Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial effects of XF drugs against Candida albicans and its biofilms

Compared with antibiotics for treating bacterial infections, there are a limited number of antifungal agents. This is due to several factors, including the difficulties of identifying suitable antifungals that target the fungal cell without damaging host cells, and the reduced rates of diagnosis of fungal infections compared with those caused by bacteria. The problem of treating fungal infections is exacerbated by an increasing incidence of antifungal resistance among human fungal pathogens. Three XF drugs (XF-73, XF-70, and DPD-207) have previously displayed innate bactericidal effects and a low propensity for microbial resistance, with XF-73 and XF-70 having a second, light-activated mechanism of action [known as photodynamic therapy (PDT)]. In an effort to expand the repertoire of antifungal agents, this research assessed the in vitro activity of XF drugs via both mechanisms of action against six strains of the fungal pathogen Candida albicans in both planktonic and biofilm cultures. In addition, this research examined the effects of XF drug treatment on biofilms of C. albicans in a reconstituted human oral epithelium model. All C. albicans strains tested were susceptible to XF-73 and XF-70, with minimum inhibitory concentrations (MICs) between 0.25 µg/mL and 2 µg/mL; DPD-207 was less potent, with MICs between 4 µg/mL and 16 µg/mL, and light activation did not enhance these MICs. Complete biofilm eradication was not reported at the tested XF drug concentrations. However, live and dead staining of C. albicans cells in biofilms after XF drug treatment demonstrated that XF-73 and XF-70 were active against most Candida biofilms tested from 64 µg/mL; again, light activation did not enhance anti-biofilm activity. Candida biofilms were more resistant to DPD-207, with fungicidal effects occurring from 256 µg/mL. XF-73 and XF-70 reduced penetration of C. albicans biofilm into reconstituted human oral epithelium (RHOE) and resulted in less damage (as determined by reduced lactate dehydrogenase release) than untreated biofilms. Overall, the results highlight the potential of XF drugs as new drugs for the management of topical infections caused by C. albicans. Further studies are warranted on the development of XF drugs as antifungals, particularly for XF-73 and XF-70.
Lightweight Unified Collaborated Relinquish Edge Intelligent Gateway Architecture With Joint Optimization

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the data sources. Hence the existing edge computing technique, heterogeneous upload of data causes unacceptable loss in data processing with backdoor functionality problems. Hence, a novel Lightweight Unified Collaborated Relinquish (LUCR) Edge Gateway Architecture with isochronal Patching is proposed to manage the dynamic updation process and increase the processing capacity and the isochronal patching is utilized to prevent backdoor functionality problems and also manage the considerable amount of hardware and software resources with its lightweight architecture. The existing approach does not take into account traversing methods, leading to results that take into account challenges and have a limited amount of resources available. A novel Aggregative Subquadratic Continual Cauchy Neural Networks (NN) has been developed, in which resources in ESP aggregates are sorted in Subquadratic sorting based on capacity vector and given to Continual Cauchy NN that provides resource sharing for ESPs resolve the contemplate problem among ESPs and increase availability. To solve the gap in providing joint optimization of edge server data processing and ESP resource utilization, a novel Permutated Block Lasso Boundary Minimization Optimization has been proposed in which edge server and ESP processes are managed with appropriate boundary minimization in the sequence thereby achieving low power consumption, high availability, throughput, quality of service and computational efficiency. The proposed system is implemented in the Python platform and the result obtained shows that the proposed system has a high QOS, and throughput as well as low cost, energy consumption, and demand vector due to balancing edge server operation and resource sharing.
The application of intelligent sensors in medical research: a review

The rapid advancement of biomedicine in the twenty-first century has been facilitated by the constant innovation in biomedical technology. The most crucial issue in the field of medicine is to use sensor technology to gather information from primitive organisms, particularly the human body. Design, development, and application of biomedical sensors in the study of clinical diseases’ diagnosis and therapy have all been significantly aided by the advancement of medicine. The interest in creating sensors significantly increased in the 1960s. Chemical and biological sensors have been swiftly created in response to an urgent practical necessity, enabling the creation of selective sensors for the direct detection of diverse ions and compounds. The traditional large-size sensors are quickly turning into miniature sensors and are rapidly applied in biological and medical fields. Currently, wearable electronic blood pressure monitors, home blood glucose meters, and quick body surface digital thermometers are commonly used. The advent of a wide variety of medical-grade wearable sensors that will enable real-time biometric data tracking of a large range of physiological characteristics will likely be one of the most revolutionary, exciting, and difficult changes to come to medicine over the next several years. For possible uses in the entertainment, health monitoring, and medical care industries, high-performance flexible strain sensors connected to clothing or human skin are necessary. The use of sensors in the development of biomedical diagnostic tools and medical equipment will enhance human quality of life in the twenty-first century. This article will introduce the current medical sensor field related to sensors for physical quantities, sensors for chemical quantities, sensors for biological quantities such as electronic nose
Placental, Foetal, and Maternal Serum Metabolomic Profiles in Pregnancy-Associated Cancer: Walker-256 Tumour Model in a Time-Course Analysis

Cancer during pregnancy presents a delicate coexistence, imposing ethical and professional challenges on both the patient and medical team. In this study, we aimed to explore in a pre-clinical model the impact of tumour evolution in serum, placental and foetal metabolomics profiles during pregnancy in a time-course manner. Pregnant Wistar rats were distributed into two experimental groups: Control (C) and Walker-256 tumour-bearing (W). The rats were euthanised on three different gestational periods: at 12 days post-conception (dpc), at 16 dpc, and at 19 dpc. Serum, placenta and foetal metabolomic profiles were performed by 1H-NMR spectra following the analyses using Chenomx NMR Analysis Software V8.3. The tumour evolution was exponential, affecting the placental metabolomic profile during all the pregnancy stages. The placental tissue in tumour-bearing dams developed at a lower speed, decreasing the foetus’s weight. Associated with the serum metabolomic changes related to tumour growth, the placental metabolomic alterations impacted many metabolic pathways related to energy provision, protein synthesis and signalling, which directly harmed the foetus’s development. The development of the foetus is clearly affected by the damage induced by the tumour evolution, which alters the metabolic profile of both the serum and the placenta, impairing early embryonic development.
Urine Peptidome Analysis Identifies Common and Stage-Specific Markers in Early Versus Advanced CKD

Given the pathophysiological continuum of chronic kidney disease (CKD), different molecular determinants affecting progression may be associated with distinct disease phases; thus, identification of these players are crucial for guiding therapeutic decisions, ideally in a non-invasive, repeatable setting. Analyzing the urinary peptidome has been proven an efficient method for biomarker determination in CKD, among other diseases. In this work, after applying several selection criteria, urine samples from 317 early (stage 2) and advanced (stage 3b–5) CKD patients were analyzed using capillary electrophoresis coupled to mass spectrometry (CE-MS). The entire two groups were initially compared to highlight the respective pathophysiology between initial and late disease phases. Subsequently, slow and fast progressors were compared within each group in an attempt to distinguish phase-specific disease progression molecules. The early vs. late-stage CKD comparison revealed 929 significantly different peptides, most of which were downregulated and 268 with collagen origins. When comparing slow vs. fast progressors in early stage CKD, 42 peptides were significantly altered, 30 of which were collagen peptide fragments. This association suggests the development of structural changes may be reversible at an early stage. The study confirms previous findings, based on its multivariable-matched progression groups derived from a large initial cohort. However, only four peptide fragments differed between slow vs. fast progressors in late-stage CKD, indicating different pathogenic processes occur in fast and slow progressors in different stages of CKD. The defined peptides associated with CKD progression at early stage might potentially constitute a non-invasive approach to improve patient management by guiding (personalized) intervention.
Transformée de Hough et détection de linéaments sur images satellitaires et modèles numériques de terrain

Une methode basee sur la transformee de Hough a ete developpee pour la reconnaissance de lineaments et appliquee au resultat de detecteurs de limite. Apres un test de controle des performances sur une image bien structuree, un parcellaire regulier de Hesbaye, elle a ete appliquee a une image Landsat TM5 et un modele numerique de terrain des massifs montagneux des Cima d’Asta et des Lagorai dans les Dolomites. Les resultats obtenus sur ces deux types de donnees spatiales sont compares entre eux et a la structure geologique de la region. Leurs avantages et inconvenients respectifs sont discutes. La difficulte de reconnaitre les structures naturelles et la problematique de l’echelle spatiale de la reconnaissance sont montrees. Les limites du programme implemente sont analysees. Il est operationnel mais l’efficacite du detecteur de limites est determinante et la technique de normalisation permettant les traitements zonaux produit des effets biaisant le calcul des frequences qui ne sont pas maitrises.
Exploratory Study: How Students in Multicultural Environment Display Collaboration Skills through Jigsaw Model Implementation in Primary Years

With the increasing globalization of society, classrooms environments are becoming more diverse, incorporating students from various cultural backgrounds. This diversity holds the potential for inclusive learning environments that embrace the differences among school members. However, such differences can give rise to communication problems, misunderstandings, divergent expectations, and lack of tolerance towards diversity. Prior studies have explored the effectiveness of collaborative learning in fostering collaboration and teamwork among students from diverse cultural backgrounds. Nevertheless, the specific discussion of collaboration skills among students in Indonesian multicultural schools remains unclear. Hence, this study aims to investigate how students in Indonesian multicultural schools exhibit their collaboration skills through the utilization of the jigsaw learning model. Qualitative methods, including classroom observation and semi-structured interviews, were employed to collect data for this study. The findings indicate that students in multicultural schools effectively overcome challenges stemming from cultural differences and demonstrate positive collaborative behaviors when implementing the jigsaw learning method. These behaviors include interactive communication, respect for differing opinions, resource sharing, group identification, and a focus on achieving shared goals. However, a minority of students encountered difficulties in sharing resources due to personal factors. The study concludes with recommendations for future research endeavors in this field.
Applying AHP in Evaluation of Vietnamese Commercial Banks

Bank rankings are one of the ways to rate the bank system and to create competitive advantages, which have emerged as the central issue and considered as one of the most important organizational innovation. This research is objective to explore and to demonstrate utility of Analytic Hierarchy Process (AHP) application in banking for the purpose of proposing suitable model for partners evaluation and selecting banking strategic alliances in Vietnam. The AHP is applied to examine what criteria should be encompassed in evaluating and examining the importance weightings of influential criteria when ranking the bank system. In this study, a short review of literature regarding application AHP in banking decision-making is presented, focusing on partner evaluation criteria and methods to propose model for partner evaluation and selecting strategic banking for the current study. After a long process of calculation based on AHP, I have come up with the final rankings according expert’s interview: ACB’s percentages have change widely from each sub-criterion; finally it gets 12.98% at the top of the list. Coming very closely downwards are DAB, SeAbank etc., at the bottom of the rankings is SGB at 7.41%. By this paper, author would contribute to the ranking process of the banking system, in general, and the special case of Vietnamese banking a very modern model to apply, then to choose the right alliance for further cooperation, not only for banking system but it can be applied for a lot of industries.
Connection between singular arcs in optimal control using bridges. Physical occurence and Mathematical model

In the time minimal control problem, singular arcs are omnipresent to determine the optimal solutions and this leads to the well known turnpike phenomenon [1]. Very recently, this connection between singular arcs using a bang arc was shown to be relevant to saturate a single spin in Magnetic Resonance Imaging. Based on this example we propose a mathematical model to analyze such connection, which is called a bridge. This is applied to compute bridges in the optimization of chemical reaction networks using temperature control.
The Association between Executive Function and Performing Instrumental Daily Activities in People with Intellectual Disabilities

Institutionalized individuals with intellectual disabilities have few opportunities to participate in instrumental activities of daily living (IADLs), which probably affects higher cognitive functions, or vice versa. The objectives of this study were to evaluate the possible difference in the ability to perform IADLs and executive functioning between individuals with and without intellectual disabilities and to determine if executive functions are associated with the performance of IADLs in people with intellectual disabilities. This was a multi-center cross-sectional study, conducted between July 2019 and May 2020. Participants with intellectual disabilities were recruited from four centers for people with intellectual disabilities. Adults without these disabilities were gathered from several community centers. The sample consisted of 90 individuals with moderate intellectual disabilities and 79 individuals with no intellectual disability. Executive functions were evaluated using the Wechsler Adult Intelligence Scale—WAIS-IV, the INECO Frontal Screening test, the Semantic Verbal Fluency Test, and the Behavioural Assessment of the Dysexecutive Syndrome—BADS—Scale. The performance of the IADLs was assessed by the Lawton and Brody Scale. The results showed that the higher the function in instrumental activities, the lower the impairment of executive functions. Executive functions accounted for 81% of the total variance in the ability to perform the IADLs. In conclusion, individuals with moderate intellectual disabilities demonstrated limitations in executing the IADLs, which were partially associated with low performance in executive functions. This information could help in the development of evidence-based intervention programs and facilitate the formulation of appropriate support strategies to enhance participation in these activities.
Litter controls of microbial community composition and function in forest soils

I assessed microbial community composition as a function of altered aboveand belowground C inputs to soil in forest ecosystems of Oregon, Pennsylvania, and Hungary as part of a larger Detritus Input and Removal Treatment (DIRT) experiment. DIRT plots, which include root trenching, aboveground litter exclusion, and doubling of litter inputs, have been established in forested ecosystems in the US and Europe that vary with respect to dominant tree species, C background, N deposition rate, and soil type. These treatments provide a unique opportunity to study the feedbacks and processes controlling soil organic matter (SOM) transformations. This study used phospholipid fatty-acid (PLFA) analysis to examine changes in the soil microbial community size and composition in the mineral soil (0-10 cm) as a result of the DIRT treatments. At all sites the PLFA profiles from the plots without roots were significantly different than all other treatments, even after accounting for the loss of mycorrhizal fungi after root trenching. PLFA analysis showed that the rootless plots generally had larger quantities of actinomycete biomarkers and lower amounts of fungal biomarkers. At one of the sites, in an old-growth coniferous forest, seasonal changes in PLFA profiles were also examined. Seasonal differences in soil microbial community composition were greater than treatment differences. Throughout the year, treatments without roots continued to have a different microbial community composition than the treatments with roots, although the specific PLFA biomarkers responsible for these differences varied by season. These data provide direct evidence that root C inputs control microbial community composition in forest soils.
Herdsman Lake water quality study

The primary objective of this study was to analyse and interpret the water quality data set which has been collected from Herdsman Lake from April 1982 onwards under the direction of the State Planning Commission. The results of the data analysis were to provide information that would contribute to the Herdsman Lake Management Plan. In addition, modifications to the water quality monitoring programme were to be recommended where appropriate. Other water quality monitoring data has been included, where available, to aid interpretation of the SPC data set.
Improved Likelihood Ratio Tests for Varying Dispersion Simplex Regression Models

The simplex regression model is tailored to response variables that assume values in the standard unit interval, such as rates and proportions. The estimation of the parameters that index the model is performed by maximum likelihood, and test inferences are commonly reached using the likelihood ratio test. Such a test is based on an asymptotic approximation, and thus the resulting inferences may be misleading when the sample size is not large. In this paper, Bartlett‐corrected likelihood ratio tests are obtained for varying dispersion simplex regressions. Monte Carlo simulations are performed to compare the finite sample behavior of the corrected tests to that of standard likelihood ratio. The numerical results show that one of the corrected tests displays excellent control of the type I error frequency. An empirical application is presented and discussed.
Application specific convolutional neural networks for brain tumor detection

The research on CNN applications for medical image processing has been progressing rapidly. In the process of the development, hurdles appear and are to be overcome. The limitation in training samples is one of them, and restriction in computation resources can be another. In this paper, we present a design approach of application specific CNN (ASCNN), allowing to minimize the computational complexity of CNN systems without lowering the performance. This approach is to full-custom design CNNs for specific applications, such as brain tumor detection, so that each part of a CNN can be optimized to suit the input data and the task assigned to it. The convolution kernels and layers are made just-sufficient, nothing excessive. In this way, the randomness and the redundancy in computation can be minimized, the dependency on training samples decreased, the information density in data flow increased, the computation efficiency/quality and performance reliability improved. Three ASCNN systems for brain tumor detection are also presented as design examples. The results of the performance evaluation demonstrate that each of them delivers a high-quality detection with a computation volume of one-digit percentage, or less, of that needed by other CNN systems recently reported in reputed journals in the research area. Hence, ASCNN approach is effective to achieve high process quality at low computation cost. It can also lower the barrier of resource requirement of CNN systems to make them more implementable and applicable for general public.
Evidence for increased DNA damage repair in the postmortem brain of the high stress-response group of schizophrenia

Background Schizophrenia (SZ) is a disorder diagnosed by specific symptoms and duration and is highly heterogeneous, clinically and pathologically. Although there are an increasing number of studies on the association between genetic and environmental factors in the development of SZ, the actual distribution of the population with different levels of influence of these factors has not yet been fully elucidated. In this study, we focused on stress as an environmental factor and stratified SZ based on the expression levels of stress-responsive molecules in the postmortem prefrontal cortex. Methods We selected the following stress-responsive molecules: interleukin (IL) -1β, IL-6, IL-10, tumor necrosis factor-α, interferon-γ, glucocorticoid receptor, brain-derived neurotrophic factor, synaptophysin, S100 calcium-binding protein B, superoxide dismutase, postsynaptic density protein 95, synuclein, apolipoprotein A1 (ApoA1), ApoA2, and solute carrier family 6 member 4. We performed RNA sequencing in the prefrontal gray matter of 25 SZ cases and 21 healthy controls and conducted a hierarchical cluster analysis of SZ based on the gene expression levels of stress-responsive molecules, which yielded two clusters. After assessing the validity of the clusters, they were designated as the high stress-response SZ group and the low stress-response SZ group, respectively. Ingenuity Pathway Analysis of differentially expressed genes (DEGs) between clusters was performed, and Terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining was conducted on four cases each in the high and low stress-response SZ groups to validate DNA damage. Results We found higher prevalence of family history of SZ in the low stress-response SZ group (0/3 vs. 5/4, p = 0.04). Pathway analysis of DEGs between clusters showed the highest enrichment for DNA double-strand break repair. TUNEL staining showed a trend toward a lower percentage of TUNEL-positive cells in the high stress-response SZ group. Conclusion Our results suggest that there are subgroups of SZ with different degrees of stress impact. Furthermore, the pathophysiology of these subgroups may be associated with DNA damage repair. These results provide new insights into the interactions and heterogeneity between genetic and environmental factors.