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As institutions continuously strive to align with the standards set forth within competency-based medical education, there is an increased need to produce evidence of learner achievement in the form of observable behaviors. However, the complexity of healthcare education and clinical environments make it challenging to generate valid and reliable behavioral assessments. In this article, we utilize our interdisciplinary knowledge from the perspectives of experts in medical education, assessment, and academic administration to provide tips to successfully incorporate behavioral assessments into instructional designs. These include tips for identifying the best assessment methods fit for purpose, guiding instructors in establishing boundaries of assessment, managing instructors, selecting raters, generating behavioral assessment guides, training raters, ensuring logistics support assessment strategies, and fostering capacity for iteration. These can be used by institutions to improve planning and implementation for longitudinal behavioral assessments.

Artificial intelligence (AI), a branch of computer science, involves developing intelligent computer programs to mimic human intelligence and automate various processes [...]

AIDS is one of the deadliest diseases in the history of humankind caused by HIV. Despite the technological development, curtailing the viral infection inside human host still remains a challenge. Therapies such as HAART uses a combination of drugs to inhibit the viral activity. One of the important targets includes HIV protease and inhibiting its activity will minimize the production of mature structural proteins. However, the genetic diversity and the occurrence of drug resistant mutations adds complexity to effective drug design. In this study, we aimed at understanding the drug binding mechanism of one such subtype, namely subtype C and its insertion variant L38HL. We performed multiple molecular dynamics simulations along with binding free energy analysis of wild-type and L38HL bound to Atazanavir (ATV). From the analysis, we revealed that the insertion alters the hydrogen bond and hydrophobic interaction networks. The alterations in the interaction networks increase flexibility at the hinge-fulcrum interface. Further, the effects of these changes affect flap tip curling. Moreover, the changes in the hinge-fulcrum-cantilever interface alters the concerted motion of the functional regions leading to change in the direction of flap movement thus causing a subtle change in the active site volume. Additionally, formation of intramolecular hydrogen bonds in the ATV docked to L38HL restricted the movement of R1 and R2 groups thereby altering the interactions. Overall, the changes in the flexibility of flap together with the changes in the active site volume and compactness of the ligand provide insights for increased binding affinity of ATV with L38HL.

Abstract Interactions between electrons and phonons play a crucial role in quantum materials. Yet, there is no universal method that would simultaneously accurately account for strong electron-phonon interactions and electronic correlations. By combining methods of the variational quantum eigensolver and the variational non-Gaussian solver, we develop a hybrid quantum-classical algorithm suitable for this type of correlated systems. This hybrid method tackles systems with arbitrarily strong electron-phonon coupling without increasing the number of required qubits and quantum gates, as compared to purely electronic models. We benchmark our method by applying it to the paradigmatic Hubbard-Holstein model at half filling, and show that it correctly captures the competition between charge density wave and antiferromagnetic phases, quantitatively consistent with exact diagonalization.

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

Huntington’s disease (HD) is a progressive neurodegenerative disorder caused due to a CAG repeat expansion in the huntingtin (HTT) gene. The primary symptoms of HD include motor dysfunction such as chorea, dystonia, and involuntary movements. The primary motor cortex (BA4) is the key brain region responsible for executing motor/movement activities. Investigating patient and control samples from the BA4 region will provide a deeper understanding of the genes responsible for neuron degeneration and help to identify potential markers. Previous studies have focused on overall differential gene expression and associated biological functions. In this study, we illustrate the relationship between variants and differentially expressed genes/transcripts. We identified variants and their associated genes along with the quantification of genes and transcripts. We also predicted the effect of variants on various regulatory activities and found that many variants are regulating gene expression. Variants affecting miRNA and its targets are also highlighted in our study. Co-expression network studies revealed the role of novel genes. Function interaction network analysis unveiled the importance of genes involved in vesicle-mediated transport. From this unified approach, we propose that genes expressed in immune cells are crucial for reducing neuron death in HD.

Abstract Mitigating the devastating effect of COVID-19 is necessary to control the infectivity and mortality rates. Hence, several strategies such as quarantine of exposed and infected individuals and restricting movement through lockdown of geographical regions have been implemented in most countries. On the other hand, standard SEIR based mathematical models have been developed to understand the disease dynamics of COVID-19, and the proper inclusion of these restrictions is the rate-limiting step for the success of these models. In this work, we have developed a hybrid Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model to explore the influence of quarantine and lockdown on disease propagation dynamics. The model is multi-compartmental, and it considers everyday variations in lockdown regulations, testing rate and quarantine individuals. Our model predicts a considerable difference in reported and actual recovered and deceased cases in qualitative agreement with recent reports.

The increasing investment of China in the Sub-Sahara Africa (SSA) has continued to add value to the continent of Africa. The Chinese investment has served as growth and developmental mechanism and finance sources for viable projects. The effect of China in Sub-Sahara Africa and Africa economies in generally on different dimensions, depending on the nature of the attractiveness of foreign direct investment (FDI) and factor of production of each country. The increased of China FDI inflows to SSA has generated much important gained to the continent of African economies. The authors analyze the FDI, determinants, merits and policy implications necessary to stimulate and accelerate more FDI from China and other countries of the world. In doing this, the SSA countries must have policy direction towards business friendly that would attract more investment and also protect foreign investors against expropriation.

The deep sea is the largest ecosystem on Earth, yet little is known about the processes driving patterns of genetic diversity in its inhabitants. Here, we investigated the macro- and microevolutionary processes shaping genomic population structure and diversity in two poorly understood, globally distributed, deep-sea predators: Cuvier’s beaked whale (Ziphius cavirostris) and Blainville’s beaked whale (Mesoplodon densirostris). We used double-digest restriction associated DNA (ddRAD) and whole mitochondrial genome (mitogenome) sequencing to characterise genetic patterns using phylogenetic trees, cluster analysis, isolation-by-distance, genetic diversity and differentiation statistics. Single nucleotide polymorphisms (SNPs; Blainville’s n = 43 samples, SNPs=13988; Cuvier’s n = 123, SNPs= 30479) and mitogenomes (Blainville’s n = 27; Cuvier’s n = 35) revealed substantial hierarchical structure at a global scale. Both species display significant genetic structure between the Atlantic, Indo-Pacific and in Cuvier’s, the Mediterranean Sea. Within major ocean basins, clear differentiation is found between genetic clusters on the east and west sides of the North Atlantic, and some distinct patterns of structure in the Indo-Pacific and Southern Hemisphere. We infer that macroevolutionary processes shaping patterns of genetic diversity include biogeographical barriers, highlighting the importance of such barriers even to highly mobile, deep-diving taxa. The barriers likely differ between the species due to their thermal tolerances and evolutionary histories. On a microevolutionary scale, it seems likely that the balance between resident populations displaying site fidelity, and transient individuals facilitating gene flow, shapes patterns of connectivity and genetic drift in beaked whales. Based on these results, we propose management units to facilitate improved conservation measures for these elusive species.

Three-dimensional topological insulators have become a research focal point on topological quantum matter. Here, the authors propose the non-Hermitian analogue, the exceptional topological insulator, with anomalous surface states only existing within the topological bulk embedding.

Abstract The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, β-propensity, etc.) calculated for the CDR, FR or VL regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called “VLAmY-Pred” ( https://web.iitm.ac.in/bioinfo2/vlamy-pred/ ). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.

Cultural transmission of behaviour is an important aspect of many animal communities ranging from humans to birds. Male humpback whales (Megaptera novaeangliae) sing a repetitive, stereotyped, socially learnt and culturally transmitted song display that slowly evolves each year. Most males within a population sing the same, slow-evolving song type; but in the South Pacific, song ‘revolutions’ have led to rapid and complete replacement of one song type by another introduced from a neighbouring population. Songs spread eastwards, from eastern Australia to French Polynesia, but the easterly extent of this transmission was unknown. Here, we investigated whether song revolutions continue to spread from the central (French Polynesia) into the eastern (Ecuador) South Pacific region. Similarity analyses using three consecutive years of song data (2016–2018) revealed that song themes recorded in 2016–2018 French Polynesian song matched song themes sung in 2018 Ecuadorian song, suggesting continued easterly transmission of song to Ecuador, and vocal connectivity across the entire South Pacific Ocean basin. This study demonstrates songs first identified in western populations can be transmitted across the entire South Pacific, supporting the potential for a circumpolar Southern Hemisphere cultural transmission of song and a vocal culture rivalled in its extent only by our own.

Abstract The urgent need for a treatment of COVID-19 has left researchers with limited choice of either developing an effective vaccine or identifying approved/investigational drugs developed for other medical conditions for potential repurposing, thus bypassing long clinical trials. In this work, we compared the sequences of experimentally verified SARS-CoV-2 neutralizing antibodies and sequentially/structurally similar commercialized therapeutic monoclonal antibodies. We have identified three therapeutic antibodies, Tremelimumab, Ipilimumab and Afasevikumab. Interestingly, these antibodies target CTLA4 and IL17A, levels of which have been shown to be elevated during severe SARS-CoV-2 infection. The candidate antibodies were evaluated further for epitope restriction, interaction energy and interaction surface to gauge their repurposability to tackle SARS-CoV-2 infection. Our work provides candidate antibody scaffolds with dual activities of plausible viral neutralization and immunosuppression. Further, these candidate antibodies can also be explored in diagnostic test kits for SARS-CoV-2 infection. We opine that this in silico workflow to screen and analyze antibodies for repurposing would have widespread applications.

Acquired immunodeficiency syndrome (AIDS) is one of the most challenging infectious diseases to treat on a global scale. Understanding the mechanisms underlying the development of drug resistance is necessary for novel therapeutics. HIV subtype C is known to harbor mutations at critical positions of HIV aspartic protease compared to HIV subtype B, which affects the binding affinity. Recently, a novel double-insertion mutation at codon 38 (L38HL) was characterized in HIV subtype C protease, whose effects on the interaction with protease inhibitors are hitherto unknown. In this study, the potential of L38HL double-insertion in HIV subtype C protease to induce a drug resistance phenotype towards the protease inhibitor, Saquinavir (SQV), was probed using various computational techniques, such as molecular dynamics simulations, binding free energy calculations, local conformational changes and principal component analysis. The results indicate that the L38HL mutation exhibits an increase in flexibility at the hinge and flap regions with a decrease in the binding affinity of SQV in comparison with wild-type HIV protease C. Further, we observed a wide opening at the binding site in the L38HL variant due to an alteration in flap dynamics, leading to a decrease in interactions with the binding site of the mutant protease. It is supported by an altered direction of motion of flap residues in the L38HL variant compared with the wild-type. These results provide deep insights into understanding the potential drug resistance phenotype in infected individuals.

Huntington’s disease (HD) is a neurodegenerative disorder with autosomal dominant inheritance caused by glutamine expansion in the Huntingtin gene (HTT). Striatal projection neurons (SPNs) in HD are more vulnerable to cell death. The executive striatal population is directly connected with the Brodmann Area (BA9), which is mainly involved in motor functions. Analyzing the disease samples from BA9 from the SRA database provides insights related to neuron degeneration, which helps to identify a promising therapeutic strategy. Most gene expression studies examine the changes in expression and associated biological functions. In this study, we elucidate the relationship between variants and their effect on gene/downstream transcript expression. We computed gene and transcript abundance and identified variants from RNA-seq data using various pipelines. We predicted the effect of genome-wide association studies (GWAS)/novel variants on regulatory functions. We found that many variants affect the histone acetylation pattern in HD, thereby perturbing the transcription factor networks. Interestingly, some variants affect miRNA binding as well as their downstream gene expression. Tissue-specific network analysis showed that mitochondrial, neuroinflammation, vasculature, and angiogenesis-related genes are disrupted in HD. From this integrative omics analysis, we propose that abnormal neuroinflammation acts as a two-edged sword that indirectly affects the vasculature and associated energy metabolism. Rehabilitation of blood-brain barrier functionality and energy metabolism may secure the neuron from cell death.

Targeting the challenging tumors lacking explicit markers and predictors for chemosensitivity is one of the major impediments of the current cancer armamentarium. Triple-negative breast cancer (TNBC) is an aggressive and challenging molecular subtype of breast cancer, which needs astute strategies to achieve clinical success. The pro-survival B-cell lymphoma 2 (BCL-2) overexpression reported in TNBC plays a central role in deterring apoptosis and is a promising target. Here, we propose three novel BH4 mimetic small molecules, SM396, a covalent binder, and two non-covalent binders, i.e., SM216 and SM949, which show high binding affinity (nM) and selectivity, designed by remodeling the existing BCL-2 chemical space. Our mechanistic studies validate the selectivity of the compounds towards cancerous cells and not on normal cells. A series of functional assays illustrated BCL-2-mediated apoptosis in the tumor cells as a potent anti-cancerous mechanism. Moreover, the compounds exhibited efficacious in vivo activity as single agents in the MDA-MB-231 xenograft model (at nanomolar dosage). Overall, these findings depict SM216, SM396, and SM949 as promising leads, pointing to the clinical translation of these compounds in targeting triple-negative breast cancer.

In cuprate high-T_{c} superconductors, resonant excitation of certain lattice vibrations has been shown to induce transient terahertz reflectivity features suggestive of nonequilibrium superconductivity above the critical temperature T_{c}. A microscopic mechanism for these observations is still lacking. Here, time-resolved measurements of scattering-angle- and polarization-dependent second-harmonic generation in driven YBa_{2}Cu_{3}O_{6+x} reveal a three-order-of-magnitude amplification of a 2.5-THz electronic mode, which is unique because of its symmetry, momentum, and temperature dependence. A theory for amplification of finite-momentum Josephson plasma polaritons, which are assumed to be well formed below T_{c} but incoherent throughout the pseudogap phase, explains all these observations. A theoretical solution for the Fresnel-Floquet reflection that starts from the coherently oscillating Josephson plasma polaritons provides a possible mechanism for the nonequilibrium superconductorlike terahertz reflectivity reported earlier. Beyond the immediate case of cuprates, this work underscores the role of nonlinear mode mixing to amplify fluctuating modes above the transition temperature in a wide range of materials.

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are well-known neuronal degenerative disorders that share common pathological events. Approved medications alleviate symptoms but do not address the root cause of the disease. Energy dysfunction in the neuronal population leads to various pathological events and ultimately results in neuronal death. Identifying common therapeutic targets for these disorders may help in the drug discovery process. The Brodmann area 9 (BA9) region is affected in both the disease conditions and plays an essential role in cognitive, motor, and memory-related functions. Analyzing transcriptome data of BA9 provides deep insights related to common pathological pathways involved in AD and PD. In this work, we map the preprocessed BA9 fastq files generated by RNA-seq for disease and control samples with reference hg38 genomic assembly and identify common variants and differentially expressed genes (DEG). These variants are predominantly located in the 3′ UTR (non-promoter) region, affecting the conserved transcription factor (TF) binding motifs involved in the methylation and acetylation process. We have constructed BA9-specific functional interaction networks, which show the relationship between TFs and DEGs. Based on expression signature analysis, we propose that MAPK1, VEGFR1/FLT1, and FGFR1 are promising drug targets to restore blood-brain barrier functionality by reducing neuroinflammation and may save neurons.

Glioblastoma multiforme (GBM) is the most common infiltrating lethal tumor of the brain. Tumor heterogeneity and the precise characterization of GBM remain challenging, and the disease-specific and effective biomarkers are not available at present. To understand GBM heterogeneity and the disease prognosis mechanism, we carried out a single-cell transcriptome data analysis of 3389 cells from four primary IDH-WT (isocitrate dehydrogenase wild type) glioblastoma patients and compared the characteristic features of the tumor and periphery cells. We observed that the marker gene expression profiles of different cell types and the copy number variations (CNVs) are heterogeneous in the GBM samples. Further, we have identified 94 differentially expressed genes (DEGs) between tumor and periphery cells. We constructed a tissue-specific co-expression network and protein–protein interaction network for the DEGs and identified several hub genes, including CX3CR1, GAPDH, FN1, PDGFRA, HTRA1, ANXA2 THBS1, GFAP, PTN, TNC, and VIM. The DEGs were significantly enriched with proliferation and migration pathways related to glioblastoma. Additionally, we were able to identify the differentiation state of microglia and changes in the transcriptome in the presence of glioblastoma that might support tumor growth. This study provides insights into GBM heterogeneity and suggests novel potential disease-specific biomarkers which could help to identify the therapeutic targets in GBM.

Neurodegenerative diseases, including Alzheimer, Parkinson, Huntington, and amyotrophic lateral sclerosis, are a prominent class of neurological diseases currently without a cure. They are characterized by an inexorable loss of a specific type of neurons. The selective vulnerability of specific neuronal clusters (typically a subcortical cluster) in the early stages, followed by the spread of the disease to higher cortical areas, is a typical pattern of disease progression. Neurodegenerative diseases share a range of molecular and cellular pathologies, including protein aggregation, mitochondrial dysfunction, glutamate toxicity, calcium load, proteolytic stress, oxidative stress, neuroinflammation, and aging, which contribute to neuronal death. Efforts to treat these diseases are often limited by the fact that they tend to address any one of the above pathological changes while ignoring others. Lack of clarity regarding a possible root cause that underlies all the above pathologies poses a significant challenge. In search of an integrative theory for neurodegenerative pathology, we hypothesize that metabolic deficiency in certain vulnerable neuronal clusters is the common underlying thread that links many dimensions of the disease. The current review aims to present an outline of such an integrative theory. We present a new perspective of neurodegenerative diseases as metabolic disorders at molecular, cellular, and systems levels. This helps to understand a common underlying mechanism of the many facets of the disease and may lead to more promising disease-modifying therapeutic interventions. Here, we briefly discuss the selective metabolic vulnerability of specific neuronal clusters and also the involvement of glia and vascular dysfunctions. Any failure in satisfaction of the metabolic demand by the neurons triggers a chain of events that precipitate various manifestations of neurodegenerative pathology.

Background: PPARγ (peroxisome proliferator-activated receptor gamma) is involved in the pathology of numerous diseases, including UM and other types of cancer. Emerging evidence suggests that an interaction between PPARγ and DNMTs (DNA methyltransferase) plays a role in cancer that is yet to be defined. Methods: The configuration of the repeating elements was performed with CAP3 and MAFFT, and the structural modelling was conducted with HDOCK. An evolutionary action scores algorithm was used to identify oncogenic variants. A systematic bioinformatic appraisal of PPARγ and DNMT1 was performed across 29 tumor types and UM available in The Cancer Genome Atlas (TCGA). Results: PPAR-responsive elements (PPREs) enriched with Alu repeats are associated with different genomic regions, particularly the promotor region of DNMT1. PPARγ–DNMT1 co-expression is significantly associated with several cancers. C-terminals of PPARγ and DNMT1 appear to be the potential protein–protein interaction sites where disease-specific mutations may directly impair the respective protein functions. Furthermore, PPARγ expression could be identified as an additional prognostic marker for UM. Conclusions: We hypothesize that the function of PPARγ requires an additional contribution of Alu repeats which may directly influence the DNMT1 network. Regarding UM, PPARγ appears to be an additional discriminatory prognostic marker, in particular in disomy 3 tumors.

Cultural transmission of behaviour is important in a wide variety of vertebrate taxa from birds to humans. Vocal traditions and vocal learning provide a strong foundation for studying culture and its transmission in both humans and cetaceans. Male humpback whales (Megaptera novaeangliae) perform complex, culturally transmitted song displays that can change both evolutionarily (through accumulations of small changes) or revolutionarily (where a population rapidly adopts a novel song). The degree of coordination and conformity underlying song revolutions makes their study of particular interest. Acoustic contact on migratory routes may provide a mechanism for cultural revolutions of song, yet these areas of contact remain uncertain. Here, we compared songs recorded from the Kermadec Islands, a recently discovered migratory stopover, to multiple South Pacific wintering grounds. Similarities in song themes from the Kermadec Islands and multiple wintering locations (from New Caledonia across to the Cook Islands) suggest a location allowing cultural transmission of song eastward across the South Pacific, active song learning (hybrid songs) and the potential for cultural convergence after acoustic isolation at the wintering grounds. As with the correlations in humans between genes, communication and migration, the migration patterns of humpback whales are written into their songs.

tRNA methyltransferase 5 (Trm5) enzyme is an S-adenosyl methionine (AdoMet)-dependent methyltransferase which methylates the G37 nucleotide at the N1 atom of the tRNA. The free form of Trm5 enzyme has three intrinsically disordered regions, which are highly flexible and lack stable three-dimensional structures. These regions gain ordered structures upon the complex formation with tRNA, also called disorder-to-order transition (DOT) regions. In this study, we performed molecular dynamics (MD) simulations of archaeal Trm5 in free and complex forms and observed that the DOT residues are highly flexible in free proteins and become stable in complex structures. The energetic contributions show that DOT residues are important for stabilising the complex. The DOT1 and DOT2 are mainly observed to be important for stabilising the complex, while DOT3 is present near the active site to coordinate the interactions between methyl-donating ligands and G37 nucleotides. In addition, mutational studies on the Trm5 complex showed that the wild type is more stable than the G37A tRNA mutant complex. The loss of productive interactions upon G37A mutation drives the AdoMet ligand away from the 37th nucleotide, and Arg145 in DOT3 plays a crucial role in stabilising the ligand, as well as the G37 nucleotide, in the wild-type complex. Further, the overall energetic contribution calculated using MMPBSA corroborates that the wild-type complex has a better affinity between Trm5 and tRNA. Overall, our study reveals that targeting DOT regions for binding could improve the inhibition of Trm5.

N6-methyladenosine (m6A) editing is the most common RNA modification known to contribute to various biological processes. Nevertheless, the mechanism by which m6A regulates transcription is unclear. Recently, it was proposed that m6A controls transcription through histone modification, although no comprehensive analysis using this dataset was performed. In this study, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to a dataset composed of mouse embryonic stem cells (mESC) and a human cancer cell line (HEC-1-A) and successfully identified two sets of genes significantly overlapping between humans and mice (63 significantly overlapped genes among a total of 16,763 genes common to the two species). These significantly overlapped genes occupy at most 10% genes from both gene sets. Using these two sets of genes, we identified transcription factors (TFs) that m6A might recruit, biological processes that m6A might contribute to, and diseases that m6A might cause; they also largely overlap with each other. Since they were commonly identified using two independent datasets, the results regarding these TFs, biological processes, and diseases should be highly robust and trustworthy. It will help us to understand the mechanisms by which m6A contributes to biological processes.

The advent of topological phases of matter revealed a variety of observed boundary phenomena, such as chiral and helical modes found at the edges of two-dimensional (2D) topological insulators. Antichiral states in 2D semimetals, i.e., copropagating edge modes on opposite edges compensated by a counterpropagating bulk current, are also predicted, but, to date, no realization of such states in a solid-state system has been found. Here, we put forward a procedure to realize antichiral states in twisted van der Waals multilayers, by combining the electronic Dirac-cone spectra of each layer through the combination of the orbital moiré superstructure, an in-plane magnetic field, and interlayer bias voltage. In particular, we demonstrate that a twisted van der Waals heterostructure consisting of graphene/two layers of hexagonal boron nitride [(hBN)_{2}]/graphene will show antichiral states at in-plane magnetic fields of 8 T, for a rotation angle of 0.2^{∘} between the graphene layers. Our findings engender a controllable procedure to engineer antichiral states in solid-state systems, as well as in quantum engineered metamaterials.

This study was carried out to understand the effect of herbal maturation diet on reproductive successes in Etroplus suratensis. Three herbal maturation diets (EXD1, EXD2, EXD3) and one control diet (EXD0) were prepared with different combinations of herbal ingredients and normal diet ingredients. The experimental animal were observed for the success in reproductive performance like Gonado Somatic Index (GSI), fecundity, striping response, percentage of fertilization, percentage of hatching, percentage of deformed and formed larvae, volume of milt, number of sperm cell, percentage of sperm motility, sperm survival time, percentage of active sperm. The EXD3 diet combination increased the GSI (3.14), fecundity (1325), striping responds (87.23), percentage of fertilization (96.45) percentage of hatching (91.89), percentage of formed larvae (87.53), volume of milt (287 μl), number of sperm cell per μl (1912) percentage of sperm motility (94.18), time of sperm survival (72′15″) and percentage of active sperm cells (92.27) and reduced deformed larva percentage (4.36). From this observation it is more evident that the combination of EXD3 was the best combination and it could be utilized for the formulation of maturation diets for E. suratensis.

Density functional theory underlies the most successful and widely used numerical methods for electronic structure prediction of solids. However, it has the fundamental shortcoming that the universal density functional is unknown. In addition, the computational result—energy and charge density distribution of the ground state—is useful for electronic properties of solids mostly when reduced to a band structure interpretation based on the Kohn-Sham approach. Here, we demonstrate how machine learning algorithms can help to free density functional theory from these limitations. We study a theory of spinless fermions on a one-dimensional lattice. The density functional is implicitly represented by a neural network, which predicts, besides the ground-state energy and density distribution, density-density correlation functions. At no point do we require a band structure interpretation. The training data, obtained via exact diagonalization, feeds into a learning scheme inspired by active learning, which minimizes the computational costs for data generation. We show that the network results are of high quantitative accuracy and, despite learning on random potentials, capture both symmetry-breaking and topological phase transitions correctly.

The BAP1 (BRCA1-associated protein 1) gene is associated with a variety of human cancers. With its gene product being a nuclear ubiquitin carboxy-terminal hydrolase with deubiquitinase activity, BAP1 acts as a tumor suppressor gene with potential pleiotropic effects in multiple tumor types. Herein, we focused specifically on uveal melanoma (UM) in which BAP1 mutations are associated with a metastasizing phenotype and decreased survival rates. We identified the ubiquitin carboxyl hydrolase (UCH) domain as a major hotspot region for the pathogenic mutations with a high evolutionary action (EA) score. This also includes the mutations at conserved catalytic sites and the ones overlapping with the phosphorylation residues. Computational protein interaction studies revealed that distant BAP1-associated protein complexes (FOXK2, ASXL1, BARD1, BRCA1) could be directly impacted by this mutation paradigm. We also described the conformational transition related to BAP1-BRCA-BARD1 complex, which may pose critical implications for mutations, especially at the docking interfaces of these three proteins. The mutations affect - independent of being somatic or germline - the binding affinity of miRNAs embedded within the BAP1 locus, thereby altering the unique regulatory network. Apart from UM, BAP1 gene expression and survival associations were found to be predictive for the prognosis in several (n = 29) other cancer types. Herein, we suggest that although BAP1 is conceptually a driver gene in UM, it might contribute through its interaction partners and its regulatory miRNA network to various aspects of cancer. Taken together, these findings will pave the way to evaluate BAP1 in a variety of other human cancers with a shared mutational spectrum.

Electrified aerosols have been observed in the lower troposphere and in the mesosphere, but have never been detected in the stratosphere and upper troposphere. We present measurements of aerosols obtained during a balloon flight to an altitude of ~ 24 km. The measurements were performed with an improved version of the Stratospheric and Tropospheric Aerosol Counter (STAC) aerosol counter dedicated to the search for charged aerosols. It is found that most of the aerosols are charged in the upper troposphere for altitudes below 10 km and in the stratosphere for altitudes above 20 km. Conversely, the aerosols seem to be uncharged between 10 km and 20 km. Model calculations are used to quantify the electrification of the aerosols with a stratospheric aerosol-ion model. The percentages of charged aerosols obtained with model calculations are in excellent agreement with the observations below 10 km and above 20 km. However, the model cannot reproduce the absence of electrification found in the lower stratosphere, as the processes leading to neutralisation in this altitude range are unknown. The presence of sporadic transient layers of electrified aerosol in the upper troposphere and in the stratosphere could have significant implications for sprite formation.

Injury to the glenohumeral capsulolabral complex is the critical lesion in anterior shoulder instability. Various injury patterns have been described including the classic Bankart lesion, the bony Bankart lesion, and humeral avulsion of the glenohumeral ligament. A rare injury variant is a glenoid avulsion of the glenohumeral ligament (GAGL lesion). Careful patient setup and surgical technique are required to identify and arthroscopically repair these lesions. We describe a suture anchor–based arthroscopic GAGL repair performed with the patient in the lateral decubitus position through standard anterior and posterior portals and an accessory posterolateral 7-o'clock portal.

Intrinsically disordered regions (IDRs) and protein (IDPs) are highly flexible owing to their lack of well-defined structures. A subset of such proteins interacts with various substrates; including RNA; frequently adopting regular structures in the final complex. In this work; we have analysed a dataset of protein–RNA complexes undergoing disorder-to-order transition (DOT) upon binding. We found that DOT regions are generally small in size (less than 3 residues) for RNA binding proteins. Like structured proteins; positively charged residues are found to interact with RNA molecules; indicating the dominance of electrostatic and cation-π interactions. However, a comparison of binding frequency shows that interface hydrophobic and aromatic residues have more interactions in only DOT regions than in a protein. Further; DOT regions have significantly higher exposure to water than their structured counterparts. Interactions of DOT regions with RNA increase the sheet formation with minor changes in helix forming residues. We have computed the interaction energy for amino acids–nucleotide pairs; which showed the preference of His–G; Asn–U and Ser–U at for the interface of DOT regions. This study provides insights to understand protein–RNA interactions and the results could also be used for developing a tool for identifying DOT regions in RNA binding proteins.

Kagome materials often host exotic quantum phases, including spin liquids, Chern gap, charge density wave, and superconductivity. Existing scanning microscopy studies of the kagome charge order have been limited to nonkagome surface layers. Here, we tunnel into the kagome lattice of FeGe to uncover features of the charge order. Our spectroscopic imaging identifies a 2×2 charge order in the magnetic kagome lattice, resembling that discovered in kagome superconductors. Spin mapping across steps of unit cell height demonstrates the existence of spin-polarized electrons with an antiferromagnetic stacking order. We further uncover the correlation between antiferromagnetism and charge order anisotropy, highlighting the unusual magnetic coupling of the charge order. Finally, we detect a pronounced edge state within the charge order energy gap, which is robust against the irregular shape fluctuations of the kagome lattice edges. We discuss our results with the theoretically considered topological features of the kagome charge order including unconventional magnetism and bulk-boundary correspondence.