Miracle-dz/UniFAR-FTUs huggingface dataset:
A mechanism of salt bridge–mediated resistance to FtsZ inhibitor PC190723 revealed by a cell-based screen

Bacterial cell division proteins, especially the tubulin homolog FtsZ, have emerged as strong targets for developing new antibiotics. Here, we have utilized the fission yeast heterologous expression system to develop a cell-based assay to screen for small molecules that directly and specifically target the bacterial cell division protein FtsZ. The strategy also allows for simultaneous assessment of the toxicity of the drugs to eukaryotic yeast cells. As a proof-of-concept of the utility of this assay, we demonstrate the effect of the inhibitors sanguinarine, berberine and PC190723 on FtsZ. Though sanguinarine and berberine affect FtsZ polymerization, they exert a toxic effect on the cells. Further, using this assay system, we show that PC190723 affects Helicobacter pylori FtsZ function and gain new insights into the molecular determinants of resistance to PC190723. Based on sequence and structural analysis and site-specific mutations, we demonstrate that the presence of salt-bridge interactions between the central H7 helix and beta-strands S9 and S10 mediate resistance to PC190723 in FtsZ. The single-step in vivo cell-based assay using fission yeast enabled us to dissect the contribution of sequence-specific features of FtsZ and cell permeability effects associated with bacterial cell envelopes. Thus, our assay serves as a potent tool to rapidly identify novel compounds targeting polymeric bacterial cytoskeletal proteins like FtsZ to understand how they alter polymerization dynamics and address resistance determinants in targets.
Doctoring Direct-to-Consumer Genetic Tests with DNA Spike-Ins

Direct-to-consumer (DTC) genetic testing companies have provided personal genotyping services to millions of customers. Customers mail saliva samples to DTC service providers to have their genotypes analyzed and receive back their raw genetic data. Both consumers and the DTC companies use the results to perform ancestry analyses, relative matching, trait prediction, and estimate predisposition to disease, often relying on genetic databases composed of the data from millions of other DTC-genotyped individuals. While the digital integrity risks to this type of data have been explored, we considered whether data integrity issues could manifest upstream of data generation through physical manipulation of DNA samples themselves, for example by adding synthetic DNA to a saliva sample (“spiked samples”) prior to sample processing by a DTC company. Here, we investigated the feasibility of this scenario within the standard DTC genetic testing pipeline. Starting with the purchase of off-the-shelf DTC genetic testing kits, we found that synthetic DNA can be used to precisely manipulate the results of saliva samples genotyped by a popular DTC genetic testing service and that this method can be used to modify arbitrary single nucleotide polymorphisms (SNPs) in multiplex to create customized doctored genetic profiles. This capability has implications for the use of DTC-generated results and the outcomes of their downstream analyses.
Novel Antibody Interfaces Revealed Through Structural Mining

Antibodies are fundamental effectors of humoral immunity, and have become an enormously successful class of therapeutics. There is increasing evidence that antibodies utilize transient homotypic interactions to enhance function, and elucidation of such interactions can provide insights into their biology and new opportunities for their optimization as drugs. Yet the transitory nature of weak interactions makes them difficult to investigate. Capitalizing on their rich structural data and high conservation, we have characterized all the ways that antibody Fab regions interact crystallographically. This approach led to the discovery of previously unrealized interfaces between antibodies. While diverse interactions exist, β-sheet dimers and variable-constant elbow dimers are recurrent motifs. Disulfide engineering enabled interactions to be trapped and investigated structurally and functionally, providing experimental validation of the interfaces and illustrating their potential for optimization. This work provides first insight into previously undiscovered oligomeric interactions between antibodies, and enables new opportunities for their biotherapeutic optimization.
Potential impact of TE-derived sRNA on gene regulation in the grass Brachypodium distachyon

In plants, the transcriptional and post-transcriptional repression of TEs involved the production of small interfering RNAs (siRNAs) that guide the de novo methylation and transcriptional silencing of TEs. Because siRNAs act via sequence complementarity, they indiscriminately target TEs and possible non-TE transcripts. TEs and their derived siRNAs might thus provide a reservoir for long-distance gene regulation. To test this hypothesis, we infected Brachypodium distachyon with Mangnaporte oryzae, the agent of Rice Blast. The infection caused the differential expression of multiple TE copies as well as a global change in gene expression. By sequencing the small RNA fraction, we identified TE-derived sRNA that are identical in sequence to motifs contained in the UTR region of differentially expressed genes. Our study opens new avenues of research to further investigate to what extent TEs may serve as a source for in trans gene regulation.
Reference genome-independent taxonomic profiling of microbiomes with mOTUs3

Background Taxonomic profiling is a fundamental task in microbiome research that aims to detect and quantify the relative abundance of microorganisms in biological samples. Available methods using shotgun metagenomic data generally depend on the availability of sequenced and taxonomically annotated reference genomes. However, the majority of microorganisms have not been cultured yet and lack such reference genomes. Thus, a substantial fraction of microbial community members remains unaccounted for during taxonomic profiling of metagenomes, particularly in samples from underexplored environments. To address this issue, we have developed the mOTU profiler, a tool that enables reference genome-independent species-level profiling of metagenomes. As such, it supports the identification and quantification of both “known” and “unknown” species based on a set of select marker genes. Results Here, we present mOTUs3, a command line tool that enables the profiling of metagenomes for >33,000 species-level operational taxonomic units. To achieve this, we leveraged the reconstruction and analysis of >600,000 draft genomes, most of which are metagenome assembled genomes (MAGs), from diverse microbiomes, including soil, freshwater systems, and the gastrointestinal tract of ruminants and other animals, which we found to be greatly underrepresented by reference genomes. Overall, two-thirds of all species-level taxa lacked a reference genome. The cumulative relative abundance of these newly included taxa was low in well-studied microbiomes, such as the human body sites (6-11%). By contrast, they accounted for substantial proportions (ocean, freshwater, soil: 43-63%) or even the vast majority (pig, fish, cattle: 60-80%) of the relative abundance across diverse non-human-associated microbiomes. Using community-developed benchmarks and datasets, we found mOTUs3 to be more accurate than other methods and to be more congruent with 16S rRNA gene-based methods for taxonomic profiling. Furthermore, we demonstrate that mOTUs3 greatly increases the resolution of well-known microbial groups into species-level taxa and helps identify new differentially abundant taxa in comparative metagenomic studies. Conclusions We developed mOTUs3 to enable accurate species-level profiling of metagenomes. Compared to other methods, it provides a more comprehensive view of prokaryotic community diversity, in particular for currently underexplored microbiomes. To facilitate comparative analyses by the research community, it is released with >11,000 precomputed profiles for publicly available metagenomes and is freely available at: https://github.com/motu-tool/mOTUs.
Anticorrosion performance of Anthocleista djalonensis on steel-reinforced concrete in a sulphuric-acid medium

ABSTRACT This paper employs electrochemical monitoring methods for studying the anticorrosion performance of the leaf-extract admixture of Anthocleista djalonensis (A. djalonensis) on steel-reinforced concrete immersed in the aggressive medium of a sulphuric-acid (H2SO4) test solution. The electrochemical monitoring methods employed include corrosion potential, corrosion current and corrosion rate. These were used for testing the corrosion responses of specimens of duplicated steel-reinforced concretes in the industrial/microbial simulating test environment. Analyses of these test measurements, as per American Society for Testing and Materials (ASTM) G16-95 R04, shows that the corrosion rate correlates directly with the leaf-extract admixture concentration and inverse functions of noise resistance; the ratio of the standard deviation of the corrosion potential to that of the corrosion current. It is concluded that 0.4167% A. djalonensis (per weight of cement) is the optimum modelling efficiency, from the ranked predictions of the correlation fitting model, by having an inhibition efficiency of η = 96.11 ± 1.14% (predicted) or 89.84 ± 2.46% (experimental) in the medium. Fittings of the predicted and experimental data show that they both follow the Frumkin adsorption isotherm model with a coefficient of determination r2 = 96.17% (predicted) and r2 = 89.88% (experimental). The results of the experimental and predicted model agree in support of using A. djalonensis as an eco-friendly inhibitor of corrosion in the reinforcing steel in concrete designed for the industrial/microbial service environment.
MAD for visual tracker fusion

Existing tracking methods vary strongly in their approach and therefore have different strengths and weaknesses. For example, a single tracking algorithm may be good at handling variations in illumination, but does not cope well with deformation. Hence, their failures can occur on entirely different time intervals on the same sequence. One possible solution for overcoming limitations of a single tracker and for benefitting from individual strengths, is to run a set of tracking algorithms in parallel and fuse their outputs. But in general, tracking algorithms are not designed to receive feedback from a higher level fusion strategy or require a high degree of integration between individual levels. Towards this end, we introduce a fusion strategy serving the purpose of online single object tracking, for which no knowledge about individual tracker characteristics is needed. The key idea is to combine several independent and heterogeneous tracking approaches and to robustly identify an outlier subset based on the "Median Absolute Deviations" (MAD) measure. The MAD fusion strategy is very generic and only requires frame-based object bounding boxes as input. Thus, it can work with arbitrary tracking algorithms. Furthermore, the MAD fusion strategy can also be applied for combining several instances of the same tracker to form a more robust ensemble for tracking an object. The evaluation is done on public available datasets. With a set of heterogeneous, commonly used trackers we show that the proposed MAD fusion strategy improves the tracking results in comparison to a classical combination of parallel trackers and that the tracker ensemble helps to deal with the initialization uncertainty of a single tracker.
BYU ScholarsArchive BYU ScholarsArchive Controller Design for Coordinated Encirclement of Moving Targets Controller Design for Coordinated Encirclement of Moving Targets

This thesis presents controllers that use relative range and bearing measurements to steer unmanned aerial vehicles (UAVs) to circular trajectories around stationary, constantly moving and constantly accelerating targets. The range and bearing to the target, along with their derivaties are estimated. These, along with the estimated local heading of the UAVs, are used in the control law, and to estimate the velocity of the target. In this work, six controllers are presented. The controller for a stationary target is derived using Lyapunov’s direct method, and feedback linearization is used for the constant velocity and accelerating targets. A new controller is proposed to control the direction of encirclement for moving targets, using Lyapunov’s direct method. Additional terms are introduced to maintain a temporally equi-spaced formation around the targets. Theoretical proofs are provided for all controllers using Lyapunov theory. Numerical simulations show vehicles converging to circular formations around both stationary and moving targets. Results are shown using MATLAB simulations with Gaussian noise added to the measurements. Further, a technique is proposed for information exchange between UAVs, with bounds provided for the time taken to dissipate information throughout the system in a scenario with multiple groups of UAVs tracking multiple targets.
The Impact of Government Subsidies and Scientific and Technological Innovation Investment on The Business Performance of Cultural Industry Enterprises

The purpose of this study is to verify the impact of government subsidies and technological innovation on the business performance of Chinese cultural industry enterprises. Therefore, this study takes 238 listed cultural industry enterprises in China from 2015 to 2020 as the object, collects 1175 samples, and uses Stata16 software for empirical analysis. The analysis results are as follows. First, government subsidies have a positive impact on the business performance of Chinese cultural industry enterprises. Second, government subsidies have a positive impact on the scientific and technological innovation of Chinese cultural industry enterprises. Third, scientific and technological innovation has a positive impact on the business performance of Chinese cultural industry enterprises. Fourth, scientific and technological innovation plays a partially mediating role in the relationship between government subsidies and business performance of Chinese cultural industry enterprises. Based on the research results, measures to improve the business performance of cultural industry companies are as follows. First, establish a modern cultural industry market system. Second, the government should expand financial and tax support for cultural industry companies. Third, promote the integration of cultural industries with scientific and technological innovation.
FINANCIAL MARKET PREDICTION T ECHNIQUES : REVIEW

: Sentiment analysis has seen an incredible growth within the past few years. Sentiment analysis or opinion mining may be a process of collecting users’ opinion from user generated content. It’s various applications, like stock exchange prediction, products’ review collection, etc. an outsized amount of labor has been wiped out this field by applying sentiment analysis to varied applications. the most goal of this paper is to review the varied methods used for sentiment analysis. Further we explain the overview of varied related papers and their performances.
New high-order universal ΣΔ modulator

A new high order sigma delta modulator called MSCL (multistage closed-loop) is presented. It uses a global feedback to lower the sensitivity to circuit imperfections and no digital prefiltering is required before summing up the output of each stage. Stable modulators can be realised to the sixth order. Simulation and experimental results are presented.
Information fusion in fingerprint authentication

Although the problem of automatic fingerprint matching has been extensively studied, it is nevertheless, not a fully solved problem. In this thesis, an information fusion approach is adopted to address some of the limitations of existing fingerprint matching systems. A hybrid fingerprint system that utilizes both minutiae points and ridge feature maps to represent and match fingerprint images has been developed. The hybrid matcher is shown to perform significantly better than a traditional minutiae-based matcher. The ridge feature maps extracted by this technique have also been used to align and register fingerprint image pairs via a correlation process, thereby obviating the need to rely on minutiae points for image registration. To address the problem of partial prints obtained from small-sized sensors, a fingerprint mosaicking scheme has been developed. The proposed technique constructs a composite fingerprint template from two partial fingerprint impressions by using the iterative control point (ICP) algorithm that determines the transformation parameters relating the two impressions. To mitigate the effect of non-linear distortions in fingerprint images on the snatching process, an average deformation model has been proposed. The model is developed by comparing a fingerprint impression with several other impressions of the same finger and observing the common ridge points that occur in them. An index of deformation has been suggested in this context to aid in the selection of an ‘optimal’ fingerprint impression from a set of impressions. Finally, techniques to combine fingerprint information with the other biometric traits of a subject (viz., face and hand geometry) are presented. To enhance user convenience, a learning methodology has been used to compute user-specific parameters in a multibiometric system. Information fusion systems, as presented in this thesis, are expected to be more reliable and robust than systems that rely on a single source of information.
Prognostic Performance of the Derived Neutrophil-to-Lymphocyte Ratio in Stage IV Melanoma Patients Treated with Immune Checkpoint Inhibitors

The purpose was to evaluate the prognostic performance of the derived neutrophil–to-lymphocyte ratio (dNLR) in patients with metastatic cutaneous melanoma (CM) treated with immune checkpoint inhibitors (ICI). We retrospectively investigated 41 CM patients with stage IV disease who had the indication for treatment with ICI. dNLR as well as NLR were routinely determined prior to the start of ICI treatment. The dNLR and NLR were calculated as follows: dNLR = absolute neutrophil counts (ANC)/white blood cell count −ANC and NRL = ANC/absolute lymphocyte counts, respectively. Follow-up of the patients was performed in line with current guidelines. In univariate analysis, dNLR (p = 0.027 and p = 0.032) as well as NLR (p = 0.0023 and p = 0.0036) were the only parameters which were significantly associated with the best overall response (BOR) and disease control rate (DCR) on ROC curve analyses. NLR negatively correlated with CM-specific survival (r = −0.32, p = 0.043). CM-specific deaths were significantly associated with the absence of immune-related adverse events (p = 0.043), elevated S100 calcium-binding protein B (S100B) at baseline (p = 0.0006), and dNLR (p = 0.024). In multivariate analyses, NLR was the only significant independent predictor for BOR (p = 0.014; odds ratio: 1.7; and 95% CI 1.11 to 2.61) and DCR (p = 0.019; odds ratio: 1.5; and 95% CI 1.07 to 2.19). Regarding CM-specific death, however, normal baseline S100B was the only significant independent predictor (p = 0.0020; odds ratio: 0.074; and 95% CI 0.014 to 0.38) for survival. Our data demonstrate that baseline NLR seems to be superior to dNLR in the prediction of ICI response in CM patients.
Comparative study of DL-based methods performance for camera model identification with multiple databases

Camera identification is an important topic in the field of digital image forensics. There are three levels of classification: brand, model, and device. Studies in the literature are mainly fo-cused on camera model identification. These studies are increas-ingly based on deep learning (DL). The methods based on deep learning are dedicated to three main goals: basic (only model) triple (brand, model and device) - open-set (known and unknown cameras) classifications. Unlike other areas of image processing such as face recognition, most of these methods are only evaluated on a single database (Dresden) while a few others are publicly available. The available databases have a diversity in terms of camera content and distribution that is unique to each of them and makes the use of a single database questionable. Therefore, we conducted extensive tests with different public databases (Dres-den, SOCRatES, and Forchheim) that combine enough features to perform a viable comparison of DL-based methods for camera model identification. In addition, the different classifications (basic, triple, open-set) pose a disparity problem preventing com-parisons. We therefore decided to focus only on the basic camera model identification. We also use transfer learning (specifically fine-tuning) to perform our comparative study across databases.
The proportionality of merger remedies in differentiated product markets

Horizontal mergers that raise competition concerns may require divestitures that eliminate the identified concerns. At the same time, divestitures should be proportionate, not going above and beyond what is necessary to eliminate the identified concerns. In this article, we argue that in differentiated product markets out-of-market substitutes exert stronger competitive pressure on some products within the relevant product market than on others. As a result, a divestiture of products that are subject to strong competitive pressure from out-of-market substitutes may not be needed if other products that are subject to weaker competitive pressure are divested. A simplistic solution that requires divestitures in all areas of horizontal overlap could result in overly broad remedies that violate the proportionality principle.
Linkage Between Culture, Leadership, and Knowledge Sharing in MNCs: Moderating Role of Training and Development

The paper develops a theoretical model that focuses on the integrated effect of organizational factors on knowledge sharing, taking the moderating effect of training and development. The proposed hypotheses were tested with structural equations modeling using self-reports of 200 personnel working in Bngladesh-based MNCs. The organizational factors such as respect for people, job stability, aggressiveness, transformational leadership, transactional leadership and decentralization have a direct influence on knowledge sharing. The substantial effect of training and development was not evident in moderating the relationship between organizational factors and knowledge sharing. However, this study reveals that training and development moderates only the relationship between team orientation and knowledge sharing. the findings broaden the understanding of the impact of organizational culture, leadership, and training and development, and knowledge sharin. This study helps managers to formulate strategies and provide new knowledge to researchers to look further into this field.
Principles Of Environmental Engineering And Science

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