Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 and avian corona viruses: epidemiological comparison and genetic approach. J Life Sci Biomed, 2020; 10(3): 21-28; https://dx.doi.org/10.51145/jlsb.2020.4

Aim. This study aimed to collect and analyse available information on COVID-19 and avian corona viruses in order to conduct a systematic review of the genetic data concerning them. Methods. All available research was done according to the strictest data selection criteria, and the databases like NCBI genebank were quantitatively searched in the currently available scientific literature using keywords, analytical statistic and genomic software. All studies on the coronavirus family were dedicated to provide an overview towards an advanced statistical analysis of the collected data. The first step was a descriptive study of COVID-19 and avian corona viruses by an epidemiological comparison between the two cases. Results. All corona viruses usually tend to have relatively A-T rich DNAs which is linked to their highly A-T rich codon biases. The results indicate genetic differences between the two viruses, but the results of a percentage analysis showed that the nucleotides A+T are both more abundant and energetically cheaper than nucleotides G+C, this gives viruses a selection advantage. Conclusion. These results give us a future positive view of this type of virus with AT-rich genomes which is selectively promoted at the host level. Recommendation. A recommendation by our study reveals that thought about the vaccine is very early but prevention has proven to be effective for this virus in chickens. Original Article PII: S225199392000004-10 Rec. 18 April 2020 Rev. 07 May 2020 Pub. 25 May 2020
Metabolic Network Modelling of Neuropsychiatric Disorders and COVID-19

Distinguishing neuropsychiatric disorders is challenging due to the overlap in symptoms and genetic risk factors. People suffering from these disorders face personal and professional challenges. Understanding the dysregulation of brain metabolism under disease condition can aid in effective diagnosis and in developing treatment strategies based on the metabolism. In this study, we reconstructed the metabolic network of three major neuropsychiatric disorders, schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) using transcriptomic data and constrained based modelling approach. We integrated brain transcriptomic data from six independent studies with a recent comprehensive genome-scale metabolic model, Recon3D. The analysis of the reconstructed network revealed flux-level alterations in the peroxisome-mitochondria-golgi axis in neuropsychiatric disorders. We also extracted reporter metabolites and pathways that distinguish these three neuropsychiatric disorders. We found differences with respect to fatty acid oxidation, aromatic and branched chain amino acid metabolism, bile acid synthesis, glycosaminoglycans synthesis and modifications, and phospholipid metabolism. Further, we predicted network perturbations that transform the disease metabolic state to a healthy metabolic state for each disorder. These analyses provide local and global views of the metabolic changes in SCZ, BD and MDD, which may have clinical implications. In light of the current pandemic of COVID-19, we also focussed on understanding the pathogenesis in SARS-CoV-2 infection. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on the host metabolism is yet to be fully understood. We analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swabs, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand the metabolic alterations in response to SARS-CoV-2 infection. We explored the expres-
An Updated Review on Recent In-Vitro, In-Vivo and Clinical Researches of Avipattikar Churna

Avipattikar Churna is used in clinical practice in dealing with the case of gastric disorders since the 19 th century. Amlapitta is one of the chief disorders of Gastro-intestinal tract. Colloquial term for Amla-pitta is Hyperacidity. Though various scientific researches were performed by varies Ayurvedic researchers, data of their outcome are not compiled to completely understand the pharmacology. This formulation promptly relives from major symptoms like Amlokalesh, Shirovedna, Ura-Pradesh Daha, Aruchi, Amlodgara. Neither, any complication(s)/Side effect(s) were reported so far, nor it produces addiction. 20 times of its normal dose (500mg/Kg body wt.) does not produce any acute toxicity in rats. Maximum numbers of constituents of it have Anti-ulcerogenic and Anti-oxidant property. It is an inference from all clinical study that 2145 days period is required to show marked improvement in the disease. Allopathic medicine uses to treat hyperacidity share a good portion of the drug market. Due to its higher safety and efficacy, it may be a good substitute for acid-lowering drugs of today.
Panoptic Segmentation Without Bounding Box Proposals

With the help of recent works, panoptic segmentation has become a popular topic of discussion in the world of computer vision. In order to perform both semantic and instance segmentation, early panop-tic segmentation networks used bounding boxes to locate and identify the instances of an image [1]. Although this method locates the objects accurately, it does not allow us to use data from the entire image. In this work, we describe a panoptic segmentation network that does not require bounding-box detection. There have been some networks that have accomplished this task, but to our knowledge, we are the ﬁrst to generate instance centers using each instance’s pixel-level offsets as well as include the use of capsules.
Bullying among adolescents: The role of skills

Bullying cannot be tolerated as a normal social behavior portraying a child's life. This paper quantifies its negative consequences allowing for the possibility that victims and nonvictims differ in unobservable characteristics. To this end, we introduce a factor analytic model for identifying treatment effects of bullying in which latent cognitive and noncognitive skills determine victimization and multiple outcomes. We use early test scores to identify the distribution of these skills. Individual‐, classroom‐ and district‐level variables are also accounted for. Applying our method to longitudinal data from South Korea, we first show that while noncognitive skills reduce the chances of being bullied during middle school, the probability of being victimized is greater in classrooms with relatively high concentration of boys, previously self‐assessed bullies and students that come from violent families. We report bullying at age 15 has negative effects on physical and mental health outcomes at age 18. We also uncover heterogeneous effects by latent skills, from which we document positive effects on the take‐up of risky behaviors and negative effects on schooling attainment. Our findings suggest that investing in noncognitive development should guide policy efforts intended to deter this problematic behavior.
P-OPT: Practical Optimal Cache Replacement for Graph Analytics

Graph analytics is an important workload that achieves suboptimal performance due to poor cache locality. State-of-the-art cache replacement policies fail to capture the highly dynamic and input-specific reuse patterns of graph application data. The main insight of this work is that for graph applications, the transpose of a graph succinctly represents the next references of all vertices in a graph execution; enabling an efficient emulation of Belady’s MIN replacement policy. In this work, we propose P-OPT, an architecture solution that uses a specialized compressed representation of a transpose’s next reference information to enable a practical implementation of Belady’s MIN replacement policy. Our evaluations across multiple applications and inputs reveal that P-OPT improves cache locality for graph applications providing an average performance improvement of 33% (56% maximum) over LRU replacement.
A Cognitive Sciences System for Symbol Grounding

OUf work addresses the issue of symbol grounding in natural language processing using notions from psycholinguistics, neurophysiology and cognitive psychology. OUf main idea is to inspire ourselves from several aspects of the way a child learns meanings of words. We consider that, from a natural language processing viewpoint, to learn meanings implies to model a subject and to use dynamic representations. We have realized a computational system which can build a network of semantic proximity between word meanings from textual corpora. This network is related to another one which memories what stands for experience in our model.
XQA: Relationship Explanation in Knowledge Graphs via Graph-Augmented Neural Networks

Knowledge graphs capturing heterogeneous relationships between different type of entities are used to support search capabilities for a wide variety of domains such as general web search, social media, healthcare and business intelligence. Explaining the relationship be-tween entities that appear as anomalies or are found to be statistically correlated in the data is an important task. For instance, a doctor will want to understand the relationship between a symptom and a disease, and a financial analyst will seek to explain the association of a company and supply-chain materials. The problem of finding interesting paths that explain the relationship between two entities has been studied in the literature. However, finding “interesting” paths as an explanation of relationship is highly subjective and the challenge is amplified as the number of entity and relation types grow. We propose a novel algorithm named XQA (eXplanatory QA) that integrates structured knowledge from graphs to augment the memory of a LSTM network and performs neural network-guided graph search. We perform user studies on Amazon Turk for capturing explanation preferences for entity pairs in multiple prominent KBs, and formulate metrics that capture properties of what humans consider as good answers. XQA outperforms a reinforcement learning based state-of-the-art system by finding 21% more answers on 1-hop relationship queries for NELL, and 90X more for longer-hop queries on NELL and Wikispeedia dataset. Empirical evaluation demonstrates the capability of XQA to find intuitive answers that outperform human subjects in many cases.
Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel disease

Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn's disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from a complex interaction between the host and environmental factors. Investigating drug efficacy in cultured human fresh IBD tissues can improve our understanding of the reasons why certain medications are more or less effective for different patients. We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to informatively integrate multi-modal data to predict inter-patient specific variation in drug response. Using explanation of our models, we interpret the models' predictions inferring unique combinations of important features associated with human tissue pharmacological responses. The inferred multi-modal features originate from multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data and all are associated with drug efficacy generated by a preclinical human fresh IBD tissue assay. To pharmacologically assess patient variation in response to IBD treatment, we used the reduction in the release of the inflammatory cytokine TNFa; from the fresh IBD tissues in the presence or absence of test drugs, as a measure of drug efficacy. The TNF pathway is a common target in current therapies for IBD; we initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor on the production of TNFa; however, we later show the approach can be applied to other targets, test drugs or mechanisms of interest. Our best model was able to predict TNFa; levels from a combination of integrated demographic, medicinal and genomic features with an error as low as 4.98% on unseen patients. We incorporated transcriptomic data to validate and expand insights from genomic features. Our results showed variations in drug effectiveness between patients that differed in gender, age or condition and linked new genetic polymorphisms in our cohort of IBD patients to variation in response to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models drug response in a relevant human tissue model of IBD while also identifying its most predictive features as part of a transparent ML-based precision medicine strategy.
The Missing Link: A Closed Form Solution to the Kermack and McKendrick Model Equations

Susceptible infectious recovered (SIR) models are widely used for estimating the dynamics of epidemics. Such models project that containment measures flatten the curve, i.e., reduce but delay the peak in daily infections, cause a longer epidemic, and increase the death toll. These projections have entered common understanding; individuals and governments often advocate lifting containment measures such as social distancing to shift the peak forward, limit societal and economic disruption, and reduce mortality. It was, then, an extraordinary surprise to discover that COVID-19 pandemic data exhibit phenomenology opposite to the projections of SIR models. With the knowledge that the commonly used SIR equations only approximate the original equations developed to describe epidemics, we identified a closed form solution to the original epidemic equations. Unlike the commonly used approximations, the closed form solution replicates the observed phenomenology and quantitates pandemic dynamics with simple analytical tools for policy makers. The complete solution is validated using independently measured mobility data and accurately predicts COVID19 case numbers in multiple countries.
Model-based evaluation of transmissibility and reinfection for the P.1 variant of the SARS-CoV-2

The variant of concern (VOC) P.1 emerged in the Amazonas state (Brazil) and was sequenced for the 1st time on 6-Jan-2021 by the Japanese National Institute of Infectious Diseases. It contains a constellation of mutations, ten of them in the spike protein. Consequences of these mutations at the populational level have been poorly studied so far. From December-2020 to February-2021, Manaus was devastated by four times more cases compared to the previous peak (April-2020). Here, data from the national health surveillance of hospitalized individuals were analysed using a model-based approach to estimate P.1 parameters of transmissibility and reinfection by maximum likelihood. Sensitivity analysis was performed changing pathogenicity and the period analysed (including/excluding the health system collapse period). In all analysed cases, the new variant transmissibility was found to be about 2.5 times higher compared to the previous variant in Manaus. A low probability of reinfection by the new variant (6.4%) was estimated, even under initial high prevalence (68%) by the time P.1 emerged. Consequences of a higher transmissibility were already observed with VOC B.1. 1.7 in the UK and Europe. Urgent measures must be taken to control the spread of P.1.
Genome sequencing and analysis of an emergent SARS-CoV-2 variant characterized by multiple spike protein mutations detected from the Central Visayas Region of the Philippines

The emergence of SARS-CoV-2 variants of concern such as the B.1. 1.7, B.1. 35 and the P.1 have prompted calls for governments worldwide to increase their genomic biosurveillance efforts. Globally, quarantine and outbreak management measures have been implemented to stem the introduction of these variants and to monitor any emerging variants of potential clinical significance domestically. Here, we describe the emergence of a new SARS-CoV-2 lineage, mainly from the Central Visayas region of the Philippines. This emergent variant is characterized by 13 lineage-defining mutations, including the co-occurrence of the E484K, N501Y, and P681H mutations at the spike protein region, as well as three additional radical amino acid replacements towards the C-terminal end of the said protein. A three-amino acid deletion at positions 141 to 143 (LGV141_143del) in the spike protein was likewise seen in a region preceding the 144Y deletion found in the B.1. 1.7 variant. A single amino acid replacement, K2Q, at the N-terminus of ORF8 was also shared by all 33 samples sequenced. The mutation profile of this new virus variant warrants closer investigation due to its potential public health implications. The current distribution of this emergent variant in the Philippines and its transmission are being monitored and addressed by relevant public health agencies to stem its spread in nearby islands and regions in the country.
A Survey on Visible Light Positioning from Software Algorithms to Hardware

The prevalence of illumination equipment and the inherent advantages of the Visible Light Communication (VLC) technique have resulted in a growing interest in Visible Light Positioning (VLP). There exist many excellent VLP techniques over the past several years. However, one limitation of most VLP survey works is that they mainly focus on the analysis from the perspective of techniques but ignore the equally important hardware aspect, since the hardware part directly affects the performance and cost of VLP systems and also determines whether it can be put into practical use. Different from most surveys concentrating on a single perspective, we provide an intensive overview of VLP systems from software algorithms to hardware devices. A novel-innovative classification method is used in the software algorithms, while the hardware aspect is introduced in terms of transmitters, modems, and receivers, making up for the deficiencies of the previous works. Massive papers including pioneering papers and the state-of-the-art ones in related areas are gathered and categorized. These solutions have also been evaluated in terms of accuracy, cost, range, and complexity. Furthermore, current open issues and tendencies regarding VLP are also illustrated in this paper.
A Model for Through Drying of Tissue Paper at Constant Pressure Drop and High Drying Intensity

A mathematical model for through drying of paper at constant pressure drop was developed. The model is based on physical properties; hence, basis weight, pressure drop, drying air temperature, pore size distribution, initial gas fraction, and tortuosity are important input parameters to the model. The model was solved for different combinations of the variables basis weight, drying air temperature, and pressure drop corresponding to industrial conditions and the results were compared with data from bench-scale experiments. The simulations show that the drying rate curve is very sensitive to the air flow rate and that correctly modeling the correlation between pressure drop and air flow rate is the most important factor for a successful model for through drying. The model was tuned by adjusting the parameters initial gas fraction and tortuosity in order to give the best possible fit to experimental data. For a given basis weight and pressure drop, different drying air temperatures resulted in relatively constant values of the fitted parameters. This means that the model can well predict the effects of changes in drying air temperature based on a tuning of the model performed at the same basis weight and pressure drop. However, for a given basis weight, an increase in pressure drop yielded fitted parameters that were somewhat different; i.e., a lower initial gas fraction and a higher tortuosity, a change that increases the resistance to air flow. This implies that the correlation between pressure drop and air flow rate in the model does not quite capture the nonlinear relationship shown by the experiments.
The determination of phosphorus in Kjeldahl digests of plant material by automatic analysis

Abstract A method is proposed for the determination of phosphorus in plant material using a modified Kjeldahl digestion and a Technicon Autoanalyzer. The procedure depends upon the formation of phosphomolybdate and its reduction to molybdenum blue by ferrous ammonium sulphate. The method enables the simultaneous determination of phosphorus and nitrogen on the same plant digest.
High‐Quality Alkyl Monolayers on Silicon Surfaces

Covalent attachment of functionalized monolayers onto silicon surfaces (see Figure for examples) is presented here as a strategy for surface modification. The preparation and structure of both unfunctionalized and functionalized alkyl-based monolayers are described, as are potential applications, for example, in the surface passivation of Si solar cells and for photopatterning of silicon surfaces.
Artificial Neural Networks, ADHD, and the Construction of a Virtual “Subject”: Fluid Intelligence & Time Management

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. Our lab is currently conducting a pilot study to assess the effects of the online game Minecraft as a therapeutic video game (TVG) to train executive function deficits in children with ADHD. The effect of the TVG intervention in combination with stimulants is being investigated to develop a drug-device combination therapy that can address both the dopaminergic dysfunction and executive function deficits present in ADHD. Although the results of this study will be used to guide the clinical development process, additional guidance for the optimization of the executive function training activities will be provided by a computational model of executive functions built with artificial neural networks (ANNs). This model uses ANNs to complete virtual tasks resembling the executive function training activities that the study subjects practice in the Minecraft world, and the schedule of virtual tasks that result in maximum improvements in ANN performance on these tasks will be investigated as a method to inform the selection of training regimens in future clinical studies. This study first proposes the use of recurrent neural networks to model the fluid intelligence executive function. This model is then combined with a previously developed model using convolutional neural networks to model working memory and prepotent impulsivity to produce virtual “subjects” who complete a computational simulation of a Time Management task that requires the use of both of these executive functions to complete. The capability of this model to produce groups of virtual “subjects” with significantly different levels of performance on the Time Management task is demonstrated.