Miracle-dz/UniFAR-FTUs huggingface dataset:
Bacterial sialoglycosidases in Virulence and Pathogenesis

Human oral microbiome and dysbiotic infections have been recently evidently identified. One of the major reasons for such dysbiosis is impairment of the immune system. Periodontitis is a chronic inflammatory disease affecting the tissues that surround and support the teeth. In the United States. , approximately 65 million people are affected by this condition. Its occurrence is also associated with many important systemic diseases such as cardiovascular disease, rheumatoid arthritis, and Alzheimer’s disease. Among the most important etiologies of periodontitis is Porphyromonas gingivalis, a keystone bacterial pathogen. Keystone pathogens can orchestrate inflammatory disease by remodeling a normally benign microbiota causing imbalance between normal and pathogenic microbiota (dysbiosis). The important characteristics of P. gingivalis causing dysbiosis are its virulence factors which cause effective subversion of host defenses to its advantage allowing other pathogens to grow. Some of the mechanisms involved in these processes are still not well-understood. However, various microbial strategies target host sialoglycoproteins for immune dysregulation. In addition, the enzymes that break down sialoglycoproteins and sialoglycans are the “sialoglycoproteases”, resulting in exposed terminal sialic acid. This process could lead to pathogen-toll like receptor (TLR) interactions mediated through sialic acid receptor ligand mechanisms. Assessing the function of P. gingivalis sialoglycoproteases, could pave the way to designing carbohydrate analogues and sialic acid mimetics to serve as drug targets.
Book review

This book addresses the most topical scientific challenge in social science: behaviour change (BC). Given evidence suggesting that many prevalent social and health problems can be traced to problematic behaviours, health promotion and policy decisions addressing these behaviours should be evidencebased and effectively implemented to justify investments and guarantee success. This book provides a comprehensive and state-of-the-art overview of the fundamentals of evidence-based BC for improving social and public health, and includes important methodological considerations as well as useful references and web-links. The authors argue that effective BC programmes must be based on rigorously tested evidence and discuss opportunities and difficulties in implementing evidence in clinical settings. They also address the scientific basis for knowledge transfer, translational research, implementation and sustainability of evidence-based programmes in particular contexts and within particular populations. Individual chapters summarise current evidence of BC in a range of health-related behaviours including eating and nutrition, sexual behaviour, alcohol and drug abuse, physical activity and exercise, tobacco smoking, injury prevention and self-management. The closing discussion emphasises future challenges in designing and developing effective BC programmes, the role of standardised reporting following the CONSORT (Consolidated Standards of Reporting Trials) statement and the Behaviour Change Consortium as well as the role of systematic reviewing in providing best evidence. This book provides an excellent contemporary overview of evaluating, reviewing, implementing and disseminating BC interventions, and demonstrates how knowledge derived from rigorous research can be translated into sustainable and effective programmes. But where is the science of BC? How good is the evidence? What are the actual techniques of BC and how do they relate to theory? As is characteristic of the field, such questions remain unanswered. Given the emphasis on evidence, it is surprising that the brief opening chapter on ‘models of BC’ does not appear to be itself guided by best evidence. Moreover, most models presented are prediction rather than change models, and there is no consistent and systematic reference to the presented theories in the subsequent chapters.
New species and records of bioluminescent Mycena from Mexico

ABSTRACT Seven species of bioluminescent fungi are recorded from the cloud forests in Mexico. Six represent new species belonging to the genus Mycena, whereas Mycena globulispora is a new distribution record for the country. Descriptions, illustrations, photographs, and an identification key to bioluminescent fungi species from Mexico are provided. Sequences of nuc rDNA internal transcribed spacer (ITS1-5.8S-ITS2 = ITS) region were generated for barcoding purposes and comparisons with similar species.
Evaluating the efficacy and safety of atorvastatin + ezetimibe in a fixed-dose combination for the treatment of hypercholesterolemia

ABSTRACT Introduction: Cardiovascular disease is a major cause of morbidity and mortality throughout the world and hypercholesterolemia is one of the key risk factors. Statins are the first line treatment to reduce atherogenic lipids and there is substantial and robust evidence with atorvastatin for reduction of cardiovascular events and mortality. Ezetimibe can be combined with any dose of atorvastatin for incremental lipid-lowering effects. Areas covered: In this review, the authors summarize the pharmacokinetics, pharmacodynamics and clinical efficacy of the components and the combination of ezetimibe and atorvastatin. Clinical benefits have been seen with ezetimibe combined with simvastatin but studies of its combination with atorvastatin are generally limited to the effects on lipid parameters where the addition of ezetimibe to atorvastatin is generally more effective than titrating the atorvastatin dose. Expert opinion: Although there are no cardiovascular outcomes studies with the combination of ezetimibe and atorvastatin, the greater reduction in atherogenic lipids can be assumed to have greater benefits in reducing cardiovascular events. The ezetimibe–atorvastatin combination is very effective in this respect and well tolerated. Fixed-dose combinations improve medication adherence and this combination should be useful for patients who cannot reach their lipid targets with maximally tolerated statin doses.
Evaluation of oxidative stress biomarkers of rabbits’ liver exposed to thermooxidized virgin olive oil obtained from blanquette olive cultivars

Abstract Objective: This work aimed to study the effect of temperature in variation of fatty acid profiles of virgin olive oil (VOO) and to evaluate the impact of the consumption of this thermo oxidized VOO on metabolic oxidative stress. Materials and methods: Effect of consumption of fresh or thermo-oxidized VOO on rabbits was studied. The animals were distributed in groups of six rabbits each and were fed for four weeks with the incorporation of 15% VOO either fresh or heated during one, two, three or four hours. The liver was examined for the level of reduced glutathione (GSH), glutathione S-transferase (GST), catalase (CAT) and activity malondialdehyde (MDA). Results: The diet of VOO heated for three and four hours resulted in a significant increase (p < 0.01) in liver of MDA and GST compared to the control group. The results also showed a significant decrease in GSH levels and CAT activity induced in rabbits of the group treated with VOO heated for three and four hours. Conclusions: The consumption of thermo-oxidized VOO is more dangerous to health than the consumption of fresh olive oil or low oxidation value VOO.
Novel functions of the ubiquitin-independent proteasome system in regulating Xenopus germline development

ABSTRACT In most species, early germline development occurs in the absence of transcription with germline determinants subject to complex translational and post-translational regulations. Here, we report for the first time that early germline development is influenced by dynamic regulation of the proteasome system, previously thought to be ubiquitously expressed and to serve ‘housekeeping’ roles in controlling protein homeostasis. We show that proteasomes are present in a gradient with the highest levels in the animal hemisphere and extending into the vegetal hemisphere of Xenopus oocytes. This distribution changes dramatically during the oocyte-to-embryo transition, with proteasomes becoming enriched in and restricted to the animal hemisphere and therefore separated from vegetally localized germline determinants. We identify Dead-end1 (Dnd1), a master regulator of vertebrate germline development, as a novel substrate of the ubiquitin-independent proteasomes. In the oocyte, ubiquitin-independent proteasomal degradation acts together with translational repression to prevent premature accumulation of Dnd1 protein. In the embryo, artificially increasing ubiquitin-independent proteasomal degradation in the vegetal pole interferes with germline development. Our work thus reveals novel inhibitory functions and spatial regulation of the ubiquitin-independent proteasome during vertebrate germline development. Summary: Two inhibitory mechanisms, translational repression and ubiquitin-independent proteasomal turnover, regulate Dnd1 protein levels in Xenopus oocytes, revealing novel functions and spatial regulation of the ubiquitin-independent proteasome during vertebrate germline development.
Identification Of Mango Leaves Using Artificial Intelligence

: Classifying just from the naked eye what plant variety a certain crop is very difficult and is sometimes not accurate. This requires expertise in the field of agriculture. At the point when there is next to zero earlier education about the distribution of data, or the data itself may be subjective and uncertain, the use of artificial intelligence will improve the automated classification. The use of fuzzy logic and K-nearest neighbor was implemented in the classification of mango variety in the Philippines, specifically the I ndian, carabao and saperada mango, through their leaves‘ physical characteristics. Necessary image processing methods were applied to the gathered images of mango plant leaves to ensure identification was accurate. These methods include the enhancement of the images to fit for analyzing and the extraction of some morphological features from binary images such as minor axis length, area
Design and Implementation of Fast Compressive Tracking

: The primary point of the undertaking is to plan "Quick Compressive Following of the hearty item with Kalman algorithm". It is an exceptionally extreme undertaking to create powerful and effective appearance models for strong article following because of the different variables, for example, light change, posture variety, movement smear, and impediment. Existing following algorithms are typically overhaul models with specimens removed from reconnaissance late edges. Despite the fact that algorithms are effective however there are a few issues stay to be tended to. In any case, while these adaptable appearance models are data needy, there does not exist sufficient measure of data for online computations to learn toward the starting. Second, internet following algorithms regularly encounters the buoy issues. As an issue of readied toward oneself learning, misaligned cases are at risk to be incorporated and corrupt the appearance models. In this paper, we propose an essential yet practical and capable after count with an appearance model concentrated around contrivances expelled from a multi-scale picture idiosyncrasy space with data self-sufficient reason. We pack example pictures of the front line target and the establishment using the same lacking estimation system. The accompanying task is arranged as an issue gathering through a pure Bayes classifier with online upgrade in the pressed territory. A coarse-to-fine interest strategy is grasped to further diminish the computational versatile quality in the distinguished method. Powerful visual after is essential to track different hindered things. Kalman channel and shade information taking after figurings are completed uninhibitedly in most of the rhythmic movement research. The proposed strategy merges enlarged Kalman channel with past and shade information for emulating distinctive inquiries under high hindrance. The proposed method is overwhelming to establishment exhibiting framework. Object distinguishing proof is completed using spatio-common Gaussian mixture model. After embodies two steps: to some degree blocked thing after and significantly obstructed article emulating. Taking after partially hindered articles, created Kalman channel is ill-used with past information of thing, however for significantly blocked article emulating, color information and size attributes are used. The structure was attempted in certifiable application and successful results were gotten.
‘ONLY’ INCREASES EXPECTATIONS FOR CAUSAL COHERENCE RELATIONS

The focus particle ‘only’ is exclusive, that is, an utterance containing ‘only’ is true if it holds for the focused element but not for any of its alternatives. The discourse unit following a proposition with ‘only’ often discusses an alternative to the focused element. This is illustrated in example (1), where the focused constituent is written in capital letters and its alternative is underlined.
The amount and scope of phrase-final lengthening in Seoul Korean

Phrase-final lengthening is a well-established phenomenon. However, what determines the amount and scope of the effect is still unclear. Previous studies have reported prominence as a key factor, but these findings rely on data from stress languages. Here, we use electromagnetic articulography to examine the amount and scope of phrase-final lengthening in Seoul Korean, a language with no lexical-level prominence, as a function of factors that are associated with prominence, i.e., focus position and accentual phrase (AP) length. Stimuli sentences included the test words either in phrase-final or phrase-medial positions with focus position (initial AP, final AP) and final AP’s length (long, short) manipulated. Formation and release durations of the test words’ consonant constrictions were calculated. Phrase-final lengthening affects the final syllable, with greater amount of lengthening found on its coda as opposed to its onset, suggesting that lengthening is progressive, i.e., decreasing with distance from the boundary. Neither focus position nor AP length affect the scope of lengthening, but AP length affects the amount of lengthening of the final coda. Finally, boundary-related shortening is detected prior to the lengthening effect, and is presumably anticipatory. The implications of these results for prosodic structure, prosodic typology and speech planning are discussed.
Larval feeding of Cydalima perspectalis on box trees with a focus on the spatial and temporal distribution

The box tree moth Cydalima perspectalis is an invasive pest on box trees originating from Eastern Asia which spread throughout Europe. We assessed the efficacy of photo-eclectors (emergence boxes) for the detection of its larvae. We also investigated their spatial distribution on the hosts and the spatial and temporal distribution of the leaf damage caused by this pest in Slovakia. Our results showed non-uniform vertical distribution of the overwintering larvae and leaf damage on the trees. The larval abundance in the spring was significantly affected by the height of the branches above the ground. During spring, the larvae occurred most abundantly in the upper parts of the trees. The leaf damage was greatest in the lower parts throughout the growing season. During the progress of the infestation, the development of the damage in the lower and middle parts was similar. In the upper part, the initial increase in the damage was slow, but accelerated four months before the complete defoliation. The field estimation of the proportion of damaged leaves and the accurate assessment based on counting the damaged leaves suggest a consis tency between these estimates. Hence, a quick field estimation of the leaf damage may be utilised by horticultural practices.
SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics

Motivation Polygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training data sets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenotypic data in an independent GWAS validation dataset. We are motivated to develop methods for approximating AUC of PRSs based on the summary level data of the validation dataset, which will greatly facilitate the development of PRS models for complex diseases. Results We develop statistical methods and an R package SummaryAUC for approximating the AUC and its variance of a PRS when only the summary level data of the validation dataset are available. SummaryAUC can be applied to PRSs with SNPs either genotyped or imputed in the validation dataset. We examined the performance of SummaryAUC using a large-scale GWAS of schizophrenia. SummaryAUC provides accurate approximations to AUCs and their variances. The bias of AUC is typically less than 0.5% in most analyses. SummaryAUC cannot be applied to PRSs that use all SNPs in the genome because it is computationally prohibitive. Availability https://github.com/lsncibb/SummaryAUC Contact Jianxin.Shi@nih.gov
Indirubin Analogues Inhibit Trypanosoma brucei Glycogen Synthase Kinase 3 Short and T. brucei Growth

The protozoan parasite Trypanosoma brucei is the causative agent of human African trypanosomiasis (HAT). The disease is fatal if it remains untreated, whereas most drug treatments are inadequate due to high toxicity, difficulties in administration, and low central nervous system penetration. ABSTRACT The protozoan parasite Trypanosoma brucei is the causative agent of human African trypanosomiasis (HAT). The disease is fatal if it remains untreated, whereas most drug treatments are inadequate due to high toxicity, difficulties in administration, and low central nervous system penetration. T. brucei glycogen synthase kinase 3 short (TbGSK3s) is essential for parasite survival and thus represents a potential drug target that could be exploited for HAT treatment. Indirubins, effective leishmanicidals, provide a versatile scaffold for the development of potent GSK3 inhibitors. Herein, we report on the screening of 69 indirubin analogues against T. brucei bloodstream forms. Of these, 32 compounds had potent antitrypanosomal activity (half-maximal effective concentration = 0.050 to 3.2 μM) and good selectivity for the analogues over human HepG2 cells (range, 7.4- to over 641-fold). The majority of analogues were potent inhibitors of TbGSK3s, and correlation studies for an indirubin subset, namely, the 6-bromosubstituted 3′-oxime bearing an extra bulky substituent on the 3′ oxime [(6-BIO-3′-bulky)-substituted indirubins], revealed a positive correlation between kinase inhibition and antitrypanosomal activity. Insights into this indirubin-TbGSK3s interaction were provided by structure-activity relationship studies. Comparison between 6-BIO-3′-bulky-substituted indirubin-treated parasites and parasites silenced for TbGSK3s by RNA interference suggested that the above-described compounds may target TbGSK3s in vivo. To further understand the molecular basis of the growth arrest brought about by the inhibition or ablation of TbGSK3s, we investigated the intracellular localization of TbGSK3s. TbGSK3s was present in cytoskeletal structures, including the flagellum and basal body area. Overall, these results give insights into the mode of action of 6-BIO-3′-bulky-substituted indirubins that are promising hits for antitrypanosomal drug discovery.
Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components

This paper focuses on the dynamical behavior for a class of memristor-based bidirectional associative memory neural networks (BAMNNs) with additive time-varying delays in discrete-time case. The necessity of the proposed problem is to design a proper state estimator such that the dynamics of the corresponding estimation error is exponentially stable with a prescribed decay rate. By constructing an appropriate Lyapunov–Krasovskii functional (LKF) and utilizing Cauchy–Schwartz-based summation inequality, the delay-dependent sufficient conditions for the existence of the desired estimator are derived in the absence of uncertainties which are further extended to available uncertain parameters of the prescribed memristor-based BAMNNs in terms of linear matrix inequalities (LMIs). By solving the proposed LMI conditions the estimation gain matrices are obtained. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed results.
Generating Lexical Representations of Frames using Lexical Substitution

Semantic frames are formal linguistic structures describing situations/actions/events, e.g. Commercial transfer of goods. Each frame provides a set of roles corresponding to the situation participants, e.g. Buyer and Goods, and lexical units (LUs) – words and phrases that can evoke this particular frame in texts, e.g. Sell. The scarcity of annotated resources hinders wider adoption of frame semantics across languages and domains. We investigate a simple yet effective method, lexical substitution with word representation models, to automatically expand a small set of frame-annotated sentences with new words for their respective roles and LUs. We evaluate the expansion quality using FrameNet. Contextualized models demonstrate overall superior performance compared to the non-contextualized ones on roles. However, the latter show comparable performance on the task of LU expansion.
A Deep Learning Approach For Brain Tumor Segmentation Using Convolution Neural Network

: The objective of this project is to develop a fully automated system for segmentation and classification of brain tumor using convolutional neural networks. Tumors are of any shape and size in the brain. This motivates us to use a deep neural network of high capacity. This paper gives the summary of the work done by us and the methodology we used. We used BraTS dataset to develop this project and to test the trained models. This dataset contains the MRI images of 200 patients. The MRI scans are segmented using patch wise segmentation. This methodology shows approximately 95.6% accuracy on the test dataset. Many experiments are done by us to find the depth of the neural network and to know the best architectures that can be used. The exact location of the gliomas is found by the convolutional neural network. For finding the spectral location of the gilomas in the glilac cells, we used combination of deep neural network and convolutional neural network . This architecture can also be used to predict the future status of the organs.