Miracle-dz/UniFAR-FTUs huggingface dataset:
In silico identification of agriculturally important molecule(s) for defense induction against bacterial blight disease in Soybean (Glycine max)

The productivity of Glycine max (Soybean), one of the economically important crops of India, is seriously affected by bacterial blight disease which is mainly caused by Psedomonas syringae. The disease results in significant yield losses in Soybean crops. Since no proven resistant source is available against bacterial blight, the only option remaining is to utilize biotechnological strategies which could lead to inhibition of pathogenic proteins of the Psedomonas syringae responsible for disease progression. Phytoalexins are well known to inhibit bacterial growth and trigger defense response against diseases in crop plants. The present study was conducted to identify the molecules which could inhibit the growth and development of bacteria. A few proteins were selected from literature analysis viz., Ornithine carbamoyl transferase 2, phaseolotoxin-insensitive, avirulence protein AvrRpt2, HarpinHrpZ, Sensor protein GacS, and Translation initiation factor IF-3 of Psedomonas syringae as possible molecular targets of phytoalexins. The molecular modeling of these proteins were done by using their amino acid sequence on Phyre2 and I-TASSER tool followed by model validation through energy minimization and Ramachandran plot analysis. Subsequently molecular docking was performed using some selected phytoalexins produced by members of Brassicaceae, Fabaceae, Solanaceae, Vitaceae and Poaceae family with each modeled protein structure by AutoDock vina. Based on the molecular docking study, we identified efficient defense molecules, which can be used for the development of agrochemicals for protection of G. max against infection of Psedomonas syringae.
Occurrence of Ordered and Disordered Structural Elements in Postsynaptic Proteins Supports Optimization for Interaction Diversity

The human postsynaptic density is an elaborate network comprising thousands of proteins, playing a vital role in the molecular events of learning and the formation of memory. Despite our growing knowledge of specific proteins and their interactions, atomic-level details of their full three-dimensional structure and their rearrangements are mostly elusive. Advancements in structural bioinformatics enabled us to depict the characteristic features of proteins involved in different processes aiding neurotransmission. We show that postsynaptic protein-protein interactions are mediated through the delicate balance of intrinsically disordered regions and folded domains, and this duality is also imprinted in the amino acid sequence. We introduce Diversity of Potential Interactions (DPI), a structure and regulation based descriptor to assess the diversity of interactions. Our approach reveals that the postsynaptic proteome has its own characteristic features and these properties reliably discriminate them from other proteins of the human proteome. Our results suggest that postsynaptic proteins are especially susceptible to forming diverse interactions with each other, which might be key in the reorganization of the postsynaptic density (PSD) in molecular processes related to learning and memory.
Multi-omics Integrative Analysis of Acute and Relapsing Malaria in a Non-Human Primate Model of P. vivax infection

Systems-scale analysis of multiple layers of molecular and cellular data has significant potential for providing novel insights into malaria pathology and immunity. We present here a unique longitudinal multi-omics dataset encompassing Macaca mulatta blood and bone marrow responses to infection by Plasmodium cynomolgi, a non-human primate (NHP) parasite species used to model P. vivax malaria acute and relapsing infections in humans. We analyzed relationships across multiple biological layers using a mutual information-based machine learning approach to integrate heterogeneous longitudinal datasets and constructed an atlas of multi-omics relatedness networks (MORNs). Using this technique, we were able to detect signatures that defined both acute and relapsing infections. Importantly, relapse infections could be distinguished from both acutely-infected and uninfected NHP, suggesting that the host-parasite interactions during relapses are unique compared to acute Plasmodium infections. To our knowledge, this is the first report of large-scale, longitudinal multi-omics analysis of malaria in any system. This dataset, along with the method used to analyze it, provides a unique resource for the malaria research community and demonstrates the power of longitudinal infection study designs, NHP model systems and integrative multi-omics analyses.
Long Noncoding RNA ASB16-AS1 Promotes Proliferation, Migration, and Invasion in Glioma Cells

Glioma is a lethal, malignant intracranial tumor that becomes progressively common. It has been shown that long noncoding RNAs (lncRNAs) serve important roles in numerous diseases such as gliomas. lncRNAs can regulate the expression of targeted genes through various mechanisms. To identify a novel lncRNA that may be critical in glioma, the present study downloaded the RNA expression profiles of 171 glioma tissues and 5 normal tissues from The Cancer Genome Atlas (TCGA) database using the TCGAbiolinks package in R. Then, lncRNAs in the downloaded TCGA data were identified using the HUGO Gene Nomenclature Committee (HGNC). Based on the fragments per kilobase million value, differential expression analysis was conducted using the limma package in R. In addition, receiver operating characteristic (ROC) analysis was performed, and the area under the curve (AUC) was evaluated using the ROCR package in R. A total of 178 lncRNAs corresponding to differentially expressed genes with an AUC >0.85 were selected. Upon identifying the differential lncRNAs, ceRNA networks were constructed with these differential lncRNAs using the starbase database. From these networks, the top 10% hub genes were selected. In addition, the present study randomly selected 4 lncRNAs for quantitative polymerase chain reaction validation in tissue samples. The results revealed that lncRNA ASB16-AS1 exhibited significantly differential expression in tissue samples and was significantly associated with tumor staging and grading. Furthermore, the proliferation, invasion, and migration of U87MG and U251 glioblastoma stem-like cells (U87GS, U251GS) were significantly inhibited upon inhibition of ASB16-AS1, and the expression of key proteins in the EMT signaling pathway was affected by knocking down ASB16-AS1. Overall, the present study revealed that lncRNA ASB16-AS1 improves the proliferation, migration, and invasion of glioma cells.
A Review of Phenotypic and Genotypic Methods for Detection of Drug Resistance in Mycobacterium tuberculosis

Tuberculosis is one of the most dangerous infectious diseases in the world and causes nearly two million deaths each year, especially in developing countries. Meanwhile, multidrug resistance tuberculosis (MDR-TB) is due to the resistance of Mycobacterium tuberculosis (Mtb) strains to two effective first-line drugs, isoniazid and rifampin, which is increasing worldwide. MDR-TB strains are mainly caused by inadequate treatment of TB patients. The emergence and spread of these strains is an obstacle to the control and management of tuberculosis as well as a threat to the World Health Organization's goal of eliminating the disease by 2050. Proper management of MDR-TB relies on early recognition of the disease. Recently, phenotypic and genotypic diagnostic methods have been developed to rapidly identify MDR strains in tuberculosis patients. Some of them are also economically suitable to use in developing countries. Proper treatment of patients with drug-resistant TB requires the rapid detection of resistant strains and appropriate drug administration. Regular monitoring of patients' side effects of medications as well as enhancing the quality of bacterial tests is essential to identify resistant strains. Therefore, in this review, we will describe the available phenotypic and genotypic tests for drug-resistant tuberculosis detection and discuss their advantages and limitations.
Signal irrelevance of corporate governance practices during Initial Public Offerings in India

A sample of initial public offerings (IPOs) of firms listed on the Bombay Stock Exchange between April 2003 and March 2014 has been used to investigate the relevance of corporate governance in the post-IPO capital market performance. Signal theory has been used to understand the phenomenon of post-IPO capital market performance vis-à-vis signal cast by corporate governance attributes. The study finds investors ’ indifference to governance mechanism put in place by IPO-firms in compliance with the listing requirements. The outcomes of the study provide essential feedback for IPO-firms and the Security Exchange Board of India, the Indian capital market regulator. Keyw ords: Initial Public Offering, Underpricing, corporate governance,
Efficient peripheral nerve firing characterisation through massive feature extraction

Peripheral nerve decoding algorithms form an important component of closed-loop bioelectronic medicines devices. For any decoding method, meaningful properties need to be extracted from the peripheral nerve signal as the first step. Simple measures such as signal amplitude and features of the Fourier power spectrum are most typically used, leaving open whether important information is encoded in more subtle properties of the dynamics. We here propose a feature-based analysis method that identifies changes in firing characteristics across recording sections by unsupervised dimensionality reduction in a high-dimensional feature-space and selects single efficiently implementable estimators for each characteristic to be used as the basis for a better decoding in future bioelectronic medicines devices.
Disentangling the contribution of socioeconomic pathways to future climate-related risks: The case of heat stress

Assessing future climate-related risks requires looking at the future states of both climate and socioeconomic systems. The overarching objective of this thesis is to advance the use of socioeconomic scenarios in climate-related risk assessment studies. The study investigates the operationalization of the SSP-RCP (Representative Concentration Pathways - Shared Socioeconomic Pathways) framework for the assessment of heat stress risk at multiple temporal and spatial scales, through case studies on Europe, African cities and the Houston metropolitan area. Results show that future socioeconomic and climatic conditions are highly uncertain, the case studies depicting an extremely broad range of possible outcomes. The co-use of socioeconomic and climate scenarios is therefore crucial to explore the spread of plausible futures. Recommendations for further research are made on improving consistency between local and global SSPs, further exploring feedbacks between SSPs and RCPs and increasing collaboration between socio-economic and natural sciences.
The antecedents and the outcomes of a firm's dominant logic - the dynamic managerial capability perspective

A firm's dominant logic is one of the most critical elements of the firm to make resource allocation and deployment decision. Drawing on dominant logic theory and dynamic managerial capability, this study articulates the antecedents and the outcomes of the practice of a firm's dominant logic. The paper proffers that entrepreneurial sensing, seizing, and transforming capabilities play very crucial role as antecedents to a firm's dominant logic and subsequently improve organisational performance. Eight propositions have been developed to conceptualise the research model. Entrepreneurs should have higher magnitude of dynamic managerial capabilities to increase their abilities to bootstrap resources, which leads to better dominant logic in order to attain superior performance. The outcome of the research will not only benefit academicians but also inform policymakers by providing profound insights that aim at linking and integrating diverse sets of policies pertaining to the creation of entrepreneurial ventures.
The mitochondrial genome of the ciliate Pseudourostyla cristata (Ciliophora, Urostylida)

Abstract The mitochondrial genomes (mitogenomes) of ciliates are linear and relatively large (<80 kb). Until now, only 11 ciliate mitogenomes, either partial or complete, have been reported. The aim of the present study was to characterize the mitogenome of Pseudourostyla cristata (Ciliophora, Urostylida). The resulting mitogenome represents the first complete mitogenome for order Urostylida and is the largest sequenced ciliate mitogenome (∼76 kb), containing 31 protein-coding genes, 9 transfer RNAs (tRNAs), 2 ribosomal RNAs (rRNAs), and 21 unclassified open reading frames. Among the 11 ciliates whose mitogenomes have been identified, the mitogenome of P. cristata is most similar to that of Oxytricha trifallax, which is in the same subclass. However, the mitogenome of P. cristata is missing two genes (nad3 and nad6) and three split genes (nad4, nad5, and rpl6) that are found in the mitogenome of O. trifallax. Furthermore, the locations of the protein-coding and tRNA genes in P. cristata are different than those of the same genes in either Euplotes minuta or E. crassus, even though the species belong to the same class. This suggests that mitogenome structure is unlikely conserved in class level phylogenetic comparison.
Seismotectonics of the Inner Tienshan: Suusamyr Basin and adjacent areas

The Ms=7.3 Suusamyr earthquake of August 19, 1992 occurred in an area reputedly aseismic. Because it was not expected there, this event attracted worldwide attention of researchers in seismology and seismotectonics, but their results have not been included in the most recent seismic zoning map of Kyrgyzstan. New studies of neotectonic structures and focal mechanisms of earthquakes in the Suusamyr area and adjacent areas give reason to revise the established notions about the seismicity of the region. The seismic hazard in Inner Tienshan appears important and Mmax are comparable to those of the Northern and Southern Tienshan, where numerous destructive events were documented in the XIX and XX centuries. For the southern parts of the study area, along Naryn River, where hydroelectric power stations are planned, the new data should be used.
A combined neural network- and physics-based approach for modeling the plasmasphere dynamics

<p>Plasmasphere is a torus of cold plasma surrounding the Earth and is a very dynamic region. Its dynamics is driven by space weather. Having an accurate model of the plasmasphere is very important for wave-particle interactions and radiation belt modeling. In recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct the global plasmasphere dynamics in the equatorial plane [<em>Bortnik et al</em>. , 2016, <em>Zhelavskaya et al</em>. , 2017, <em>Chu et al</em>. , 2017]. These neural network-based models have been able to capture the large-scale dynamics of the plasmasphere, such as plume formation and the erosion of the plasmasphere on the night side. However, NNs have one limitation. When data is abundant, NNs perform really well. In contrast, when the coverage is limited or non-existent, as during geomagnetic storms, NNs do not perform well. The reason is that since these data are underrepresented in the training set, NNs cannot learn from the limited number of examples. This limitation can be overcome by employing physics-based modeling during such intervals. Physics-based models perform stably during high geomagnetic activity time periods if initialized and configured correctly. In this work, we show the combined approach to model the global plasmasphere dynamics that utilizes advantages of both neural network- and physics-based modeling and produces accurate global plasma density reconstruction during extreme events. We present examples of the global plasma density reconstruction for a number of extreme geomagnetic storms that occured in the past including the Halloween storm in 2003. We validate the global density reconstructions by comparing them to the IMAGE EUV images of the He+ particles distribution in the Earth&#8217;s plasmasphere for the same time periods. </p>
A Simulated Semiparametric Estimation of Nonlinear Errors-in-Variables Models∗

This paper deals with a nonlinear errors-in-variables model where the error distributions are nonparametric and a prediction equation for unobserved covariates is available. Consistent and asymptotically normally distributed estimator is constructed using the combined approach of the method of nonparametric estimation and the method of simulated moments (MSM) of McFadden (1989) or Pakes and Pollard (1989). Necessary and sufficient conditions of identifiability of the model are derived by using the Fourier deconvolution method.
Estimating entrepreneurial risk in Russian small and medium-sized enterprises

Identifying risks is essential to ensure the efficiency of activities of small and medium-sized enterprises who are most exposed to the effects of risks due to limited resources and their structural features. The purpose of this study was to define the levels of entrepreneurial risk prevailing in recent years in Russian micro, small and medium-sized enterprises. The study used data from the official statistical collection of activities of all Russian enterprises for the period 2015 to 2017, representing 82 Russian regions and thirteen types of economic activity. The highest level of acceptable entrepreneurial risk was observed in the trade industry. The highest level of critical entrepreneurial risk was observed in enterprises specializing in the production and distribution of electricity, gas and water, and in mining. The highest level of catastrophic risk was observed in enterprises specializing in building and construction, transport and communications, and in wholesale and retail trade. These results could be of interest to entrepreneurs when choosing the type of sectors to engage in, and to policymakers in supporting enterprises in particular economic sectors of Russia. This paper aims to contribute to the scholarly debate on the establishment of levels of entrepreneurial risk.
DISTRIBUTIONS AND PHASE-RESOLVED PATTERNS

Corona discharge is a form of partial discharges (PD) occurring at non-uniform electric field in air. This kind of discharges could be very useful in practical electrotechnology application, however in many cases corona discharges lead to serious technical problems. Corona discharge is a source of electromagnetic interferences of wide frequency range, and e.g. during electrical power equipment diagnostic measurements at AC testing voltages it produces unneeded quasi-synchronic disturbances. Paper presents results of corona discharges investigations by means of partial discharge phase-resolved measurement method. Model needle-to-plane electrode systems were used during laboratory experiments as well defined corona discharge sources. PD forms and phase-resolved PD patterns were observed and analyzed for different setup and testing voltage parameters.
Biometric authentication using curvelet transform

In this paper, we propose a feature extraction method for two-dimensional image authentication algorithm using curvelet transform and principal component analysis (PCA). Since wavelet transform can not adequately describe facial curves features, the proposed approach involves image denoising applying a 2D-Curvelet transform to achieve compact representations of curves singularities. To assess the performance of the presented method, we have employed three classiﬁcation techniques: Neural networks (NN), K-Nearest Neighbor (KNN) and Support Vector machines (SVM). Extensive experimental results and comparison with the existing methods show the effectiveness of the proposed recognition method in the ORL face database and CASIA iris database.
Chapter 4 INTERACTIONS OF HIGHLY CHARGED IONS WITH C AND SURFACES

Slow collisions between highly charged ions and many-electron targets, such as large atoms, molecules, clusters, or surfaces, usually lead to the transfer of several electrons from the complex target to the projectile. The efcient capture of target electrons is related to the relatively long (on an atomic time scale) interaction time of typically several femtoseconds and the strong Coulomb force of the highly charged projectile that acts like a vacuum cleaner for loosely bound target electrons while offering a large number of excited projectile states into which electrons can be captured. This chapter will cover part of the interesting physics involved in collisions with two particular types of complex targets: gaseous C (as a representative for easily mass-selected carbon-cage molecules called “fullerenes”) and solid surfaces. The theoretical description of these collisions is presented as simply as possible, with a strong emphasis on the modeling of basic electronic interaction mechanisms. For C targets, a variety of observables recently have been measured in laboratories around the world against which existing models can be scrutinized. While for C targets modern coincidence experiments allow for the selection of distant projectile trajectories that do not result in the destruction of the target’s carbon cage, even in the most grazing collisions between highly charged ions and surfaces, close encounters cannot be avoided. This complicates the study of collisions with surfaces to the extent that the interaction mechanisms which dominate while the ion is close to (and possibly inside) a surface are not yet understood in full detail. These mechanisms are a matter of intense ongoing research to which the reader will be introduced
The Effect of Company Performance to Managerial Risk Taking: an Insight from Prospect Theory

One of the fundamental principle in Finance is that people behave in risk averse manner. Risk is only taken if it will result in higher expected value. The higher the risk, the higher also value to be expected. However, Prospect Theory predicts that in loss situation people will exhibit risk seeking behaviour. We test this hypothesis by evaluating how company performance influence managerial risk taking. It is proposed that low performance will induce managers to be risk seeking, and thus increase the level of managerial risk taking. The result is important as past performance might shift the amount of risk from optimal level, and thus affecting company value negatively. Company management might need to device certain procedures to neutralize the effect of performance to managerial risk taking.
Asymptotic Analysis of Multilevel Best Linear Unbiased Estimators

We study the computational complexity and variance of multilevel best linear unbiased estimators introduced in [D. Schaden and E. Ullmann, SIAM/ASA J. Uncert. Quantif. , (2020)]. We specialize the results in this work to PDE-based models that are parameterized by a discretization quantity, e.g., the finite element mesh size. In particular, we investigate the asymptotic complexity of the so-called sample allocation optimal best linear unbiased estimators (SAOBs). These estimators have the smallest variance given a fixed computational budget. However, SAOBs are defined implicitly by solving an optimization problem and are difficult to analyze. Alternatively, we study a class of auxiliary estimators based on the Richardson extrapolation of the parametric model family. This allows us to provide an upper bound for the complexity of the SAOBs, showing that their complexity is optimal within a certain class of linear unbiased estimators. Moreover, the complexity of the SAOBs is not larger than the complexity of Multilevel Monte Carlo. The theoretical results are illustrated by numerical experiments with an elliptic PDE.