Miracle-dz/UniFAR-FTUs huggingface dataset:
Phylophenomic and Phylogenomic analysis for Ovis aries reveals distinct identity of newly reported breed

Domestication and phylogenetics for Ovis aries is an important species to study, since there exists enormous biodiversity in terms of habitat and utility of sheep. The present study aimed at identification of the biodiversity existing within sheep breeds reared in different agroclimatic zones of the state West Bengal (Garole, Birbhum, Bonpala and Chotanagpuri) through phylogenetic analysis of phenotypic traits as growth and biomorphometric traits through principal component analysis, factor analysis, genetic correlation, multivariate cluster analysis through Hierarchial classification and k-means cluster analysis. Confirmation of the phylophenomic studies were later on carried out with phylogenomic analysis with microsatellite markers for sheep. Birbhum sheep from dry arid region of West Bengal is reported as the newly reported breed of sheep with distinct genetic identity.
Comprehensive analysis using DNA metabarcoding, PCR, and HPLC unveils the adulteration in Brahmi herbal products

Background The herbal products market is expanding and creating a bottleneck for raw materials. Hence, economically motivated adulteration has a high prevalence. DNA barcoding and species-specific PCR assays are now revolutionising the molecular identification of herbal products and are included in a number of pharmacopoeias for the identification of raw materials. High-throughput sequencing with barcoding advances toward metabarcoding, which enables the identification of unintentionally or intentionally unlabelled plant material present in herbal products. Brahmi is one of the most commercially significant and nootropic botanicals, with great controversy over the terms “Brahmi” being used to describe both Bacopa monneri (BM) and Centella asiatica (CA) species. Purpose This study evaluates DNA-based methods for Brahmi herbal products with the traditional HPLC-based analytical approach in order to assess their effectiveness. Methods We employed a species-specific PCR assay, DNA metabarcoding using rbcL minibarcode, and HPLC to detect the presence of the Brahmi (either BM or CA) in eighteen market samples. All the methods have been validated using in-house blended formulations. Results Comprehensive analysis of all three methods revealed the presence of 22.2%, 55.6%, and 50.0% of Brahmi by PCR assay, DNA metabarcoding, and HPLC, respectively, in Brahmi market formulations, whereas blended formulations only exhibited targeted plant species with all three methods. Conclusion Species-specific PCR can be used as a cost-effective and rapid method to detect the presence of the Brahmi, while in high-throughput methods, DNA metabarcoding can be used to detect the presence of widespread adulterated botanicals, and further, bioactive compounds could be detected by HPLC. These results emphasise the need for quality control of the marketed Brahmi herbal products as well as the implementation of all methodologies in accordance with fit for purpose.
Appetitive and aversive cue reactivities differentiate biotypes of alcohol drinkers

Craving reflects the subjective urge to use drugs and can be triggered by both positive and negative emotional states. However, no studies have systematically investigated the relative roles of these mechanisms in the pathophysiology of substance misuse or distinguished the mechanisms in individual vulnerability to substance use disorders. In the current study, we performed meta-analyses of drug cue-elicited reactivity and win and loss processing in the monetary incentive delay task to identify distinct neural correlates of appetitive and aversive responses during cue exposure. We then characterized the appetitive and aversive cue responses in seventy-six alcohol drinkers performing a cue craving task during fMRI. Imaging data were processed according to published routines. The appetitive circuit involved medial cortical regions and the ventral striatum, and the aversive circuit involved the insula, caudate and mid-cingulate cortex. We observed a significant correlation of β estimates of cue-elicited activity of the appetitive and aversive circuit. However, individuals varied in appetitive and aversive cue responses. From the regression of appetitive (y) vs. aversive (x) β, we identified participants in the top 1/3 each of those with positive (n = 15) and negative (n = 11) residuals as “approach” and “avoidance” biotype, with the others as the “mixed” biotype (n = 50). For clinical characteristics, the avoidance biotype showed higher sensitivity to punishment. In contrast, the approach biotype showed higher levels of sensation seeking and alcohol expectancy for social and physical pressure. The findings highlighted distinct neural underpinnings of appetitive and aversive components of cue-elicited reactivity and substantiated the importance of biotyping substance misuse.
Cryo-electron tomography suggests tubulin chaperones form a subset of microtubule lumenal particles with a role in maintaining neuronal microtubules

The functional architecture of the long-lived neuronal microtubule (MT) cytoskeleton is maintained by various MT-associated proteins (MAPs), most of which are known to bind to the MT outer surface. However, electron microscopy (EM) has long ago revealed the presence of particles inside the lumens of neuronal MTs, of yet unknown identity and function. Here, we use cryogenic electron tomography (cryo-ET) to analyze the three-dimensional (3D) structures and organizations of MT lumenal particles in primary hippocampal neurons, human induced pluripotent stem cell-derived neurons and pluripotent P19 cells. We obtain in-cell 3D maps of several lumenal particles from the respective cells and detect structural features that are common to all cell-types, underscoring their potential overarching functions. Mass spectrometry-based proteomics combined with structural modeling suggests a subset of lumenal particles could be tubulin-binding cofactors (TBCs) bound to tubulin monomers. A different subset of smaller particles, which remains unidentified, exhibits densities that bridge across the MT protofilaments. We show that increased lumenal particle concentration within MTs is concomitant with neuronal differentiation and correlates with higher MT curvatures. Enrichment of lumenal particles around MT lattice defects and at freshly polymerized MT open-ends suggest a MT protective role. Together with the identified structural resemblance of a subset of particles to TBCs, these results hint at a role in local tubulin proteostasis for the maintenance of long-lived neuronal MTs.
High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display

Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.
Fine-tuning Pre-Trained Transformer based model for Hate Speech and Offensive Content Identification in English Indo-Aryan and Code-Mixed (English-Hindi) languages

Hate Speech and Offensive Content Identification is one of the most challenging problem in the natural language processing field, being imposed by the rising presence of this phenomenon in online social media. This paper describes our Transformer-based solutions for identifying offensive language on Twitter in three languages (i.e., English, Hindi, and Marathi) and one code mixed (English-Hindi) language, which was employed in Subtask 1A, Subtask 1B and Subtask 2 of the HASOC 2021 shared task. Finally, the highest-scoring models were used for our submissions in the competition, which ranked our IRLab@IITBHU team 16th of 56, 18th of 37, 13th of 34, 7th of 24, 12th of 25 and 6th of 16 for English Subtask 1A, English Subtask 1B, Hindi Subtask 1A, Hindi Subtask 1B, Marathi Subtask 1A, and English-Hindi Code-Mix Subtask 2 respectively.
Automatic Depression Detection Based on Merged Convolutional Neural Networks using Facial Features

Clinical depression is one of the crucial medical conditions affecting a substantial portion of today’s world population. This paper proposes a novel technology based on deep learning tools for efficient detection of clinical depression in potential patients. A novel algorithm is proposed to label a recent AFEW-VA dataset in terms of creating the depressed class and the non-depressed class based on the valence and arousal values for various individuals from their video frames. Furthermore, the full facial regions, the eye regions, and the mouth regions from the classified dataset are extracted as the regions of interest (ROIs) to be utilized to train three different pre-trained 2DCNN models, namely, ResNet50, VGG16, and InceptionV3 by using transfer learning. For each 2D-CNN architecture, a novel algorithm is proposed to merge the models trained on the three ROIs. It is observed that the merged model, combining all the three ROIs outperforms the individual models or a merged model merging only two of the three ROIs in terms of obtaining a higher accuracy of depression detection. It is also observed that the merged models based on the ResNet50 architecture results in the best accuracy value of 0.95 as compared to the VGG16 and InceptionV3 architectures.
Chromatin state modeling across individuals reveals global patterns of histone modifications

Epigenetic mapping studies across individuals have identified many positions of epigenetic variation in various human tissues and conditions. However the relationships between these positions, and in particular global patterns that recur in many regions of the genome remains understudied. In this study, we use a stacked chromatin state model to systematically learn global patterns of epigenetic variation across individuals and annotate the human genome based on them. We applied this framework to histone modification data across individuals in lymphoblastoid cell lines and across autism spectrum disorder cases and controls in prefrontal cortex tissue. We find that global patterns are correlated across multiple histone modifications and with gene expression. We used the global patterns as a framework to predict transregulators, identify trans-QTL, and study complex disease. The frameworks for identifying and analyzing global patterns of epigenetic variation are general and we expect will be useful in other systems.
Joint DOA Estimation in Spherical Harmonics Domain using Low Complexity CNN

Direction of arrival (DOA) estimation for multi-channel speech enhancement is a challenging problem. In this context, this paper proposes a new method for joint DOA estimation using a low complexity convolutional neural network (CNN) architecture. The spherical harmonic (SH) coefficients of the received speech signal are obtained from the spherical harmonics decomposition (SHD). The magnitude and phase features are extracted from these SH coefficients and combined as a single feature for training the CNN. A single CNN model is trained using these combined features in contrast to two CNN models used in earlier work. Both azimuth and elevation are then obtained for estimation of DOA from this single CNN. Extensive simulations are also conducted for the performance evaluation of the proposed low complexity CNN model. It is observed that the proposed CNN model provides robust DOA estimates at the various signal to noise ratios (SNR) and reverberation times with reduced computational complexity. Performance evaluated in terms of the gross error (GE) and run-time complexity also provides interesting results motivating the use of the proposed model in practical applications.
Hate Speech Detection using LIME guided Ensemble Method and DistilBERT

Hate Speech classification has crucial applications in the social media domain. We describe the performance of our classifiers in the Hate Speech and Offensive Content Identification Track (HASOC) of FIRE 2021 conference. The dataset provided is for Indo-European Languages. We chose English tweets and developed two main classifiers as part of HASOC Track 1, which had two Subtasks 1A and 1B. Subtask 1A is a binary Hate Speech identification task, and Subtask 1B is multi grained classification of hate, profane, offensive and neutral content. Our team ”Beware Haters” studied Support Vector Machine, Random Forest, Logistic Regression, Bidirectional Long Short Term Memory Model and an Ensemble of the listed models for the Subtask 1A and the highest Macro F1 score we achieved was 0.7722 by our Ensemble model which combined the advantages of SVM, Logistic Regression and Random Forest. We used a model interpretation tool LIME, before integrating the models in a weighted Ensemble approach. For Subtask 1B, we obtained better results using a DistilBERT model that achieved a Macro F1 score of 0.6311. We have compared the performance of the basic DistilBERT Model with a fine tuned version.
biCourage: ngram and syntax GCNs for Hate Speech detection

Hate Speech identification is a challenging task given the world knowledge required. Moreover, it is even more complex in the social media context due to language and media specificities. Despite these challenges, advances in this task may help improving collective well-being on social media. In this context, the biCourage team participated in the English version of Task 1 of HASOC 2021, a shared task for “Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages”. Our participation in this campaign aimed to examine the suitability of Graph Convolutional Neural Networks (GCN), due to their capability to integrate flexible contextual priors, as a computationally effective so-lution compared to more computationally expensive and relatively data-hungry methods, such as fine-tuning. Specifically, we explored and combined two text-to-graph strategies based on different language modelling objectives, comparing them with fine-tuned Bert. We submitted the results of several deep learning architectures, comprised of different arrangements of GCNs and transformer architectures. Our team achieved the best results in both subtasks using the GCNs based architectures combining two text-to-graph strategies ranked in 21st and 20th positions in Subtasks 1A and 1B. Assessing the models’ prediction, we identify complementary capabilities in the text-to-graph strategies that further research on their combination can explore. Moreover, the proposed GCN model is 3.85 times faster than fine-tuned Bert in training speed and still outperforms it by 2.3% and 5.41% on the F1 score of Subtasks 1A and 1B, respectively.
Neural network‐derived Potts models for structure‐based protein design using backbone atomic coordinates and tertiary motifs

Computational protein design has the potential to deliver novel molecular structures that can function as binders or catalysts. Neural network models that use backbone coordinate-derived features perform exceptionally well on native sequence recovery tasks and can be applied to design new proteins. A statistical energy-based framework for modeling protein sequence landscapes using Tertiary Motifs (TERMs) has also demonstrated performance on protein design tasks. In this work, we combine the two methods to make neural structure-based models more suitable for protein design. Specifically, we supplement backbone-coordinate features with TERM-derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM-based and coordinate-based information, and COORDinator, which uses only coordinate-based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine-tuned on experimental data to improve predictions. Our results suggest that using TERM-based and coordinate-based features together may be beneficial for protein design and that structure-based neural models that produce Potts energy tables have utility for flexible applications in protein science. Code Code will be publically released at a later date.
Structural insights into acetylated histone ligand recognition by the BDP1 bromodomain of Plasmodium falciparum

Plasmodium falciparum requires a two-host system, moving between Anopheles mosquito and humans, to complete its life cycle. To overcome such dynamic growth conditions its histones undergo various post-translational modifications to up-regulate and down-regulate certain genes required for invasion and replication. The P. falciparum genome encodes six bromodomaincontaining proteins, of which Bromodomain Protein 1 (PfBDP1) has been shown to interact with acetylated lysine modifications on histone H3 to regulate the expression of invasion-related genes. Here, we investigated the ability of the PfBDP1 bromodomain to interact with acetyllsyine modifications on additional core and variant histones. A crystal structure of the PfBDP1 bromodomain (PfBDP1-BRD) reveals it contains the conserved bromodomain fold, but our comparative analysis between the PfBDP1-BRD and the 8 human bromodomain families indicates it has a unique binding mechanism. Solution NMR spectroscopy and ITC binding assays carried out with acetylated histone ligands demonstrate that it preferentially recognizes tetra-acetylated histone H4, and we detected weaker interactions with multi-acetylated H2A.Z in addition to the previously reported interactions with acetylated histone H3. Our findings indicate PfBDP1 may play additional roles in the P. falciparum life cycle, and the distinctive features of its bromodomain binding pocket could be leveraged for the development of new therapeutic agents to help overcome the continuously evolving resistance of P. falciparum against currently available drugs.
Service-Based Management Architecture for On-Demand Creation, Configuration, and Control of a Network Slice Subnet

The management of a 5G system comprises Operation and Management aspects defined by 3GPP, including Network Slicing, and the Management and Orchestration aspects specified in ETSI’s Network Function Virtualization framework. Our Proof-of-Concept demonstrates the implementation of an on-demand provisioning procedure of a Network Slice Subnet composed of Virtual Network Functions from potentially different vendors. The demonstration includes a Network Management System conforming to the 3GPP Service-Based Management Architecture, an ETSI MANO orchestrator, and a Network Function Virtualization Infrastructure.
Customer Incivility towards Frontline Employees’ Deviant Behaviour: The Moderating Roles of Emotional Labour

Customer interaction is considered to be the heart of a hospitality employee's daily activities. Customer service expectations have risen to such an extent that they can become upset at the slightest delay of service delivery, such as waiting to check-in, slow Wi-Fi speed, tardy service, etc. Frontline service employees tend to be used as punching bags by dissatisfied customers and become targets of their impolite and incautious behavior. Nevertheless, restaurants are found to struggle in maintaining a productive work environment and reported to have an immense incivility and deviant behaviour, which in long run can create huge productivity loss. The purpose of this study is to investigate the enormous effects of customer incivility on employees who engage in deviant behaviour, using emotional labour as the moderator. Purposive sampling technique was applied and a total of 120 questionnaires were distributed to the frontline employees of casual dining restaurants in the Klang Valley. The results pointed out that there was a significant and positive moderating effect of emotional labor (β= .806, p<.05) towards customer incivility toward deviant behaviour among casual dining restaurants’ frontline employees. It is recommended that restaurant managers to send their frontline employees for emotional or psychological well-being training as well as rewarding them for handling difficult customers well. In addition, both monetary and non-monetary rewards should be considered in upholding the frontline employees’ motivation and work performance.
Edinburgh Research Explorer ArdA proteins from different mobile genetic elements can bind to the EcoKI Type i DNA methyltransferase of E. coli K12

Anti-restrictionandanti-modi ﬁ cation(anti-RM)istheabilitytopreventcleavagebyDNArestriction – modi ﬁ cation (RM) systems of foreign DNA entering a new bacterial host. The evolutionary consequence of anti-RM is the en- hanced dissemination of mobile genetic elements. Homologues of ArdA anti-RM proteins are encoded by genes presentinmanymobilegeneticelementssuchasconjugativeplasmidsandtransposonswithinbacterialgenomes. The ArdA proteins cause anti-RM by mimicking the DNA structure bound by Type I RM enzymes. We have inves-tigatedArdAproteinsfromthegenomesof Enterococcusfaecalis V583, Staphylococcusaureus Mu50and Bacteroides fragilis NCTC 9343, and compared them to the ArdA protein expressed by the conjugative transposon Tn 916 . We ﬁ nd that despite having very different structural stability and secondary structure content, they can all bind to the EcoKI methyltransferase, a core component of the EcoKI Type I RM system. This ﬁ nding indicates that the less structured ArdA proteins becomefully folded upon binding.Theability ofArdA from diverse mobile elements toinhibitTypeIRMsystemsfromotherbacteriasuggeststhattheyareanadvantagefortransfernotonlybetween closely-related bacteria but also between more distantly related bacterial species. © 2013 The Authors. Published by Elsevier B.V. All rights reserved.
Entropy Valuation of Option: Using Option-implied Information

This paper studies an entropy valuation method by incorporating informative risk-neutral moments recovered from options as constraints. Within this entropy framework, a unique distribution close enough to the correct one is obtained and its risk-neutrality is deeply verified based on simulations. Using this resultant risk-Entropy: neutral distribution, a sample of risk-neutral underlying price paths is generated and ultimately the option prices are computed. The pricing performance and analysis in simulations demonstrate that this proposed valuation is completely comparable to the benchmarks, and can produce amazingly accurate prices for options.