Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of Nsp3 papain-like protease

The COVID-19 pandemic has emerged as the biggest life-threatening disease of this century. Whilst vaccination should provide a long-term solution, this is pitted against the constant threat of mutations in the virus rendering the current vaccines less effective. Consequently, small molecule antiviral agents would be extremely useful to complement the vaccination program. The causative agent of COVID-19 is a novel coronavirus, SARS-CoV-2, which encodes at least nine enzymatic activities that all have drug targeting potential. The papain-like protease (PLpro) contained in the nsp3 protein generates viral non-structural proteins from a polyprotein precursor, and cleaves ubiquitin and ISG protein conjugates. Here we describe the expression and purification of PLpro. We developed a protease assay that was used to screen a custom chemical library from which we identified Dihydrotanshinone I and Ro 08-2750 as compounds that inhibit PLpro in protease and isopeptidase assays and also inhibit viral replication in cell culture-based assays.
Immunogenicity of a public neoantigen derived from mutated PIK3CA

Public neoantigens (NeoAgs) represent an elite class of shared cancer-specific epitopes derived from recurrent mutations in driver genes that are restricted by prevalent HLA alleles. Here, we report on a high-throughput platform combining single-cell transcriptomic and T cell receptor (TCR) sequencing to establish whether mutant (Mut) PIK3CA, among the most common genomically altered driver oncogenes, generates an immunogenic public NeoAg. Using this method, we developed a library of TCRs that recognize an endogenously processed neoepitope containing a common PIK3CA hotspot mutation that is restricted by HLA-A*03:01. Mechanistically, immunogenicity to this public NeoAg arises primarily from enhanced stability of the neopeptide/HLA complex caused by a preferred HLA anchor substitution. Structural studies indicated that the HLA-bound neopeptide presents a relatively “featureless” surface dominated by the peptide’s backbone. To overcome the challenge of binding such an epitope with high specificity and affinity, we discovered that a lead TCR clinical candidate engages the neopeptide through an extended interface aided by an unusually long β-chain complementarity-determining region 3 (CDR3β) loop. In a pan-cancer cohort of patients with diverse malignancies that express the PIK3CA public NeoAg, we observed spontaneous immunogenicity, NeoAg clonal conservation, and in a limited number of cases, evidence of targeted immune escape. Together, these results establish the immunogenic potential of Mut PIK3CA, creating a framework for off-the-shelf immunotherapies targeting this public NeoAg.
Public good-driven release of heterogeneous resources leads to genotypic diversification of an isogenic yeast population

Adaptive diversification of an isogenic population, and its molecular basis has been a subject of a number of studies in the last few years. Microbial populations offer a relatively convenient model system to study this question. In this context, an isogenic population of bacteria (E. coli, B. subtilis, and Pseudomonas) has been shown to lead to genetic diversification in the population, when propagated for a number of generations. This diversification is known to occur when the individuals in the population have access to two or more resources/environments, which are separated either temporally or spatially. Here, we report adaptive diversification in an isogenic population of yeast, S. cerevisiae, when propagated in an environment containing melibiose as the carbon source. The diversification is driven due to a public good, enzyme α-galactosidase, leading to hydrolysis of melibiose into two distinct resources, glucose and galactose. The diversification is driven by a mutations at a single locus, in the GAL3 gene in the GAL/MEL regulon in the yeast.
Image-based deep learning reveals the responses of human motor neurons to stress and ALS

Although morphological attributes of cells and their substructures are recognized readouts of physiological or pathophysiological states, these have been relatively understudied in amyotrophic lateral sclerosis (ALS) research. In this study we integrate multichannel fluorescence high-content microscopy data with deep-learning imaging methods to reveal - directly from unsegmented images - novel neurite-associated morphological perturbations associated with (ALS-causing) VCP-mutant human motor neurons (MNs). Surprisingly, we reveal that previously unrecognized disease-relevant information is withheld in broadly used and often considered ‘generic’ biological markers of nuclei (DAPI) and neurons (βIII-tubulin). Additionally, we identify changes within the information content of ALS-related RNA binding protein (RBP) immunofluorescence imaging that is captured in VCP-mutant MN cultures. Furthermore, by analyzing MN cultures exposed to different extrinsic stressors, we show that heat stress recapitulates key aspects of ALS. Our study therefore reveals disease-relevant information contained in a range of both generic and more specific fluorescent markers, and establishes the use of image-based deep learning methods for rapid, automated and unbiased testing of biological hypotheses.
Technology Hotspot Tracking: Topic Discovery and Evolution of China's Blockchain Patents Based on a Dynamic LDA Model

Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.
Multi-Scale and Multi-Branch Convolutional Neural Network for Retinal Image Segmentation

The accurate segmentation of retinal images is a basic step in screening for retinopathy and glaucoma. Most existing retinal image segmentation methods have insufficient feature information extraction. They are susceptible to the impact of the lesion area and poor image quality, resulting in the poor recovery of contextual information. This also causes the segmentation results of the model to be noisy and low in accuracy. Therefore, this paper proposes a multi-scale and multi-branch convolutional neural network model (multi-scale and multi-branch network (MSMB-Net)) for retinal image segmentation. The model uses atrous convolution with different expansion rates and skip connections to reduce the loss of feature information. Receiving domains of different sizes captures global context information. The model fully integrates shallow and deep semantic information and retains rich spatial information. The network embeds an improved attention mechanism to obtain more detailed information, which can improve the accuracy of segmentation. Finally, the method of this paper was validated on the fundus vascular datasets, DRIVE, STARE and CHASE datasets, with accuracies/F1 of 0.9708/0.8320, 0.9753/0.8469 and 0.9767/0.8190, respectively. The effectiveness of the method in this paper was further validated on the optic disc visual cup DRISHTI-GS1 dataset with an accuracy/F1 of 0.9985/0.9770. Experimental results show that, compared with existing retinal image segmentation methods, our proposed method has good segmentation performance in all four benchmark tests.
Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models

Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models.
Optimisation of dietary energy utilisation for poultry – a literature review

SUMMARY Feed energy is an important production factor in poultry, representing 75% of the total cost of feed. Therefore, maximising energy digestion and utilisation is essential for cost-effectiveness and sustainability in poultry production. Consequently, accurate energy evaluation of raw material and animal requirements for energy is valuable for precision feeding and optimised benefits in growing chickens. Two key strategies to enhance the utilisation of energy from feed ingredients are the use of exogenous enzymes, such as carbohydrases, and accurate energy requirement prediction. Exogenous carbohydrases can enhance nutrient digestion and absorption, especially in diets with viscous ingredients, in which carbohydrases can enhance the digestibility of saturated fat and protein, by 33% and 3%, respectively, and about 4% energy utilisation. This can improve not only energy utilisation, but also gut health by reducing nutrient flow into the hindgut, as the presence of undigested nutrients fuels pathogenic bacteria proliferation. Moreover, accurate energy bioassays are required to provide values of dietary energy and true availability of energy to the birds. Currently, metabolisable energy (ME) systems are commonly used to evaluate poultry energetics. However, ME does not represent the total energy available to the birds, as it cannot measure the proportion of dietary energy that is lost as heat during feed ingestion, absorption and metabolism. In fact, the ME system can underestimate energy provided by fat by 13% and overestimate energy from proteins by 20% in chicken feeds. As net energy (NE)/ME ratio can vary from 59% to 77% depending on dietary composition, the NE systems are suggested as alternative, more accurate energy measurement methods, as they provide energy values corrected for heat increment. This paper reviews energy sources for poultry and addresses the potential to use NE measurements as a tool to evaluate the ability of feeds and feed additives to improve the exploitation of energy utilisation.
Identifying High-Level Concept Clones in Software Programs Using Method's Descriptive Documentation

Software clones are code fragments with similar or nearly similar functionality or structures. These clones are introduced in a project either accidentally or deliberately during software development or maintenance process. The presence of clones poses a significant threat to the maintenance of software systems and is on the top of the list of code smell types. Clones can be simple (fine-grained) or high-level (coarse-grained), depending on the chosen granularity of code for the clone detection. Simple clones are generally viewed at the lines/statements level, whereas high-level clones have granularity as a block, method, class, or file. High-level clones are said to be composed of multiple simple clones. This study aims to detect high-level conceptual code clones (having granularity as java methods) in java-based projects, which is extendable to the projects developed in other languages as well. Conceptual code clones are the ones implementing a similar higher-level abstraction such as an Abstract Data Type (ADT) list. Based on the assumption that “similar documentation implies similar methods”, the proposed mechanism uses “documentation” associated with methods to identify method-level concept clones. As complete documentation does not contribute to the method’s semantics, we extracted only the description part of the method’s documentation, which led to two benefits: increased efficiency and reduced text corpus size. Further, we used Latent Semantic Indexing (LSI) with different combinations of weight and similarity measures to identify similar descriptions in the text corpus. To show the efficacy of the proposed approach, we validated it using three java open source systems of sufficient length. The findings suggest that the proposed mechanism can detect methods implementing similar high-level concepts with improved recall values.
Rotation Invariant Predictor-Corrector for Smoothed Particle Hydrodynamics Data Visualization

In order to extract the vortex features more accurately, a new method of vortex feature extraction on the Smoothed Particle Hydrodynamics data is proposed in the current study by combining rotation invariance and predictor-corrector method. There is a limitation in the original rotation invariance, which can only extract the vortex features that perform equal-speed rotations. The limitation is slightly weakened to a situation that the rotation invariance can be used, given that a specific axis is existed in the fluid to replace the axis needed for it. Therefore, as long as the axis exists, the modified rotation invariant method can be used. Meanwhile, the vortex features are extracted by predictor-corrector method. By calculating the cross product of the parallel vector field, the seed candidates of vortex core lines can be obtained, and the real seed points can be gained from the rotation invariant Jacobian. Finally, the seed point and a series of candidates based on the predictor-corrector method are connected to draw the vortex core lines. Compared with the original method, the rotation invariant predictor-corrector method not only expands the application scope, but also ensures the accuracy of extraction. Our method adds the steps of calculating the rotation invariant Jacobian, the performance is slightly lower, but with the increase of the particle number, the performance gradually tends to the original method.
New perspective on nano-porous gallium nitride formation on p-type silicon with plasma focus device

ABSTRACT For the first time, the formation of nano-porous (NP) gallium nitride (GaN) on p-type silicon (Si) (100) substrate with the plasma focus device was observed. Four and six main plasma focus shots with GaN as the target on the Si substrate were applied. Subsequently, two shots of nitrogen plasma without the GaN target were applied to both of the Si surfaces. Then the substrates were gone through a 1050°C nitrogen treatment by a thermal chemical vapor deposition (TCVD) device. Field emission scanning electron microscopy and atomic force microscopy indicated NP structures on the surface of the substrates. With the increase in the amount of shots, thicker and rougher NP structures were formed. Additionally, X-ray diffraction shows GaN polycrystalline formation on the surface with the energy-dispersive X-ray spectroscopy, indicating that there was nitrogen deficiency on the Si substrates.
Artificial Intelligence and technology in COVID Era: A narrative review

Application of artificial intelligence (AI) in the medical field during the coronavirus disease 2019 (COVID-19) era is being explored further due to its beneficial aspects such as self-reported data analysis, X-ray interpretation, computed tomography (CT) image recognition, and patient management. This narrative review article included published articles from MEDLINE/PubMed, Google Scholar and National Informatics Center egov mobile apps. The database was searched for “Artificial intelligence” and “COVID-19” and “respiratory care unit” written in the English language during a period of one year 2019-2020. The relevance of AI for patients is in hands of people with digital health tools, Aarogya setu app and Smartphone technology. AI shows about 95% accuracy in detecting COVID-19-specific chest findings. Robots with AI are being used for patient assessment and drug delivery to patients to avoid the spread of infection. The pandemic outbreak has replaced the classroom method of teaching with the online execution of teaching practices and simulators. AI algorithms have been used to develop major organ tissue characterization and intelligent pain management techniques for patients. The Blue-dot AI-based algorithm helps in providing early warning signs. The AI model automatically identifies a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection sound pressure, and light level detection. There is now no looking back as AI and machine learning are to stay in the field of training, teaching, patient care, and research in the future.
Understanding Skin Chronobiology to Unveil and Transform Dermal Chronotherapeutics: Paving the Way for the Future

Chrono pharmacology elaborates on how the outcomes of drug changes with biological timing and endogenous rhythm. It assists in optimization of drug action and accordingly lessens adverse effects by timing medication relative to biological periodicity [1]. Circadian rhythm is being stimulated by zeitgeber mechanisms like light–darkness, warmth–cold, eating–fasting, etc. Zeitgeber is a stimulus which tends to initiate as well as regulate all the circadian rhythms of our body [2]. Basically, the circadian clock consists of three components: an input, central clock, and an output. The major input of zeitgeber is light which is cascaded through the retino-hypothalamic tract and geniculo-hypothalamic tract through messengers like glutamate, pituitary adenyl cyclase-activating peptide, and substance P. Central clock or the pacemaker of the circadian rhythm is suprachiasmatic nucleus (SCN) which is located in basal hypothalamus [3]. SCN tends to control outputs via neural or humoral pathways [4]. SCN fibers target endocrine neurons—such as gonadotropin-releasing hormone, thyrotropin-releasing hormone, and corticotropin-releasing hormone—and autonomic neuron projecting preganglionic sympathetic as well as parasympathetic neurons in the spinal cord. Understanding Skin Chronobiology to Unveil and Transform Dermal Chronotherapeutics: Paving the Way for the Future
Cajanus Cajan (l.) Millsp Aqueous Extracts against Melanoma Cell Line and their Proteases

Aims: Extract proteins with protease inhibitor (PI) activity from fresh organs of Cajanus cajan, using aqueous systems; study the activity of melanoma secreted proteases; investigate inhibitory effect of C. cajan extracts on melanoma proteases; and evaluated the effect of the extract, with the most protease inhibitor activity, on melanoma cell line (SK-MEL-28) viability. Material and Methods: Extracts of C. cajan leaves, stems, and roots were prepared using different aqueous systems. Protein content was evaluated by Bradford method, protein profile by gel electrophoresis by Laemmli method and, extracts PI activity against trypsin, papain, and pepsin. Melanoma cell line was cultured in Dulbecco's medium, and secreted proteases was obtained from culture supernatant. Characterization of melanoma proteases included substrates activity, optimum pH, and effect of specific PIs and cations on protease activity. Anticancer activity was investigated Original Research Article Teixeira et al.; EJMP, 32(2): 1-14, 2021; Article no.EJMP. 65869 2 using C. cajan extracts (containing PIs) and SK-MEL-28 extracellular fraction (containing proteases). Cytotoxic effect of extract was assayed on SK-MEL-28 cells line using methylthiazol tetrazolium method. CC-P was submitted to thin layer chromatography to identify alkaloids, coumarins, flavonoids, saponins and terpenes. Results: C. cajan extracts showed different protein contents and protein profiles in electrophoresis analysis. C. cajan organs presented PIs activities against serine, cysteine, and aspartic proteases. Leaf extract prepared using phosphate buffer (CC-P) and stems extract prepared with water (CCCA) had the best inhibitory activities against trypsin (~58%). Pepsin was the lowest inhibited (1129%) and papain was the most inhibited (14-100%). Protease activity of melanoma fraction was the highest using casein as substrate, and two proteins with 150 and 100 kDa with gelatinase activity. These proteases has maximal activity at pH 7.0 and 9.0, and was importantly inhibited by benzamidine, 1,10-phenanthroline and EDTA, suggesting that serine and metalloproteases are secreted by SK-MEL-28 cells. CC-P was the most important inhibitor of melanoma proteases, and induced cytotoxicity on SK-MEL-28 cells in culture. Although there is correlation between melanoma protease inhibition and cell death, CC-P has secondary metabolites, as coumarins, flavonoids and terpenes that can have synergy of antitumor activity. Conclusion: C. cajan extracts have serine, cysteine, and aspartic protease inhibitor activities. CCP had the best inhibition on melanoma proteases and it was also cytotoxic to melanoma cell in culture. Therefore, these PIs can be important strategy for cancer treatment because tumor cells secrete proteases that are crucial to cancer progression.