Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully automated ab initio protein structure prediction using I-STES, HMMSTR and ROSETTA

MOTIVATION The Monte Carlo fragment insertion method for protein tertiary structure prediction (ROSETTA) of Baker and others, has been merged with the I-SITES library of sequence structure motifs and the HMMSTR model for local structure in proteins, to form a new public server for the ab initio prediction of protein structure. The server performs several tasks in addition to tertiary structure prediction, including a database search, amino acid profile generation, fragment structure prediction, and backbone angle and secondary structure prediction. Meeting reasonable service goals required improvements in the efficiency, in particular for the ROSETTA algorithm. RESULTS The new server was used for blind predictions of 40 protein sequences as part of the CASP4 blind structure prediction experiment. The results for 31 of those predictions are presented here. 61% of the residues overall were found in topologically correct predictions, which are defined as fragments of 30 residues or more with a root-mean-square deviation in superimposed alpha carbons of less than 6A. HMMSTR 3-state secondary structure predictions were 73% correct overall. Tertiary structure predictions did not improve the accuracy of secondary structure prediction.
Distributed One-Class Support Vector Machine

This paper presents a novel distributed one-class classification approach based on an extension of the ν-SVM method, thus permitting its application to Big Data data sets. In our method we will consider several one-class classifiers, each one determined using a given local data partition on a processor, and the goal is to find a global model. The cornerstone of this method is the novel mathematical formulation that makes the optimization problem separable whilst avoiding some data points considered as outliers in the final solution. This is particularly interesting and important because the decision region generated by the method will be unaffected by the position of the outliers and the form of the data will fit more precisely. Another interesting property is that, although built in parallel, the classifiers exchange data during learning in order to improve their individual specialization. Experimental results using different datasets demonstrate the good performance in accuracy of the decision regions of the proposed method in comparison with other well-known classifiers while saving training time due to its distributed nature.
Ieee Transactions on Visualization and Computer Graphics Globally Optimal Surface Mapping for Surfaces with Arbitrary Topology

— Computing smooth and optimal one-to-one maps between surfaces of same topology is a fundamental problem in computer graphics and such a method provides us a ubiquitous tool for geometric modeling and data visualization. Its vast variety of applications includes shape registration/matching, shape blending, material/data transfer, data fusion, information reuse, etc. The mapping quality is typically measured in terms of angular distortions among different shapes. This paper proposes and develops a novel quasi-conformal surface mapping framework to globally minimize the stretching energy inevitably introduced between two different shapes. The existing state-of-the-art inter-surface mapping techniques only afford local optimization either on surface patches via boundary cutting or on the simplified base domain, lacking rigorous mathematical foundation and analysis. We design and articulate an automatic variational algorithm that can reach the global distortion minimum for surface mapping between shapes of arbitrary topology, and our algorithm is sorely founded upon the intrinsic geometry structure of surfaces. To our best knowledge, this is the first attempt towards numerically computing globally optimal maps. Consequently, our mapping framework offers a powerful computational tool for graphics and visualization tasks such as data and texture transfer, shape morphing, and shape matching.
Preclinical antitumor activity of ST7612AA1: a new oral thiol-based histone deacetylase (HDAC) inhibitor

ST7612AA1 (property of Sigma-Tau), a thioacetate-ω (γ-lactam amide) derivative, is a potent, second generation, oral pan-histone deacetylase inhibitor (HDACi). Aim of the study was to assess the efficacy of ST7612AA1 in solid and haematological tumors, and to characterize its mechanism of action. In vitro, ST7612AA1 potently inhibited different class I and class II HDACs, leading to restore the balance of both histone and non-histone protein acetylation. In vivo, it induced significant anti-tumor effects in xenograft models of lung, colon, breast and ovarian carcinomas, leukemia and lymphoma. This was likely due to the modulation of different HDAC substrates and induction of transcriptional changes with respect to several genes involved in key processes, such as cell cycle regulation, DNA damage checkpoints, immune response, cell adhesion and epithelial-to-mesenchymal transition. PK analysis confirmed the pro-drug nature of ST7612AA1, which is rapidly absorbed and converted to ST7464AA1 after a single oral dose in mice. ST7612AA1 was selected from a novel generation of oral HDAC inhibitors. Its high efficacy correlated with its potent and selective inhibitory activity of HDAC and was combined with a favorable pharmacodynamics profile. These aspects support a clinical development of ST7612AA1 towards a broad spectrum of human solid and haematologic malignancies.
Automated Detection and Classification of Defects in Pcb Using Deep Learning Techniques: A Comparison of Resnet50v1 and Efficient Det D1

To keep pace with the fast-growing Printed Circuit Board (PCB) industry, automated processes are necessary for producing flawless products. Automated visual PCB inspections have become indispensable for addressing the challenges posed by conventional manual inspection systems, as they offer faster, more quantitative, and objective assessments. The aim of this research is to determine an appropriate and effective Convolutional Neural Network (CNN) algorithm for detecting and classifying different PCB defects. Two CNN algorithms, namely Single Shot Detector-Residual Neural Network 50 V1 Model (SSD ResNet50v1) and Efficient Object Detector Model D1 (EfficientDet D1), are evaluated for their classification performance, and their results are compared. The training of these models is conducted on Google Colaboratory, a cloud-based service that provides a free online Jupyter notebook environment for deep learning model training on NVIDIA's Graphical Processing Unit (GPU). The PCB dataset comprises 10,668 images of size 600 × 600 with six common PCB defects frequently found in the industry. The study calculates the classification performance of both algorithms and presents their results. The accuracy achieved by SSD ResNet50v1 is 81.68%, while EfficientDet D1 reaches 71.75%. A comparison of both architectures' accuracies shows that SSD ResNet50v1 performs better than EfficientDet D1.
Selective interactions between epithelial tumour cells and bone marrow mesenchymal stem cells

This work is a comparative study on the features displayed by an epithelial metastatic breast cancer cell line (MCF-7) when set in co-culture with human bone marrow mesenchymal stem cells (MSC) or a feeder layer of 3T3 fibroblasts. MSC, a subset of non-haematopoietic cells in the marrow stroma, display a potential for self-renewal, proliferation and differentiation into precursors for bone, cartilage, connective and muscular tissue. Adhesion of MCF-7 cells to monolayers of MSC or 3T3 was high (95 and 85% respectively). Once attached, MCF-7 grow well on both monolayers. Morphology of MCF-7 cells, as analysed by light and epifluorescence microscopy, revealed that MCF-7 cells grow in clusters on 3T3, but disperse on MSC. Concomitant with the lost of their aggregation status, MCF-7 on MSC express low levels of the intercellular adhesion molecules, E-cadherin and epithelial-specific antigen (ESA). These results suggest that MSC represent an appropriate cell target to investigate the cellular and molecular events occurring at the interface of epithelial-marrow stromal interactions. Together, the model here described should permit to further evaluate the significance and prognostic impact of the shift of micrometastatic cells from a cluster-aggregated into a single-cell status. © 2000 Cancer Research Campaign
Channel-aware distributed dynamic spectrum access via learning-based heterogeneous multi-channel auction

We consider the design of a distributed online learning and access mechanism for dynamic spectrum access, where channel availability statistics are unknown to each secondary user (SU). Unlike existing distributed access policies, we explore the instantaneous channel gain of SUs' channels for multi-user multi-channel diversity gain. We consider an auction-based approach. For the primary channels with heterogeneous statistics, we apply the unit demand auction [1] to determine each SU's selection of a primary channel based on its instantaneous rate over each channel. We further propose a learning based unit demand (LBUD) auction, where each SU only bids for the M-best channels estimated by itself through distributed learning. The new mechanism not only reduces communication overhead, but also improves the throughput performance when the primary channels have dissimilar availability statistics. In addition, we show that the LBUD auction preserves the strong property of unit demand auction, i.e. it is dominant strategy incentive compatible. To improve the convergence speed of the iterative procedure of channel allocation in the auction, we also propose an adaptive price increment algorithm. Simulations show the effectiveness of our proposed auction mechanism in throughput gain by exploring instantaneous channel fade.
From interrogation to investigative interviewing; strategic trends in police questioning

The Court of Appeal has recently released a number of people who were convicted of serious charges because it could no longer rely on confessions obtained during Police questioning. A detailed questionnaire study in 1987 of 80 detectives at four busy inner-London Police Stations indicated that there had been improvements in the way Police question suspects. This is a result of the combined effects of new legislation, technology, and organizational policies designed to encourage an ethical approach to investigation. The majority of detectives have moved away from questioning purely to obtain a confession, towards questioning that is more of an inquiry—examining and adding to the existing evidence. However, a small number of detectives still indicated a preference for a dominant, confrontational style of interviewing, which stands in marked contrast to the majority emphasis on co-operative interviewing styles. It appeared from this study that the legislation would succeed in making the questioning of suspects less inherently coercive. Subsequent empirical studies have shown this trend to be continuing. The concept of ‘investigative interviewing’ as an alternative to interrogation has been developed with the assistance of psychologists and is being promoted through a national training programme intended to restore confidence in evidence obtained through Police questioning.
Performance Evaluation of MIMO Symbol Detection Techniques in LTE Downlink Extended Pedestrian Environment for Spatial Multiplexing

In wireless Multiple Input Multiple Output (MIMO) channels, the accurate detection of the transmitted information becomes complex with the order of MIMO. The complexity issues become even more pronounced with the order of modulation viz. 16-QAM, 64-QAM and 256-QAM. This paper evaluates the performance of Long Term Evolution (LTE) downlink in the extended pedestrian environment for various MIMO symbol detection techniques. Channel Detector algorithms such, Minimum Mean Square Error (MMSE), Zero Forcing (ZF) and Sphere Decoding have been evaluated for various modulation schemes. The investigation being undertaken is in terms of Physical layer BER with specific application to Spatial Multiplexing.
Combined Treatment of Hydroxytyrosol with Carbon Monoxide-Releasing Molecule-2 Prevents TNFα-Induced Vascular Endothelial Cell Dysfunction through NO Production with Subsequent NFκB Inactivation

This study investigated the atheroprotective properties of olive oil polyphenol, hydroxytyrosol (HT), in combination with carbon monoxide-releasing molecule-2 (CORM-2) that acts as a carbon monoxide donor using vascular endothelial cells (VECs). Our results showed that CORM-2 could strengthen the cytoprotective and anti-apoptotic effects of HT against TNFα-induced cellular damage by enhancing cell survival and the suppression of caspase-3 activation. While HT alone attenuated NFκBp65 phosphorylation and IκBα degradation triggered by TNFα in a dose-dependent manner, combined treatment of HT with CORM-2 but not iCORM-2 nearly completely blocked these TNFα effects. Furthermore, combined action of both compounds results in the inhibition of NFκB nuclear translocation. Results also indicate that both compounds time-dependently increased eNOS phosphorylation levels and the combination of HT with CORM-2 was more effective in enhancing eNOS activation and NO production in VECs. The NOS inhibitor, L-NMMA, significantly suppressed the combined effects of HT and CORM-2 on TNFα-triggered NFκBp65 and IκBα phosphorylation as well as decreased cell viability. Together, these data suggest that carbon monoxide-dependent regulation of NO production by the combination of HT with CORM-2 may provide a therapeutic benefit in the treatment of endothelial dysfunction and atherosclerosis.
Of Scapegoats and Signals

If an organization’s management is caught in the act of misconduct, it may call for a changing of the guard. Surprisingly, though, there is little empirical evidence examining the presumed benefits of executive turnover in the aftermath of wrongdoing. In this study, we explore investor reactions to CEO turnover following financial misrepresentation. We theorize and find that firms can be successful at managing investor reactions to organizational misconduct by either scapegoating or signaling change, but middle-ground approaches that do not commit to one or the other are less successful. We test our ideas in a firm-level event study of market reactions to CEO successions following a material financial statement restatement. We discuss the results, which generally support our predictions, and their implications for development of the scapegoating and signaling literatures and research on both executive succession and restoring corrupt organizations.
Studies on Techniques to Improve YOLO in Fault Detection Using RGB Images of Solar Panels

Solar panels can have short lifespan and significantly lower efficiency with the occurrence of various faults. Currently, most fault detection methods suffer from cost-ineffectiveness due to either requiring expensive equipment, such as a thermal camera or lack of information on the source of the faults. Most of the research of using You Only Look Once (YOLO) to detect faults in solar panels revolves around thermal images too. This paper aims to propose an alternative method to detect faults (primarily cracks and soiling) in solar panels by applying YOLOv7 on Red, Green, Blue (RGB) images captured by an Unmanned Aerial Vehicle (UAV). To further improve the performance of this scheme, this paper will focus on applying various techniques to improve the accuracy and training time of the model, which include gray scale conversion, data augmentation and change of Gradient Descent (GD) optimizer. Based on preliminary results, it was found that the combination of gray scale conversion and data augmentation leads to an accuracy of 95.91% while Nadam stands out as the best optimizer because it reduces required training time for convergence by 75 epochs.
Theory-driven and Corpus-driven Computational Linguistics and the Use of Corpora

Computational linguistics and corpus linguistics are closely-related disciplines: they both exploit electronic corpora, extract various kinds of linguistic information from them, and make use of the same methods to acquire this information. Moreover, both were heavily affected by "paradigm shifts" from the prevailing empiricism of the 1950s, to rationalism, then back again with a revival of empirical methods in the 1990s. Computational linguistics deals with the formal modeling of natural language. The formal models can be used to draw conclusions about the structure and functioning of the human language system. They also form the basis of implemented systems for the analysis and generation of spoken or written language, in a variety of applications. The methods applied in building these models are of different kinds since, as a result of the above-mentioned paradigm changes, work in computational linguistics has taken two different paths. Both branches of computational linguistics aim to build models of natural language, but each exploits different techniques: the rationalist's branch focuses on theory-driven, symbolic, nonstatistical methods, whilst the empiricist's branch focuses on corpus-driven and statistical techniques. As we will see later however, the distinction between the branches is these days less clear, and the two fields seem to be coming together again as people successfully combine concepts and methods from each field. Obviously, the corpus-driven branch of computational linguistics has a natural affinity to corpus linguistics, and a shared interest in corpus exploitation. As a consequence, many research topics can be attributed equally well to either computational linguistics or corpus linguistics; examples include part-of-speech tagging (see article 25), treebanking (article 17), semantic tagging (article 27), coreference resolution (article 28), to name just a few. At opposite extremes of computational and corpus linguistics, the ultimate goals of corpus exploitation do however diverge: certain domains of corpus-driven computational linguistics aim to build "optimal" models "no matter how", and the particular corpus features that find their way into such models are not seen as interesting per se; in contrast, corpus linguistics could be said to target exactly these features, the "ingredients" of the models.
Differential susceptibility on myosin heavy chain isoform following eccentric-induced muscle damage

Based on myosin heavy chain (MHC) isoform, human skeletal muscle fibers can be categorized into three fiber types, type I, IIa, IIx fibers, and each fiber type has different characteristics. Typical characteristics are difference in force production, shortening velocity, and fatigue resistance. When the muscle is contract and stretched by a force that is greater than the force generated by the muscle, contraction-induced muscle damage frequently occurs. Several experimental models involving both human and animal have considered the susceptibility of different muscle fiber type and part of muscles to eccentric induced muscle damage. General consensus is a greater susceptibility of fast-twitch fiber or type II fiber to damage following eccentric contractions. However, the results from these previous efforts were not enough to conclude the susceptibility between each individual fiber types after eccentric contraction. Therefore, the purpose of this review is to explore detail limitation and interpretation of previous studies, and review the recent study that eliminated the prior limitations, such as strain magnitude, lengthening velocity, fiber type heterogeneity, and muscle architecture issue.
Efficacy of Videotape Self-Modeling in Treating an Electively Mute Child

We evaluated the efficacy of videotape self-modeling to treat a black third-grade male who had been selectively mute in the school environment for 4 years. Two self-modeling videotapes were made via a staging-and-editing process. The first self-modeling videotape showed the child answering direct questions from his teacher. The second self-modeling videotape showed the child volunteering to answer questions asked of the class as a whole. A multiple-baseline design across the two behaviors was used to assess the effectiveness of the interventions. The first self-modeling videotape produced a clear increase in the child's rate of answering direct questions from his teacher. The second tape produced equivocal results.
Visual acuity impairment and vision-related quality of life: the Barbados Eye Studies

In addition to clinical criteria, self-perceptions of vision-related functioning and well-being are recognized as important measures for characterizing the disability associated with visual impairment. This report assessed the impact of visual impairment in a subset of the African-origin participants of the population-based Barbados Eye Studies, using the US National Eye Institute's 25-item Visual Functioning Questionnaire (NEI-VFQ-25). Visual impairment was associated with significantly (P ≤ 0.01) lower VFQ scores in multidimensional aspects of vision-related quality of life (QOL), including vision-related activities and psychosocial functioning. Adjusted mean composite scores were 12 points (95% confidence interval: 9.5, 14.5) and 31 points (26.7, 34.6) lower for low vision and blindness, respectively, compared with no impairment, and 13 points (7.7, 17.6) lower for visual impairment not correctable, compared with correctable with refraction. In this population with high rates of visual disability, impaired visual acuity adversely impacted the self-perceived QOL, as measured by this instrument.
Triumph From Anguish: The Inspiration of the Special Olympics

Through violent revolution or quiet changes to laws, societies across the globe are working toward the ideal of equality for all, but this equality is particularly elusive for one segment of the human family—people with intellectual disabilities. In every society on Earth, this population is routinely denied access to health care, education, employment, social contact, and other opportunities that lead to long, fulfilling lives. The Special Olympics movement is waging a dignity revolution worldwide to change this reality though the efforts of hundreds of thousands of volunteers working with more than 4 million athletes. The equality challenge for Special Olympics is unlike that of any other because it does not rest on the ideal of achieving sameness but rather on the opposite ideal of welcoming difference. This will require not just the addition of services for “them” but an attitude change for all of “us. ” When international bodies and policymakers meet, they will need to include people with intellectual disabilities at the table. When metrics are set, leaders will need unequivocal targets to reach. When censuses and other measurements are made, they will need to include this population and ensure they are no longer hidden. Finally, people the world over will need to adopt an open mind and an unleashed heart. Then, we can declare victory in the dignity revolution for people with intellectual disabilities.