The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. By examining deep learning models' performance in scenarios like stock market crashes and highlighting the significance of cross-validation techniques, this study aims to offer practitioners insights into constructing robust risk management systems. It advocates for a comprehensive approach integrating quantitative analysis with macroeconomic factors to enhance financial risk prediction and analysis in volatile markets. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.

Abstract: Online services have transformed into a boon for various industries and globalization as a whole. One of the ventures reliant upon the Web is finance. Pattern prediction and risk reduction in the stock market and other financial decisions are made possible by some technologies. These technologies promise to expand innovation, knowledge, and efficiency opportunities in the financial sector. Finance frequently uses machine learning technology to identify risks based on probabilistic statistics and historical data to support investment decisions. Additionally, it can be utilized for risk assessment and risk management planning. Internet technology, which enables real-time data analysis, streamlined transactions, and improved customer communication, has particularly benefited the financial sector. However, these advancements come with significant challenges, such as the need for robust online credit protection and security threats. This study looks at how advanced machine learning methods are used in the financial sector and focuses on how they have changed credit scoring, algorithmic trading, and predictive modeling.

Potential permanent magnet (Fe0.7Co0.3)2B has large saturation magnetization and high Curie temperature but a moderate magnetocrystalline anisotropy (MCA) and displays relatively low coercivity. One way to improve coercivity is to combine the contribution from magnetocrystalline- and magnetic shape anisotropy by preparing (Fe0.7Co0.3)2B nanowires. We study the effect of size, morphology, and surface defect on the hard magnetic properties of nanowires using micromagnetic simulation. The hard magnetic properties of (Fe0.7Co0.3)2B nanowire bonded magnet have been estimated, including the role of inter-wire magnetostatic interaction. By considering the existence of local reduction of MCA energy up to 30% on the surface layer of nanowires, the anisotropic bonded magnet with 65% vol. of (Fe0.7Co0.3)2B nanowires displays typical magnetic properties of remanence Br=8.4 kG, coercivity Hci=9.9 kOe, and maximum energy product (BH)m=17.8 MGOe. Developing effective technology for synthesizing nanowires and fabricating corresponding bonded magnets is promising for manufacturing practical magnets based on magnetic phase with relatively low or moderate MCA, such as (Fe0.7Co0.3)2B.

In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable.

It is important to consider both requirements and limitations for the generation and sharing of genomic data. For example, human genomic sequence data should typically not be deposited publicly without the appropriate "explicit consent" to do so. This includes genetic identifying data that may exist within raw reads for bulk RNA Sequencing (RNA-Seq) data and genomic data from cell lines that are available to purchase. At the same time, data sharing is important for re-analysis and reproducibility/replication in the scientific community. A review of known rules and guidelines for genomic data sharing is provided. This includes specific rules for NIH-funded data for controlled access deposit of HeLa cell line data, including bulk RNA-Seq data. The ability to create “partial” Gene Expression Omnibus (GEO) public deposits with only processed data is described, with search criteria that can identify many “partial” GEO deposits for a variety of data types where reads were not made public for patient privacy concerns. However, it is the opinion of the author that this should be considered a short-term solution, and additional considerations and action should be carried out for genomic data generated in future experiments. Information about how to learn more about what is known for consent for cell line samples is also briefly provided, along with search results for genomic data from widely available cell lines that is deposited in controlled access databases. Finally, it should be made clear that this article presents some amount of opinion. Additionally, open feedback for this preprint is encouraged to further enhance knowledge and communication.

A two scalar field model that incorporates non Riemannian Measures of integration or usually called Two Measures Theory (TMT) is introduced, in order to unify the early and present universe. In the Einstein frame, a K-essence is generated and as a consequence for the early universe, we can have an Inflation for the very early universe and then, after inflation, an early dark energy period and a late dark energy period for the present universe dark, both dark energy epochs have a consistent generation of dark energy (DE), dark matter (DM) and stiff matter. This is obtained when the scale invariance is introduced and then is spontaneously broken from the integration of the degrees of freedom associated with the modified measures. The resulting effective potentials and K-essence in the Einstein frame produce the three flat regions corresponding to the different epochs mentioned before. For the first flat region, we can associate an inflationary universe. Also assuming this first plateau, we study the inflation in the framework of the slow-roll approximation. In this scenario under the slow roll approximation, we obtain a linear combination that is a constant. The corresponding cosmological perturbations in our model are determined and we also obtain the different constraints on the parameter-space from the Planck data. The slow roll approximation can be arranged so that the transition from inflation to the next flat region can be achieved without problems. In the following two flat regions, in addition to the inflation phase, representing the Early Dark Energy and Late Dark Energy Periods, with DE and also the DM, do not need to be introduced separately, they are instead a result of a K-essence induced by the multi measures, multi field theory. Therefore the scalars generate both the DE and the DM . Also a stiff matter component is automatically generated from the K-essence theory from two scalar fields. From the perturbative analysis associated to the perturbation solution of background, we find a correlation between the two scalars. Besides, we obtain that our model during the dark epoch has a behaviour of tracking freezing mode

It's been a growing concern that many microbes are developing resistance against many antibiotics, lowering the effectiveness and progress expected from that treatment option. To overcome the re-sistance developed by microbes, metallic and non-metallic-based nanoparticle treatments are starting to present a very promising approach. Furthermore, polyurethanes are used in a wide range of biomedical applications due to their variety of physical-chemical, mechanical, and structural properties, and biotic and abiotic degradation. They are widely used in bio-imaging procedures when metallic-based filler particles are incorporated, making the final product radiopaque. It would be advantageous, however, if polyurethanes with intrinsic radiopacity could be produced in their synthesis, avoiding a series of disadvantages in the processing and final product and also presenting potential antimicrobial activities. This review’s objective was to study the radiopacifying charac-teristics of nanoparticles as well as the physical principles of radiopacity and the variety of medical applications of polyurethanes with nanoparticles. It was found in this study that the synthetization of radiopaque polyurethanes is not only possible but also that the efficiency of synthetization was improved when using atoms with high electron density as part of the back chainbone or grafted, making them great multipurpose materials.

The use of biomass as a source of energy has been identified to be energy intensive which involves high cost of handling. However, pelletization reduces the bulk density of biomass, thereby reducing the cost of handling and enhancing ease of use. This study modified and evaluated an existing hand operated fish feed pelleting machine. The parts of the machine that were redesigned are hopper and power transmission unit. Corncob was used to evaluate the modified machine using die hole diameter (5,6 and 7 mm) and binder quantity (0, 2.5 and 5 wt%) as factors. The average results obtained for machine efficiency, throughput, pellet length and bulk density were 58.83%, 4.24 kg/h, 15.51 mm and 0.160 g/cm3. Die hole diameter had significant effect on pellet length only. Binder quantity had significant effect on machine efficiency, throughput and pellet length. Machine efficiency and throughput reduces as the quantity of binder increases and pellet length increases with increasing quantity of binder.

Smart grids represent the evolution of traditional electrical grids through the integration of digital and communication technologies. These grids are capable of monitoring and managing the distribution of electricity efficiently and resiliently. Artificial Intelligence (AI) plays a crucial role in this context, offering advanced solutions for optimization, forecasting, and energy management. This paper explores the main AI technologies applied to smart grids, highlighting their advantages, challenges, and impacts on energy efficiency and sustainability. The methodology employed includes a comprehensive literature review, analysis of current AI applications in smart grids, and examination of case studies to illustrate the practical implementation and benefits of AI technologies in this field.

The planned closure of potash mines, achieved through the injection of highly mineralized brines into the worked-out area, is a complex and not fully studied process. A critical concern arises when brines obstruct the aerodynamic connections between the flooded mine's airspace and the atmosphere, potentially leading to the formation of closed cavities where explosive gases can accumulate. To address this hazard, it is imperative to develop systems capable of extracting the gas-air mixture from the unflooded domed portions of the worked-out area. This is the focus of this study, in which we analyze gas-dynamic processes within the potassium mine worked-out area during planned flooding. Two distinct scenarios are examined: the first involves controlled flooding with saturated brines, while the second contemplates flooding resulting from a hypothetical breakthrough of supra-salt strata, leading to the ingress of groundwater into the worked-out area. A novel mathematical model is introduced to predict the evolution of gas-air mixture parameters in the unflooded dome segment of the worked-out area. Utilizing this model, we assess the effectiveness of proposed measures designed to eliminate explosive gases from the worked-out area. Specifically, a pipeline system is proposed for the removal of gases, followed by their dilution below the maximum permissible concentrations in the effluent jet. The findings from this study contribute valuable insights into ensuring the safe and efficient closure of potash mines, shedding light on potential risks and effective mitigation strategies for gas-related hazards during planned flooding.

This study examines the contribution of early executive functions (EFs) in the association between fluency in reading and arithmetic. Kindergarten children (N = 1,185) were assessed on Executive Functions skills and on reading and arithmetic fluency in Grade 1 and Grade 3. The analysis revealed that beyond the connection within each domain there is a unidirectional effect between fluency measures, with Grade 1 reading fluency significantly influencing the development of arithmetic fluency in Grade 3. Furthermore, the findings indicate that kindergarten EFs significantly contribute to arithmetic fluency at both time points and to reading fluency in the first grade. Early EF skills also emerged as a mediator of associations between fluency performance in reading and arithmetic, suggesting that the influence of EFs extends beyond individual academic domains. These findings have implications for understanding the cognitive mechanisms that underlie the relations between these academic skills.

African swine fever virus (ASFV), a highly contagious pathogen characterized by a complex structure and a variety of immunosuppression proteins, causes hemorrhagic, acute, and aggressive infectious disease that severely injure the pork product and industry. However, there is no effective vaccine or treatment. The main reasons are not only the complex mechanisms that lead to immunosuppression, but also the unknown functions of various proteins. This review summarizes the interaction between ASFV and host immune system, along with the involvement of virulence related genes and proteins, as well as the corresponding molecular mechanism of immunosuppression of ASFV, encompassing pathways such as cGAS-STING, NF-κB, JAK/STAT, apoptosis, and other modulation. The aim is to summarize the dynamic process during the ASFV infection and entry into host cell, provide a rational insight into development of vaccine and a better clear knowledge of how ASFV impacts host.

A nation's social, economic, and political emancipation is ineluctably connected with functional education systems in which people learn to create new institutions, utilise new technologies, cope with vagaries and difficulties in their environment, and positively alter their behavioural patterns. Education in a broad sense improves the capabilities of individuals and the capacity of institutions and becomes a catalyst for the closely interrelated economic, social, cultural, and political changes that are parts of the hallmarks of national development. Nevertheless, a teacher stands out as a crucial figure within any educational institution, wielding significant influence in effecting these changes and transformations. Teacher education is broad. It is based on the praxis that teachers are made in contrary to the assumption that teachers are born. Since teaching is considered an art and a science, the teacher has to acquire not only knowledge but also requisite skills needed in carrying out his or her inestimable duties or services. The efficiency and effectiveness of every educational system hinge significantly on the educational achievements of teachers. After all, no education can surpass the quality of its educators. Thus, every nation places demands and expectations on the teacher, which need to be addressed by both initial and continuing teacher education. On this note, this paper examines teacher education with respect to its praxis and professionalism, examines the nexus between teacher education and socioeconomic as well as political emancipation, analyses the challenges of teacher education in Nigeria, and in the final analysis, gives recommendations based on the identified challenges.

The financial performance of local governments is closely related to the economic growth of a region. Strong financial performance provides a solid foundation for sustainable economic development. This study analyses the relationship between local government financial performance, capital expenditure, and sustainable economic growth in Banten Province. A quantitative approach was employed, using secondary data from Regional Revenue and Expenditure Budgets and economic growth statistics. Purposive sampling was utilised, and data analysis was conducted using Path Analysis. The findings reveal that several financial ratio variables significantly impact economic growth directly and indirectly through capital expenditure as a mediating variable. Directly, the independence ratio, effectiveness ratio, efficiency ratio, fiscal decentralisation ratio, dependency ratio, and capital expenditure significantly influence economic growth. The independence and effectiveness ratios have a positive impact, indicating that improvements in these variables directly foster economic growth. However, the efficiency and fiscal decentralisation ratios have a negative impact, suggesting that increases in these variables may reduce economic growth. Indirectly, through capital expenditure, the independence, effectiveness, dependency, and compatibility ratios significantly affect economic growth, with the independence ratio being the most dominant. Conversely, the fiscal decentralisation and efficiency ratios do not show significant indirect effects, indicating that capital expenditure is not an effective mediator for these variables.

Intracranial metastases from thyroid cancer are rare. Although the prognosis of thyroid cancer patients is generally favorable, the prognosis of patients with intracranial metastases from thyroid cancer has been considered unfavorable having a lower survival rate than those without intracranial involvement. Many questions about their management remain unclear. The aim of this study was to analyze the characteristics, treatments, and outcomes of patients with brain metastases from thyroid cancer. Among 4320 patients with thyroid cancer recorded in our institutional database over a 30-year period, 20 patients with brain metastasis were retrospectively collected and analyzed. The clinical characteristics, histological type of primary cancer and metastatic brain tumor, additional previous distant metastasis, treatment modalities, locations and characteristics on radiologic findings, time interval between the first diagnosis of the primary thyroid cancer and brain metastasis, and survival were analyzed. The mean age at initial diagnosis was 59.3 ± 14.1 years, and at the manifestation of diagnosis of cerebral metastasis 64.8 ± 14.9 years. The histological types of the primary thyroid cancers were papillary in ten patients, follicular in seven, and poorly differentiated carcinoma in three. The average interval between the diagnosis of thyroid cancer and brain metastasis was 63.4 ± 58.4 months (range, 0-180 months). Ten patients had a single intracranial lesion and 10 patients had multiple lesions. Surgical resection was primarily performed in 15 patients and whole-brain radiotherapy, radiotherapy or tyrosine kinase inhibitors were applied in the other 5 patients. Overall median survival time was 15 months after diagnosis of BM from TC (range: 1-252 months). Patients with thyroid cancer can develop brain metastasis even many years after diagnosis of the primary tumor. Our study demonstrates an increased overall survival of patients younger than 60 years of age at the time of diagnosis of brain metastasis. There was no difference in survival between patients with brain metastasis from papillary or follicular thyroid carcinoma.

Objectives: To examine the relationship among edentulism, nutritional intake, and the progress of multimorbidity among older Americans. Methods: We used data from the Health and Retirement Study (2006-2018), a longitudinal survey of older Americans that has collected data biennially since 1992. Edentulism was assessed in 2006 while nutritional intake was assessed in 2013. Multimorbidity was indicated by 5 self-reported chronic conditions: diabetes, heart conditions, lung diseases, cancer, and stroke. Individuals with two or more conditions at baseline were excluded from the analysis. Nutritional intake was calculated by summing 10 nutrients (Protein, Vitamins C, D, B12, E, Calcium, Zinc, Polyunsaturated fatty acids, Folate, and ß- carotene). Structural Equation Modelling (SEM) was used to examine the nutritional pathway between edentulism (2006) and the increase in multimorbidity from 2006- 2018. Results: Individuals included in the analysis were 3,463. The incidence of multimorbidity between 2006 and 2018 was 24.07%. while the percentage of edentate participants in 2006 was 16.42%. The mean of total nutrition in 2013 was 4.50 (4.43, 4.55). SEM analysis showed that edentulism was negatively associated with nutritional intake {estimate -0.15(95% CI: -0.30, - 0.01)}. A negative association was found between total nutrition and multimorbidity {estimate -0.008 (95%CI: -0.01, -0.002)}. Age, wealth and smoking were included in the analysis, and had statistically significant associations with multimorbidity. Conclusion: The analysis demonstrated a longitudinal association between edentulism, nutritional intake, and the progress of multimorbidity.

Gc protein-derived Macrophage Activating Factor (GcMAF), a powerful immunostimulant endowed with anti-cancer and anti-angiogenetic activities, offers significant advantages when combined with radiation therapy. A stronger immune response improves the effectiveness of radiation therapy by allowing the body to better eliminate residual cancer cells after treatment. In addition, by boosting the immune system, GcMAF mitigates some of the immunosuppressive side effects of radiation therapy, leading to faster recovery. In order to fully exploit the potential of GcMAF in cancer therapy, knowledge of the molecular interactions with its receptor is essential. This study proposes the first extracellular domain (residues 1-34) of the CCR1 protein as the GcMAF receptor. The CCR1 gene, expressed in monocytes and 168 other cell types or tissues, encodes this transmembrane protein. Electrostatic and hydrophobic interactions, along with hydrogen bonds mediate the molecular interactions between the TPT^420-GalNAcELAK (or TPK⁴²⁰ELAK) sequences of GcMAF (or Gc2 protein variant) and the TTEDYDTTT sequence of its receptor.

A new formalism is introduced that makes it possible to elucidate the physical and geometric content of quantum spacetime. It is based in the Minimum Group Representation Principle (MGRP). Within this framework new results for entanglement and geometrical/topological phases are found and implemented in cosmological and black hole space-times. Our main results here are: {\bf(i)} We find the Berry phases for inflation and for the cosmological perturbations and express them in terms of the observables, as the spectral scalar and tensor indices, $n_S$ an $n_T$, and the tensor to scalar ratio $r$. The Berry phase for de Sitter inflation is imaginary with the sign describing the exponential acceleration. {\bf(ii)} The pure entangled states in the minimum group (metaplectic) $Mp(n)$ representation for quantum de Sitter space-time and black holes are found. {\bf(iii)} For entanglement, the relation between the Schmidt type representation and the physical states of the $Mp(n)$ group is found: This is a {\it new non-diagonal} coherent state representation complementary to the known Sudarshan diagonal one. {\bf(iv)} Mean value generators of $Mp(2)$ are related to the adiabatic invariant and topological charge of the spacetime (matrix element of the transition $-\infty < t < \infty$). {\bf(v)} The basic {\it even} and {\it odd} $n$-sectors of the Hilbert space are intrinsic to the quantum spacetime and its discrete levels (in particular continuum for $n \rightarrow \infty$), they do not require any extrinsic generation process as the standard Schrodinger cat states, and are {\it entangled}. {\bf(vi)} The gravity or cosmological domains in one side and another of the Planck scale are {\it entangled}. Examples: The quantum primordial trans-Planckian de Sitter vacuum and the classical late de Sitter vacuum today; the central quantum gravity reqion and the external classical gravity region of black holes. The classical and quantum dual gravity regions of the space-time are entangled. {\bf(vii)} The general classical-quantum gravity duality is associated to the Metaplectic $Mp(n)$ group symmetry which provides the complete full covering of the phase space and of the quantum space-time mapped from it.

Beneficial microbes are crucial for improving crop adaptation and growth under vari-ous stresses. They enhance nutrient uptake, improve plant immune responses, and help plants tolerate stresses like drought, salinity, and heat. The yield potential of any crop is significantly influenced by its associated microbiomes and their potential to im-prove growth under different stressful environments. Therefore, it’s crucial and excit-ing to understand the mechanisms of plant-microbe interactions. Maize (Zea mays L.) is one of the primary staple foods worldwide, in addition to wheat and rice. Maize is also an industrial crop globally, contributing 83% of its production for use in feed, starch, and biofuel industries. Maize requires significant nitrogen fertilization to achieve opti-mal growth and yield. Maize plants are highly susceptible to heat, salinity, and drought stresses and require innovative methods to mitigate the harmful effects of environmental stresses and reduce the use of chemical fertilizers. This review summarizes our current understanding of the beneficial interactions between maize plants and specific microbes. These beneficial microbes improve plant resilience to stress and in-crease productivity. For example, they regulate electron transport, downregulate cata-ase, and upregulate antioxidants. We also review the roles of plant growth promoting rhizobacteria (PGPR) enhancing stress tolerance in maize. Additionally, we explore the application of these microbes in maize production and identify major knowledge gaps that need to be addressed to utilize the potential of beneficial microbes fully.

Attention-Deficit/Hyperactivity Disorder (ADHD) exhibits a notable prevalence across diverse populations, with estimates ranging from 4% to 6%. This wide range prompts an investigation into the evolutionary and genetic underpinnings of ADHD, especially considering the markedly different environments faced by ancestral human populations compared to today's structured societies. This study examines the potential adaptive advantages of ADHD-associated traits in hunter-gatherer contexts, where hyperactivity, impulsivity, and novelty-seeking behaviors may have conferred survival benefits. We employ the principles of population genetics, including Hardy-Weinberg equilibrium, selection coefficients, and genetic drift, to model the dynamics of ADHD allele frequencies over time. Additionally, we discuss balancing selection as a mechanism for maintaining genetic diversity and how gene-environment interactions (GxE) contribute to the expression of ADHD in contemporary settings. Our analysis suggests that traits associated with ADHD, while potentially maladaptive in modern structured environments, could have been preserved through evolutionary processes due to their adaptive value in ancestral contexts. Understanding these dynamics provides a comprehensive view of ADHD's prevalence and highlights the significance of genetic and environmental interplay in shaping human behavior.

This study examines the relationship between semantic memory and social cognition in schizophrenia, addressing how these cognitive domains intersect. Semantic memory, which includes general world knowledge and word meanings, was evaluated using verbal fluency tasks and the Camels and Cactus Test. Social cognition, essential for social interaction, was assessed through emotion recognition (Faces Test) and Theory of Mind (Hinting Task). Participants included 50 individuals with schizophrenia and 30 controls. The schizophrenia group showed significantly lower performance on both semantic memory and social cognition tasks. Notably, strong correlations were found between the Camels and Cactus Test and social cognition measures, suggesting that social cognition deficits in schizophrenia may be linked to semantic memory impairments. Regression analyses highlighted that the Camels and Cactus Test significantly predicted social cognition performance, independent of symptomatology. These findings underscore the interconnectedness of semantic memory and social cognition in schizophrenia, suggesting that semantic memory deficits, particularly in non-categorical associations, play a important role in social cognitive impairments. This study provides new insights into the cognitive underpinnings of schizophrenia, emphasizing the need for further research to explore these relationships and their implications for cognitive models and therapeutic interventions.

Principal Component Analysis (PCA) is a method that identifies common directions within multivariate data and presents the data in as few dimensions as possible. One of the advantages of PCA is its objectivity, as the same results can be obtained regardless of who performs the analysis. However, PCA is not a robust method and is sensitive to noise. Consequently, the directions identified by PCA may deviate slightly. If we can teach PCA to account for this deviation and correct it, the results should become more comprehensible. The methods for doing this and an issue with this are presented.

The antimicrobial peptide LRGG (LLRLLRRGGRRLLRLL-NH2) was designed and chemically synthesized in a study conducted by Jia et al. Gram-negative bacteria were found to be sensitive to LRGG and exhibited a high therapeutic index. Genetic engineering methods were used to create the prokaryotic fusion expression vector pQE-GFP-LRGG, and the resulting corresponding fusion protein GFP-LRGG was subsequently expressed and purified. The precursor GFP was then removed by TEV proteolysis, and pure LRGG was obtained after another round of purification and endotoxin removal. The prokaryotic-expressed antimicrobial peptide LRGG displays a broad-spectrum antibacterial effect on gram-negative bacteria and can eliminate 99.99% of E. coli within 100 minutes at the minimal MIC concentration. Compared to the chemically synthesized LRGG, the prokaryotic-expressed LRGG exhibits similar temperature, pH, salt ion, serum stability, and cell selectivity. Furthermore, prokaryotic-expressed LRGG showed excellent therapeutic effects in both the infection model of cell selectivity and no embryotoxicity in a Galleria mellonella infection model. The mechanism by which LRGG causes bacterial death was found to be disruption of Gram-negative cell membrane.

Due to the host immunological disease brought on by infection is sepsis, multiple organ dysfunction syndrome (MODS), which has a high morbidity and mortality rate, could be the result. With advances in pathogen detection technologies, breakthroughs in anti-infection drugs, and the creation of organ function support, the clinical diagnosis and treatment of sepsis have advanced significantly. Pathogen-induced abnormal immunological responses, which can range from excessive inflammation to immunosuppression, are recognized to play a significant role in the high death rate. Unfortunately, no medications that are specifically intended to treat sepsis have been licensed. In order to offer a theoretical foundation for the management of sepsis, we will now discuss current advancements in research on immunological responses in sepsis. The prognosis of sepsis patients may be improved by developing and optimizing a dynamic immune system treatment plan based on anti-infection therapy, fluid replacement, support for organ function, and timely application of immunomodulatory therapies

Considering the health benefits of the kombucha, already widely studied, the objective of the study was to development of the beverage with the addition of infusion of special arabica coffee from the first fermentation varying the proportion of special coffee (2% a 13%) and evaluate the pH, volatile acidity, degree of alcohol, centesimal composition, sodium contents, colour parameters as like acceptability of the beverages. With respect to the pH, all the formulations conformed with the kombucha identity standard, but K3 and K4 were below the established minimum for volatile acidity. Except for K4, all the other formulations were classified as alcoholic kombuchas, although the values were very close to the limit for non-alcoholic. The formulations presented low sugar and sodium content, which corroborated the low caloric value. Therefore, coffee-flavored kombucha fermented for 18 days becomes an option for consumers looking for low-calorie, refreshing and healthy drinks. The luminosity of the beverages decreased as the proportion of coffee increased, and consequently the values for a* and b* increased, indicating a strong tendency for a yellowish red colour. Sensorially, the formulations K4 and K3 were the most well accepted in all respects and can therefore be considered formulations with high commercialization potential.

ABSTRACT Underwater mines may destroy friendly submarines and marine habitats, compromising nonmilitary security. Finding credible information is vital to avoiding scams and spotting hazards. This article employs Supervised Machine literacy models to appropriately categorize subsea items like mines or gems based on Sonar sounds. Python constructs three double classifier models using Gorman and Sejnowski (1988) training data. Through the extraction of key aspects from the Sonar signals, such as spectral components and intensity, the models are able to match newly obtained data with the gemstone order or mine. Delicacy decreases the chance of misidentifications in nonmilitary activities. Machine literacy offers full validation that accommodates real-world Sonar data noise and unpredictability. Based on test results, the highest performing algorithm will deliver the required ratio of accuracy to perceptivity for the bracket task. Central keywords: vaticination model, bracket algorithms, supervised machine literacy, SONAR, and aquatic mining emphasize particular foundations. Improved sophistication in spotting undersea Mines let nonmilitary defence systems respond to proven threats only, preventing friendly submarines or sea life from unnecessary damage owing to false duplicity. This work is a promising start to real-world mine detection devices. Further developments may incorporate greater training data sets, real-time testing on subsea platforms equipped with integrated sonar, and optimization for the discovery of new mine-types. Overall, machine learning and Sonar vision decrease signal fluctuation and noise, making it easier to determine accurate boundaries for non-military applications and environmental protection. The recommended automated discovery technique must balance computational efficiency, generalizability, and delicacy. Prior a real crime, new quality control and verification procedures could boost trust even further.

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.

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

Coconut is an important multipurpose crop wherein different stages of its maturity are used for different products. Traditional approach of its classification use sound formed by tapping the fruit, and the tapper will base its judgment through the sound he heard. The procedure was highly subjective and attempts had been made by other researchers to automate and classify the adulthood of the coconut objectively. Nowadays, deep learning techniques are being utilized to solve classification problems. One such method is Convolutional Neural Network (CNN) that takes raw pixel data, learns to extract features, and ultimately classifies the input. In this study, a portable device was developed that takes audio signals generated from tapping the fruit mechanically. This audio clip was converted to spectrogram and was used as input to a LeNet CNN architecture. We argue that CNN classifier has improved accuracy compared to the non-deep learning system. Our evaluation confirmed that the use of CNN had improved the accuracy of classifying coconut by about 15%.

Major depressive disorder (MDD) and substance use disorder (SUD) are of immense medical and social concerns. Although significant progress on neuronal involvement in mood and reward circuitries has been achieved, only relatively recently the role of glia in these disorders have attracted attention. Detail understanding of the glial function in these devastating diseases could offer novel interventions. Here, following a brief review of circuitries involved in mood regulation and reward perception, specific contribution of the neurotrophic factors, neuroinflammation and gut microbiota to these diseases are highlighted. In this context, the role of specific glial cells (e.g., microglia, astroglia, oligodendrocytes and synantocytes) on phenotypic manifestation of MDD or SUD are emphasized. In addition, potential use of this knowledge in developing novel therapeutics is touched upon.

The wide circulation of conspiracy narratives and their frequent intertwining with populist rhetoric is both an element of concern and a topic of intense scientific and philosophical debate. The depth of the link between conspiracy theories and populism represents a crucial issue whose comprehension can facilitate understanding their specific nature and the factors behind their diffusion in public communication. To this end, it is necessary to cultivate an interdisciplinary approach and great critical attention, eschewing monocausal explanations. This paper addresses the question of the essentially political nature of conspiracism confronting the recent epistemological debate that, by putting the positivist paradigm aside, has sought to explore and understand the socio-cultural roots of conspiracy rhetoric, with its sceptical, antagonistic and hermetic traits. By integrating the reflections of epistemologists such as Cassam or Harris with the considerations of political scientists such as Taggart and with Schmitt's radical reflections on politics, it is perhaps possible to reintegrate the different approaches to populist conspiracism into an overall social genealogical perspective, thanks also to recent demographic elaborations. Thus, we could ascribe the spread of conspiracism to the prevalence in societies of a hyper-political discursive regime, i.e. founded on the principle of opposition, without the possibility of compromise, between different groups and interests. At the basis of such Manichaeism, it is plausible to place in the first place the growing inequalities and related social disintegration, which hinder the circulation of trust and recognition between individuals and groups, thus ending up undermining democracy at its roots, as a political system that legitimises and thus peacefully regulates conflict.

While the simple model of the total atmospheric carbon sink effect as a linear function of concentration has provided excellent prediction results, several problems remained to be investigated and solved. The most obvious open issue is the correct treatment of land use change emssions. It turns out that the model improves by mostly neglecting them after 1959. This effectively implies that land use change emissions have been constant and small since then. The key investigation starts with the observation, that the total carbon sink has a short term component which be explained by temperature changes commonly known as El Nin˜o. The apparent paradox is analysed and explained, why contrary to the short term changes no temperature caused trend can be detected in the model, despite the fact that several contributing processes exhibit temperature dependency. The result of this analysis leads to the model extension, where the total effect of sinks and natural emissions are a linear function of concentration and temperature. This model not only explains current measurements but also paleo climate from ice-core time series.

Leukemia is a form of blood cancer that results in an increase in the number of white blood cells in the body. The correct identification of Leukemia at any stage is essential. The current traditional approaches rely mainly on field expertise' knowledge, which is time consuming. The poor understanding and a long period of examination might damage the human body. In this situation an automated Leukemia identification delivers more reliable and accurate diagnostic information. To effectively diagnose acute lymphoblastic Leukemia from blood smears pictures, a new strategy based on traditional image analysis techniques with machine learning techniques, and a composite learning approach was constructed in this experiment. To identify the type of acute Leukemia first, four well-known machine learning models were utilized. It was discovered that Support Vector Machine (SVM) Provides the highest accuracy in this scenario. To boost the performance, a deep learning model Resnet50 was hybridized with SVM model. Finally, it was revealed that this composite approach achieved 99.9% accuracy.

This study investigates the effects of subclinical hypothyroidism and BDNF on telomere length in T1DM mothers and their neonates. Methods: In this prospective cohort study, 70 pregnant women with T1DM were enrolled. Subclinical hypothyroidism during the first trimester was characterized by elevated thyroid-stimulating hormone (TSH) levels ranging from 2.5 to 5.0mIU/L alongside normal free thyroxine (FT4) concentrations. Participants were stratified into two groups based on TSH levels: a maternal subclinical hypothyroid group (SHG, n=35) and a maternal euthyroid group (ETG, n=35). Leukocyte telomere length (LTL) measurements were conducted using whole blood samples from the mothers and umbilical veins. Results: The maternal LTL mean was 1.7 (SD=1.1), which was significantly shorter than the neonatal telomeres, with a mean of 2.3 SD (1.2). A significant correlation was observed between maternal and neonatal LTL (r=0.750, p&lt;0.001). A positive association was found between neonatal LTL and BDNF levels in maternal circulation [β=0.421 (95% CI 0.536; 1.873; p=0.001)]. Neonatal LTL was negatively associated with gestational weight gain [β=-0.266 (95% CI-0.138; -0.009)], and with TSH in the first trimester [β=-0.237; (95% CI -0.493; - 0.010)]. Notably, neonates in the SHG exhibited shorter LTL, with a mean of 2.1 (1.0), compared to those in the ETG, who had a mean LTL of 2.7 (1.4). A positive association was found between maternal LTL and BDNF levels in maternal circulation [β=0.523 (95% CI 0.790; 1.950; P&lt;0.001)]. Maternal LTL was negatively associated with gestational weight gain [β=-0.259 (95% CI -0.122; -0.010)], and with TSH in the first trimester [β=-0.231; (95% CI -0.422; - 0.015)]. Conclusions: Genetic factors affect neonatal LTL. Maternal subclinical hypothyroidism reduces neonatal LTL. The link between maternal and neonatal LTL and BDNF reveals the complex endocrine-neurotrophic interaction in T1DM telomere dynamics.

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

Most existing coal-fired power plants were designed for sustained operation at full load to maximize efficiency, reliability, and revenue, as well as to operate air pollution control devices at design conditions. Depending on plant type and design, these plants can adjust output within a fixed range in response to the plant operating or market conditions. The need for flexibility driven by increased penetration of variable and non-dispatchable power generation such as wind and solar is shifting the traditional mission profile of thermoelectric power plants in three ways: more frequent shutdowns when market or grid conditions warrant, more aggressive load ramp rates (rate of output change), and lower minimum sustainable load, which provides a wider operating range and helps avoid costly plant shutdowns. Recent studies have shown that flexibility of a coal-fired power plant can be improved by energy storage. The objective of this work was to analyze a set of energy storage options and determine their impact on flexibility and economics of a representative coal-fired power plant. The effect of three energy storage systems integrated with a coal power plant on plant flexibility and economics was investigated. The results obtained in this project show that Energy Storage Systems integrated with a thermal power plant improves plant flexibility and participation in the Energy and Ancillary Services markets which improves plant financial performance. The study was funded by the U.S. Department Office of Fossil Energy FE-1 under Award Number DE-FE0031903.

In this work, we explore the intrinsic disorder status of the three members of the synuclein family of proteins - α-, β-, and γ-synucleins. We also analyzed the peculiarities of the amino acid sequences and modeled 3D structures of the human synuclein family members to find potential effects of pathological mutations. Furthermore, we conducted a comparative sequence-based analysis of the synuclein proteins from various evolutionary distant species and evaluated their levels of intrinsic disorder using a set of commonly used bioinformatics tools. Next, we conducted a detailed functional disorder analysis of the proteins in the interactomes of the human synuclein family members using various web-based disorder analysis and prediction tools, such as RIDAO, D2P2 and FuzDrop. We identify that the interactome of human α-synuclein has relatively higher levels of intrinsic disorder, as compared to the interactomes of human β- and γ- synucleins. These observations highlight the critical importance of intrinsic disorder of human α-synuclein and its interactors in neuronal processes as compared to other proteins of the synuclein family.

Sentence stimuli pervade psycholinguistics research. Yet limited attention has been paid to the automatic construction of sentence stimuli. Given their linguistic capabilities, this study investigated the efficacy of ChatGPT/AI tools in generating sentence stimuli. In three psycholinguistic experiments, this study examined the acceptability and validity of AI-formulated auditory sentences and written sentences in two languages: English and Arabic. Participants gave English AI-generated stimuli similar to or higher acceptability ratings than human-composed stimuli, but the opposite was observed for Arabic AI-generated stimuli. The validity of AI-developed stimuli relied on the study design, with only Experiment 2 demonstrating the target psycholinguistic effect (L1/L2 morphosyntactic prediction). These results highlight the promising role of AI as a stimuli developer which could facilitate psycholinguistic research and increase its diversity. Implications for psycholinguistic research were discussed.

Neonatal electronic cardiac monitoring in labor and delivery room (DR-ECG) is shown to be sustained at a tertiary care regional perinatal center despite COVID-19 and other complex organizational challenges. In this contemporary cohort, initial increase in chest compressions at birth associated with the introduction of DR-ECG monitoring was mitigated by focused educational interventions on effective ventilation with no difference in neonatal mortality. DR-ECG may help our understanding of human and system factors, identify potential better practices for optimal resuscitation team performance and assess impact of targeted training initiatives on clinical outcomes.

Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.

Radical reactions are powerful in the synthesis of diverse molecular scaffolds bearing functional groups. In pervious review articles, we have presented 1,2-difunctionalizations, remote 1,3-, 1,4-, 1,5-, 1,6- and 1,7-difunctionalizations, and addition followed by cyclization reactions. Presents in this paper are radical cyclization followed by the second functionalization reactions. The second functionalization could be realized by atom transfer reactions, radical or transition metal-assisted coupling reactions, and reactions with neutral molecules, cationic and anionic species.

The synchronization of complex networks is believed to be closely related to network topology. How to study synchronization of real network without clear topology and parameters? In this Letter, a synchronized stability boundary equation is derived that is system- and disturbance-independent and applicable to arbitrarily coupled grids. The results imply that spontaneous synchronization in a network may be network independent. These conclusions provide new research paths for network synchronization and analyze the synchronization stability of grids in a unified way.

Cognitive impairment is a condition which affects aging patient populations and is becoming more prevalent as the geriatric population expands. It is categorized by the extent of cognitive decline, memory loss, functional abilities, and the capability to perform daily tasks. Based on the degree of impairment, it is classified as mild cognitive impairment or dementia. Considering the debilitating nature of cognitive decline and its impact on quality of life, prevention is key. In addition, given the gradual progression of the underlying causes of cognitive decline, early diagnosis and appropriate management has the potential to offer clinical advantages and better patient outcomes. Management options discussed vary from pharmaceutical regimens to neurosurgical interventions, with differing outcomes based on etiology of cognitive impairment.

LPA3 receptors were expressed in TREx HEK 293 cells, and their signaling and phosphorylation were studied. The agonist, lysophosphatidic acid (LPA), increased intracellular calcium and ERK phosphorylation through pertussis toxin-insensitive processes. Phorbol myristate acetate, but not LPA, desensitizes LPA3-mediated calcium signaling. The agonists and the phorbol ester-induced LPA3 internalization. Pitstop 2 (clathrin heavy chain inhibitor) markedly reduced LPA-induced receptor internalization; in contrast, phorbol ester-induced internalization was only delayed. LPA induced rapid β-arrestin-LPA3 receptor association. The agonist and the phorbol ester induced marked LPA3 receptor phosphorylation, and phosphorylation sites were detected using mass spectrometry. Phosphorylated residues were detected in the intracellular loop 3 (S221, T224, S225, and S229) and in the carboxyl terminus (S321, S325, S331, T333, S335, Y337, and S343). Interestingly, phosphorylation sites are within sequences predicted to constitute β-arrestin binding sites. These data provide insight into LPA3 receptor signaling and regulation.

Lysophosphatidic acid (LPA) type 3 (LPA3) receptor mutants were generated in which the sites detected phosphorylated were substituted by non-phosphorylatable amino acids. Substitutions were made in the intracellular loop 3 (IL3 mutant), the carboxyl terminus (Ctail), and both domains (IL3/Ctail). The wild-type (WT) receptor and the mutants were expressed in T-REx HEK293 cells, and the consequences of the substitutions were analyzed employing different functional parameters. Agonist- and LPA-mediated receptor phosphorylation was diminished in the IL3 and Ctail mutants and essentially abolished in the IL3/Ctail mutant, indicating that the main sites were substituted and distributed in both domains. The WT and mutant receptors increased intracellular calcium and ERK 1/2 phosphorylation in response to LPA and PMA. An increase in baseline ERK phosphorylation was detected in the IL3/Ctail mutant. LPA and PMA-induced receptor interaction with β-arrestin 2 and LPA3 internalization were severely diminished in the mutants. Mutant-expressing cells also exhibit increased baseline proliferation and response to different stimuli, which were inhibited by the antagonist Ki16425, suggesting a role of LPA receptors in this process. Migration in response to different attractants was markedly increased in the Ctail mutant, which the Ki16425 antagonist also attenuated.

Prostate cancer lung metastasis represents a clinical conundrum due to their implication of advanced disease progression and the complexities they introduce in treatment planning. As the disease progresses to distant sites such as the lung, the clinical management becomes increasingly intricate, requiring tailored therapeutic strategies to address the unique characteristics of metastatic lesions. This review seeks to synthesize the current state of knowledge surrounding prostate cancer metastasis to the lung, shedding light on the diverse array of clinical presentations encountered, ranging from subtle radiological findings to overt symptomatic manifestations. By examining the diagnostic modalities utilized in identifying this metastasis, including advanced imaging techniques and histopathological analyses, the review aims to provide insights into the diagnostic landscape and the challenges associated with accurately characterizing lung metastatic lesions in prostate cancer patients. Moreover, the review delves into the nuances of therapeutic interventions employed in managing prostate cancer lung metastasis, encompassing systemic treatments such as hormonal therapies and chemotherapy, as well as metastasis-directed therapies including surgery and radiotherapy.

We investigate the impact of aging on exchangeable inter-arrival times in mixed renewal processes, exploring its implications for reliability and survival analysis. In this study, first, we revisit the definition of renewal point processes, where inter-event time intervals are considered as exchangeable non-negative random variables. Then, we define the concept of statistical aging as latency in the observational process of event counting. Latency affects event detection but preserves exchangeability. However, it may alter the statistical properties of inter-event time intervals. Our analytical and numerical assessments highlight the significance of aging in exchangeable lifetimes, offering insights into key metrics such as the failure survival function, renewal function, and hazard rate function. Through archetypal examples and empirical findings, we illustrate the implications of aging on renewal processes. In particular, employing a Bayesian perspective, we analyze high-frequency currency exchange rate data to assess the impact of aging on volatility risk evaluation. This study contributes novel insights to the literature of renewal theory and survival analysis, emphasizing the role of latency aging in stochastic point processes under exchangeability assumption.

Badminton is one of the most popular racket sports played by children up to the elderly worldwide. Everyone can practice and play badminton as a leisure or competitive sport, regardless of age, experience, or skill level. It does not require physical contact among players and expensive equipment. Compared with closed-skill exercise (e.g., running, swimming), open-skill exercise (e.g., badminton, table tennis) has been reported to intervene more with cognitive function and prevent cognitive decline. In this review, we outline the characteristics of badminton’s beneficial effects on physical and cognitive aspects. The characteristics presented here indicate that intervention with regular badminton exercise could effectively improve cognitive function and prevent cognitive decline in the elderly.

Rotavirus (RV) replicates within viroplasms, membrane-less electron-dense globular cytosolic inclusions with liquid-liquid phase properties. In these structures occur the virus transcription, replication, and packaging of the virus genome in newly assembled double-layered particles. The viroplasms are composed of virus proteins (NSP2, NSP5, NSP4, VP1, VP2, VP3, and VP6), single and double-stranded virus RNAs, and host components such as microtubules, perilipin-1 and chaperonins. The formation, coalescence, maintenance, and perinuclear localization of viroplasms rely on their association with the cytoskeleton. A stabilized microtubule network involving microtubules and kinesin Eg5 and dynein molecular motors is associated with NSP5, NSP2, and VP2, facilitating dynamic processes such as viroplasm coalescence and perinuclear localization. Key post-translation modifications, particularly phosphorylation events of RV proteins NSP5 and NSP2, play pivotal roles in orchestrating these interactions. Actin filaments also contribute, triggering the formation of the viroplasms through the association of soluble cytosolic VP4 with actin and the molecular motor myosin. This review explores the evolving understanding of RV replication, emphasizing the host requirements essential for viroplasm formation and highlighting their dynamic interplay within the host cell.

In all mammals, the circulating pool of MLT is synthesized in the pineal gland, during the night darkness hours. Its main function is that of synchronizing the organism in the photoperiod. In contrast, extra-pineal MLT is synthesized in peripheral organs, does not follow any circadian rhythm nor circulates and plays a detoxifying and cytoprotective action. Circulating MLT may stimulate both innate and acquired immune responses by its circadian action and by activating high affinity receptors on immunocompetent cells. Extra-pineal MLT may have anti-oxidant and anti-inflammatory effects dampening the innate immune response. These two seemingly divergent roles may be considered as two faces of the same coin. In fact, the integration of both circulating and extra-pineal MLT functions might generate a balanced and effective immune response against microbial pathogens. The studies described in this review investigated the effect of exogenous MLT in various models of infectious diseases using extremely different doses and treatment schedules. None of them evaluated the possibility to integrate the non-circadian anti-inflammatory effect with the circadian immunoenhancing action of MLT. As a consequence, in spite that most studies agree that MLT has a beneficial effect against infections, it seems difficult to draw any definite conclusion about its possible therapeutic use.

The catastrophic failure of two dams in Libya in the night of 10th and 11th September 2023, resulted in a significant amount of water and debris destroying a large part of the city of Derna, which is located downstream of the two dams. The flood caused more than 6000 fatalities and the dis-placement of thousands of people from the city. The dams failed due to heavy rainfall caused by the Medicane Daniel, resulting in intense precipitation and the dams overspilling after reaching full capacity. This paper analyses the consequences of the dam breach by modelling the flow discharge and the flooding of the city of Derna. The analysis compares the computed quantities with the findings of the post-event satellite image analysis from UNOSAT and Copernicus, the field reports, and information gathered from the social media through AI detection. The results allows to better understand the dynamics of the dam breach, its initial parameters and an assessment of the accu-racy of the results.

(1) Background: Tooth extraction is one of the oldest and most well-known surgical procedures in dental medicine. It is still routinely performed to date by both general practitioners, oral surgeons, and dental undergraduates. The Benex extraction system allows for the extraction of teeth in vertical direction, which avoids most trauma against the surrounding alveolar bone and soft tissues; (2) Methods: The study included 56 patients who were recruited from the department of Oral Surgery at the Faculty of Dental Medicine, Medical University – Plovdiv. Patients were divided into two groups of 28 patients – Group I (control group) and Group II (study group). For each group the success of the extraction, buccal cortical plate preservation, pain experience and early wound healing were assessed; (3) Results: There was no statistical significance between the success of the extractions in both groups. The Benex extractions preserved the buccal cortical plate in 95% of the cases, whereas the forceps extractions only in 71.8%, which shows a statistical significance. On the seventh day patients in group II reported less pain, without a significant difference. There was a significantly bigger number of completely healed extraction wounds on the 10th day; (4) Conclusions: Atraumatic extractions allow for more hard and soft tissues to be preserved in the extraction site. This is all crucial for the successful outcome and aesthetically pleasing results of a following prosthetic or implant restoration.

The most frequent cause of cardiac failure globally is ischemic heart disease, with chemothera-py-related cardiovascular side effects emerging as the second most frequent cause. Cancer therapy can result in acute cardiac events, such as coronary artery spasm, acute myocardial infarction, thromboembolism, myocarditis, bradycardia, tachyarrhythmias, atrio-ventricular blocks, QT pro-longation, torsades de pointes, pericardial effusion and hypotension, as well as chronic conditions, such as hypertension, systolic and diastolic left ventricular dysfunction presenting clinically as heart failure or cardiomyopathy. In cardio-oncology, when referring to cardiac toxicity and cardiovas-cular hypersensitivity, there is a great deal of misunderstanding. When a dose-related cardiovas-cular side effect continues even after the causative medication is stopped, it is referred to as cardi-otoxicity. A fibrotic response is the ultimate outcome of cardiac toxicity, which is defined as a dose-related cardiovascular adverse impact that lasts even after the causative treatment is stopped. Cardiotoxicity can occur after a single or brief exposure. On the other hand, the term cardiac or cardiovascular hypersensitivity describes an inflammatory reaction that is not dose-dependent, can occur at any point during therapy, even with very low medication dosages, and can show up as Kounis syndrome. It may also be accompanied by anti-drug antibodies and tryptase levels. In this comprehensive review, we present the current views on cardiac toxicity and cardiovascular hypersensitivity, together with the reviewed cardiac literature on the chemotherapeutic agents inducing hypersensitivity reactions. Cardiac hypersensitivity seems to be the pathophysiologic basis of coronary artery spasm, acute coronary syndromes such as Kounis syndrome and myocardi-tis caused by cancer therapy.

The aging populations observed across numerous countries worldwide necessitate a thorough exploration of interventions aimed at promoting healthy aging. As individuals age, they undergo significant physiological changes that impact their functional capacity and often necessitate increased reliance on external support systems. Enhancing the autonomy and well-being of elderly individuals emerges as a critical imperative, serving not only individual interests but also broader societal goals. This review investigates various interventions designed to optimize the health and independence of aging populations, offering insights into effective strategies for promoting healthy aging and mitigating the societal burdens associated with an aging demographic.

We analyze the transformation of a very broad class of metrics that can be put in terms of static coordinates. Starting from a general ansatz, we obtain a relation for the parameters in which one can impose further symmetries, or restrictions. One of the simplest restrictions leads to FLRW cases, while transforming from the initial static to other static-type coordinates can lead to near horizon coordinates, Wheeler-Regge, isotropic coordinates, among others. As less restrictive cases, we show an indirect route for obtaining Kruskal-Szekeres within this approach, as well as Lema\^{\i}tre coordinates. We use Schwarzschild spacetime as prototype for testing the procedure in individual cases. However, application to other spacetimes, such as de-Sitter, Reissner-Nordstr\"{o}m or Schwarzschild de Sitter, can be readily generalized.

The new regulation was introduced to the franchise market of South Korea in that an applicant who wants to be a franchiser must set up direct retail stores and run them for at least one year before recruiting franchisees. Considering the purpose of the regulation, it is inferred that once franchisers run their own stores, their franchisees would be better off than otherwise. This paper addresses one reason that the Korea Fair Trade Commission has no choice but to introduce the regulation. Along the line, this paper investigates whether franchisees would have the more likelihood to make profit in the case of that franchisers operate their own stores. The result demonstrates that this case could be true since the operation of franchiser owned stores could be helpful in reducing cost of franchisees.

DNA barcoding has emerged as a powerful tool in the field of taxonomy, transforming the way we identify and classify species. This review article aims to provide an up-to-date overview of the advancements in DNA barcoding techniques, their applications, and their impact on taxonomy and biodiversity studies. We will explore the evolution of DNA barcoding, the development of standardized barcode regions, and the challenges and limitations associated with this methodology. Additionally, we will delve into the diverse range of taxonomic groups for which DNA barcoding has been applied, highlighting key case studies and their contributions to our understanding of biodiversity. Furthermore, we will discuss the integration of DNA barcoding with other molecular and bioinformatic approaches, such as metabarcoding and machine learning, to enhance species identification and biodiversity assessment. This review aims to provide a comprehensive perspective on how DNA barcoding is reshaping the field of taxonomy and its implications for conservation, ecology, and evolutionary biology.

Throughout this paper the effects of injuries on athletes are highlighted in different aspects. Exertional compartment syndrome is a specific injury related to athletes that is thoroughly explored in this paper through an analysis of the anatomy, symptoms, diagnosis and treatments of the syndrome. Exertional compartment syndrome is an exercise-induced muscle and nerve condition that causes pain, swelling as well as disability in the affected muscles of the lower leg. This syndrome commonly develops in young athletes involved in sports with excessive running and repetitive impact such as track and field, soccer, basketball and lacrosse. Exertional compartment syndrome is commonly misdiagnosed as other less severe injuries such as shin splints, which is symptomatic of pain along the tibia bone of the lower leg caused by overuse. The misdiagnosis of exertional compartment syndrome allows the syndrome to worsen and intensify resulting in the only effective treatment to be fasciotomy, a surgical procedure to release the pressure within the compartment of the lower leg. There is no definitive protocol developed for patients after fasciotomy is performed. Athletes experiencing similar symptoms after surgery are usually treated for their symptoms rather than the syndrome as a whole, which does not have a high degree of success. The development of a definitive protocol utilizing the techniques of manual therapy, mobility stretching and corrective strength training can improve the adverse effects of exertional compartment syndrome as well as restore functionality to the athlete. Raising awareness of this syndrome in athletics will be effective in treating the athlete early on in order to avoid severe effects and invasive procedures.

We discuss three key points: defining mathematically the notions of phase, symmetry breaking, topological defects and their stability in a formal language of homotopy theory and fiber bundle formalism; postulating and proving two theorems for successive symmetry breaking scenarios in condensed matter physics and cosmology, and demonstrating application of theorems to superfluid Helium 3 and Kibble-Lazarides-Shafi (KLS) walls, supporting and mathematically justifying the results of [Volovik, 2020].

The case of a patient with autoimmune haemolytic anaemia and an anastomosing haemangioma, is presented. The main dilemma was whether the haemangioma was the cause or the consequence of the autoimmune haemolytic anaemia and how both processes should be management. The diagnostic methods used are analytical and imaging techniques, which have not been conclusive in the diagnosis of anastomosing haemangiomas. Preoperative corticosteroid treatment does not prevent the recurrence of autoimmune haemolytic anaemia in the postoperative period. Surgical indication is established by the progression of proliferative activity, tumor enlargement and compression of the vena cava over time. Surgical resection of the tumor is the procedure used. The indication for surgical treatment of the anastomosing haemangioma and the decrease in alpha- fetoprotein after tumor resection are the most relevant results, although it increases again after a few weeks. The development of an anastomotic haemangioma may be secondary to autoimmune haemolytic anaemia. Angiogenesis resulting from the formation of erythroblastic islands at the periphery of the tumor and clonal expansions may be a mechanism to compensate for the haemolysis caused by autoimmune haemolytic anaemia

The consistent and continuous growth in the world’s population is creating many challenges for public policymakers in the different life areas, including dealing with the increasing amounts of waste that are generating problems involving air and land pollution and a shortage of land for waste disposal. This study presents the results of public policy on managing municipal waste, as manifested in the quantity and rate of waste collected throughout Israel in recent years and disposed of in the various landfills and in analysis of the political and economic factors affecting this policy. The study combines a quantitative and qualitative approach, where the quantitative study includes analysis of statistical data based on information from the Central Bureau of Statistics, the Ministry of Environmental Protection, the Ministry of Finance, and others, and the qualitative study relies on reading and analyzing primary documents of the different government ministries on Israel’s waste disposal policy and information in the media on this issue. The research findings attest to an increase in the amount of municipal waste dumped in Israel, a merely slight decrease in the rate of landfilling as a proportion of all municipal waste disposal, and a merely slight increase in the rate of municipal waste recycled in recent years. The research conclusions stress the effects of the landfill levy and the Cleanliness Maintenance Fund on one hand, and of the government instability in the Ministry of Environmental Protection, the positivist policy embraced by decision makers in the Ministry of Environmental Protection, and the power struggles between Israel’s different ministries on the other, as the respective economic and political factors affecting Israeli policy on municipal waste management.

Rarely has a chemical elicited as much controversy as has dichloroacetate (DCA). DCA was initially considered a dangerous toxic industrial waste product. Then it was a potential treatment for lactic acidosis. However, the main controversies started in 2008 when DCA was found to have anti-cancer effects in experimental animals. These publications showed contradictory results in vivo and in vitro, so that a thorough consideration of this compound in cancer is merited. Despite 50 years of experimentation, DCA’s future in therapeutics is uncertain. Without adequate clinical trials and lack of health authorities’ approval, DCA has been introduced in off -label cancer treatments in alternative medicine clinics in Canada, Germany, and other European countries. The lack of well-planned clinical trials and its use by people without medical training has discouraged consideration by the scientific community. There are limited thorough clinical studies of DCA, and many publications are individual case reports. Case reports of DCA benefits against cancer have been increasing recently. Furthermore, it has been shown that DCA synergizes with conventional treatments and other repurposable drugs. Beyond the classic DCA target, pyruvate dehydrogenase kinase, new target molecules have also been recently discovered. These findings have renewed interest in DCA. This paper explores whether existing evidence justifies further research on DCA for cancer treatment and it explores the role DCA may play in oncology treatment.

This paper examines the interplay between media and the “social side of soils” whereby climate change action implicates individual and collective capacity to adapt, participate in groups or organizations, networks, and respond to challenges and opportunities across a system. The context examined here is Canadian agriculture, and soil health initiatives in the province of Ontario. Our review of relevant literature points to a knowledge gap on the role of the media in enabling climate action in the Canadian agricultural sector with a focus on soil management practices. This study conducted a media content analysis of 100 English-language news articles published between 2022 and 2024 and conducted 31 surveys with media professionals. Approximately one-quarter of the screened news articles contained any relevant coverage of soil health-related climate change mitigation issues. Journalist surveys identified the resource constraints on soil health media coverage with a wide range of traditional and Internet-based journalism on climate change issues, motivated particularly by crisis communication and one-off “parachute reporting”. Going forward, the engagement of key stakeholders of soil health in Canada such as farmers for media and communication about climate change mitigation needs attention. Key policy structures at the federal and provincial levels can help to make this happen.

The canonical decomposition of a bipartite graph is a new decompositionmethod that involves three operators, parallel, series and K+S. The class of weak bisblit graphs is totally decomposable with respect of these operators, and the class of bicographs is totally decomposable with respect of parallel and series operators. We prove in this paper that the class of bipartite (P_6,C_6)-free graphs is exactly the class of bipartite graphs that are totally decomposable with respect of parallel and K+S operators. We simplify the recognition algorithm of weak-bisplit graphs to adepte with bipartite (P_6,C_6)-free graphs. As a result of this adapted algorithm, we prsent efficient solutions in this class of graphs for two optimization graph problems, the first is the maximm balanced bi-clique problem and the the second is the maximum independent set problem.

This article presents a critical review of the current and future state of “business architecture” within the enterprise architecture frameworks. The review is conducted in the following four stages. In the first stage of analysis, the research and publication questions are established. In the second stage, the search for information is designed and implemented using the Prisma methodology, and once the selection criteria were applied, 75 article-type documents were selected from Scopus, WoS, and Dimensions databases. In the third stage, the information analysis is conducted, and then it is synthesized. Finally, in fourth stage, the discussion is elaborated. This research contributes in understanding the current and future trends of business architecture in Industry 4.0. Considering the challenges of the changing market, the results show the need to implement architectures and dynamic business models, including their artifacts, in a globalized context immersed in new technologies.

Handedness reflects functional body and hemispheric lateralization, proving relevant to contextualize cognitive and behavioral profiles. This study aimed to determine the lateralization of the hands, feet and eyes in patients with various neurological disorders and to test the Edinburgh Handedness Inventory (EHI), a widely used handedness measure. The study design included a literature overview and a clinical study. The overview spanned the literature of the past 50 years since the EHI was developed. Three hundred seventy-four patients with immune-mediated (IM) or noIM neurological diseases and healthy subjects underwent personal and family history of handedness, stressful events and IM diseases, and EHI. The literature overview showed that handedness, which develops under the influence of genetic and acquired factors, is an indirect indicator of hemispheric lateralization; hemispheric lateralization is bidirectionally related to the immune system. In the clinical study, the EHI showed appropriate reliability and structural validity; its scores related to chronological age, onset age, and disease duration and helped distinguish patients with IM disease from other patients and healthy subjects. Therefore, handedness may change over the course of life under the influence of pathology and could predict IM neurological diseases. The EHI is an appropriate measure of handedness.

Background: To investigate the safety and efficacy of palliative arterial embolization for metastases of the scapula. Materials and Methods: We studied the files of the patients who underwent palliatrive arterial embolization for metastases of the scapula from January 2016 to January 2023, 15 patients (8 M, and 7 F) with a mean age of 62 years (SD 10.2) with two patients receiving a second procedure, for a total of 17 embolizations. Data on technical and clinical success, as well as changes in tumor size, were collected. All embolization-related complications were evaluated according to the CIRSE classification system for complications. Results: The average follow-up was 18 months (Range 6-36 months). The main primary tumor were renal, lung and breast cancer. The primary arteries embolized were the circumflex scapular artery, the dorsal scapular artery, and the thoracoacromial trunk. A mixture of Glubran and Lipiodol in a 1:2 ratio was used for embolization, with an average of 1.1 mL (Range 0.5-1.5 mL) used per procedure. The efficacy of embolization was measured using the Visual Analogue Scale (VAS), with values recorded at baseline, at 1, 3, 6, and 12 months post-embolization, and at 6-month intervals after. Baseline VAS scores averaged 7.2 (SD 1.5), decreasing to 2.8 (SD 0.9) at one month. Progressive reduction was observed at 3, 6, and 12 months with scores of 2.3 (SD 0.8), 1.9 (SD 0.7), and 1.6 (SD 0.6) respectively. Metastatic tumor size was reduced from a mean of 192.2 cm3 (range 38.7 to 480.1 cm3) pre-embolization to a mean of 177.3 cm3 (range 37.2 to 455.5 cm3) at the 12-month follow-up (p

During the last few years, advancement in the area of biochemistry, science of the material world, engineering and computer-aided testing has directed towards advancement of high-throughput tools for the sake of profiling information encoded in a gene. Single-cell RNA-sequencing (scRNA-seq) tools capable to examine the sequence data from each individual cells and it shows within population variety and permit exploring of cell conditions and transformation by extreme resolution, possibly give out cell sub-types or gene expression fluctuations that are shaded in mass sequencing processes, which shows population-averaged evaluations. Yet, the major disadvantage for this tool is the lack of success to pick out location related details of the RNA transcriptome, as this needs tissue detachment and cell isolation. Location based transcript determination is among the advancements in the area of medical biotechnology as this can find out the molecules like RNA dataset in their intact physical placement in tissue segment with spatial context at the scale of a single-cell, which is very advantageous as compare to single-cell sequencing techniques. These approaches give key observation in the areas of biomedical field sub-disciplines like neurology, embryology, carcinoma study, immune cell investigation and histological activities. Generally, this review mainly focused towards single-cell sequencing methods, technology development, its challenges observed, different expression data analysis mechanisms and their applications in different areas, such as in cancer research, microbes, central nervous system, sex organs and immune-biology, intensifying the essentiality of sequencing tools at single-cell level for the characterization of extremely dynamic individual cells.

The study examines academic burnout syndrome and its relation to personal and academic variables among university students in nursing and early childhood education programs in Spain. A total of 606 university students (Primary Education: 49.7%; Nursing: 49.7%) of both sexes (71.5% female), and average age 20.68 years (SD = 1.65) participated. An ex post facto retrospective single-group design was planned. The instruments used were the Maslach Burnout Inventory-Student Survey (MBI-SS) and the NEO Five Factor Inventory (NEO-FFI). Nursing students, who showed more study hours, less sleep, and lower grades, had higher academic burnout scores. Linear regression models were proposed to analyze the relationship between academic burnout, personality, and sociodemographic variables. Nursing students scored higher in emotional exhaustion and lower in cynicism, and they scored higher in neuroticism and openness. Furthermore, 16.1% of the variance in academic burnout was explained by personality variables as degree, course and study hours. These findings suggest the importance of considering both academic and personality variables in understanding academic burnout in university students.

Drug-induced movement disorders (DIMDs) are a group of neurological conditions caused by the use of certain medications. These disorders can significantly impact a person's quality of life and are essential to recognize and manage. This article provides an overview of the causes, symptoms, and treatment of DIMDs. DIMDs can manifest in various forms, including parkinsonism, tardive dyskinesia, dystonia, and akathisia. They are most commonly associated with medications such as neuroleptics, antidepressants, antipsychotics, antiemetics, and anticonvulsants. Risk factors for developing DIMDs include age, genetics, duration of medication use, and dose of medication. Symptoms of DIMDs can range from mild to severe and may include tremors, rigidity, bradykinesia, and involuntary movements. These symptoms can significantly impact a person's ability to perform daily activities and lead to social isolation and decreased quality of life. DIMD diagnoses are based on clinical evaluation, including a thorough medical history and physical examination. Imaging studies such as MRI or CT scans may also be used to rule out other possible causes of symptoms. Treatment of DIMDs involves the withdrawal of the offending medication if possible. Pharmacological treatments such as anticholinergic drugs, benzodiazepines, and dopamine agonists may also be used to manage symptoms. Non-pharmacological treatments such as physical, occupational, and speech therapy can also be beneficial. DIMDs are necessary neurological conditions that can significantly impact a person's quality of life. Early recognition and management of these disorders are essential for optimizing outcomes. Further research is needed to understand the underlying mechanisms of DIMDs better and to develop more effective treatments.

The industrial metaverse paradigm is generally a virtual environment that integrates various technologies such as augmented reality (AR) and mixed reality (MR) to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the industrial Metaverse to drive sustainability and resource efficiency is compelling. Utilizing advanced technologies like the Industrial Metaverse is essential for staying competitive in today’s rapidly evolving business environment. However, the environmental impact of the technologies underpinning the industrial metaverse, like data centres and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that by 2025, without sustainable AI practices, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains. As the metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. However, the specific extent to which this substitution can alleviate environmental concerns remains understudied, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the industrial metaverse as well as explore the environmental impact of the industrial metaverse. The methodological approach is that of integrative literature review design wherein multiple sources from web science and databases such as ACM library, IEEE Library, and Google Scholar, were analysed to provide a comprehensive understanding of the developments in the industrial metaverse. Specifically, firstly by considering the Industrial metaverse architecture we elucidate the concept of the "industrial metaverse" and the associated enabling technologies. Secondly, an exploration through discussion, of the prevalent use cases and deployment of the emerging industrial metaverse. Thirdly, exploration of the impact of the industrial metaverse on the environment. In the fourth instance, we address novel security and privacy risks as well as upcoming research challenges, keeping in mind that the industrial metaverse is based on a strong data fabric. Results point to the Industrial Metaverse as having both positive and negative environmental effects by energy consumption, e-waste, and pollution. Overall research, however, indicates that most industrial metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Ultimately for sustainability in the industrial metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, examining the effects of products on the environment as well as creating a circular economy.

This publication aims to create 4K UHD video sequences with a specific focus on smart cities and transport. The created database will be accessible to all experts in the field and can be further used for research purposes. All reference video sequences will be transcoded into different quality settings. The resolution, compression standard, and bitrate will be changed. The work will aim to find out the best combination of video parameters from the measured values. Thanks to the overall evaluation, we will ensure for each single video sequence the quality with the best experience for the providers that will offer the service. The database will also be available with its objective and subjective ratings, with dynamicity information characterized by spatial and temporal information. After, It will be possible to compare the results of the subjective evaluation of a selected sample of our respondents with the work of other researchers who can evaluate with a different sample of evaluators.

Oscillons, fundamental to various physical domains, are examined in-depth in this paper. From Josephson junctions in superconducting circuits to classical and quantum field theories, oscillators reveal profound insights into diverse phenomena. The study primarily focuses on oscillons, distinctive field configurations with enduring localization, establishing intriguing connections with well-known models like the Sine-Gordon breather. Surprising parallels in the weak coupling limit illuminate the rich dynamics of oscillators. A semi-classical quantization method, discretizing the field into spatially homogeneous regions, exposes stability angles, providing insights into the quantum nature of oscillons. The application of this quantization framework to specific cases, including the Sine-Gordon breather, showcases its versatility. The results offer a comprehensive perspective on the quantization of oscillons, unraveling the intricate interplay between classical and quantum dynamics. In conclusion, this paper provides a profound exploration of quantum oscillators, unveiling novel connections and presenting a rigorous quantization framework. The gained insights contribute significantly to the broader understanding of oscillatory systems and their pivotal role in diverse physical phenomena.

The advantage of FPGAs lies in the ability to implement a fully hardware solution for interfacing with various input/output (I/O) devices. Each block can work in parallel with all the others, simplifying the satisfaction of timing constraints. However, this hardware utilization consumes FPGA resources that could otherwise be allocated to the primary project. An alternative involves employing a small "soft-core" processor implementing I/O in software. With the goal to design and evaluate a new tiny soft-core processor optimized for FPGA resources in I/O , a novel processor named Baby8 is developed. It is an 8-bit CISC soft-core processor optimized for reduced FPGA resources including program size for 8-bit applications. The number of instructions is not large but any instruction can access arbitrary memory locations. The performance and resource utilization of the newly designed processor are evaluated and compared with a variety of other soft-core processors. The results demonstrate its competitive performance, achieving an average maximum clock frequency of approximately 57 MHz and a power consumption of around 2mW. Furthermore, it conserves nearly half of the FPGA resources in implementation.

Rheometric investigations facilitate the rheological analysis of cementitious suspensions. However, the choice of the rheometric device and raw data conversion affect the results. Contrary to small gap rheometry, where the flow data shear stress

The rapid integration of Information and Communication Technology (ICT) is transforming the traditional electrical grid into a {\em Smart Grid}. Smart grids enable two-way communication and improved monitoring and control between utilities and customers. However, due to its heterogeneous nature, public exposure, and weak security at low-powered devices, the Smart Grid has vulnerabilities to various malicious threats, adversaries, and cyber attacks, which may affect cost and service availability. Additionally, when the systems' confidentiality, integrity, or availability are compromised, the resulting fallout can threaten national security and have cascading effects on human lives. Given the extreme consequences of an attack, smart-grid technology must be thoroughly tested for correct operation and security {\em before} it is deployed. As a result, vulnerability testing of smart grids, not only for correctness but for security purposes, has been the subject of numerous studies by academics, government agencies, and private companies. This paper reviews the vulnerabilities associated with the smart grid and spotlights simulation as the vulnerability testing methodology conducted in recent pertinent research works. It also presents various security aspects of the smart grid, including grid applications, system and network infrastructure and components, cyber threats and attacks, simulation, and different mitigation techniques. Finally, we analyze the gaps in the current research works, focusing on simulation. We briefly present a real-time simulation testbed that mimics customer behaviour and integrates hardware in the loop to apply attack methods, analyze vulnerabilities and risk mitigation associated with the smart grid system, and propose future work to improve the current framework.

Advances in gastric cancer screening and endoscopic technology have made it possible to detect and treat gastric cancer at an early stage. As a result, about 40% of all gastric cancers have now been resected by endoscopic treatment in Japan. In endoscopic diagnosis, magnifying endoscopic observation using band-limited light such as narrow band imaging (NBI) has significantly improved the accuracy of gastric cancer diagnosis. At the same time, in endoscopic treatment, endoscopic submucosal dissection (ESD) has contributed to increase explosively the number of gastric cancers which could be resected endoscopically. In recent years, the construction of algorism for endoscopic diagnosis and treatment with a view to personalized medicine has been explored, and endoscopic use for gastric cancer managements is expected to develop further in the future. Keywords (5 words):

In the paper, a study of the performance of unmanned ground vehicle (UGV), establishes numerical simulation model of the chassis system by three-dimensional software and finite element analysis software. Based on the numerical modelling and analysis, the work focuses on study of the strength and stiffness of the vehicle's chassis and researched the distribution of stress and deformation under the extremal condition. The paper also presents an autopilot design with new cascade control system for an autonomous motion of an unmanned ground vehicle based on Proportional-Integral-Derivative (PID) and Feed-Forward (FF) control. The PID-FF controller is a part of dedicated for presented UGV used in a hybrid control system for precise control and stabilization, necessary to increase the vehicle motion stability and maneuvers precision. The hybrid PID-FF control system proposed for the ground vehicle model gives a satisfactory quality of control, while maintaining the simplicity of the control system. Presented tests performed in area of mechanical design and control analysis gives good results and proves usefulness of designed unmanned device.

Most recent university campuses follow the North American model, built on city limits or countryside, with large separate buildings in open green spaces. Studies suggest that prevalence and severity of mental health issues among university students are increasing over the past decade in most countries. University services were created to face this growing problem, however individual based interventions have limited effects on mental health and well-being of a large population. Our aim was to verify if and how the natural environment in Campuses is focused on programs to cope with the issue of mental health and well-being among students. Systematic review of literature was undertaken with search in Scopus and LILACS with the keywords “green areas” AND “well-being” AND “Campus”, following PRISMA guidelines. As a result, 32 articles were selected. Research on the topic is recent, mostly in the USA, Bulgaria, and China. Most studies used objective information on campuses’ greenness and/or university students’ perception. Mental health was usually measured by validated scores. Findings of all the studies indicated positive association between campus greenery and well-being of students. We conclude that there is a large potential for use of university campuses in programs and sites for students´ restoration and stress relief.

This study aimed to identify the most beneficial attitudes adopted by enterprise participants during their engagement in open-source software (OSS) activities. For this purpose, we selected repositories from GitHub, featuring the top 10 with the highest stars and forks, classified the participants according to their sentiment states, and compared their productivity. The results indicated that participants with positive sentiments were more productive than those with negative sentiments. Consequently, it can be inferred that encouraging organizations to express positive sentiments towards their developers publicly may stimulate OSS community activities and enhance productivity, thereby potentially leading to increased open innovation and sustainability.

Energy and information are fundamental concepts that play crucial roles in the foundations of science, each contributing to different aspects of our understanding of the natural world. Here we encounter that Fisher' measure is able to link them. We also investigate some interesting relations between Fisher's information, Poisson's distribution, and thermal quantifiers in a grand canonical ensemble. These connections permit one to visualize interesting novel artifacts in statistical mechanics. We clearly see them emerge in discussing the ideal gas. For example, we encounter that the Poisson distribution is intrinsically involved in the physics of the ideal gas, and we are able to give a Fisher-estimation of the difference in informational content between the grand canonical and canonical ensembles.

Saprotrophic fungi are key in global carbon cycling, capable of decomposing lignocellulose in wood. This study, for the first time, explores the influence of oxygen (O2) and temperature on methane (CH4) production by two fungi, Laetiporus sulphureus and Pleurotus sapidus. We examined CH4 formation under varying O2 levels (0 to 98%) and temperatures (17, 27, and 40 °C) when the fungi grew on pine wood, beech wood, and grass under sterile conditions. Our findings reveal a strong dependency of fungal CH4 production on O2 mixing ratios. Methane formation was highest when O2 levels exceeded 5%, whilst no CH4 formation was observed after complete O2 consumption by the fungi. Reintroducing O2 immediately resumed fungal CH4 production. When CH4 formation was normalized to O2 consumption (CH4_norm) a different pattern between species was observed. L. sulphureus showed higher CH4_norm rates with higher O2 levels, whereas P. sapidus showed elevated rates between 0-5% O2. Temperature also significantly influenced CH4 and CH4_norm rates, with the highest production at 27 °C, and comparatively lower rates at 17 and 40 °C. These results demonstrate that O2 levels and temperature are critical in controlling fungal CH4 emissions, a consideration necessary for future CH4 source predictions.

The prognosis of cancer patients has greatly improved in the last years, owing to the development of novel chemotherapeutic agents. However, this progress comes with an increasing occurrence of cardiovascular adverse reactions. A serious side effect is arterial hypertension, which is the most frequent comorbidity encountered in cancer patients, influencing the outcomes in cancer survivors. Even though secondary hypertension related to specific chemotherapeutic agents, such as vascular endothelial growth factor inhibitors, is usually mild and reversible, in rare instances it can be severe, leading to discontinuation of chemotherapy. In addition, hypertension per se has been studied as a potential risk factor for cancer development. The relationship is even more complex than previously thought, as concerning evidence recently highlighted the potential oncogenic effects of antihypertensive drugs, such as thiazide diuretics predisposing to skin cancer. As a result, under the light of the similar risk factors and overlapping pathophysiological mechanisms between hypertension and cancer, a promising concept of onco-hypertension has emerged, aiming to improve the understanding of the complicated interplay between these two pathologies and maintain a balance between the efficacy and risks of both antihypertensive drugs and chemotherapy agents.

Gene editing is now easy to make new disease models through in-vivo and in vitro tests. It can potentially be used to make animals with single-gene or multiple-gene changes. The mutant strains with changed germlines are no longer needed with in vivo gene editing, which uses the CRISPR-Cas9 system to target cells of interest in their normal tissues. Whereas, the AAVs and other viral vectors have made it possible to change cells selectively. Gene editing hade made it possible to use human induced pluripotent stem cells (iPSC) to model diseases that run in families. Researchers can compare and contrast the human genomes of many different ethnic and racial groups using this method. Scientists may be able to make a disease in a lab dish using iPSCs from a patient. Using CRISPR, iPSCs made from patient cells can be fixed if they have certain problems. This shows that gene therapy is possible and shows what happens when cells aren't working right. The fact that CRISPR-Cas9 can change the DNA by just one nucleotide has had a huge effect on biological studies. CRISPR is becoming more and more popular, which shows how useful, easy, and effective it is. With the broad use of CRISPR-based apps, the tool is now used for much more than just changing genes. This method can be used to screen the whole genome, control the translation of genes based on their sequence, and edit several genes at the same time. Scientists can now model diseases in different species and learn more about how genes work because of these advances. Genome-wide association studies and genome-editing tools like CRISPR are giving us a good look at the future of personalized medicine.

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the image-based features of microscopy. In this comprehensive review article, we take a historical perspective and delve into the evolution of this methodology, presenting its past, exploring current state-of-the-art and forecasting potential future advancements. The inception of ImFC arose from the innovative combination of a flow cytometer's hydraulics with the technology of an advanced camera. This synergistic coupling facilitated the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. However, the application of ImFC faced several challenges. Among the most prominent ones, is the development and implementation of software systems that can reliably and efficiently handle the robust data acquisition and analysis inherent in ImFC. The scale and complexity of the data generated by ImFC necessitates the creation of novel analytical tools that can effectively manage and interpret this data, thus allowing us to unlock the full potential of ImFC. Interestingly, artificial intelligence (AI) algorithms have begun to be implemented on ImFC, offering a promise for enhancing analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

This overview initially describes insect immune reactions and then brings together present knowledge of the interactions of vector insects with their invading parasites and pathogens. It is a way of introducing this special issue with subsequent papers presenting the latest details of these interactions in each particular group of vectors. Hopefully, this paper will fill a void in the literature since brief descriptions of vector immunity have now been brought together in one publication and could form a starting point for those interested and new to this important area. Descriptions are given on the immune reactions of mosquitoes, blackflies, sandflies, tsetse flies, lice, fleas and triatomine bugs. Cellular and humoral defences are described separately but emphasis is made on the co-operation of these processes in the completed immune response. The paper also emphasises the need of great care in extracting haemocytes for subsequent study as appreciation of their fragile nature is sometimes overlooked with non-sterile media, smearing techniques and excessive centrifugation sometimes used. The potential vital role of eicosanoids in the instigation of many of the immune reactions described is also discussed. Finally, the priming of the immune system, mainly in mosquitoes, is considered and one possible mechanism is presented

Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of iot data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan 'an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase of sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control.

In the discussion on environmental policies, the notion of eco-efficiency is often used. Eco-efficiency is defined as the delivery of products and services with competitive values, while reducing the ecological impacts and satisfying human needs. In an environment of great competitiveness in which the companies operate, the improvements in the productive process is one of the differentiating factors for guaranteeing a strong competition. Also it is essential rationalizing energy consumption and natural resources. For this it is necessary to understand the main operations and dynamics of the company. This work presents an empirical study of a Portuguese company in the industrial sector. The problematic here presented is based on the company’s growing concern to reduce the amount of scrap produced. From the literature research that was performed, a strong dependence relation between the different variables in the extrusion process was found. The main objective of this work is to model the aluminium extrusion process, with the aim of minimizing the production of scrap. For this several variables involved in the process are taking into account. Using statistical techniques, in particular multiple linear regressions, it was possible to identify the importance of the variables under study for the scrap production.

Neural-objective and image optimization approaches for drilling fluid rheology automation are crucial for drilling engineering optimization. A myriad of intelligent computational models are employed to predict and monitor the parameters of mud rheology and filter cake permeability posture using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and a model optimization tool. 498 datasets of synthetic-based mud (SBM) flat rheology from a drilling mud laboratory at Nazarbayev University were imported into a user-friendly MATLAB application using image processing and nftool. Apart from the Google TensorFlow Sequential API DNN architecture used, a Levenberg-Marquardt training algorithm with input sigmoid hidden neurons 10, 12, and 18 coupled with a linear output layer was also used to predict the SBM flow index. OBM and SBM filter cakes were processed for void spaces; however, a model study was extrapolated to maximize filtration volume Vf. The study’s findings show that the ANN-FF model employed for rheological property monitoring and prediction had higher steady and exponential levels of accuracy and a correlation coefficient of 0.96-0.99. More so, SBM and OBM image processing presented an area of void spaces of 1790M2 and 1739M2, respectively. The porosity and permeability postures of both SBM and OBM resulted in a significant void space capable of maximizing the flow index. In addition, the single-objective modelling based on a genetic algorithm did validate the experimental rheological data for flow index or filtration volume Vf optimization; the study resulted in the finding that the constrained physics-informed objective function hindered maximizing oil recovery and instead predicted possible formation damage. It is empirical to note that automating flow predictions with neural, objective, and image functions has presented an alternative novel method for non-programmers using MATLAB and Google Colabs that is capable of enhancing mud rheological parameters, drilling efficiency, and hydrocarbon recovery.

Background: Rapid advancements in Distributed Ledger Technology (DLT), including blockchain, are foundational to a new era of digital innovation. This innovation has catalyzed the emergence of ‘Decentralized Science (DeSci),’ a new concept and movement that aims to address the challenges of modern science. Objective: Given the novelty of the field of DeSci, this study aims to provide a comprehensive definition of the term as well as explore and conceptualize shared values and guiding principles inherent to DeSci. Methods: In line with the objectives of this study, an exploratory literature review was conducted to identify and synthesize the scholarly and secondary literature. The search and selection process included six databases (PubMed, Google Scholar, Web of Science, IEEE Xplore, arXiv, and Social Science Research Network), and the search period was limited to the last 15 years, from 2008 to 2023. To identify relevant secondary literature, such as articles, reports, blog posts, and website content, a keyword search was conducted in three search engines (Google.com, Bing.com, and Yahoo.com). Owing to the novelty of the concept and movement of DeSci, the exploratory literature review was supplemented by an anonymous online-based expert survey using a combination of single-choice and open-ended questions. The experts were selected based on predefined inclusion criteria, in association with their activities in the field of DeSci. The responses to the single-choice questions were subject to statistical analysis, whereas the open-ended questions were analyzed using qualitative content analysis. Results: Seven studies were selected for evaluation as part of the search and selection process to identify relevant scholarly literature. Following the review of secondary literature, additional 24 publications were included in the analysis. In the expert survey, 39 valid datasets were collected and analyzed. Following the synthesis of the results of the exploratory literature review and expert survey, a comprehensive definition of the term ‘Decentralized Science’ (DeSci) was formulated to reflect recurring themes. As no publications that explicitly discussed or addressed the values or principles of DeSci in the exploratory literature review could be identified, a set of shared values and guiding principles for DeSci were defined based on the results of the expert survey. Conclusion: The results of this study underscore the emerging nature of DeSci, as evidenced by the limited availability of relevant information and scarcity of academic publications. While this study proposes a comprehensive definition of DeSci as well as a set of shared values and guiding principles, the results of this study highlight the importance of ongoing evaluation and validation. Furthermore, the results of this study indicate a clear need for future research in the field of DeSci, emphasizing its dynamic and developing nature.

This review is a first attempt at bringing together various concepts from research on wall- and magnetically-bounded turbulent flows. Brief reviews of both fields are provided: The main similarities identified are coherent (turbulent) structures, flow generation and transport barriers. Examples are provided and discussed.

Background: Parkinson's disease (PD) is a global disease. However, there is a lack of research specifically focused on the AfrAbia population. Climate change, driven mainly by global warming, is linked to detrimental health impacts, including an increased frequency and intensity of heat waves. Neurological diseases, including neurodegenerative syndromes like Alzheimer's disease, Parkinson's disease, and motor neuron illnesses, have been increasingly seen and recorded in scientific literature. Although there is much literature demonstrating the growth in neurodegenerative disorders, the relationship between environmental variables such as global warming, air pollution, heavy metals, and pesticides and the higher occurrence of these illnesses is still unknown. Objective: The current article primarily aims to examine the relationship between climatic changes, air pollution, pesticides, heavy metals, and the occurrence of PD. Initially, we evaluate the influence of exposure to these variables on the cause or origin of Parkinson's disease. In addition, we will investigate how these variables disrupt processes such as excitotoxicity, oxidative stress, and neuroinflammation, all of which are strongly linked to Parkinson's disease (PD). Conclusions: This research presents a plausible rationale for the adverse effects of environmental influences on the brain, which are pivotal in the progression or onset of Parkinson's disease from an AfrAbian standpoint.

The algorithmic complexity vulnerability (ACV) that may lead to denial of service attacks greatly disrupts the security and availability of applications, and due to the widespread use of third-party libraries, its impact may be amplified through the software supply chain. The existing work in the field is dedicated to abstract loop or iterative patterns and fuzzing the entire application to discover algorithm complexity vulnerabilities, but they still face efficiency and effectiveness issues. Our research focuses on: (1) Proposing a representation and extraction method for code features related to algorithmic complexity vulnerabilities, helping analysts quickly understand program logic; (2) Providing a new ACV detecting model, focusing on the spatial complexity anomalies caused by deep recursion structures, and proposing a new filtering method; (3) Aiming at the difficulty of efficiently generating complex-data-type-related payloads using existing symbol execution techniques, a call-chain-guided payload construction method is proposed. We tested third-party components in the open-source Java Maven Repository, identified many unexposed vulnerabilities and 8 of them received CVE (Common Vulnerabilities &amp; Exposures) identifiers, and demonstrated that our method can discover more algorithmic complexity vulnerabilities compared to existing tools with better performance.

The emergence of diverse infections worldwide, which is a serious global threat to human existence, necessitate the urgent development of novel therapeutic candidates that can combat these diseases with efficacy. Isatin, a secondary metabolite from tryptophan is considered a privileged scaffold and favorable pharmacophore with a unique structural moiety and varied chemical and biological properties resulting in its widespread applications by medicinal chemists. Molecular hybridization is one of the most common and important techniques use in the synthesis of their bioactive hybrids in drug discovery. This review containing published articles from 2005 to 2022 focuses on isatin hybrids which have been synthesized and reported in the literature alongside a discussion on their biological properties. The purpose of this review is to set up the direction for the design and development of isatin-hybrids with tailored biological properties as effective therapeutic candidates inspired by nature.

Evidence from large epidemiological studies has shown that obesity may predispose to increased Th2 inflammation and increase the odds of developing asthma. On the other hand, there is growing evidence suggesting that metabolic dysregulation that occurs with obesity, and more specifically hyperglycemia and insulin resistance, may modify immune cell function and in some degree systemic inflammation. Insulin resistance seldom occurs on its own, and in most cases constitutes a clinical component of metabolic syndrome, along with central obesity and dyslipidemia. Despite that, in some cases, hyperinsulinemia associated with insulin resistance has been proven a stronger risk factor than body mass in developing asthma. This finding has been supported by recent experimental studies showing that insulin resistance may contribute to airway remodeling, promotion of airway smooth muscle (ASM) contractility and proliferation, increase of airway hyper-responsiveness and release of pro-inflammatory mediators from adipose tissue. All these effects indicate the potential impact of hyperinsulinemia on airway structure and function, suggesting the presence of a specific asthma phenotype with insulin resistance. Epidemiologic studies have found that individuals with severe and uncontrolled asthma have a higher prevalence of glycemic dysfunction, whereas longitudinal studies have linked glycemic dysfunction to an increased risk of asthma exacerbations. Since the components of metabolic syndrome interact with one another so much, it is challenging to identify each one's specific role in asthma. This is why, over the last decade, additional studies have been conducted to determine whether treatment of type 2 diabetes mellitus affects comorbid asthma as shown by the incidence of asthma, asthma control and asthma-related exacerbations. The purpose of this review is to present the mechanism of action, and existing preclinical and clinical data, regarding the effect of insulin resistance in asthma.

In many developing countries of the world including Pakistan motorcycle steeling is one of the main issue that needs to be dealt with an iron hand as it leads to economic losses as well as crime increase in the society, according to the DOWN REPORT there were 28609 motorcycle stolen in 2019 and this number has been increased to 34908 in 2020. Now due to the increasing motorcycles stealing crime what measures can we take to reduce the number of motorcycle theft the rule of technology is enormous in this regards specially information technology (IT) , information technology can play an important role in the security of motorcycles from being theft by the thieves, Information technology provide us the GSM based control system which can be very help full to construct a security system for motorcycle in order to make it antitheft.

Guided image filter implicitly balances noise and details based on local variance, achieving noise suppression while preserving prominent edges. However, we have observed limitations in using variance to distinguish between noise and image details, resulting in the suppression of fine structures alongside noise. Furthermore, the goal of enhancing image details, which is crucial in image filtering, has not been adequately addressed in edge-preserving filter design. In this paper, we approach image filtering as a problem of spectral energy reconfiguration, where the gain of each spectral band is adaptively regulated to achieve simultaneous noise suppression and detail enhancement. Instead of relying on variance, we employ a linear noise estimator to obtain the Signal-to-Noise Ratio (SNR) measurement necessary for dynamic gain regulation. Similar to the guided image filter, we determine the filter coefficients by solving the closed-form solution of the optimizer. We demonstrate that the proposed extended guided filter effectively integrates information from multiple spectral bands locally, allowing for the joint enhancement of meaningful details while simultaneously smoothing noise.

Background: The association of echocardiographic findings and subsequent risk of left-sided native valve endocarditis (LS-NVE) is undefined. The aim of this study was to determine if transthoracic echocardiographic (TTE) measurements are associated with the subsequent development of LS-NVE in patients without cardiac predisposing conditions. Methods: Institutional databases were evaluated for adults diagnosed with LS-NVE from 2008 to 2020. Patients with prosthetic valves, cardiovascular implantable electronic devices, intracardiac devices, injection drug use, and predisposing cardiac conditions were excluded. Only patients who had a TTE performed 6 months to 3 years before the development of LS-NVE were included as cases. Controls were patients within the same Mayo location with a TTE report and were matched in a 1:3 ratio according to age, gender, Charlson Comorbidity Index, and echocardiography date. Results: There were 148 cases and 431 matched controls. As compared to controls, IE cases had a higher prevalence of diabetes mellitus (46.6% vs. 30.4%) and chronic kidney disease (46.6% vs. 28.1%) (p<0.001). Left ventricular outflow tract velocity (p=0.017), left ventricular ejection fraction (p=0.018), and E: e’ ratio (p=0.050) were associated with LS-NVE. Conclusions: Echocardiographic measurements were associated with subsequent LS-NVE development in this pilot study. A larger cohort of LS-NVE patients, however, is needed to validate these findings.

This study examines the absorption and concentration of anthracene (AN) in the garden radish. Studies were conducted to grow radishes from the sowing stage in soil contaminated with AN, and to grow radishes in soil contaminated with AN following maturity (27 days after sowing). For the growth in AN-containing soil from sowing onward, AN levels immediately after seeding ranged from 12,500-36,300 ng/g-ww. AN concentrations during growing period ranged from 7140-16,000 ng/g-ww in soil and from 38.2-2160 ng/g-ww in radish, respectively. For the growth in AN-containing soil after maturity, AN concentrations in soil and radish ranged from 9560-22,100 ng/g-ww and 342-2270 ng/g-ww. The concentrations of AN in the roots and leaves had a higher density than those in the stems. In these experiments, positive relationships between AN levels in soil and radishes were observed. It is inferred that the greater the degree of soil contamination, the greater the effect on the amount of AN assimilated by the radishes. The concentration ratio (radish-to-soil; Cr/Cs) decreased exponentially with days since AN application. This indicates that the concentration ratio is affected by time in contact with AN. According to the results for the change over time of Cr/Cs and contents by radish part, it is thought that AN in the soil has good absorption through the roots to circulate within the organism, and is preferentially distributed and accumulated in the roots and leaves, which have high organic matter content.

Clinical work conducted over the last seventeen years at the EEG Institute in Los Angeles and by other neurofeedback providers around the world has demonstrated the utility of extending frequency-based neurofeedback deep into the infra-low frequency (ILF) region, using the method of endogenous neuromodulation described herein. The method is characterized by the absence of any overt reinforcements, which makes it possible to extend the clinical reach to extremely low frequencies. As the training frequency is lowered, the signal becomes more difficult to discriminate, and ultimately it can only be discerned by the brain itself, in the process of endogenous neuromodulation. The method emulates how the brain does skill learning in general: It must observe itself performing the skill, with feedback on its performance. While the immediate target of ILF neurofeedback is enhanced self-regulatory competence--with symptomatic relief and functional recovery the secondary consequences, progressive lowering of the target frequencies has led to improved outcomes in application to challenging dysfunctions such as episodic suicidality, migraine, seizures, and bipolar mood swings. The work has also yielded insights into how the frequency domain is organized. The training proceeds best at frequencies that are specific to each individual, and these are referred to as optimal response frequencies (ORFs). These frequencies differ for various placements but stand in two fixed relationships to one another, one that holds over the EEG spectral range, and another that holds over the entire ILF range. Training in the ILF region engages the dynamics of the glial-neuronal networks, which govern tonic, resting state regulation. The collective clinical experience with ILF neuromodulation within a large practitioner network supports the case for making protocol-based, individualized ‘homeodynamic’ regulation a therapeutic priority, particularly for our most impacted clinical populations: addiction, trauma formations, traumatic brain injury, and the dementias. The case is made for further outcome studies and foundational research.