Miracle-dz/UniFAR-FTUs huggingface dataset:
Organophosphorus Nerve Agents: Types, Toxicity, and Treatments

Organophosphorus compounds are extensively used worldwide as pesticides which cause great hazards to human health. Nerve agents, a subcategory of the organophosphorus compounds, have been produced and used during wars, and they have also been used in terrorist activities. These compounds possess physiological threats by interacting and inhibiting acetylcholinesterase enzyme which leads to the cholinergic crisis. After a general introduction, this review elucidates the mechanisms underlying cholinergic and noncholinergic effects of organophosphorus compounds. The conceivable treatment strategies for organophosphate poisoning are different types of bioscavengers which include stoichiometric, catalytic, and pseudocatalytic. The current research on the promising treatments specifically the catalytic bioscavengers including several wild-type organophosphate hydrolases such as paraoxonase and phosphotriesterase, phosphotriesterase-like lactonase, methyl parathion hydrolase, organophosphate acid anhydrolase, diisopropyl fluorophosphatase, human triphosphate nucleotidohydrolase, and senescence marker protein has been widely discussed. Organophosphorus compounds are reported to be the nonphysiological substrate for many mammalian organophosphate hydrolysing enzymes; therefore, the efficiency of these enzymes toward these compounds is inadequate. Hence, studies have been conducted to create mutants with an enhanced rate of hydrolysis and high specificity. Several mutants have been created by applying directed molecular evolution and/or targeted mutagenesis, and catalytic efficiency has been characterized. Generally, organophosphorus compounds are chiral in nature. The development of mutant enzymes for providing superior stereoselective degradation of toxic organophosphorus compounds has also been widely accounted for in this review. Existing enzymes have shown limited efficiency; hence, more effective treatment strategies have also been critically analyzed.
Challenges in Machine Learning based approaches for Real-Time Anomaly Detection in Industrial Control Systems

Data-centric approaches are becoming increasingly common in the creation of defense mechanisms for critical infrastructure such as the electric power grid and water treatment plants. Such approaches often use well-known methods from machine learning and system identification, i.e., the Multi-Layer Perceptron, Convolutional Neural Network, and Deep Auto Encoders to create process anomaly detectors. Such detectors are then evaluated using data generated from an operational plant or a simulator; rarely is the assessment conducted in real time on a live plant. Regardless of the method to create an anomaly detector, and the data used for performance evaluation, there remain significant challenges that ought to be overcome before such detectors can be deployed with confidence in city-scale plants or large electric power grids. This position paper enumerates such challenges that the authors have faced when creating data-centric anomaly detectors and using them in a live plant.
The communicative power of knowledge visualizations in mobilizing information and communication technology research

ABSTRACT This editorial explores knowledge visualization, a field of study that investigates the power of visual formats to represent knowledge, as a strategy to enhance knowledge mobilization of results from ICT4D research. We highlight the fact that there are evidence-based guidelines for creating and crafting visualizations in academic writing. We also provide some visualization examples that highlight general knowledge visualization criteria such as anchor and extend, familiarity, clarity and consistency, include text, prudent simplicity and aesthetics. Although visualization is not the central theme of any of the papers in this issue, the papers offer a variety of visualization techniques as appropriate to the knowledge domain.
Recognising lepidopteran images based on locality-constrained linear coding and SVM

In this paper, a novel lepidopteran insect images recognition method is proposed. The captured insect images are first preprocessed to separate the foreground from the complicated background. Then, two wings are cut out from the insect, calibrated and segmented into a number of superpixels. The values of l, a, b, x, y of each superpixel are calculated as feature descriptors. Those descriptors are encoded with locality-constrained linear coding and then pooled into a feature vector. Finally, the multi-class linear support vector machine is used for classification. The approach is testified in a data-set including 576 images of insect specimens from 10 different lepidopteran species and the recognition accuracy is over 99% in average. The experimental results suggested that the proposed method performs well in lepidopteran insect species recognition.
Pollination Ecology, Specialization, and Genetic Isolation in Sympatric Bee-Pollinated Salvia (Lamiaceae)

Premise of research. Previous pollination ecology studies of Salvia have shown that there is low specialization for certain subgroups of bees and that pollinator number varies with species and locality. We studied 12 Salvia species (three clades with different corolla morphologies and staminal lever mechanisms) that in part co-occur and coflower to examine prezygotic isolation mechanisms and the degree of specialization versus generalization in pollination ecology. Methodology. Pollinators were identified using field observations, photos, and related literature across three sites in Turkey. Video documentation of the visitation rate and the site of pollen placement on the pollinator body, morphometric measurements between flowers and pollinators, flowering time, flower color, handling time, and stigma contact ratio were analyzed. Plant-pollinator networks were constructed. Pivotal results. Mechanical, phenological, and ethological isolation occurs among sympatric Salvia species. A morphological fit is evident between flower tube length and the proboscis length of the main pollinators. Pollinator networks indicate that most species are ecological generalists and that only a few are specialists. Conclusions. The 12 Salvia species, though phenotypically and functionally specialized by their zygomorphic, bilabiate flowers and bee-pollination syndrome, differ in the degree of their ecological specialization. Most of the sympatric Salvia species tend to be ecologically generalized, with two or more main pollinators and a few additional secondary pollinators, while a few Salvia species are clearly specialized. Some floral traits (e.g., flower color, morphology, size, corolla tube length and width of the corolla tube entrance, type and size of the staminal lever mechanism, small numbers of flowers in the inflorescence), a short flowering time, and a small population size appear to be correlated with the degree of ecological specialization.
Gangliosides in the Brain: Physiology, Pathophysiology and Therapeutic Applications

Gangliosides are glycosphingolipids highly abundant in the nervous system, and carry most of the sialic acid residues in the brain. Gangliosides are enriched in cell membrane microdomains (“lipid rafts”) and play important roles in the modulation of membrane proteins and ion channels, in cell signaling and in the communication among cells. The importance of gangliosides in the brain is highlighted by the fact that loss of function mutations in ganglioside biosynthetic enzymes result in severe neurodegenerative disorders, often characterized by very early or childhood onset. In addition, changes in the ganglioside profile (i.e., in the relative abundance of specific gangliosides) were reported in healthy aging and in common neurological conditions, including Huntington’s disease (HD), Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), stroke, multiple sclerosis and epilepsy. At least in HD, PD and in some forms of epilepsy, experimental evidence strongly suggests a potential role of gangliosides in disease pathogenesis and potential treatment. In this review, we will summarize ganglioside functions that are crucial to maintain brain health, we will review changes in ganglioside levels that occur in major neurological conditions and we will discuss their contribution to cellular dysfunctions and disease pathogenesis. Finally, we will review evidence of the beneficial roles exerted by gangliosides, GM1 in particular, in disease models and in clinical trials.
HIV-1 Infection and Glucose Metabolism Reprogramming of T Cells: Another Approach Toward Functional Cure and Reservoir Eradication

With the emerging of highly active antiretroviral therapy, HIV-1 infection has transferred from a fatal threat to a chronic disease that could be managed. Nevertheless, inextricable systemic immune activation and chronic inflammation despite viral suppression render patients still at higher risk of HIV-1-associated non-AIDS complications. Immunometabolism has nowadays raised more and more attention for that targeting metabolism may become a promising approach to modulate immune system and play a role in treating cancer, HIV-1 infection and autoimmune diseases. HIV-1 mainly infects CD4+ T cells and accumulating evidence has brought to light the association between T cell metabolism reprogramming and HIV-1 pathogenesis. Here, we will focus on the interplay of glycometabolism reprogramming of T cells and HIV-1 infection, making an effort to delineate the possibility of utilizing immunometabolism as a new target towards HIV-1 management and even sterilizing cure through eliminating viral reservoir.
Influences of molecular structure on the isothermal crystallization behavior and mechanical properties of β-nucleated isotactic polypropylene

ABSTRACT To investigate the influence of molecular structure of isotactic polypropylene (iPP) on the crystallization behavior and mechanical properties of its β-nucleated product, in this study, four iPP samples were selected. Results of molecular structure characterizations revealed that the molecular weight of the iPP resins are close, while the average isotacticities and the isotacticity distributions of the samples are quite different from each other. Isothermal crystallization kinetics study suggested that due to their different average isotacticities and isotacticity distributions, the crystallinity difference of the four samples is as high as 5.3%, and the crystallinity of β-crystal difference is as high as 7.2%. Generally, the higher the average isotacticity of the iPP and the wider its distribution are, the stronger the β-crystallization ability of the sample is. Moreover, the mechanical measurement revealed that high average isotacticity and the wide isotacticity distribution of the iPP resulted in the strong the β-crystallization ability and thus better the toughness. Graphical abstract
Leonurine Regulates Treg/Th17 Balance to Attenuate Rheumatoid Arthritis Through Inhibition of TAZ Expression

Leonurine, an active alkaloid extracted from Herba leonuri, is reported to have potent anti-inflammatory activity against rheumatoid arthritis (RA). However, the molecular mechanism of action of leonurine in RA remains poorly understood. In this study, we detected 3,425 mRNAs differentially expressed between CD4+ T cells of RA patients and those of healthy individuals using microarray raw data mining. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that transcriptional coactivator with PDZ-binding motif (TAZ) regulates a variety of biological processes including T-helper (Th)-17 cell development, and was thus selected for functional verification. In a naïve CD4+ T cell differentiation assay, we found that TAZ overexpression was associated with impaired balance between T regulatory (Treg) and Th17 cells in vitro. TAZ overexpression increased the levels of the pro-inflammatory cytokines interleukin (IL)-17, IL-1β, and tumor necrosis factor (TNF)-α and decreased that of the anti-inflammatory cytokine IL-10. Leonurine treatment had a direct recovery effect on the impaired balance and reduced the expression of TAZ and led to normalization of IL-17, IL-1β, and TNF-α and IL-10. Furthermore, IL-6 was found to promote the expression of TAZ and receptor activator of nuclear factor kappa-B ligand (RANKL), and RANK. Leonurine significantly inhibited TAZ-mediated expression of RANKL, and RANK and IL-6 in synovial fibroblasts. We conclude that the therapeutic effect of leonurine was through suppression of TAZ led to restoration of Treg/Th17 balance and suppression of synovial fibroblast action.
Portal-Based Path Perturbation for Metropolis Light Transport

Light transport simulation in scenes with difﬁcult visibility still remains a challenging problem. Markov chain Monte Carlo (MCMC) rendering is often employed for such conﬁgurations. It generates a sequence of correlated light transport paths by iteratively mutating the current state, a path, to another. Since the proposed path is correlated to the current path, MCMC can explore regions of the path space, also with difﬁcult visibility, once they have been found. To improve the efﬁciency of the exploration, we propose a path mutation strategy making use of the concept of portals. Portals are user-deﬁned objects in the scene to guide the sampling of the difﬁcult visibility, which have been employed in the context of non-MCMC rendering. Our mutation strategy perturbs a path edge around the intersection point of the edge and the portal, instead of perturbing the edge by moving a path vertex as in the ordinary path mutation strategies. This reduces the probability for the proposed path being rejected due to changes in visibility.
When 5G Meets Deep Learning: A Systematic Review

This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration.
ncRNA-Agents : anotação de RNAs não-codificadores baseada em sistema multiagente

Non-coding RNAs (ncRNAs) are an important subset of the transcripts produced in the cells of organisms, since they a ect many cellular processes. Although there are e cient and fast computational methods to identify proteins, annotation of ncRNAs is now focus of intensive research once their characteristics and signals are not yet entirely known. In this context, in this thesis, we present an architecture for ncRNAs annotation based on the multi-agent system paradigm. The implementation of a system, called ncRNA-Agents, uses collaborative agents, where each agent has knowledge and reasonig (simulating biologists) about a speci c aspect of RNA, which contributes to a curated ncRNA annotation, with associated quality and explanations based on the results of the tools used by the system to recommend the annotation. In addition, we performed three case studies with three fungi, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Paracoccidioides brasiliensis, to evaluate the performance of the system and its ability to annotate known ncRNAs and predict new ncRNAs. This tool is publicly available at http://www.biomol. unb.br/ncrna-agents.
Observation of the Disomic Inheritance of Four Allelic Pairs in the Octoploid Cultivated Strawberry

Although recent genetic studies suggest that octoploid cultivated strawberry, Fragaria 3ananassa Duch. , is highly diploidized, there have been no reports that directly and clearly described the behavior of four allelic pairs. In this study, we demonstrated the disomic inheritance of four allelic pairs inF. 3ananassa by using two highly polymorphic simple sequence repeat markers. By genotyping the offspring of 'Summer-princess' (containing eight distinct alleles of marker FxaHGA02P13) 3 'Dekoruju' (containing another four distinct alleles for this marker), four allelic pairs could be identified in 'Summer-princess'. In a similar way, four allelic pairs could be identified for cultivar Ohishi-shikinari and marker EMFvi136. The results of this study provide direct evidence that at least part of the octoploid strawberry genome is fully diploidized and that the genome composition hypothesis AAA#A#BBB#B# is possible as suggested by other recent genetic studies.
Performance of HepaRG and HepG2 cells in the high-throughput micronucleus assay for in vitro genotoxicity assessment

ABSTRACT The micronucleus (MN) assay is a core test used to evaluate genotoxic potential of xenobiotics. The traditional in vitro MN assay is usually conducted in cells lacking metabolic competency or by supplementing cultures with an exogenous rat S9 metabolic system, which creates a significant assay limitation for detecting genotoxic metabolites. Our previous study demonstrated that compared to HepG2, HepaRG cells exhibited a significantly higher level of CYP450 enzyme activities and detected a greater portion of genotoxic carcinogens requiring metabolic activation using the Comet assay. The aim of this study was to assess the performance of HepaRG cells in the flow cytometry-based MN assay by testing 28 compounds with known genotoxic or carcinogenic modes of action (MoA). HepaRG cells exhibited higher sensitivity (83%) than HepG2 cells (67%) in detecting 12 indirect-acting genotoxicants or carcinogens. The HepaRG MN assay was 100% specific and 93% accurate in detecting genotoxic potential of the 28 compounds. Quantitative comparison of the MN concentration-response data using benchmark dose analysis showed that most of the tested compounds induced higher % MN in HepaRG than HepG2 cells. In addition, HepaRG cells were compatible with the Multiflow DNA damage assay, which predicts the genotoxic MoA of compounds tested. These results suggest that high-throughput flow cytometry-based MN assay may be adapted using HepaRG cells for genotoxicity assessment, and that HepaRG cells appear to be more sensitive than HepG2 cells in detecting genotoxicants or carcinogens that require metabolic activation.
A novel method based on Adaptive Periodic Segment Matrix and Singular Value Decomposition for removing EMG artifact in ECG signal

Background: The Electrocardiogram (ECG) signals are usually used to detect and monitor human health. However, the Electromyogram (EMG) artifact also can be obtained during measurement, these make difficult for doctors in correct diagnosis. In general, the ECG signal is periodic while EMG artifact is non-stationary and has overlapped with it under the frequency domain. According to these characteristics, it is necessary to extract clean ECG signals from noisy EMG artifact signals by using the periodic separation method. Method: A novel Adaptive Periodic Segment Matrix (APSM) based on Singular Value Decomposition (SVD) is proposed for extracting clean ECG from EMG artifact. Firstly a periodic segment estimation method is proposed by obtaining an average periodic length and RR intervals constraint via envelope spectrum of the measured signal. Secondly, the R wave peaks and its position of the ECG signals are detected by these. After that, APSM with rank one is formed using R wave peaks and the calculated RR intervals constraint, Then SVD is processed on this matrix, the restructured ECG signals will be obtained by the first maximum singular value of the formed matrix. The validation of proposed method is made by applying the algorithm to ECG records from the MIT-BIH Arrhythmia Database. The zero-mean percent root-mean-square difference (PRD 1 ), Cross-correlation coefficient and output signal to noise ratio (SNR output ) have been calculated for presenting the algorithm performance by comparison with other methods. Finally two heart disease cases have been studied for P wave and ST segment detection under noisy ECG with EMG artifact.Results: The proposed methods achieved significant improvement in output signal-to-noise ratio, percentage root-mean-square differences and lead to the higher value of cross-correlation coefficient between the original (clean) ECG and the denoised ECG signal. Also, the reconstructed ECG signal can be better able to follow the trend of original (clean) ECG signal under the EMG noise. Conclusion: The proposed periodic segment estimation method can adaptively find the periodic length in ECG signal by using envelope spectrum. Also, the more strict rank one trajectory matrix has been formed in APSM by using R wave peaks and RR intervals constraint. The results show that the proposed APSM-SVD method is effective for EMG artifact removal and extracting the clean ECG signal. The R peak, P wave, QRS complex and ST segment can be preserved in the reconstructed ECG signal.