies associated with the oxidation-induced dysregulation of GAPDH are also discussed, featuring the importance of the redox regulation of GAPDH in neurodegeneration and metabolic disorders.

Spike (S) protein of Coronaviruses help in receptor attachment and virus entry into the host cells. While S protein is required for virus entry, it is also important as an immunogen as it is the most accessible part of the virus architecture. S protein form knob like structures (viral spikes) protruding outwards in the form of homotrimers containing an S1 and S2 as monomers. Mutations in structural proteins of virus play crucial role in determining virulence and also in many instances influencing emergence of antibody escape variants and cellular tropism. In this paper we have performed in depth analyses of spike protein sequences from various parts of the world and tried to correlate the data with possible functional relevance of such mutations.

Much of the debate on Plan S seems to concentrate on how to make toll access journals open access, taking for granted that existing open access journals are Plan S compliant. We suspected this was not so, and set out to explore this using DOAJ's journal metadata. We conclude that an overwhelmingly large majority of open access journals are not Plan S compliant, and that it is small HSS publishers not charging APCs that are least compliant and will face major challenges with becoming compliant. Plan S need to give special considerations to smaller publishers and/or non-APC-based journals.

Adlay seed samples were collected from 3 adlay growing regions (Yeoncheon, Jeonnam and Eumseong regions) in Korea during 2012. Among all the samples collected, 400 seeds were tested for fungal occurrence by standard blotter and test tube agar methods and different taxonomic groups of fungal genera were detected. The most predominant fungal genera encountered were Fusarium, Phoma, Alternaria, Cladosporium, Curvularia, Cochliobolus and Leptosphaerulina. The occurrence of Fusarium species were 45.6% and based on the combined sequences of two protein coding genes, EF-1a, Beta-tubulin and phylogenetic analysis, 10 species were characterized as F. incarnatum (11.67%), F. kyushense (10.33%), F. fujikuroi (8.67%), F. concentricum (6.00%), F. asiaticum (5.67%), F. graminearum (1.67%), F. miscanthi (0.67%), F. polyphialidiom (0.33%), F. armeniacum (0.33%) and F. thapsinum (0.33%). The ability of these isolates to produce mycotoxins fumonisin (FUM) and zeralenone (ZEN) were tested by ELISA quantitative analysis method. The result revealed that fumonisin (FUM) was produced only by F. fujikuroi and zeralenone (ZEN) by F. asiaticum & F. graminearum. Mycotoxigenic species were then examined for their morphological characteristics to confirm their identity. Morphological observations of the species correlated well with their molecular identification and confirmed as F. asiaticum, F. fujikuroi and F. graminearum.

Gilbert's syndrome (GS) is a common hereditary condition characterized by mild increases in serum bilirubin levels due to inherited defects in bilirubin metabolism. This review, based on data from peer-reviewed articles and journals spanning from 1977 to January 2024, provides a comprehensive overview of over four decades of research on GS. Early studies primarily focused on defining the clinical and genetic characteristics of the syndrome, laying the foundation for subsequent investigations. More recent research has delved into the genetic mechanisms underlying the reduced expression of bilirubin UDP-glucuronosyltransferase, which has significantly enhanced our understanding of the pathogenesis of GS. Furthermore, recent studies have shed light on the clinical implications of GS, including its association with non-alcoholic fatty liver disease and mortality risk, highlighting the complex interplay between genetic factors, bilirubin metabolism, and clinical outcomes. Despite its generally benign nature, thorough research is essential to fully understand the impact of GS on patients' lives. This review emphasizes the importance of ongoing research to deepen our understanding of GS and its broader implications.

Fraudulent transactions pose a significant threat to financial institutions and e-commerce platforms.Machine learning models, trained on historical labeled data (fraudulent vs. legitimate transactions), are often employed to identify and prevent fraud. However, real-world datasets frequently exhibit class imbalance, where fraudulent transactions (minority class) are significantly outnumbered by legitimate transactions (majority class). Machine learning models may perform poorly as a result of this imbalance, underestimating fraud and favouring the majority class. This paper proposes a novel approach to address class imbalance and improve fraud detection accuracy. We explore the implementation of FROST (Feature space RObust Synthetic saTuration) oversampling, a technique specifically designed to generate synthetic samples for the minority class. The FROST function leverages the k-nearest neighbors (KNN) algorithm and a user-defined amplification factor (m) to create synthetic data points that closely resemble existing minority class instances. We integrate the FROST-enhanced oversampling technique into the machine learning pipeline for fraud detection. The paper evaluates the effectiveness of this approach compared to traditional oversampling methods and analyzes its impact on classification accuracy metrics.

This paper provides a new discovery that adds to the contemporary theory of modelling of non-stationary queues, by capturing analytically the behavior of the root parameter, for the G1/M/1 PSFFA model of non-stationary Ek/M/1 queue. Additionally, it has been also revealed that the resulting root parameter reduces to the PSFFA model of non-stationary queue. Potential applications of PSFFA to Computer engineering are provided. Some challenging open problems are addressed combined with closing remarks and future research directions.

Malaria, leishmaniasis, and African trypanosomiasis are protozoan diseases that constitute a major global health problem, especially in developing countries; however the development of drug resistance coupled with the toxicity of current treatments have hindered their management. The implication of certain enzymes (dihydrofolate reductase, DHFR) or proteins (potassium channels) in the pathogenesis of these protozoan diseases is undeniable. In this study, a series of three DHFR inhibitors (6-5 fused heterocyclic derivatives X, Y and Z) and one K+ channel blocker (E4031) were screened for their inhibitory effect on Leishmania donovani, Plasmodium falciparum, and Trypanosoma brucei. Resazurin assay was used to assess antitrypanosomal and antileishmanial activities of test compounds, whereas the antimalarial activity was appraised through SYBR Green I test. Moreover, cytotoxicity of test compounds was evaluated on Vero, Raw 264.7 and HepG-2 cells using the resazurin-based test, whilst their pharmacokinetic properties were predicted using the online tool pkCSM. As a result, compound Y revealed a selective (selectivity index range: 2.69 - >61.4; Vero, Raw 264.7 and HepG-2 cells) and broad-spectrum antiprotozoal activity against L. donovani promastigotes (IC50: 12.4 µM) and amastigotes (IC50: 4.28 µM), P. falciparum (IC50: 0.028 µM), and T. brucei brucei (IC50: 0.81 µM). In addition, compound X inhibited the growth P. falciparum (IC50: 0.0052 µM) and T. brucei brucei (IC50: 6.49 µM). In silico screening of the active antiprotozoal compounds revealed positive drug-likeness scores as none of Lipinski's rule of five’s criteria was violated by these compounds. However, in-depth pharmacokinetic and mechanistic studies are warranted to support discovery of novel antiprotozoal agents against malaria, leishmaniasis, and African trypanosomiasis by repurposing K+ channel blocker and DHFR inhibitors.

The wind flow patterns and pressure distribution around a plus-shaped building are significantly influenced by the dimensions of its re-entrant corners and the overall building height. The present study aims to find the wind effects and study of the pressure distribution with different flow patterns. Plan area of the building is around 300 square meter while the height of the building is 50 meters by keeping the equal length of the re-entrant corners. The research is performed using ANSYS CFX academic version and the results are depicted in various forms such as pressure coefficient (Cpe), stream lines in both vertical as well as in specific surface and pressure contours. The analysis is carried out by varying the wind incident angles, from 0° to 90° at the interval of 15°. The building dimension are scale down to perform the test on length scale of 1:100. Since the considered building model exhibits symmetry along both the axes, present study mostly emphasis on examining re-entrant corners located in the building. This method yields valuable information about how air circulates and how pressure is distributed across a broad spectrum of wind angles. This study provides valuable insights into flow circulation and pressure distribution for a wide range of wind angles.

The large quantity of information retrieved from communities, public data repositories, web pages, or data mining can be sparsed and poorly classified. This work shows how to employ unsupervised classification algorithms such as K-means proper to classify user reviews into their closest category, forming a balanced data set. Moreover, we found that the text vectorization technique significantly impacts the clustering formation, comparing TF-IDF and Word2Vec. The value for mapping a cluster with movie genre was 81.34% ± 20.48 of the cases when the TF-IDF was applied, whereas Word2Vec only yielded a 53.51% ± 24.1. In addition, we highlight the impact of the removal of stop-words. Thus, we detected that pre-compiled lists are not the best method to remove stop-words before clustering because there is much ambiguity, centroids are poorly separated, and only 57% of clusters could match a movie genre. Thus, our proposed approach achieved a 94% of accuracy. After analyzing the classifiers’ results, we appreciated a similar effect when divided by the stop-words method removal. Statistically significant changes were observed, especially in precision metric and Jaccard scores in both classifiers, using custom-generated stop lists rather than pre-compiled ones. Reclassifying sparse data is strongly recommended as using custom-generated stop lists.

The present work is a numerical simulation of velocity and mixture fraction fields in a turbulent non-reaction Propane jet flow issuing into parallel co-flowing air, in isothermal condition which has been experimentally described here: http://www.sandia.gov/TNF/DataArch/ProJet.html. The objective is a better understanding of the flow structure and mixing process, a situation in which there is no chemical interaction and heat-transfer. The two-equation Realizable k-ε eddy viscosity turbulence model has been used to simulate the turbulent flow field on a 2D plane (i.e., on a 5-degree sector of the experimental domain), because Realizable k-ε more accurately predicts the spreading rate of both planar and round jets and presents the best proficiency in comparison with all versions of the k-ε models. Afterward, axial and radial profiles of mean velocities, turbulence energy, mean mixture fraction, the mixture fraction half radius (Lf), and the mass flux diagram have been numerically elicited for grid independent mesh (mesh B) and compared with corresponding experimental data to assess the numerical model. To obtain turbulence kinetic energy, k, due to lack of w' in the experimental data, the assumption of w'=v' seems to be valid. Simulations have demonstrated that mean mixture fraction (at radial profiles at locations x/D=0, 4, 15, 30 and 50) and its half radius, Lf, which characterizes jet width expanse, are prominently well-captured; Moreover, mean vertical velocity fields (in X-direction: Umean) have revealed less accuracy but still conspicuously well-captured. However, mean vertical velocity fields (in Y-direction: Vmean) have disclosed less resolution; Likewise, turbulence kinetic energy, k, have manifested moderate accuracy (between Umean and Vmean). It should be noticed that although numerical results for absolute pressure, p, have been obtained on aforementioned sections, there were no experimental data to compare with them. Thus, the corresponding numerical data has not been demonstrated in this study.

In this paper, we give explicit asymptotic formulas for some sums over primes involving generalized alternating hyperharmonic numbers of types I, II and III. Analogous results for numbers with $k$-prime factors will also be considered.

Human endogenous retroviruses (HERVs) are ancient retroviral DNA sequences established into germline. They contain regulatory elements and encoded proteins few of which may provide benefits to hosts when co-opted as cellular genes. Their tight regulation is mainly achieved by epigenetic mechanisms which can be altered by environmental factors, e.g. viral infections, leading to HERV activation. Aberrant expression of HERVs associates with neurological disease, such as multiple sclerosis (MS) or amyotrophic lateral sclerosis (ALS), inflammatory processes and neurodegeneration. This review summarizes recent advances on the epigenetic mechanisms controlling HERV expression and the pathogenic effects triggered by HERV derepression. The article ends describing new promising therapies targeting HERV elements, one of which, temelimab, has completed phase II trials with encouraging results in treating MS. The information gathered here may turn helpful in the design of new strategies to unveil epigenetic failures behind HERV-triggered disease, opening new possibilities for druggable targets and/or for extending the use of temelimab to treat other associated diseases.

Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the k-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex & Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) signals is a key signal processing problem. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new SWD method with a low computational complexity that can be easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the Morlet 1-D decomposition is applied. The generalized Gaussian distribution (GGD) statistical model is fitted to the resulting wavelet coefficients. A k-nearest neighbors (k-NN) self-supervised classifier is trained using the GGD parameters to detect the spike-and-wave pattern. Experiments were conducted using 106 spike-and-wave signals and 106 non-spike-and-wave signals for training and another 96 annotated EEG segments from six human subjects for testing. The proposed SWD classification methodology achieved 95 % sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These results set the path to new research to study causes underlying the so-called absence epilepsy in long-term EEG recordings.

Specifically this work is based on the concept of ‘dark energy’ as a phenomenal effect of expansion through which a theoretical value of Hubble’s constant has been introduced. It is a constant that is based on a postulate that the square root of ratio of the Siva’s constant ‘K’ and Hubble’s constant ‘H’ is exactly equal to the diameter of the neutral hydrogen atom. That is the, exact theoretical value of Hubble’s constant. This is always constant .However, space-time conversions and the changes in the velocity of light will affect the distance and velocity in Hubble’s equation. Deviation factors in velocity, distance and the Hubble’s constant have been calculated separately which are affected by change of velocity of light with expansion of space time. The observations are related to red shift. So the change factor in terms of Hubble’s constant has been calculated with respect to red shift. Thus a factor which affects the theoretical Hubble’s constant has been calculated to find the exact value of Hubble’s constant that satisfies the experimental results. The present theoretical value of Hubble’s constant has been calculated. The experimental results by plank, ACT (Atacama Cosmology Telescope), WMAP (Wilkinson Microwave Anisotropy Probe) etc. experiments are at par with the theoretical result. Thus the ontology of space time introduced in this theoretical work supposed to be correct and leads to a new theory of origin of universe for which a brief description has been provided.

Hyperspectral imaging provides vital information about the objects and elements present inside the image. That’s why they are very useful in satellite imagery as well as image forensics. Hyperspectral document analysis (HSDI) can be used for document authentication using ink analysis which can provide sufficient information about the composition and type of ink. In this project, we have implemented HSDI based ink classification technique using Principle Component Analysis for dimensionality reduction and K-means clustering for ink classification. This is unsupervised learning approach and it is very simple and efficient in order to classify limited number of bands. We have used this technique to classify 33 different bands of ink.

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network(NN) along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and Mean Squared Error (MSE) of 0.1168.

Imaging techniques such as computed tomographies (CT) play a major role in clinical imaging and diagnosis of malignant lesions. In recent years, spectral CT has emerged in the field of computed tomographies, utilizing detailed information from extracted spectral parameters of the specimen. Metal nanoparticle platforms enable effective payload delivery for this technique. Due to the possibility of surface modification, metal nanoparticles are predestined to facilitate molecular tumor targeting. In this work, we demonstrate the feasibility of anti-plasma membrane Heat shock protein 70 functionalized gold nanoparticles (AuNPs) for tumor specific multimodal imaging. Membrane-associated Hsp70 is exclusively presented on the plasma membrane of malignant cells of multiple tumor entities, but not on corresponding normal tissue cells, predestining this target epitope for tumor-selective in vivo targeting. In vitro microscopical analysis revealed the presence of cmHsp70.1-AuNP in the cytosol of tumor cell lines, being internalized via the endosomal-lysosomal pathway. In tumor bearing mice the biodistribution as well as the intratumorally enrichment of AuNP were examined 24h after i.v. application, in vivo. In parallel to spectral CT analysis, histological analysis confirmed the presence of tumor cells. In contrast to control NP, a significant enrichment of cmHsp70.1-AuNPs has been detected selectively in tumors of different preclinical mouse models. Furthermore, the biodistribution of AuNP, following i.v. injection, was analyzed by a machine-learning approach on digitalized slides. In summary, utilizing mHsp70 on tumor cells for guidance of cmHsp70.1 antibody functionalized nanoparticles enables sufficient enrichment and uniform distribution of AuNPs in mHsp70-expressing tumor cells, adequate for various microscopical imaging techniques and spectral-CT-based tumor delineation, in vivo.

Various peptides or non-structural amino acids are recognized by their specific target proteins and perform biological role in various pathways in vivo. Understanding the interactions between target protein and peptides (or non-structural amino acids) provides key information on the molecular interactions, which can be potentially translated to the development of novel drugs. However, it is experimentally challenging to determine the crystal structure of protein-peptide complexes. To obtain structural information on substrate recognition of peptide-recognizing enzyme, X-ray crystallographic studies were performed using triglycine (Gly-Gly-Gly) as main-chain of peptide. The crystal structure of Parengyodontium album Proteinase K in complex with triglcyine was determined at 1.4 Å resolution. Two different bound conformations of triglycine were observed at the substrate recognition site. The triglycine backbone forms stable interactions with β5-α4 and α5-β6 loops of main-chain. One of the triglycine-binding conformations was identical with the binding mode of a peptide-based inhibitor from a previously reported crystal structure of Proteinase K. Triglycine has potential application X-ray crystallography to identify substrate recognition sites in peptide binding enzymes.

A non-Newtonian fluid model is used to investigate the two-dimensional pulsatile blood flow through a tapered artery with a stenosis. The mixed convection effects of heat and mass transfer are also taken into account. By applying non-dimensionalization and radial coordinate transformation, we simplify the system in a tube. Under the finite difference scheme, numerical solutions are calculated for velocity, temperature concentration, resistance, impedance, wall shear stress and shearing stress at the stenosis throat. Finally, Quantitative analysis is carried out.

Although the signaling function of Na/K ATPase is been studied for decades, the chasm between the pumping function and the signaling function of Na/K – ATPase is still an open issue. This article explores the relationship between ion pumping and signaling with attention to the amplification of oxidants through this signaling function. Starting with some experimental observations published by our laboratories and others, we develop some predictions regarding cellular oxidant stress.

: This Systematic Review focused on the use of Bifidobacterium infantis and Bifidobacterium breve in neurodegenerative diseases. The study of metabolomics has demonstrated the relationship between dysbiosis and neuronal death. The modulation of the gut microbiota in the intervention of these pathologies has become a promising area of study, showing beneficial results such as the improvement of synaptic function, the reduction of neuroinflammation, as well as the improvement of associated neuropsychiatric symptoms. Several studies have addressed the administration of Bifidobacterium infantis and Bifidobacterium breve, alone, in conjunction or in combination with other strains, in the intervention of neurodegenerative diseases. This Systematic Review includes the results of 17 research articles (n=5 in humans and n=12 in animal models) and the main result was that the use of these Bifidobacterium strains, alone, in conjunction or in combination with other strains, produced improvements in neurodegenerative disease-related symptomatology and degenerative cellular processes. However, some studies shown neutral results in certain variables. These data suggest that the use of these probiotics has a neuroprotective effect that may delay the progression of the disease. This research provides relevant and updated information on the use of these Bifidobacterium strains in neurodegenerative pathologies such as Alzheimer's disease and Parkinson's disease.

We report positive correlations between letter frequency and symmetry (more symmetric symbols are more frequent) in English and Russian languages and in four types of numerals (Western, Roman, Arabic, and Chinese) with the coefficients of determination of R2=0.899 (English), R2=0.693 (Russian) and R2=0.740 (numerals). For comparison, we studied "symbol-like" 2D colloidal clusters and found negative correlations between frequency and symmetry (R2=0.814 and R2=0.994). While in human-made systems the frequency of using of a symbol is the driving force which leads to symbols simplification and symmetrizing, in natural system, symmetry leads to lower frequency of occurrence because there are fewer ways of building symmetric clusters than asymmetric ones. We interpret these results as a correlation between the symbol as a type and as a token and we attribute this correlation to the inherent iconicity of symbols

Although a lot of research has been carried out on hand sanitizer as an effort to prevent Covid-19, there is still limited research on non-alcoholic hand sanitizer lotion from the brown algae Scytosiphon lomentaria (S. lomentaria) as a raw material that provides antibacterial effects and maintains healthy skin. Therefore, this study aimed to analyze microbial inhibitory ability, physical and chemical characteristics of hand sanitizer lotion made from S. lomentaria algae. S. lomentaria powder was tested for phytochemicals, total phenolic content, free radical scavenging activity (IC50), and antibacterial tests. S. lomentaria slurry was prepared by adding distilled water (1:1) for lotion preparations in four treatment groups, such as normal control (lotion base without adding algae), lotion with the addition of algae 20%, 30%, and 40%. The lotion preparations for each treatment group were tested for antibacterial activity of Staphylococcus aureus and Escherichia coli, phytochemicals, pH, adhesion, spreadability, homogeneity, emulsion type, and hedonic. S. lomentaria powder contains bioactive polyphenolic compounds with a total phenolic content of 117.70±1.40 mg GAE/g dry weight and an IC50 value of 455±3.4 ppm, while lotion with the addition of S. lomentaria contains polyphenols and saponins The best treatment was shown by 40% S. lomentaria hand sanitizer lotion with inhibition of 6.30 ± 0.64 mm E. coli, 6.85 ± 1.12 mm S. aureus, hedonic test for color, texture and absorption with likes to likes very much level, neutral pH, greenish-white color, O/W emulsion type, high adhesion, homogeneous, and easily absorbed by the skin. This research can be an alternative reference in making non-alcoholic hand sanitizer lotion that is safe for the skin.

Parkinson’s disease (PD) is a common multisystem neurodegenerative disorder affecting 1% of the population above 60 years. The main neuropathological features of PD are the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of alpha synuclein (Syn)-rich Lewy bodies both manifesting with classical motor signs. Syn has emerged as a key protein in PD pathology as it can spread through synaptic networks to reach several anatomical regions of the body contributing to the appearance of non-motor symptoms (NMS) considerate prevalent among individuals before PD diagnosis and persisting throughout the patient’s life. NMS mainly include loss of taste and smell, constipation, psychiatric disorders, dementia, rapid eye movement (REM) sleep behavior impairment, urogenital dysfunction, and cardiovascular impairment. This review summarizes the more recent findings showing the impact of Syn deposits on several prodromal NMS and emphasizes the importance of early detection of Syn toxic species in biofluids and peripheral biopsies as prospective biomarkers in PD.

The excessive and prolonged use of the electronic devices with digital screens may cause some serious health problems. The ocular surface problems particularly Dry Eye Syndrome may be the most common and preventable one of them. In this study, unfavourable and potentially harmful effects of digital screens on lacrimal system and tear functions were evaluated in 221 participants. Their ocular surface conditions were evaluated by Ocular Surface Disease Index (OSDI) scores and their lacrimal functioning was measured by Schirmer’s test.. A reciprocal and statistically significant relationship was found between dry eye symptoms and OSDI scores. This relationship was more prominent especially in long duration use of smartphones with brighter background lights..

Parkinson’s disease (PD) is the second most common degenerative disease which may cause blood pressure abnormalities due to autonomic dysfunction. Early morning rise of blood pressure which is called morning surge is found to be related with some cardiovascular diseases when it is exaggerated. We wanted to search the relation of Morning blood pressure surge (MBPS) and inflammation in PD. Method: Ambulatory blood pressure monitoring for 24 hours, fibrinogen to albumin ratio, hs-CRP are used in this retrospective study. Fifty idiopathic PD patients and 50 age and sex matched control cases were included. MBPS, it has been defined as the difference between morning BP (measured 2 hours after awakening) and the lowest night-time BP. Body mass index (BMI) is used as an independent variable. Results: Median value of MBPS for PD group (21,63) was significantly different from the control group values (18,63) (p

Alzheimer´s disease (AD) is characterized by the brain deposition of senile plaques composed by amyloid-β peptides (Aβ) that increase inflammation. An endogenous peptide derived from the insulin-like growth factor (IGF)-I, glycine-proline-glutamate (GPE) has IGF-I-sensitizing and neuroprotective actions. Here, we examined the effects of GPE on Aβ levels and hippocampal inflammation generated by the intracerebroventricular infusion of Aβ25-35 during 2 weeks (300 pmol/day) in ovariectomized rats and the signaling-related pathways and levels of Aβ-degrading enzymes associated with these GPE-related effects. GPE prevented the Aβ-induced increase in the phosphorylation of p38 mitogen-activated protein kinase and the reduction in activation of signal transducer and activator of transcription 3, insulin receptor substrate-1 and Akt, as well as on interleukin (IL)-2 and IL-13 levels in the hippocampus. The functionality of somatostatin, measured as the percentage of inhibition of adenylate cyclase activity and the levels of insulin-degrading enzyme were also preserved by GPE co-treatment. These findings indicate that GPE co-administration may protect from Aβ insult by changing hippocampal cytokine content and somatostatin functionality through regulation of leptin- and IGF-I-signaling pathways that could influence the reduction in Aβ levels through modulation of levels and/or activity of Aβ proteases.

With the increasing occurrence of rotator cuff injuries every year, there is a great need for a reliable treatment option. Wharton’s Jelly contains several components that can positively impact the replacement and repair of musculoskeletal defects. The overall objective of this study is to evaluate the improvement of patient-reported pain scales after applying Wharton’s Jelly (WJ) in rotator cuff defects. Eighty-seven patients with rotator cuff defects who failed at least eight weeks of conservative treatment were selected from the retrospective repository. 2cc of WJ flowable allograft was applied to the specific affected anatomy, the most common being supraspinatus tendon, biceps tendon insertion, labral tear, and subscapularis tear. No adverse reactions were reported. Statistically significant improvements were found from the initial to Day 90 in all scales. Patient satisfaction was calculated using minimal clinically important differences. No statistically significant differences were found in mean changes between gender, BMI, and age. Scanning Electron Microscopy images reveal the similarities between the collagen matrix in WJ and the rotator cuff. The significant improvement in patient outcomes coincides with current literature analyzing WJ applications with other structural defects around the body. WJ is a promising alternative for musculoskeletal defects when the standard of care fails.

Cow’s Milk (CM) is a healthy food consumed worldwide by individuals of all ages. Unfortunately, 'lactase deficient' individuals cannot digest milk's carbohydrate lactose depriving themselves of highly beneficial milk proteins like casein, lactoalbumin and lactoglobulin, due to lactose intolerance (LI), while other individuals develop cow milk allergy specifically against these proteins (CMPA). The management of these conditions is distinctly different and an inappropriate diagnosis or treatment may have significant implications for the patients, especially if they are infants or very young children, resulting in unnecessary dietary restriction or avoidable adverse reactions. Omics technologies play a pivotal role in elucidating the intricate interactions between nutrients and the human body, spanning from genetic factors to the microbiota profile and metabolites. This comprehensive approach enables the precise delineation and identification of distinct cohorts of individuals with specific dietary requirements, so that tailored nutrition strategies can be developed. This is what is called personalized nutrition or precision nutrition (PN), the area of nutrition that focuses on the effects of the nutrients over the genome, proteome and metabolome, promoting well-being and health, preventing diseases, reducing chronic disease incidence, and so increasing life expectancy. Here, we report the scientific community opinion proposing to replace the approach “one size fits all” with tailor-made nutrition programs, designed by integrating nutrigenomic data together with clinical parameters and microbiota profiles, taking into account the individual lactose tolerance threshold and needs in terms of specific nutrients intake. This customized approach could help LI patients to improve their quality of life, overcoming depression or anxiety, often resulting from the individual perception of this condition as different from a normal state.

This research proposes an algorithm to sign messages using the ElGamal asymmetric cryptosystem, called: Digital signature algorithm based on ElGamal, Diffie-Hellman protocol and the number π (DSAπ). This proposal has a high level of security like the DSA standard, in addition, execution times are reduced in relation to it. The Diffie-Hellman protocol and the number π were used in the construction of the digital signature algorithm in conjunction with the Hash-Sha-512 function. Some experiments are presented to provide evidence that the bits on the right side of the decimal point of π appear randomly, such as: a color image was constructed of a string of consecutive bits chosen at random, of the number π. Subsequently, parameters such as: entropy, correlation, discrete Fourier transform, goodness-of-fit test χ2 were evaluated. It is mentioned that an S-box 8×8 is involved in the signature. The sensitivity of the signature was measured, that is, when small changes are made to the sender’s private key, or small changes to the message. Finally, an image was constructed using the signatures of random texts, and subsequently the randomness of its pixels was obtained, to verify that there is no relationship between the different signatures.

Intrusion detection is generally carried out by matching network traffic patterns with known attack patterns or by identifying abnormal network traffic patterns. One statistical methodological approach used in intrusion detection is Statistical Process Control (SPC) by constructing a control chart. Hotelling’s T2 control chart is a multivariate control chart commonly used to monitor the mean process. The performance of the T2 chart in monitoring mean shifts can be increased if a robust estimator is utilized. Based on previous research, T2 based on the Fast-MCD estimator has good performance in monitoring low to medium outlier contaminated data. Therefore, the MRCD estimator can be used to detect intrusion. On the other hand, this research focuses on developing a bootstrap-based robust Hotelling’s T2 charts with Fast-MCD and MRCD estimators for evaluating performance in detecting intrusion on intrusion detection datasets. Based application of UNSW-NB15, the proposed chart has better performance than the conventional T2 and Fast-MCD-based T2 despite the longer execution time.

The Sidi Bou Othmane (SBO) pegmatite district is in the Central Jebilet massif, Occidental Meseta domain, Morocco. The SBO district is hosted essentially in a volcano-sedimentary series composed of the Late-Devonian Sarhlef shales. Pegmatite bodies crop out as dykes, which are oriented from N-S to E-W and are generally variably deformed with ductile and/or brittle structures with ante, syn- or post kinematic criteria. Petrographic observations of pegmatite dykes show that feldspars (i.e., albite, microcline) are the most abundant mineral phases, followed by quartz, micas, with tourmaline and accessory mineral as garnet, and zircon and secondary minerals as clinochlore, sericite, and illite. The geochemical study indicates that, according to the major pegmatite elements dykes of SBO have mainly a S-type granitic composition and are peraluminous granite with calc-alkalic affinity. The study of trace elements pointed out that SBO pegmatite formed under post-orogenic syn-collision context during the Variscan orogeny, by the partial melting of argilliferous sediment and can be ascribed to the muscovite-bearing pegmatite; moreover, they have a good potential on ceramic and contain minerals, such as feldspar, recently assessed as Critical raw materials by European Union.

The work is devoted to the development of methods for constructing asymptotic formulas as x→∞ of a fundamental system of solutions of linear differential equations generated by a symmetric two-term differential expression of odd order. The coefficients of the differential expression belong to classes of functions that allow oscillation (for example, those that do not satisfy the classical Titchmarsh-Levitan regularity conditions). As a model equation, the 5th order equation i2p(x)y′′′′′+p(x)y′′′′′+q(x)y=λy, for which various cases of behavior of the coefficients p(x),q(x), is investigated. New asymptotic formulas are obtained for the case when the function h(x)=−1+p−1/2(x)∉L1[1,∞) significantly influences the asymptotics of solutions to the equation. The case when the equation contains a nontrivial bifurcation parameter is studied.

Blood biomarkers represent a promising future for studying cognitive impairment, particularly in Alzheimer's Disease (AD), as they offer a non-invasive alternative to cerebrospinal fluid tests and have potential as population screening tools. However, the relationships between these biomarkers and specific cognitive functions, as well as their utility in predicting longitudinal cognitive decline, are not yet fully understood. This descriptive review surveys the literature from 2018 to 2023, focusing on the associations between Amyloid-β, Total Tau, Phosphorylated Tau (p-tau), Neurofilament Light (Nfl), and Glial Fibrillary Acidic Protein (GFAP) with cognition. The reviewed studies are heterogeneous, varying in design and population (mixed, cognitively impaired, or unimpaired), and show results that are sometimes conflicting. Generally, cognition positively correlates with Aβ levels, especially when evaluated through the Aβ42/Aβ40 ratio. In contrast, Tau, Nfl, and GFAP levels typically show a negative correlation with cognitive performance. While p-tau measures tend to show stronger associations with cognitive functions than other biomarkers, no single blood marker has emerged as predominantly associated with a specific cognitive domain. These findings add to our understanding of the complex relationship between blood biomarkers and cognitive performance and underscore their potential utility in clinical evaluations of cognition.

It is important to develop new antibacterial drugs since the emergence of bacterial resistance to available antibiotics. Metal nanoparticles have the potential to combat antibiotic resistance and are effective antibacterial agents. Here, we demonstrate the biogenic production of zinc nanoparticles using Rhododendron arboreum stem bark. The biosynthesized ZnNPs were characterized through different analytical techniques such as UV-vis, FTIR, EDX, SEM, and XRD. Clinical isolates such as E. coli, S. aureus, and K. pneumonia were used to check the antibacterial efficacy of the biosynthesized zinc nanoparticles. The MIC and MBC values against K. pneumonia, S. aureu, and E. coli were found to be 34±0.21 and 11.71±0.47, 47±0.11 and 23.86±0.84 and 94±0.18 and 40.43±0.16 µg/mL, respectively. It dramatically decreased the integrity of the cell membrane and markedly increased the permeability of both inner and outer membranes. The green synthesized ZnNPs have good catalytic activity. It degraded 65% of methyl orange dye within 6h when the mixture was kept in direct sunlight, thus demonstrating good photodegradation potential and can be employed as a good photocatalyst. According to these results, biosynthesized zinc nanoparticles could be a potential therapy option for controlling bacterial pathogenesis and an effective photocatalyst for the elimination of hazardous water contaminants.

Following on from work already carried out in our laboratory on a new biomass-derived green solvent, eucalyptol, we are now turning our attention to sabinene as a new green solvent. Sabinene is also derived from biomass. We have shown that it can be used as such or distilled to synthesize thiazolo[5,4-b]pyridine heterocycles by thermal or microwave activation. This new solvent was compared with various conventional and green solvents. The conditions were optimized to enable us to carry out the syntheses in good yields, and we were able to show that sabinene, a natural bicyclic monoterpene, could be used effectively as a solvent.

Perinatal stress experienced by mothers of very premature newborns may influence the mother’s milk and the infant’s intestinal microbiota. This prospective study of mothers of very preterm infants fed with mother´s own milk (MOM) was carried out in a tertiary hospital over a 2-year period. Assessment of maternal stress in 45 mothers of 52 very preterm newborns using the parental stress scale (PSS:NICU) that revealed an inverse relationship between stress and MOM production in the first days of life (p=0.012). The greatest contributor to stress was the one related to the establishment of mother-child bond. Maternal stress was lower in mothers in whom the kangaroo method was established early (p=0.011) and in those with a higher educational level (p=0.032). Levels of fecal calprotectin (FC) decrease with the passage of days and were directly correlated with birthweight (p=0.044). FC levels 7 days post-delivery were lower in newborns that received postnatal antibiotics (p=0.027). High levels of maternal stress resulted in progressive decreases and increases in the proportions of Firmicutes and Proteobacteria species, respectively, over 15 days post-delivery, both in MOM and in fecal samples from premature newborns. These findings underscore the importance of recognizing and appropriately managing maternal stress in neonatal units, given its marked influence on both the microbiota of maternal milk and the intestinal microbiota of premature newborns.

Currently, a wide variety of velocity profiles are used in motion control. Thus, this paper compares the energy consumption of three parabolic, trapezoidal, and S-curve profiles. We studied the case of using embedded systems since they are a powerful solution to monitor and coordinate the movement of devices and machinery in various industries combined with motion control applications. These integrated systems allow precise and efficient control of motors and actuators through control and feedback algorithms. In addition, we propose an alternative methodology for implementing motion controllers using an Advanced RISC Machine (ARM) microcontroller, which computes the trajectory and the control action in real-time. We conducted an experiment using a linear plant composed of a direct current (DC) motor coupled to an endless screw where a carriage is mounted that can move mechanically along a rail a distance of 1.16m. In addition, a 4096 pulses per revolution (PPR) encoder is connected to the motor to measure position and angular velocity, which compute the distance the carriage has moved in meters. A current sensor is used to assess the energy consumption, and 40 tests for each profile were carried out to compare the energy consumption for the three-motion profiles considering cases with and without load on the carriage.

A simulation test system for the two-dimensional (2D) fuze overload loading process was designed to address the loading issues of recoil overload and centrifugal overload in fuze safety and arming (S&A) devices. By incorporating centrifuge rotation energy storage, impact acceleration simulation, and equivalent centrifugal rotation simulation, a block equipped with a fuze S&A device was accelerated instantly upon being impacted by a centrifuge-driven impact hammer, simulating recoil overload loading. The impact hammer was retracted instantaneously by adopting an electromagnetic brake, which resulted in the centrifugal rotation of the block around its track, to simulate the centrifugal overload loading. The dynamic equations of the simulation test system and the equations of impact hammer motions were established, whereby the rotation speed of the centrifuge and the braking force of the electromagnetic brake were calculated and selected. A dynamic model of the collision between the impact hammer and block was established using ANSYS/LS-DYNA software for simulation analysis. The acceleration curves of the recoil overload and centrifugal overload with variations in the centrifuge speed, cushion material, and buffer thickness were obtained, which verified the feasibility of the proposed loading simulation method. 2D overload loading simulation tests were performed using the developed simulation system, and the acceleration curves of the recoil overload and centrifugal overload were measured. The test results indicated that the proposed system can accomplish 2D overload loading simulations for a recoil overload of several 10000 g and centrifugal overload of several 1000 g.

A newly UHPLC-MS/MS method development and validation for S-Allyl Cysteine was used to evaluate the comparative pharmacokinetic parameters. SC PLGA NPs were developed by the emulsion solvent evaporation method. SC PLGA NPs showed their drug loading and encapsulation efficiency i.e. 5.13±0.10% and 82.36±4.01%, respectively. SC PLGA NPs showed a spherical morphology on an average size (134.8±4.61nm), PDI: 0.277±0.004, and −25.3±1.03mV zeta-potential is suitable for oral delivery. Development and validation of the UHPLC-MS/MS bioanalytical method were performed successfully for PK-parameters examinations with 1.219-minutes RT, MS (162.00/73.10), and a total run-time was 2.0-minutes. 1.0–1000.0ng/mL was a linear-range with inter & intra-day accuracy (92.55–99.40%) followed by precision (1.88–4.23%). SC PLGA NPs oral bioavailability was significantly higher (**p<0.01) as compared to SC-S treated groups (iv & oral). We found that the antimicrobial activity of SC PLGA NPs was more effective than pure S-Allyl-L-Cysteine with significant results (p<0.01) as compared to SC-S. SC PLGA NPs showed fitted physicochemical and enhanced antimicrobial properties which can be helpful for oral administration. On the basis of our observations, SC PLGA NPs suggested the highest potential for the improvement of oral bioavailability with a sustained and controlled release of S-Allyl-L-Cysteine delivery.

Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen and is the leading cause of infection in patients with cystic fibrosis (CF). The ability of P. aeruginosa to evade host responses and develop into chronic infection causes significant morbidity and mortality. Several mouse models have been developed to study chronic respiratory infections induced by P. aeruginosa, with the bead agar model being the most widely used. However, this model has several limitations, including the requirement for surgical procedures and high mortality rates. Herein, we describe novel and adapted biologically relevant models of chronic lung infection caused by P. aeruginosa. Three methods are described, a clinical isolate infection model, utilizing isolates obtained from patients with CF, an incomplete antibiotic clearance model, leading to bacterial bounce-back and the establishment of chronic infection, and an adapted water bottle chronic infection model. These models circumvent the requirement for surgical procedure and importantly, can be induced with clinical isolates of P. aeruginosa and in wild-type mice. We also demonstrate successful induction of chronic infection in the transgenic βENaC murine model of CF. We envisage that the models described will facilitate the investigations of host and microbial factors, and the efficacy of novel antimicrobials, during chronic P. aeruginosa respiratory infections.

The research is a deep study of the CBOE Volatility Index (VIX), which represent the market sentiments of the S & P 500 Index (SPX) over a period of 33 years. It evaluates how the two indexes perform in the stock market crash and how reliable VIX is in predicting market sentiments. The research distributes the market rally in two forms, and one is a trust rally, when investors are happy to see the market in a stable form, and investors' sentiments are positive towards the stock market. Second is a fear rally, where investors' sentiments are negative, and their outlook is bearish for various reasons. It observes the pattern of rallies, like the longest trust or fear rally, in which year and its duration. The research analyzes data to reach a meaningful conclusion and various inferences that can be drawn from the study of historical data.

Methyl Salicylate (MS), the principal constituent of Wintergreen oil (WO) was obtained from acetyl salicylic acid (ASA) by sequential transesterification-esterification reaction promoted by NbCl5for the first time. The reagents were added simultaneously, and the reaction process involved the transesterification and esterification reactions, which were accompanied by thin layer chromatography and gas chromatography. The conversion rate via GC was 100%, and the MS yield was 94%. A cytotoxicity of 50% and 64% for cultured S. aureus and metastatic melanoma cells, respectively, was observed for a concentration of 0.6 mg/mL, whereas no cytotoxicity for non-tumor cells was observed for this concentration, and it is considered to be the optimum concentration.

In response to the new concept of the impact of total climate production factors on plant phenology, this study will verify the feasibility of simulating plant phenology and triggering thresholds based on total climatic production factors by using the phenological and meteorological observation data of S. krylovii plant from 1985 to 2018 at the Xilinhot National Climate Observatory of China Meteorological Administration. The results indicate that the total climate production factors influencing plant phenological changes can be well used for phenological simulation and its triggering thresholds. The mutation of cumulative climate production potential based on total climate production factors can effectively indicate the green-up date and the wilting date of S. krylovii plant, and their triggering thresholds depend on the parameters of climate resource change and plant biology, which are (0.085, –5.363) and (0.086, –27.620), respectively. The cumulative climate production potential based on total climate production factors can effectively indicate the heading date of S. krylovii plant, and its triggering thresholds also depends on the parameters of climate resource change and plant biology, which is (394.632，–38026.268). Furthermore, the results support the viewpoint that the climate abrupt changes determine the beginning and the ending of plant growth, while the accumulative climate resources determine the other phenological dates. This study provides new ideas for the study of plant phenology.

With the explosion of the generation, transmission and sharing of image data over the Internet and other unsecured networks, the need and significance for the development of novel image encryption algorithms is unprecedented. In this research work, we propose a novel framework for image encryption that is based on two hyperchaotic maps, utilized in conjunction with the single neuron model (SNM). The framework entails three successive stages, where in every stage, a substitution box (S-box) is applied, then XORing with an encryption key is carried out. The S-boxes and the encryption keys are generated from the numerical solutions of the hyperchaotic maps and the SNM. The performance of the proposed framework is gauged through a number of metrics, reflecting superior performance and complete asymmetry between the plain images and their encrypted versions. However, the main advantages of this work are: (1) The attained width of the key space; and (2) The achieved efficiency in software implementation. The superior key space of 2^{2551} is the result of employing the two hyperchaotic maps, while the improved efficiency, resulting in an average encryption rate of 8.54 Mbps, is the result of the utilization of the single neuron model, as well as the employment of optimized parallel processing techniques. In addition, the proposed encryption framework is shown to output encrypted images that pass the NIST SP 800 suite.

From the merge of arts and crafts towards a practice for mass production of desirability, consumption and product development in a capitalist economy of scale, design has lost its natural ability of problem comprehension and tension alleviation. The modern world needs the creativity, flexibility, and responsiveness embedded into design practices, mostly when a behavioural change, either individual or organizational, is intended. Still, the informality nature of the field is creating a gap between the study, research, and industrial design practice. Here it is presented the Biology of Creativity Model (BoC) which is a design-by-analogy method that promotes an empowered design practice through analysis of mostly biology reference texts for enhanced creativity and innovation performance in a diverse array of contexts.

Cotton is one of the most important economic crops in the world, whereas insect attacks is a prime cause of its losses on yield and quality. Despite it is important, little is known about the mechanism of cotton response to insect. In this study, we simulated insect feeding by applying insect oral secretions (OS) to wounds, and combined transcriptome and metabolome analysis to comprehensively investigate how OS from two major pest species (Helicoverpa armigera and Spodoptera litura) affect cotton defense responses. Obvious differences were observed that 12,668 and 13,379 genes differently expressed respectively in cotton after being treated with different OS when compared with wounding along. Upon the addition of OS, JA-signaling pathway were rapidly and strongly induced, however SA-biosynthesis related gens were significantly down-regulated. By constructing the co-expression gene network, we identified a hub gene which encoding a leucine-rich repeat receptor kinase may play important role in recognition and signal transduction at early stage. We also found that OS from different insect species altered abundance of flavonoid related compound with different patterns. Interestingly, Gossypol which is a well-known anti-insect compound was kept a relative low content after OS application relative to wounding which implied the existence of the suppression effects of OS to cotton defense response. This study illustrated the transcriptional and metabolic changes of cotton in responding to OS from two chewing insect species, identified potential key gene during the interaction process and finds the evidence for OS’s inhibition effects on cotton defense response.

In this paper, we introduced many new notions and new contraction named as S-weakly contraction after that we obtained the p-common best proximity point theorems for different types of contractions in the setting of complete metric spaces by using weak P_p-property and proved the uniqueness of these points. Also we presented some examples to prove the validity of our results.

Spectral photon counting cone-beam CT imaging is challenged by individual pixel response behaviour, which leads to noisy projection images and subsequent image artefacts like rings. Existing methods to correct for this either use calibration measurements, like signal-to-thickness calibration (STC), or perform post-processing ring artefact correction of sinogram data or scan reconstructions, without taking the pixel response explicitly into account. Here we present a novel post-processing method (DAC-shifting), which explicitly measures the current pixel response using flat field images, and subsequently corrects the projection data. The DAC-shifting method was evaluated with a repeat series of spectral photon counting imaging (Medipix3) of a phantom with different density inserts, and in iodine K-edge imaging. The method was also compared against PMMA based STC. The DAC-shifting method was shown to be effective in correcting individual pixel response, and was robust against detector instability. On the contrary, STC correction showed varying results, which was almost as good as DAC-shifting when data was acquired just after STC calibration, but worse with larger time differences. In K-edge imaging, DAC-shifting provides a sharper attenuation peak, and more uniform CT values, which is expected to benefit iodine concentration quantification.

Several efforts have been made to estimate the prevalence of different types of antibiotic resistance in different regions of Nigeria. The study investigated the prevalence of Klebsiella pneumoniae encoding genes for CTX-M, TEM, and SHV-1 extended-spectrum beta-lactamases (ESBL) enzymes isolated from urine samples of non-hospitalized patients with suspected UTI in Lagos State, Nigeria. A 4-month retrospective study, spanning from May 2023 to August 2023, was conducted. K. pneumoniae was isolated from urine samples using standard microbiological methods. The modified Kirby-Bauer disc diffusion method was used to test for antibiotic susceptibility, and Clinical and Laboratory Standard Institute (CLSI) guidelines were followed for interpretation. The double-disk synergy method was used to confirm the phenotypic identity of ESBL producers, and the results were interpreted in accordance with CLSI guidelines. The phenotypic method was utilized for ESBL screening, employing the standard disk diffusion method. The CTX-M ESBL gene, TEM gene, and SHV gene were found using multiplex PCR. Results revealed that 41 K. pneumoniae isolates were resistant to third-generation Cephalosporins; the isolates showed resistance to ceftazidime in 100% of cases and ceftriaxone in 80.49% of cases respectively. It was determined that 8 (19.51%) of the isolates produced ESBL. Among Klebsiella isolates, the frequencies of the SHV, TEM, and CTX-M genes were 31 (75.61%), 4 (9.76%), and 10 (24.39%), in that order. Four isolates (9.67%) had the SHV, TEM, and CTX-M genes, while eight (19.51%) of the isolates possessed both the SHV and CTX-M genes. This investigation demonstrates high frequency, wide range of patterns and coexistence of ESBL genes in K. pneumoniae isolates from outpatient UTI patients. Although several phenotypic tests have been used for ESBL detection, testing directly for the presence of ESBL genes is always more accurate. The majority of the isolates of Klebsiella pneumoniae ESBL producers had the SHV gene identified.

In a context of increasingly necessary energy transition, precise modelling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSO) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modelling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow the estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

The agricultural sector in Palestine has a significant role in its economy. However, the production of this sector is affected by different kinds of plant diseases, specifically leaf diseases. Automatic agricultural leaf disease detection is essential for the early diagnosis and controls the overall health of fields. Image segmentation techniques, clustering, and deep learning are often used to detect diseased leaves. This study proposes a novel hybrid approach based on image classification. The hybrid approach combines the k-means clustering algorithm with Convolutional Neural Network (CNN), where k-means is used to detect the leaf’s infected area, then CNN is used for specifying the disease. We used the PlantVillage dataset for experimental verification as it contains several crops with different kinds of challenging diseases. We also examined the selection of optimal k-value using the Silhouette coefficient, Elbow method, and Kneedle Algorithm. The Silhouette technique was analyzed using three distance metrics; Euclidean, Manhattan, and Cosine. Its scores for the three-distance metrics were low, near-zero, and failed to produce the optimum k value. Besides, the Elbow method was complicated to use in image segmentation in terms of executing and visualizing the k value in its graph plot. Based on verification results, the Kneedle Algorithm produced better results in the consistency of choosing the optimal k value and showed superiority over other approaches. Therefore, the processed images were segmented with the k-means clustering algorithm with a Kneedle algorithm-based k value. Finally, a Convolutional Neural Network (CNN) is trained to classify the type of disease based on analyzing and testing leaf images. The hybrid model achieved high accuracy of 93.79% in disease identification, confirming the proposed model’s robustness.

To determine the metamorphic ages of the accretionary complexes in the Northern Chichibu Belt in SW Japan, K–Ar dating was conducted using weakly metamorphosed sedimentary rocks collected from the Kanto Mountains, Central Japan. Whole-rock ages were obtained for chert and red shale samples, and the mineral ages of fine-grained illite with the grain size less than 4 μm were obtained for chert, red shale, mudstone, acidic tuff, and basic tuff. The K–Ar ages of chert and red shale presented large variations, with systematically older ages compared to those of mudstone and tuff in the same strata. The influence of submarine hydrothermal activities on chert and red shale before subduction is a possible cause of this deviation. The illite samples, which were fractionated into four grain-size classes using a suspension method, yielded older ages and higher illite crystallinity (i.e., smaller values of Kübler’s crystallinity index) for larger grain-size classes. The peak metamorphic ages were determined from the K–Ar ages of the 3–4 μm class illite in mudstone and tuff. The Late Jurassic to the Earliest Cretaceous accretionary complex of the lowest structural unit (Kashiwagi Unit) was dated within a small range between 117–110 Ma, which is distinctly older than the K–Ar ages of white mica reported from the Sanbagawa Belt. The peak metamorphic age of acidic tuff (113 Ma) at the type locality of the Mikabu Greenstones indicates that the subducted Mikabu seamount is a constituent of the Kashiwagi Unit. The peak metamorphic ages of the Manba and Kamiyoshida Units were obtained as 132–107 Ma and 163–144 Ma, respectively. Major structural discontinuity is suggested within the Middle Jurassic accretionary complexes.

BACKGROUND. Two sequelae of non-alcoholic steatohepatitis (NASH), ESLD and HCC, have become the leading causes for liver transplantation in the Western. The present study aims to approach the cellular metabolic disturbances involved in NASH progression that are associated with microbiota community changes. METHODS. Metabolic effects and microbiota community changes were explored in the murine with NASH progression by blocking the Na/K-ATPase/Src/reactive oxygen amplification loop using the synthetic targeting peptide pNaKtide. DNA from the terminal ileum microbiota habitat was obtained and amplified by PCR to develop DNA bacterial phylogenic sequence analysis of wild type and treated animals at 12, 24 and 48 weeks. Induced changes by pSrc normalization at 24 weeks were correlated with liver morphological changes, intestinal CD4+/CD8+ ratio, and liver macrophage CD14+ expression. Differences among groups were evaluated by ANOVA/t-test and Principal Component Analysis (PCA). RESULTS. Microbiota communities varied significantly at all time points (12, 24 and 48 weeks), with an increase of Verrucomicrobia and a decrease of Bacteroidetes and Firmicutes in the HFD group. Microbiota community changes regressed to their wild-type state at 24 weeks on treated animals, and those changes were associated with a decrease in liver inflammation and senescence, lower ileum CD4+/CD8+ T cells and higher liver CD14+ cells (p<0.05). Concomitantly, the metabolic disturbances in our diet-induced NASH model were normalized by NKA/Src signaling blockage and exercise with a paucity of apoptotic activity, mitigation of cell senescence, and regression of liver fibrosis (p<0.01). CONCLUSIONS. pSrc inhibition at caveolar α1-Na/K-ATPase rescinded NASH-related metabolic disturbances establishing resident physiological microbiota communities with concomitant paucity on apoptotic activity and regression of liver fibrosis; effects that were associated with both gut and liver T-lymphocyte responses.

Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is automatic device classification with the logically semantic type and using that as a parameter for device context management. This would enable smart security solutions. In this paper, a device classification approach is proposed for the context management of ubiquitous devices based on unsupervised machine learning. To classify unknown devices and to label them logically, a proactive device classification model is framed using a k-Means clustering algorithm. To group devices, it uses the information of network parameters such as Received Signal Strength Indicator (rssi), packet_size, number_of_nodes in the network, throughput, etc. Experimental analysis suggests that the well-formedness of clusters can be used to derive cluster labels as a logically semantic device type which would be a context for resource management and authorization of resources. This paper fulfills an identified need of proactive device classification for device management.

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal-Organic Frame as Cu(BPDC), and 7 different polymer films including PVA, PEDOT: PSS, PFO, SB, SW, SG, PB were used for functionalizing of QCM sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models including Principal Component Analy-sis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

Hyper spectral imaging (HSI) is a technique that is used to obtain the spectrum for each pixel in the image. It helps in finding objects and identifying materials etc. Such an identification is very difficult using other imaging techniques. It allows the researchers to investigate the documents without any physical contact. Nowadays detection of unequal Ink mismatch based on HSI has shown vast improvement in distinguishing the inks. Detection of unequal Ink mismatch is an unbalanced clustering problem. This paper used K-means Clustering for ink mismatch detection. K-means Clustering find same subgroups in the data based on Euclidean distance. This paper demonstrates performance in unequal Ink mismatch based on HSI.

Based on the insight gained from single jet analysis performed earlier [1], CFD analysis on turbulent jets impinging on one another at an angle was performed. Multiple impingement angles were considered for this study to gain better understanding of the parameters affecting resultant jet growth and velocity distribution. From the study of single jet, it was concluded that the SST k-ω turbulence model was the ideal turbulence model capable of accurately predicting the flow physics of the jets exiting a fully developed pipe at low Reynolds number. Hence, for the study of impinging jets, SST k-ω turbulence model was used to study the velocity and jet growth characteristics. It became evident that the mesh alignment with the velocity vector at exit of the pipe domain plays a crucial role in the accuracy of the results. The parameter used to evaluate this condition was identified as False Diffusion and was observed to affect the TKE parameter significantly. Methods to reduce false diffusion are also discussed in this article. Based on the mesh obtained from the grid sensitivity study, jets impinging at 30, 45 and 60 degrees at Reynolds number of 7500 were numerically analyzed. It was observed that the profile of the resultant jet closely matched with the prediction of elliptical profile predicted by past researchers, [2] and [3]. Also, it was seen that higher jet growth was predicted in case of jets impinging at a higher impingement angle.

This paper considers the detection of fluctuating target in heavy-tailed clutter through the use of dynamic programming based on track-before-detect (DP-TBD) in radar systems. The clutter is modeled in terms of K-distribution, which can be widely used to describe non-Gaussian clutter received from high-resolution radars and radars working at small grazing angle. Swerling type 1 is considered to describe the target fluctuation between scans. Conventional TBD techniques suffer from significant performance loss in heavy-tailed environments due to the more frequent occurrences of target-like outliers. In this paper, we resort to DP-TBD algorithm based on prior information, which can enhance the detection performance by using the environment and target fluctuating information during the integration process of TBD. Under non-Gaussian background, the expressions of the likelihood ratio merit function for Swerling type 1 target are derived first. However, the closed analytical form of the merit function is difficult to be obtained. In order to reduce the complexity of evaluating the merit function and the computational load, an efficient approximation method as well as a two-stage detection approach is proposed and used in the integration process. Finally, several numerical simulations of the new strategy and the comparisons are presented to verify that the proposed algorithm can improve the detection performance, especially for fluctuating target in heavy-tailed clutter.

Nowadays, the research and development of on-chip LiDAR sensors for vehicle collision avoidance is growing very fast. Therefore, the assessment of the reliability in obstacle detection using the information provided by LiDAR sensors has become a key issue to be explored by the scientific community. This paper presents the design and implementation of a self-tuning method in order to maximize the reliability of an Internet-of-Things sensors network and to minimize the number of sensors to localize with the required accuracy obstacles by a detection threshold. In order to achieve this goal, models that predict accuracy (i.e., prediction error) for object localization using data collected by LIDAR sensors are designed and implemented in Webots Automobile 3D simulation tool. The approach is based on combining different techniques. Firstly, point-cloud clustering technique and an error prediction model library composed by a multilayer perceptron neural network with backpropagation, k-nearest neighbors and linear regression are explored. Secondly the above-mentioned techniques for modeling are also combined with a supervised and reinforcement machine learning technique, Q-learning in order to minimize the detection threshold. In addition, a IoT driving assistance simulated scenario with a LiDAR sensor network is designed in order to validate the prediction model and the optimal configuration of the sensor network to guarantee reliability in obstacle localization. The results demonstrate that the self-tuning method is appropriate to increase the reliability of the sensor network whereas minimizing the detection threshold

ABSTRACT The purpose of this paper is to review the involvement of mitochondria in the actions of nitroglycerin (glycerol trinitrate -GTN) and the redox disturbances underlying diseases it treats. The paper begins by explaining complex I as the initial site of formation of superoxide and the process whereby it leads to the development of glutathione dependent tolerance. Next, the paper describes new formulations for preventing tolerance. The paper ends by illustrating diseases that may benefit from treatment with novel glutathione based GTN formulations and treatment approaches.