Miracle-dz/UniFAR-FTUs huggingface dataset:
A multivariate clustering of AAindex database for protein numerical representation

As a first step of genomics signal processing, alphabetical sequence is mapped to numerical. The choice of mapping techniques depends on the application and affects the result of the study. Since biological function is the result of amino acids interactions, a significant method for alphabetical to numerical conversion of sequence is to use the physico-chemical and biochemical properties of amino acids. AAindex database is a rich collection of such properties that can be used for numerical representation of protein. Each of these properties gives a viewpoint in the study of biological functions. Taking into account all AAindex indices leads to a multi-viewpoint representation and provides more options to observe and study the target biological phenomena. But this advantage increases variables number, space dimension and computation time. Since there is correlation between AAindex databases, to handle the issue of space dimension increasement, compact versions of correlated indices are extracted. This paper aims at the construction of new indices through clustering of AAindex database with correlation distance. The results suggest that due to the correlation of these new maps with groups of AAindex indices (in clusters); they have the potential to be used for numerical representation of protein sequence in different studies.
A Contrario Detection of Faces: A Case Example

The a contrario framework is a statistical formulation of a perception principle that permits one to detect meaningful structures in data. It has been applied to the detection of lines and contours in images, moving objects in video, etc., but no attempt has been made to use it for the detection of faces. The goal of this paper is to show that the a contrario formulation can be adapted to the face detection method described by Viola and Jones in their seminal work. We propose an alternative to the cascade of classifiers proposed by the authors by introducing a stochastic a contrario model for the detections of a single classifier, from which adaptive detection thresholds may be inferred. The result is a single classifier whose detection rates are similar to those of a cascade of classifiers. Moreover, we show how a very short cascade of classifiers can be constructed, which improves the accuracy of a classical cascade, at a much lower computational cost. The results prove the validity of the a contrario a.
Plasma Cytokine Profiles in Subjects with High-Functioning Autism Spectrum Disorders

Background Accumulating evidence suggests that dysregulation of the immune system is involved in the pathophysiology of autism spectrum disorders (ASD). The aim of the study was to explore immunological markers in peripheral plasma samples from non-medicated subjects with high-functioning ASD. Methodology/Principal Findings A multiplex assay for cytokines and chemokines was applied to plasma samples from male subjects with high-functioning ASD (n = 28) and matched controls (n = 28). Among a total of 48 analytes examined, the plasma concentrations of IL-1β, IL-1RA, IL-5, IL-8, IL-12(p70), IL-13, IL-17 and GRO-α were significantly higher in subjects with ASD compared with the corresponding values of matched controls after correction for multiple comparisons. Conclusion/Significance The results suggest that abnormal immune responses as assessed by multiplex analysis of cytokines may serve as one of the biological trait markers for ASD.
Location of Intra- and Extracellular M. tuberculosis Populations in Lungs of Mice and Guinea Pigs during Disease Progression and after Drug Treatment

The lengthy treatment regimen for tuberculosis is necessary to eradicate a small sub-population of M. tuberculosis that persists in certain host locations under drug pressure. Limited information is available on persisting bacilli and their location within the lung during disease progression and after drug treatment. Here we provide a comprehensive histopathological and microscopic evaluation to elucidate the location of bacterial populations in animal models for TB drug development. To detect bacilli in tissues, a new combination staining method was optimized using auramine O and rhodamine B for staining acid-fast bacilli, hematoxylin QS for staining tissue and DAPI for staining nuclei. Bacillary location was studied in three animal models used in-house for TB drug evaluations: C57BL/6 mice, immunocompromised GKO mice and guinea pigs. In both mouse models, the bacilli were found primarily intracellularly in inflammatory lesions at most stages of disease, except for late stage GKO mice, which showed significant necrosis and extracellular bacilli after 25 days of infection. This is also the time when hypoxia was initially visualized in GKO mice by 2-piminidazole. In guinea pigs, the majority of bacteria in lungs are extracellular organisms in necrotic lesions and only few, if any, were ever visualized in inflammatory lesions. Following drug treatment in mice a homogenous bacillary reduction across lung granulomas was observed, whereas in guinea pigs the remaining extracellular bacilli persisted in lesions with residual necrosis. In summary, differences in pathogenesis between animal models infected with M. tuberculosis result in various granulomatous lesion types, which affect the location, environment and state of bacilli. The majority of M. tuberculosis bacilli in an advanced disease state were found to be extracellular in necrotic lesions with an acellular rim of residual necrosis. Drug development should be designed to target this bacillary population and should evaluate drug regimens in the appropriate animal models.
Excess risk of major vascular diseases associated with airflow obstruction: a 9-year prospective study of 0.5 million Chinese adults

Background China has high COPD rates, even among never-regular smokers. Little is known about nonrespiratory disease risks, especially vascular morbidity and mortality after developing airflow obstruction (AFO) in Chinese adults. Objective We aimed to investigate the prospective association of prevalent AFO with major vascular morbidity and mortality. Materials and methods In 2004–2008, a nationwide prospective cohort study recruited 512,891 men and women aged 30–79 years from 10 diverse localities across China, tracking cause-specific mortality and coded episodes of hospitalization for 9 years. Cox regression yielded adjusted HRs for vascular diseases comparing individuals with spirometry-defined prevalent AFO at baseline to those without. Results Of 489,382 participants with no vascular disease at baseline, 6.8% had AFO, with prevalence rising steeply with age. Individuals with prevalent AFO had significantly increased vascular mortality (n=1,429, adjusted HR 1.29, 95% CI 1.21–1.36). There were also increased risks of hemorrhagic stroke (n=823, HR 1.18, 95% CI 1.09–1.27), major coronary events (n=635, HR 1.33, 95% CI 1.22–1.45), and heart failure (n=543, HR 2.19, 95% CI 1.98–2.41). For each outcome, the risk increased progressively with increasing COPD severity and persisted among never-regular smokers. Conclusion Among adult Chinese, AFO was associated with significantly increased risks of major vascular morbidity and mortality. COPD management should be integrated with vascular disease prevention and treatment programs to improve long-term prognosis.
Olig2 and Hes regulatory dynamics during motor neuron differentiation revealed by single cell transcriptomics

During tissue development, multipotent progenitors differentiate into specific cell types in characteristic spatial and temporal patterns. We address the mechanism linking progenitor identity and differentiation rate in the neural tube, where motor neuron (MN) progenitors differentiate more rapidly than other progenitors. Using single cell transcriptomics, we define the transcriptional changes associated with the transition of neural progenitors into MNs. Reconstruction of gene expression dynamics from these data indicate a pivotal role for the MN determinant Olig2 just prior to MN differentiation. Olig2 represses expression of the Notch signaling pathway effectors Hes1 and Hes5. Olig2 repression of Hes5 appears to be direct, via a conserved regulatory element within the Hes5 locus that restricts expression from MN progenitors. These findings reveal a tight coupling between the regulatory networks that control patterning and neuronal differentiation, and demonstrate how Olig2 acts as the developmental pacemaker coordinating the spatial and temporal pattern of MN generation.
A Systematic Evaluation of Static API-Misuse Detectors

Application Programming Interfaces (APIs) often have usage constraints, such as restrictions on call order or call conditions. API misuses, i.e., violations of these constraints, may lead to software crashes, bugs, and vulnerabilities. Though researchers developed many API-misuse detectors over the last two decades, recent studies show that API misuses are still prevalent. Therefore, we need to understand the capabilities and limitations of existing detectors in order to advance the state of the art. In this paper, we present the first-ever qualitative and quantitative evaluation that compares static API-misuse detectors along the same dimensions, and with original author validation. To accomplish this, we develop MuC, a classification of API misuses, and MuBenchPipe, an automated benchmark for detector comparison, on top of our misuse dataset, MuBench. Our results show that the capabilities of existing detectors vary greatly and that existing detectors, though capable of detecting misuses, suffer from extremely low precision and recall. A systematic root-cause analysis reveals that, most importantly, detectors need to go beyond the naive assumption that a deviation from the most-frequent usage corresponds to a misuse and need to obtain additional usage examples to train their models. We present possible directions towards more-powerful API-misuse detectors.
Interactions between Predation and Resources Shape Zooplankton Population Dynamics

Identifying the relative importance of predation and resources in population dynamics has a long tradition in ecology, while interactions between them have been studied less intensively. In order to disentangle the effects of predation by juvenile fish, algal resource availability and their interactive effects on zooplankton population dynamics, we conducted an enclosure experiment where zooplankton were exposed to a gradient of predation of roach (Rutilus rutilus) at different algal concentrations. We show that zooplankton populations collapse under high predation pressure irrespective of resource availability, confirming that juvenile fish are able to severely reduce zooplankton prey when occurring in high densities. At lower predation pressure, however, the effect of predation depended on algal resource availability since high algal resource supply buffered against predation. Hence, we suggest that interactions between mass-hatching of fish, and the strong fluctuations in algal resources in spring have the potential to regulate zooplankton population dynamics. In a broader perspective, increasing spring temperatures due to global warming will most likely affect the timing of these processes and have consequences for the spring and summer zooplankton dynamics.
Quantitative tests of a reconstitution model for RNA folding thermodynamics and kinetics

Significance We propose and test predictions of a thermodynamic and kinetic model for RNA tertiary folding that is based on separable energetic contributions of RNA elements. We define these contributions based on the principle features of RNA, and we test the basic predictions of separability by determining whether the energetic contributions of one component are affected by changes in another component. Our results support energetic separability of RNA elements and suggest that it may be possible to deconstruct RNAs into smaller parts that can be studied in isolation such that the individual folding behaviors of these parts can be used to “reconstitute” the folding of the original RNA. Decades of study of the architecture and function of structured RNAs have led to the perspective that RNA tertiary structure is modular, made of locally stable domains that retain their structure across RNAs. We formalize a hypothesis inspired by this modularity—that RNA folding thermodynamics and kinetics can be quantitatively predicted from separable energetic contributions of the individual components of a complex RNA. This reconstitution hypothesis considers RNA tertiary folding in terms of ΔGalign, the probability of aligning tertiary contact partners, and ΔGtert, the favorable energetic contribution from the formation of tertiary contacts in an aligned state. This hypothesis predicts that changes in the alignment of tertiary contacts from different connecting helices and junctions (ΔGHJH) or from changes in the electrostatic environment (ΔG+/−) will not affect the energetic perturbation from a mutation in a tertiary contact (ΔΔGtert). Consistent with these predictions, single-molecule FRET measurements of folding of model RNAs revealed constant ΔΔGtert values for mutations in a tertiary contact embedded in different structural contexts and under different electrostatic conditions. The kinetic effects of these mutations provide further support for modular behavior of RNA elements and suggest that tertiary mutations may be used to identify rate-limiting steps and dissect folding and assembly pathways for complex RNAs. Overall, our model and results are foundational for a predictive understanding of RNA folding that will allow manipulation of RNA folding thermodynamics and kinetics. Conversely, the approaches herein can identify cases where an independent, additive model cannot be applied and so require additional investigation.
An online universal classifier for binary, multi-class and multi-label classification

Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are sub-categories of single-label classification. Several classifiers are developed for binary, multi-class and multi-label classification problems, but there are no classifiers available in the literature capable of performing all three types of classification. In this paper, a novel online universal classifier capable of performing all the three types of classification is proposed. Being a high speed online classifier, the proposed technique can be applied to streaming data applications. The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems. The results obtained are compared with state-of-the-art techniques from each of the classification types.
Upregulated expression of hematopoietic cell kinase inhibits tumor proliferation in hepatocellular carcinoma

This research focused on the role of HCK in hepatocellular carcinoma (HCC), which is one of the diseases with a high incidence and mortality. Hematopoietic cell kinase (HCK), as a Src family member of tyrosine kinases, has been reported in many tumors, such as human gastric cancer, breast cancer, colorectal cancer and non-small cell lung cancer. In our present study, we found elevated HCK in HCC specimens by western blot and immunohistochemistrical analyses. However, its role in HCC remains unclear. Furthermore, we obesrved that the expression level of HCK was positively correlated with the tumor grade, tumor size, number of tumor nodes, vein invasion and Ki-67 expression. Kaplan-Meier curve indicated that the high expression of HCK was significantly correlated with poor prognosis. In vitro, the depletion of HCK in HCC cell lines resulted in cells cycle arrest at G1/S phase. Meanwhile, the interference of HCK led to the reduction of proliferation using CCK-8 and plate colony assays. In addition, the reduced expression of HCK attenuated resistance to oxaliplatin in the HCC cells. All these findings prompted that HCK could exhibit an important role in HCC proliferation. In summary, HCK may serve as a novel prognostic marker and be a potential therapeutic target in HCC.
Rate-Dependent Modeling of a Highly Filled Vulcanizate

Many filled elastomers exhibit characteristic types of non-linear and weakly rate-dependent behavior not well represented by viscoelasticity. Previous attempts to overcome these problems are discussed. Recently, there has been particular interest in rate-independent frictional, such as triboelastic, models. In the current paper rate-dependent variants of triboelastic models are described. Essential aspects of the behavior of these models are discussed. Predictions for the models are compared with experimental data for sinusoidal deformation histories and stress relaxation tests.
Two Genetic Variants Associated with Plantar Fascial Disorders

Abstract Plantar fascial disorder is comprised of plantar fasciitis and plantar fibromatosis. Plantar fasciitis is the most common cause of heel pain, especially for athletes involved in running and jumping sports. Plantar fibromatosis is a rare fibrous hyperproliferation of the deep connective tissue of the foot. To identify genetic loci associated with plantar fascial disorders, a genome-wide association screen was performed using publically available data from the Research Program in Genes, Environment and Health including 21,624 cases of plantar fascial disorders and 80,879 controls. One indel (chr5:118704153:D) and one SNP (rs62051384) showed an association with plantar fascial disorders at genome-wide significance (p<5×10−8) with small effects (odds ratios=0.93 and 1.07 per allele, respectively). The indel chr5:118704153:D is located within TNFAIP8 (encodes a protein induced by TNF alpha) and rs62051384 is located within WWP2 (which is involved in proteasomal degradation). These DNA variants may be informative in explaining why some individuals are at higher risk for plantar fascial disorders than others.
University of Birmingham Assessment of corrosion resistance of cast cobalt-and nickel-chromium dental alloys in acidic environments

Objective: The aim of this study was the comparison of degradation resistance of nickel-chromium (Ni-Cr) and cobalt-chromium (Co-Cr) alloys used as a base material for partial dentures in contact with saliva. Methods: Wiron®99 and Wironit extra-hard® were selected as representative casting alloys for Ni-Cr and Co-Cr alloys respectively. The alloys were tested in contact with de-ion-ised water, artificial saliva and acidified artificial saliva. Material characterisation was performed by X-ray diffractometry (XRD), micro-hardness and nano-hardness testing. The corrosion properties of the materials were then analysed using open circuit potential analysis and potentiodynamic analysis. Alloy leaching in solution was assessed by inductively coupled plasma mass spectrometry techniques. Results: Co-Cr alloy was more stable than the Ni-Cr alloy in all solutions tested. Leaching of nickel and corrosion attack was higher in Ni-Cr alloy in artificial saliva compared to the acidified saliva. The corrosion resistance of the Co-Cr alloy was seen to be superior to that of the Ni-Cr alloy with the former alloy exhibiting a lower corrosion current in all test solutions. Microstructural topographical changes were observed for Ni-Cr alloy in contact with artificial saliva. The Ni-Cr alloy exhibited microstructural changes and lower corrosion resistance in artificial saliva. The acidic changes did not enhance the alloy degradation.
Auto-kNN: Brain Tissue Segmentation using Automatically Trained k-Nearest-Neighbor Classification

In this paper we applied one of our regularly used processing pipelines for fully automated brain tissue segmentation. Brain tissue was segmented in cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM). Our algorithms for skull stripping, tissue segmentation and white matter lesion (WML) detection were slightly adapted and applied to twelve data sets within the MRBrainS13 brain tissue segmentation challenge. Skull stripping is performed using non-rigid registration of 5 atlas masks. Our tissue segmentation is based on an automatically trained kNN-classifier. Training samples were obtained by non-rigid registration of 5 manually labeled scans followed by a pruning step in feature space to remove any residual erroneously sampled tissue voxels. The kNN-classification incorporates voxel intensities from a T1-weighted scan and a FLAIR scan. The white matter lesion detection is based on an automatically determined threshold on the FLAIR scan. The application of the algorithms on the data from the MRBrainS13 Challenge showed that our pipeline produces acceptable segmentations. Average resulting Dice scores were 77.86 (CSF), 81.22 (GM), 87.27 (WM), 93.78 (total parenchyma), and 96.26 (all intracranial structures). Total processing time was about 2 hours per subject.
Hydrocarbon-Degrading Bacteria Exhibit a Species-Specific Response to Dispersed Oil while Moderating Ecotoxicity

ABSTRACT The Deepwater Horizon blowout in April 2010 represented the largest accidental marine oil spill and the largest release of chemical dispersants into the environment to date. While dispersant application may provide numerous benefits to oil spill response efforts, the impacts of dispersants and potential synergistic effects with crude oil on individual hydrocarbon-degrading bacteria are poorly understood. In this study, two environmentally relevant species of hydrocarbon-degrading bacteria were utilized to quantify the response to Macondo crude oil and Corexit 9500A-dispersed oil in terms of bacterial growth and oil degradation potential. In addition, specific hydrocarbon compounds were quantified in the dissolved phase of the medium and linked to ecotoxicity using a U.S. Environmental Protection Agency (EPA)-approved rotifer assay. Bacterial treatment significantly and drastically reduced the toxicity associated with dispersed oil (increasing the 50% lethal concentration [LC50] by 215%). The growth and crude oil degradation potential of Acinetobacter were inhibited by Corexit by 34% and 40%, respectively; conversely, Corexit significantly enhanced the growth of Alcanivorax by 10% relative to that in undispersed oil. Furthermore, both bacterial strains were shown to grow with Corexit as the sole carbon and energy source. Hydrocarbon-degrading bacterial species demonstrate a unique response to dispersed oil compared to their response to crude oil, with potentially opposing effects on toxicity. While some species have the potential to enhance the toxicity of crude oil by producing biosurfactants, the same bacteria may reduce the toxicity associated with dispersed oil through degradation or sequestration.
Enterprise Architecture: What Aspects is Current Research Targeting

Enterprise architecture is an approach to aligning business and IT within a company. In this paper we present the state of the art in enterprise architecture (EA) research, our survey is based on an analysis of the publicly available publications. Our research methodology defines the analysis criteria. These criteria are: the distribution of the papers over time, their topics, authors, reference disciplines and their dispersion over the lifecycle activities, which will be defined. The evaluation included 80 papers (all referencing explicitly the term "enterprise architecture"). The results of our survey are: EA is a young discipline, but the interest in it is growing. Although a wide range of topics is covered, the discipline is lacking basic research. The main contributers to EA are consulting companies and academics. But academics do not contribute very much to the basic research in EA. Furthermore, very few other disciplines are used to enhance enterprise architecture. In addition enterprise architecture is a new discipline and it will not mature unless substantial basic research will be made.