Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of a domain wall on conductance quantization in a ferromagnetic nanowire

The effect of the domain wall (DW) on the conductance in a ballistic ferromagnetic nanowire (FMNW) is revisited by exploiting a specific perturbation theory which is effective for a thin DW; the thinness is often the case in currently interested conductance measurements on FMNWs. Including the Hund coupling between carrier spins and local spins in a DW, the conductance of a FMNW in the presence of a very thin DW is calculated within the Landauer-B\"{u}ttiker formalism. It is revealed that the conductance plateaus are modified significantly, and the switching of the quantization unit from $e^2/h$ to ``about $2e^2/h$'' is produced in a FMNW by the introduction of a thin DW. This accounts well for recent observations in a FMNW.
Transcriptome analysis of Sf9 insect cells during production of recombinant Adeno‐associated virus

The insect cell‐baculovirus expression vector system (IC‐BEVS) has emerged as an alternative time‐ and cost‐efficient production platform for recombinant Adeno‐associated virus (AAV) for gene therapy. However, a better understanding of the underlying biological mechanisms of IC‐BEVS is fundamental to further optimize this expression system toward increased product titer and quality. Here, gene expression of Sf9 insect cells producing recombinant AAV through a dual baculovirus expression system, with low multiplicity of infection (MOI), was profiled by RNA‐seq. An 8‐fold increase in reads mapping to either baculovirus or AAV transgene sequences was observed between 24 and 48 h post‐infection (hpi), confirming a take‐over of the host cell transcriptome by the baculovirus. A total of 336 and 4784 genes were identified as differentially expressed at 24 hpi (vs non‐infected cells) and at 48 hpi (vs. infected cells at 24 hpi), respectively, including dronc, birc5/iap5, and prp1. Functional annotation found biological processes such as cell cycle, cell growth, protein folding, and cellular amino acid metabolic processes enriched along infection.
Optical Photometry of GRB 021004: The First Month

We present UBVRCIC photometry of the optical afterglow of the gamma-ray burst GRB 021004 taken at the Nordic Optical Telescope between approximately 8 hours and 30 days after the burst. These data are combined with an analysis of the 87 ks Chandra X-ray observations of GRB 021004 taken at a mean epoch of 33 hr after the burst to investigate the nature of this GRB. We find an intrinsic spectral slope at optical wavelengths of βUH = 0.39 ± 0.12 and an X-ray slope of βX = 0.94 ± 0.03. There is no evidence for color evolution between 8.5 hr and 5.5 days after the burst. The optical decay becomes steeper approximately 5 days after the burst. This appears to be a gradual break due to the onset of sideways expansion in a collimated outflow. Our data suggest that the extragalactic extinction along the line of sight to the burst is between AV ≈ 0.3 and 0.5 and has an extinction law similar to that of the Small Magellanic Cloud. The optical and X-ray data are consistent with a relativistic fireball with the shocked electrons being in the slow cooling regime and having an electron index of p = 1.9 ± 0.1. The burst occurred in an ambient medium that is homogeneous on scales larger than approximately 1018 cm but inhomogeneous on smaller scales. The mean particle density is similar to what is seen for other bursts (0.1 cm-3 ≲ n ≲ 100 cm-3). Our results support the idea that the brightening seen at approximately 0.1 days was due to interaction with a clumpy ambient medium within 1017–1018 cm of the progenitor. The agreement between the predicted optical decay and that observed approximately 10 minutes after the burst suggests that the physical mechanism controlling the observed flux at t ≈ 10 minutes is the same as the one operating at t > 0.5 days.
Theoretical Description of GRB 160625B with Wind-to-ISM Transition and Implications for a Magnetized Outflow

GRB 160625B, one of the brightest bursts in recent years, was simultaneously observed by Fermi and Swift satellites, and ground-based optical telescopes in three different events separated by long periods of time. In this paper, the non-thermal multiwavelength observations of GRB 160625B are described and a transition phase from wind-type-like medium to interstellar medium (ISM) between the early (event II) and the late (event III) afterglow is found. The multiwavelength observations of the early afterglow are consistent with the afterglow evolution starting at ∼150 s in a stellar wind medium, whereas the observations of the late afterglow are consistent with the afterglow evolution in ISM. The wind-to-ISM transition is calculated to be at s when the jet has decelerated, at a distance of ∼1 pc from the progenitor. Using the standard external shock model, the synchrotron and synchrotron self-Compton emission from reverse shock is required to model the GeV γ-ray and optical observations in the early afterglow, and synchrotron radiation from the adiabatic forward shock to describe the X-ray and optical observations in the late afterglow. The derived values of the magnetization parameter, the slope of the fast decay of the optical flash, and the inferred magnetic fields suggest that Poynting flux-dominated jet models with arbitrary magnetization could account for the spectral properties exhibited by GRB 160625B.
Modelling the mid-infrared polarization in dust around young stars

The presence of crystalline silicates has been detected in the circumstellar environment of several young stars in the recent past and there is evidence of silicon carbide (SiC) detection in the envelope of pre-main sequence star SVS13. In this work, we have attempted to probe the presence of SiC in the dust around protoplanetary disks in a sample of young stars. We have modelled the linear polarization of composite dust grains in the mid-infrared (MIR: 8--13 $\mu$m) using silicates as the host with various inclusions of SiC and graphites using the Discrete Dipole Approximation (DDA) and the Effective Medium Approximation (EMA) T-Matrix methods. We have then compared our modelling results with polarimetric observations made in the protoplanetary disks surrounding two Herbig Be stars and one T-Tauri star with particular emphasis towards the 10 $\mu$m silicate feature using CanariCam mounted over the Gran Telescopio Canarias (GTC). We report the possible existence of SiC in the outer disk/envelope around one star in our sample which has been interpreted based on the shape, size, composition and fraction of inclusions by volume in our dust grain models.
Social media for talent acquisition: Win-Win situation for recruiters and jobseekers

Abstract Emerging technologies are inducing a paradigm shift in work practices for organizations. Technological advancements have led to the maximum indulgence of today’s generation in social networking sites. In this paper, researchers investigate at how effective social media platforms are as a source for talent acquisition. From recruiters’ perspective this paper highlights the current practices used by organizations to attract potential candidates who can be the best fit as per their vision, mission, goals and corporate culture. From job seekers’ perspective, social networking sites are also used by them to showcase their talent so as to get noticed for their dream organization. The mixed-method research methodology is used for the study where primary data is collected through jobseekers and recruiters from Indian IT firms and secondary data is collected via research papers, articles, and reports.
Optimizing perceived organisational support to improve job performance of IT employees

Abstract Amid Covid crisis and other market trends, the texture of the market is such that even a whiff of adverse news gets distended. In such a situation, an organization can outperform only when employees contribute to its success story. The rapid advancement in the current scenario has resulted in substantial changes in all dimensions and fields of human life. This situation drives businesses to keep up with and develop in response to these changes and innovations. It has become imperative for organisations to stimulate organisational paradigm adjustments, particularly in terms of boosting quality, effectiveness and efficiency in order to improve their performance. This calls for employees’ belief that organisations deeply care about their well-being and value their work. Such a perception is known as Perceived Organisational Support (POS). This paper seeks to understand the concept of optimizing POS to improve the job performance of employees. This research is designed by using a quantitative approach with data from 380 IT employees working in India.
Electromagnetically induced transparency in 6Li

We report electromagnetically induced transparency for the D1 and D2 lines in 6Li in both a vapour cell and an atomic beam. Electromagnetically induced transparency is created using copropagating mutually coherent laser beams with a frequency difference equal to the hyperfine ground state splitting of 228.2 MHz. The effects of various optical polarization configurations and applied magnetic fields are investigated. In addition, we apply an optical Ramsey spectroscopy technique which further reduces the observed resonance width.
An experiment on the perceived efficacy of fear-based messages in online roulette

ABSTRACT The current study examined the emotional and cognitive evaluations as well as the perceived efficacy of fear-based, text-only pop-up messages. The pop-up messages were presented when viewing a 3-minute prerecorded video of online roulette play. Fifty-nine people who gamble online viewed both low- and high-threat messages that reflected, by random assignment, either the financial (n= 27) or social (n= 32) consequences of gambling. Participants then reported their emotional and cognitive evaluations of the messages, as well as their perceived efficacy to facilitate responsible gambling. Eye-tracking was used as an objective measure of attention to the message. A 2 (message theme: social, financial) x 2 (threat level: low, high) mixed-model ANOVA was used to examine the evaluations and efficacy of the messages. The main effects of message theme/threat level were not significant. The 2 × 2 interaction for the outcome of overall effectiveness was significant, whereby the high-threat and social message combination was rated more effective than other combinations. For eye-tracking, there were no significant findings. The results suggest that fear-based social messaging may be more effective than non-fear inducing or financially-oriented messages. Further research can explore if messages that are perceived to be effective likewise lead to lower-risk gambling.
An exploratory study of factors affecting employee engagement in the IT sector during pandemic using regression analysis

Abstract The pandemic situation caused by COVID-19 has forced most IT companies in India to conduct work from home until 2021. Organizations have thought that by shifting the workspace from office to home, they would be able to combat the pandemic situation. The shift of the workplace during the pandemic has augmented employee anxiety, stress and burnout, which have further resulted in employee disengagement. The regression analysis has been used to study the impact of COVID-19 and identify the key drivers that largely impacted employee engagement during the pandemic. The present study has tried to lay a foundation for investigating employee engagement amid the pandemic and post-pandemic mainly in the IT sector.
Geometry of visual working memory information in human gaze patterns

Stimulus-dependent eye movements have been recognized as a potential confound in decoding visual working memory information from neural signals. Here, we combined eye-tracking with representational geometry analyses to uncover the very information in miniature gaze patterns while participants (n = 41) were cued to maintain visual object orientations. Although participants were discouraged from breaking fixation via real-time feedback, small gaze shifts (< 1 degree) robustly encoded the to-be-maintained stimulus orientation, with evidence for encoding two sequentially presented orientations at the same time. While the orientation encoding upon stimulus presentation was object-specific, it changed to a more object-independent format during cued maintenance, particularly when attention had been temporarily withdrawn from the memorandum. Finally, categorical reporting biases increased after unattended storage, with indications of biased gaze geometries emerging already during the maintenance periods prior to behavioral reporting. These findings disclose a wealth of information in gaze patterns during visuospatial working memory, and suggest systematic changes in representational format when memory contents have been unattended.
The Centralspindlin proteins Pavarotti and Tumbleweed work with WASH to regulate Nuclear Envelope budding

Nuclear envelope (NE) budding is a nuclear pore independent nuclear export pathway, analogous to the egress of herpesviruses, and required for protein quality control, synapse development and mitochondrial integrity. The physical formation of NE buds is dependent on the Wiskott-Aldrich Syndrome protein Wash, its regulatory complex (SHRC), and Arp2/3, and requires Wash’s actin nucleation activity. However, the machinery governing cargo recruitment and organization within the NE bud remains unknown. Here, we identify Pavarotti (Pav) and Tumbleweed (Tum) as new molecular components of NE budding. Pav and Tum interact directly with Wash and define a second nuclear Wash-containing complex required for NE budding. Interestingly, we find that the actin bundling activities of Wash and Pav are required, suggesting a structural role in the physical and/or organizational aspects of NE buds. Thus, Pav and Tum are providing exciting new entry points into the physical machineries of this alternative nuclear export pathway for large cargos during cell differentiation and development.
gGN: learning to represent graph nodes as low-rank Gaussian distributions

Unsupervised learning of node representations from knowledge graphs is critical for numerous downstream tasks, ranging from large-scale graph analysis to measuring semantic similarity between nodes. This study presents gGN as a novel representation that defines graph nodes as Gaussian distributions. Unlike existing representations that approximate such distributions using diagonal covariance matrices, our proposal approximates them using low-rank perturbations. We demonstrate that this low-rank approximation is more expressive and better suited to represent complex asymmetric relations between nodes. In addition, we provide a computationally affordable algorithm for learning the low-rank representations in an unsupervised fashion. This learning algorithm uses a novel loss function based on the reverse Kullback-Leibler divergence and two ranking metrics whose joint minimization results in node representations that preserve not only node depths but also local and global asymmetric relationships between nodes. We assessed the representation power of the low-rank approximation with an in-depth systematic empirical study. The results show that our proposal was significantly better than the diagonal approximation for preserving graph structures. Moreover, gGN also outperformed 17 methods on the downstream task of measuring semantic similarity between graph nodes.
A stable reference human transcriptome and proteome as a standard for reproducible omics experiments

In recent years, the development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits its application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. However, the transcriptome and proteome of most cell lines shift during culturing, which limits their applicability to serve as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (R2=0.966-0.995) and proteome (R2=0.934-0.976 for DDA, R2=0.942-0.986 for DIA) after 9 subculturing generations, which allows this stable standard sample to be stably produced on an industrial scale for several decades. Moreover, this stability was maintained across labs and platforms. In sum, our results justified a omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.
Ancestrally polymorphic chromosomal inversions as potential drivers of speciation

14 Chromosomal inversions may play a central role in speciation through their ability to reduce 15 recombination and therefore genetic exchange at inverted parts of the genome between diverging 16 populations. We analysed long- and short-read whole-genome data from sympatric and allopatric 17 populations of two Drosophila virilis group species, D. montana and D. flavomontana , to understand 18 how inversions have contributed to the divergence of these sister species. Using highly contiguous 19 genome assemblies for both species, we identified three large alternatively fixed inversions on the X 20 chromosome and one in each of the autosomes 2L, 4 and 5. A comparison of demographic models 21 estimated for inverted and non-inverted (colinear) chromosomal regions suggest that these inversions 22 most likely arose before or around the time of the species split. We also detected low rate of 23 interspecific gene flow (introgression) from D. montana to D. flavomontana , which was further reduced 24 inside inversions and was lower in allopatric than in sympatric populations. Together these findings 25 suggest that the inversions have reduced gene exchange between these sister taxa both before and 26 after the onset of species divergence and that low levels of recent introgression may have led to the 27 reinforcement of reproductive barriers previously detected between these species. Our results suggest 28 that ancestrally polymorphic inversions may act as drivers of speciation by allowing the accumulation 29 of divergence inside inversions, while gene flow at colinear regions continues until the evolving 30 reproductive barriers complete speciation. 31
ECOLE: Learning to call copy number variants on whole exome sequencing data

Copy number variants (CNV) are shown to contribute to the etiology of various genetic disorders. Here, authors present ECOLE, a deep learning-based somatic and germline CNV caller for WES data. Utilising a variant of the transformer architecture, the model is trained to call CNVs per exon. Copy number variants (CNV) are shown to contribute to the etiology of several genetic disorders. Accurate detection of CNVs on whole exome sequencing (WES) data has been a long sought-after goal for use in clinics. This was not possible despite recent improvements in performance because algorithms mostly suffer from low precision and even lower recall on expert-curated gold standard call sets. Here, we present a deep learning-based somatic and germline CNV caller for WES data, named ECOLE . Based on a variant of the transformer architecture, the model learns to call CNVs per exon, using high-confidence calls made on matched WGS samples. We further train and fine-tune the model with a small set of expert calls via transfer learning. We show that ECOLE achieves high performance on human expert labelled data for the first time with 68.7% precision and 49.6% recall. This corresponds to precision and recall improvements of 18.7% and 30.8% over the next best-performing methods, respectively. We also show that the same fine-tuning strategy using tumor samples enables ECOLE to detect RT-qPCR-validated variations in bladder cancer samples without the need for a control sample. ECOLE is available at https://github.com/ciceklab/ECOLE .
Predicting Antimicrobial Activity for Untested Peptide-Based Drugs Using Collaborative Filtering and Link Prediction

The increase of bacterial resistance to currently available antibiotics has underlined the urgent need to develop new antibiotic drugs. Antimicrobial peptides (AMPs), alone or in combination with other peptides and/or existing antibiotics, have emerged as promising candidates for this task. However, given that there are thousands of known AMPs and an even larger number can be synthesized, it is inefficient to comprehensively test all of them using standard wet lab experimental methods. These observations stimulated an application of machine-learning methods to identify promising AMPs. Currently, machine learning studies frequently combine very different bacteria without considering bacteria-specific features or interactions with AMPs. In addition, the sparsity of current AMP data sets of antimicrobial activity disqualifies the application of traditional machine-learning methods or renders the results unreliable. Here we present a new approach, featuring neighborhood-based collaborative filtering, to predict with high accuracy a given bacteria’s response to untested AMPs, AMP-AMP combinations, and AMP-antibiotic combinations based on similarities between bacterial responses. Furthermore, we also developed a complementary bacteria-specific link approach that can be used to visualize networks of AMP-antibiotic combinations, enabling us to suggest new combinations that are likely to be effective. Our theoretical analysis of AMP physico-chemical features suggests that there is an optimal similarity between two different AMPs that exhibit strong synergistic behavior. This principle, alongside with our specific results, can be applied to find or design effective AMP-AMP combinations that are target-specific. Author summary It is well known that combinations of different antimicrobial peptides (AMPs), in comparison to single AMP species, can lead to more efficient antimicrobial activity, but the large number of possible combinations requires the application of efficient machine-learning methods. We developed a new approach consisting of collaborative filtering, link prediction, and AMP feature analysis to predict previously-unknown, bacteria-specific activity of AMP combinations, suggest novel synergistic AMP-antibiotic combinations, and guide future design of effective AMP-AMP combinations.