Miracle-dz/UniFAR-FTUs huggingface dataset:
A general framework for model-based statistics

SUMMARY This paper presents a general framework for model-based statistics. The framework is based on extended concepts of sufficiency and ancillarity. The fundamental theorem of the new theory exhibits in a single equation the reference distributions for decision theory, model checking, and conditional inference for a general statistical model. The theorem includes the equations upon which Bayesian and frequentist statistics are founded. The general theory also fills two gaps previously missing from empirical Bayesian statistics: concepts of sufficiency and conditional inference. Section 2 discusses a simple example in depth. This example is just complicated enough to illustrate most of the new ideas. It allows comparison of the new concepts with those already in the literature. Because it is so simple, essential principles are emphasized. Section 3 describes the general framework. Extended concepts of sufficiency and ancillarity form the basis for the framework. These extended concepts culminate in a simple, but fundamental, theorem. The generality of the framework is illustrated throughout this section. In particular, frequentist, Bayesian, robust Bayesian, empirical Bayesian, and prediction models are discussed. The common framework emphasizes the similarities among various schools of statistical thought. In fact, besides varying interpreta- tions of probability, the only essential difference between schools is in the model itself. Section 4 derives new conditional empirical Bayesian results for Stein's estimator. The estimated conditional mean squared error is shown to equal the posterior mean squared error with respect to the hyperprior that gives rise to Stein's estimator. Section 5 discusses several more complicated empirical Bayesian models. Some sugges- tions are offered, but for the most part, the primary purpose of this section is to indicate the difficulties that must be faced for these problems. Section 6 offers concluding remarks. These remarks summarize the advantages of the general framework.
In Vitro Inhibition of SARS-CoV-2 Infection by Bromhexine hydrochloride

The world is enduring the SARS CoV-2 pandemic, and although extensive research has been conducted on the issue, only a few antivirals have been approved to treat patients with COVID-19. Bromhexine hydrochloride was previously identified as a potent inhibitor of TMPRSS2, an essential protease for ACE-2 virus receptor interactions. In the present study, we investigated whether bromhexine treatment could reduce SARS CoV-2 replication in vitro. To evaluate bromhexine’s effectiveness against SARS COV-2 infection, viral load was measured using Caco-2 cell lines expressing TMPRSS2. Our molecular docking results indicate that bromhexine displays an affinity with the active site of TMPRSS2. The drug significantly inhibited SARS CoV-2, both parental and P1 variant strains, infection in the Caco-2 cell line, reducing about 40% of SARS-CoV-2 entrance and about 90% of viral progeny in the supernatant 48h post-infection. Furthermore, bromhexine did not exhibit any direct virucidal activity on SARS CoV-2. In conclusion, bromhexine hydrochloride efficiently disrupts SARS CoV-2 infection in vitro and has the potential to become an effective antiviral agent in COVID-19 treatment.
CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

Reinforcement learning (RL) has become a popular paradigm for modeling animal behavior, analyzing neuronal representations, and studying their emergence during learning. This development has been fueled by advances in understanding the role of RL in both the brain and artificial intelligence. However, while in machine learning a set of tools and standardized benchmarks facilitate the development of new methods and their comparison to existing ones, in neuroscience, the software infrastructure is much more fragmented. Even if sharing theoretical principles, computational studies rarely share software frameworks, thereby impeding the integration or comparison of different results. Machine learning tools are also difficult to port to computational neuroscience since the experimental requirements are usually not well aligned. To address these challenges we introduce CoBeL-RL, a closed-loop simulator of complex behavior and learning based on RL and deep neural networks. It provides a neuroscience-oriented framework for efficiently setting up and running simulations. CoBeL-RL offers a set of virtual environments, e.g. T-maze and Morris water maze, which can be simulated at different levels of abstraction, e.g. a simple gridworld or a 3D environment with complex visual stimuli, and set up using intuitive GUI tools. A range of RL algorithms, e.g. Dyna-Q and deep Q-network algorithms, is provided and can be easily extended. CoBeL-RL provides tools for monitoring and analyzing behavior and unit activity, and allows for fine-grained control of the simulation via interfaces to relevant points in its closed-loop. In summary, CoBeL-RL fills an important gap in the software toolbox of computational neuroscience.
On whole-genome demography of world’s ethnic groups and individual genomic identity

All current categorizations of human population, such as ethnicity, ancestry and race, are based on various selections and combinations of subjectively- and/or qualitatively-defined characteristics, such as ancestral lineage/location, cultural/societal norm, language, skin color and other phenotypes and traits perceived by the members within or from outside of the categorized group. Yet, such categorization has been broadly used also in the fields of human genetics, health sciences and medical practices (e.g., 1,2,3), where the observed health characteristics are objectively and quantitatively definable, but the population categorization is not yet available. Here we show the feasibility of deriving a whole-genome-based categorization that is objectively definable and quantitatively measurable. We observe that: (a) the world’s ethnic populations form about 14 genomic groups (GGs); (b) each GG consists of multiple ethnic groups (EGs); and (c) at an individual level, approximately 99.8%, on average, of the whole genome contents are identical between any two individuals regardless of their GGs or EGs.
Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups

We present 4,157 whole-genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest whole genomic resource of Koreans. Korea4K provides 45,537,252 variants and encompasses most of the common and rare variants in Koreans. We identified 1,356 new geno-phenotype associations which were not found by the previous Korea1K dataset. Phenomics analyses revealed 24 genetic correlations, 1,131 pleiotropic variants, and 127 causal relationships from Mendelian randomization. Moreover, the Korea4K imputation reference panel showed a superior imputation performance to Korea1K. Collectively, Korea4K provides the most extensive genomic and phenomic data resources for discovering clinically relevant novel genome-phenome associations in Koreans.
Polymer folding through active processes recreates features of genome organization

From proteins to chromosomes, polymers fold into specific conformations that control their biological function. Polymer folding has long been studied with equilibrium thermodynamics, yet intracellular organization and regulation involve energy-consuming, active processes. Signatures of activity have been measured in the context of chromatin motion, which shows spatial correlations and enhanced subdiffusion only in the presence of adenosine triphosphate (ATP). Moreover, chromatin motion varies with genomic coordinate, pointing towards a heterogeneous pattern of active processes along the sequence. How do such patterns of activity affect the conformation of a polymer such as chromatin? We address this question by combining analytical theory and simulations to study a polymer subjected to sequence-dependent correlated active forces. Our analysis shows that a local increase in activity (larger active forces) can cause the polymer backbone to bend and expand, while less active segments straighten out and condense. Our simulations further predict that modest activity differences can drive compartmentalization of the polymer consistent with the patterns observed in chromosome conformation capture experiments. Moreover, segments of the polymer that show correlated active (sub)diffusion attract each other through effective long-ranged harmonic interactions, whereas anticorrelations lead to effective repulsions. Thus, our theory offers non-equilibrium mechanisms for forming genomic compartments, which cannot be distinguished from affinity-based folding using structural data alone. As a first step toward disentangling active and passive mechanisms of folding, we discuss a data-driven approach to discern if and how active processes affect genome organization.
Quinolone Signals Related to Pseudomonas Quinolone Signal-Quorum Sensing Inhibits the Predatory Activity of Bdellovibrio bacteriovorus

Bdellovibrio bacteriovorus is one of the predatory bacteria; therefore, it can act as a novel “living antibiotic,” unlike the current antibiotics. Here the predation of Escherichia coli by B. bacteriovorus was inhibited in the presence of Pseudomonas aeruginosa. This study investigated whether P. aeruginosa-induced predation inhibition is associated with bacterial quorum sensing (QS). Each las, rhl, or pqs QS mutant in P. aeruginosa was used to check the predatory activity of E. coli cells using B. bacteriovorus. As a result, the predatory activity of B. bacteriovorus increased in a mutant pqs QS system, whereas wild-type PA14 inhibited the predatory activity. Moreover, the addition of 4-hydroxy-2-heptylquinoline (HHQ) or the analog triggered the low predatory activity of B. bacteriovorus and killed B. bacteriovorus cells. Therefore, a defensive action of P. aeruginosa against B. bacteriovorus is activated by the pqs QS system, which produces some quinolone compounds such as HHQ.
Determinants of motor neuron functional subtypes important for locomotor speed

Locomotion requires precise control of the strength and speed of muscle contraction and is achieved by recruiting functionally-distinct subtypes of motor neurons (MNs). MNs are essential to movement and differentially susceptible in disease, but little is known about how MNs acquire functional subtype-specific features during development. Using single-cell RNA profiling in embryonic and larval zebrafish, we identify novel and conserved molecular signatures for MN functional subtypes, and identify genes expressed in both early post-mitotic and mature MNs. Assessing MN development in genetic mutants, we define a molecular program essential for MN functional subtype specification. Two evolutionarily-conserved transcription factors, Prdm16 and Mecom, are both functional subtype-specific determinants integral for fast MN development. Loss of prdm16 or mecom causes fast MNs to develop transcriptional profiles and innervation similar to slow MNs. These results reveal the molecular diversity of vertebrate axial MNs and demonstrate that functional subtypes are specified through intrinsic transcriptional codes.
Distinct roles of perceived teacher and peer relationships in adolescent students’ academic motivation and outcomes: Father absence as a moderator

In the learning environment, both teachers and peers are influential social agents. In the present study, we differentiated between positive and negative aspects of teacher and peer relationships and examined how they predict adolescent students’ academic interest and self-concept, which in turn lead to different levels of academic achievement and subjective well-being at school. Additionally, we explored whether father absence moderated these predictive relationships. Results based on a group of 4274 Chinese middle school students revealed that positive social relationships were more closely related to interest, self-concept, and well-being than negative ones. The predictive paths from teacher relationships to motivation and achievement were stronger than those from peer relationships. However, peers played a prominent role in helping students with absent fathers build a positive self-concept, which led to improved achievement.
Different Hazards, Different Responses: Assessments of Flooding and COVID-19 Risks among Upstate New York Residents

The coronavirus disease 2019 (COVID-19) pandemic and extreme flooding events have recently taken enormous tolls. Drawing on research into differential risk responses across hazards, the authors examine how different social processes surrounding risk from flooding and COVID-19 shape how people respond to each hazard. Data from a household survey of 498 residents in two cities in the northeastern United States reveal that levels of concern and protective measures vary across the two hazards. Whereas climate polarization does not appear to influence flood risk responses, COVID-19 responses appear strongly polarized. However, having a known risk condition can offset Republicans’ doubts about COVID-19. In addition, whereas people of color express greater concern about flooding, white people take more protective measures, and women are more likely than others to take protective measures against COVID-19. Contrasting stakes, immediacy, dread, and polarization surrounding flooding and COVID-19 intersect with social inequalities to produce differing patterns of risk response.
MATHEMATICAL MODELING OF OSCILLATORY MAGNETO-CONVECTION OF A COUPLE-STRESS BIOFLUID IN AN INCLINED ROTATING CHANNEL

A mathematical study is conducted of the oscillatory hydromagnetic flow of a viscous, incompressible, electrically conducting, non-Newtonian bio-fluid in an inclined, rotating channel with nonconducting walls, incorporating couple stress effects. The constitutive equations for a couple-stress fluid and the Maxwell electromagnetic field equations are presented and then reduced to a set of coupled partial differential equations for the primary and secondary flow. The model is then nondimensionalized with appropriate variables and shown to be controlled by the inverse Ekman number (K2 = 1/Ek), the hydromagnetic body force parameter (M), channel inclination (α), Grashof number (Gr), Prandtl number (Pr), oscillation frequency (ω), and time variable (ωT). Analytical solutions are derived using complex variables. The influence of the governing parameters on the primary velocity (u), secondary velocity (w), temperature (θ), primary and secondary flow discharges per unit depth in the channel (Qx, Qz), and friction.
Emergence of cyclonic structures due to the interaction between near‐inertial oscillations and mesoscale eddies

We show that mesoscale eddies, through their effects on near‐inertial oscillations, can induce a spatially heterogeneous diffusion that depends on the vorticity sign. In turn, this heterogeneous diffusion breaks the cyclone–anticyclone symmetry on small time‐scales (compared to the diffusive time‐scale) and favours the emergence of cyclonic structures. Copyright © 2003 Royal Meteorological Society
Finding Vulnerabilities in E-Governance Apps of Android Platform

Around the globe, mobile devices like Smartphones, PDAs & tablets are playing an essential role in every person's day to day life. Out of various operating systems, Android OS is one of the most popular and user-friendly operating system for mobile devices. The Government of India is continuously focusing on digitizing its services to the citizen, so that maximum individuals can get benefit of these services and also it removes the middle man culture and minimize the corruption. Inspired by the steps of Government of India, many state governments also works in the same direction and made their services online through mobile applications. In this paper, we have analyzed some mobile applications of Madhya Pradesh Government and an exhaustive assessment has been done for Apps Vulnerability. After analyzing these apps, authors proposed some recommendations in the conclusion of the paper.
A review of the digital transformation maturity models for SMEs in search of a self-assessment

This paper shows a summary of the review related to digital transformation maturity models used for SMEs. The dimensions and items taken into account by the different models were mainly analyzed in order to establish the key factors for measuring the level of maturity of Digital Transformation. The methodology used by the different reviewed models was also analyzed in order to find opportunities for improvement in a potential new model. The review focused on maturity models that are suitable for SMEs and are not specialized at a specific sector of company. Many similarities were found in the dimensions and elements that the models got, but it was also evident that the vast majority require experts accompanying the diagnostic process, which may be the main opportunity for improvement with the possibility of proposing a self-assessment model, which allows the organization to assess its digital maturity through an interactive questionnaire by itself without any external support.
Interfaces: nanometric dielectrics

The incorporation of nanometric size particles in a matrix to form dielectric composites shows promise of materials (nanodielectrics) with new and improved properties. It is argued that the properties of the interfaces between the particles and the matrix, which will themselves be of nanometric dimensions, will have an increasingly dominant role in determining dielectric performance as the particle size decreases. The forces that determine the electrical and dielectric properties of interfaces are considered, with emphasis on the way in which they might influence composite behaviour. A number of examples are given in which interfaces at the nanometric level exercise both passive and active control over dielectric, optical and conductive properties. Electromechanical properties are also considered, and it is shown that interfaces have important electrostrictive and piezoelectric characteristics. It is demonstrated that the process of poling, namely subjecting macroscopic composite materials to electrical stress and raised temperatures to create piezoelectric materials, can be explained in terms of optimizing the collective response of the nanometric interfaces involved. If the electrical and electromechanical features are coupled to the long-established electrochemical properties, interfaces represent highly versatile active elements with considerable potential in nanotechnology.
Using Social Interactions to Redefine Vaccination Strategy

The pandemics are believed to change the human perception and significantly affect the socio-economical, environmental and psychological outlook of affected people. The recent Covid-19 pandemic has challenged the state of art healthcare systems and has put modern day technology driven healthcare system to a task. While the doctors, biotechnologist, epidemiologist and technologist put their heart in, to model and study the impact of Covid-19; the researchers were tirelessly working on identifying a vaccine that can efficiently put an end to the pandemic. The mass vaccination has always seemed a solution to communicable diseases, pandemics and endemics. The authors believe an efficient vaccination strategy / model is needed to reach the major population in least possible time. It will facilitate to reach the goal of mass vaccination and decrease the spread of virus. The paper presents a PageRank based vaccination model that utilizes the depth first search to traverse a social graph that proves to converge faster than most widely used Random Walk. The idea is to prioritize the vaccination of the most connected individual who is more likely to be a victim or be a super-spreader. The paper also studies the hesitation and acceptance of vaccination among various communities.
NailRing: An Intelligent Ring for Recognizing Micro-gestures in Mixed Reality

Gesture interaction is currently a main interaction technology in the field of mixed reality. However, long-term and large-scale gesture in mid-air will lead to muscle fatigue and privacy problems, which cannot meet the comfort requirements of continuous interaction and inevitably hinder the development of mixed reality systems. To solve this problem, we propose NailRing, an intelligent ring to recognize fingertip micro-gestures using a micro-close-focus camera on a fingertip bracket. Such fingertip physiological characteristics as the changes in fingertip color distribution and muscle shape changes caused by fingertip pressure have been studied. According to the recognition principle, ten types of micro-gestures have been designed and used for contact interaction and one-hand interaction respectively. The accuracy of gesture recognition (cross-session $ F_{Macro}=98.3\%$; cross-person $ F_{Macro}=86.4\%$) in user studies verifies the performances of NailRing under different interaction conditions. Finally, the capability of NailRing in a series of potential application scenarios has also been discussed and analyzed.
Distributed Nonrecursive Averaging Filters for Quantized Consensus: An Edge Sensitivity Design Approach

This article studies the distributed nonrecursive averaging filter design for the quantized consensus of discrete-time first-order multiagent systems over undirected and connected networks. The quantized consensus problem is cast into the robust consensus problem with sector bound uncertainties associated with the edges of communication graphs, and then an edge sensitivity design approach is utilized for the filter parameter design. Necessary and sufficient conditions for the filter parameter for preserving robust consensus with sector bound uncertainties are provided, which are shown to be dependent on the $\mathcal {H}_{\infty }$ norm of the edge complementary sensitivity transfer matrix of the closed-loop multiagent system. Then, the optimal filter parameter design is provided by solving a constrained optimization problem, under which robust consensus with maximal allowable sector bound parameter is reached. The results show that the supremum of the sector bound parameter of the logarithmic quantizer for preserving robust consensus is independent of the communication network topologies and the sampling period.
Anatomical Patterns Recognition of Impulse Control Disorders of Parkinsonian Patients Using Deep Learning of MRI structural images

In Parkinson’s disease (PD) several neuropsychiatric conditions are highly frequent including impulse control disorders (ICD). Driven by medication’s side-effects, these disorders lead to issues in self-control over rewarding cues that produce excessive behavior. Yet, objective means to detect and diagnose presence and severity of impulsivity in PD is lacking. The method developed in this work seeks to identify anatomical patterns in magnetic resonance images, which allow to classify PD with ICD vs PD patients using deep learning techniques. The proposed method includes pre-processing techniques to standardize magnetic resonance images. Different experiments using a variety of deep learning architectures were designed to classify the patterns. A GradCAM technique was also used for identifying the regions where the anatomical patterns were identified, which differentiate the two groups. We reveal an objective way to predict the presence of patterns associated to ICD in anatomical scans. Our model achieved an accuracy of 97.4% using Convolutional Neuronal Networks with an ensemble architecture; a novel objective clinical tool to diagnose ICD and better characterize detection of this severe neuropsychiatric problem.