Miracle-dz/UniFAR-FTUs huggingface dataset:
Retina Comprehensive Molecular Diagnosis of a Large Cohort of Japanese Retinitis Pigmentosa and Usher Syndrome Patients by Next-Generation Sequencing

Citation: Oishi M, Oishi A, Gotoh N, et al. Comprehensive molecular diagnosis of a large cohort of Japanese retinitis pigmentosa and Usher syndrome patients by next-generation sequencing. Invest Ophthalmol Vis Sci. 2014;55:7369–7375. DOI: 10.1167/iovs.14-15458 PURPOSE. Retinitis pigmentosa (RP), a major cause of blindness in developed countries, has multiple causative genes; its prevalence differs by ethnicity. Usher syndrome is the most common form of syndromic RP and is accompanied by hearing impairment. Although molecular diagnosis is challenging, recent technological advances such as targeted highthroughput resequencing are efficient screening tools.
Techniques, Tools and Applications of Graph Analytic

Graphs have acute significance because of poly-tropic nature and have wide spread real world big data appli-cations, e.g., search engines, social media, knowledge discovery, network systems, etc. Major challenge is to develop efficient systems to store, process and analyze large graphs generated by these applications. Graph analytic is important research area in big data graphs dealing with efficient extraction of useful knowledge and interesting patterns from rapidly growing big data streams. Tremendously huge and complex data of graph applications requires specially designed graph databases having special data structures and effective features for data modeling and querying. The manipulation of large size of data requires effective scalable and distributed computational techniques for efficient graph partitioning and communication. Researchers have proposed different analytical techniques, storage structures, and processing models. This study provides insight of different graph analytical techniques and compares existing graph storage and computational technologies. This work also assesses the perfor-mance, strengths and limitations of various graph databases and processing models.
A QuAntitAtive Risk MAnAgeMent AppRoAch

1066-033X/15©2015iEEE Digital Object Identifier 10.1109/MCS.2014. 2364709 Date of publication: 19 January 2015 C ritical infrastructures must continuously operate safely and reliably, despite a variety of potential system disturbances. Given their strict operating requirements, such systems are automated and controlled in real time by several digital controllers receiving measurements from sensors and transmitting control signals to actuators. Since these physical systems are often spatially distributed, there is a need for information technology (IT) infrastructures enabling the timely data flow between the system components. These networked control systems are ubiquitous in modern societies [1]. Examples include the electric power network, intelligent transport systems, and industrial processes. Networked control systems are vulnerable to cyberthreats through the use of open communication networks and heterogeneous IT components. Because networked control systems are often operated through supervisory control and data acquisition (SCADA) systems and the measurement and control data in these systems are commonly transmitted through unprotected communication channels, the networked control system is vulnerable to several threats [2]. As illustrative examples, we mention the cyberattacks on power transmission networks operated by SCADA systems [3] and the Stuxnet malware that allegedly infected an industrial control system and disrupted its operation [4], [5]. Unlike other IT systems where cybersecurity mainly involves the protection of data, cyberattacks on networked control systems may influence physical processes through feedback actuation. Therefore, networked control-system security needs to consider threats at both the cyber and physical layers. Control theory has developed frameworks to handle disturbances and faults [6], [7], and these tools can be used to detect and attenuate the consequences of cyberattacks on networked control systems. However, there are substantial conceptual and technical differences between a fault-tolerant and a secure control framework. Faults are commonly considered to be physical events that affect the system behavior. Simultaneous events are assumed to be noncolluding, in the sense that events do not act in a coordinated way. On the other hand, cyberattacks may be performed over a significant number of attack points in a coordinated fashion; see, for instance, [8]–[10]. Moreover, faults are constrained by the physical dynamics and do not have an intent or objective to fulfill, as opposed to cyberattacks that do have a malicious intent and are not directly constrained by the dynamics of the physical process. Ensuring security may involve addressing a large number of threats, thus requiring the use of risk management methods [11] to prioritize the threats to be mitigated. A QuAntitAtive Risk MAnAgeMent AppRoAch
Synthesizing Datalog Programs Using Numerical Relaxation

The problem of learning logical rules from examples arises in diverse fields, including program synthesis, logic programming, and machine learning. Existing approaches either involve solving computationally difficult combinatorial problems, or performing parameter estimation in complex statistical models. In this paper, we present Difflog, a technique to extend the logic programming language Datalog to the continuous setting. By attaching real-valued weights to individual rules of a Datalog program, we naturally associate numerical values with individual conclusions of the program. Analogous to the strategy of numerical relaxation in optimization problems, we can now first determine the rule weights which cause the best agreement between the training labels and the induced values of output tuples, and subsequently recover the classical discrete-valued target program from the continuous optimum. We evaluate Difflog on a suite of 34~benchmark problems from recent literature in knowledge discovery, formal verification, and database query-by-example, and demonstrate significant improvements in learning complex programs with recursive rules, invented predicates, and relations of arbitrary arity.
Oxidative stress and successful antioxidant treatment in models of RYR 1-related myopathy

1 Department of Paediatrics, University of Michigan Medical Centre, Ann Arbor, MI 48109-2200, USA 2 Department of Neurology, University of Michigan Medical Centre, Ann Arbor, MI 48109-2200, USA 3 Inserm, U787, Institut de Myologie, Paris, (F-75013), France 4 UPMC, UMR787, Paris, (F-75013), France 5 Department of Biomedical Engineering, University of Michigan Medical Centre, Ann Arbor, MI 48109-2200, USA 6 Grenoble Institut des Neurosciences-INSERM U836, Grenoble, France 7 Biochimie et Génétique Moléculaire, CHU Grenoble/INSERM U836, Grenoble, France 8 Department of Molecular and Integrated Physiology, University of Michigan Medical Centre, Ann Arbor, MI 48109-2200, USA 9 Department Molecular, Cellular, and Developmental Biology, University of Michigan Medical Centre, Ann Arbor, MI 48109-2200, USA 10 AP-HP, Centre de Référence Maladies Neuromusculaires Paris-Est, Groupe Hospitalier Pitié-Salpêtrière, Paris, F-75013, France 11 AP-HP, Service de Pédiatrie, Centre de Référence Maladies Neuromusculaires (GNMH) Hôpital Raymond Poincaré, 92380 Garches, France
Diagnosis and management of tyrosinemia type I

&NA; Hereditary tyrosinemia type I (HTI) (MIM 276700) is an autosomal recessive disorder caused by deficiency of fumarylacetoacetase (EC 3.7.1.2), which is the last enzyme in the tyrosine degradation pathway. The enzyme block causes accumulation of toxic metabolites in the liver and kidneys, which are the organs where tyrosine is mainly degraded. The disorder is characterized by severe liver disease, which either causes liver failure in infancy or may take a more protracted course, with death often occurring during childhood or adolescence because of hepatoma development. Treatment with a diet restricted in phenylalanine and tyrosine does not prevent progression of the liver disease and development of hepatocellular carcinoma. Liver transplantation was previously the only option for these patients. Important achievements from metabolic and molecular biology studies of the disease include a new treatment for patients with HTI using an enzyme inhibitor, detection of self‐induced correction of the genetic defect in regenerative liver nodules in HTI patients, identification and development of useful animal models for HTI, and studies of the molecular genetics of HTI. These advances will have great implications for our understanding of pathogenetic mechanisms, which is the basis for improved diagnostic methods and improved treatment of patients with HTI.
Map Reduce based KeyWord Extraction and Bagging Algorithm for Review Classification in e-Commerce Websites

Customer reviews play an important role in sentiment analysis and opinion mining. With the rapid increase in the growth of e-commerce, understanding the mindset of the customer is very critical. To understand the reach of a product we can perform sentiment analysis on the user-generated reviews that are available on the internet. Since an array of information is available on the internet we need an efficient method to extract the necessary information before actually devising a method for sentiment analysis. In this work, classification of Amazon user reviews has been done using the same Key-word extraction bagging algorithm and Relative classifier for classification. To classify huge sets of reviews, components of the Hadoop such as Map-Reduce, Hive, and Pig are used in a systematic manner. Term Frequency-Inverse Document Frequency is implemented as it is highly efficient in Key-Words extraction. This algorithm classifies huge sets of data under seconds.
Choosing them to withstand them ? Working conditions and their impact on health and well-being

Evaluating the role of working conditions in the determination of workers’ health status is obviously a crucial matter when it comes to public policies, as evidenced by the recent law in France introducing the consideration of past physical exposures into pension calculation schemes. Our goal is to determine if the possibility for workers to alter their own working conditions acts as a modulator in the relationship between work strains and physical health, mental health and well-being. To do so, we use data coming from the 2010 wave of the European Working Conditions Survey (EWCS) and resort to an instrumental variable approach to handle biases related to reverse causality and omitted variables. Our results indicate varying supplementary effects of facing detrimental working conditions and being restrained in terms of decision latitude or involvement in firms’ decisions, on top of the classical effects of these indicators considered separately. Our results, much like those of the literature, demonstrate mixed and rather inconsistent effects, yet with interesting differences according to gender and the health and well-being outcome retained.
Multi-view Manifold Learning for Media Interestingness Prediction

Media interestingness prediction plays an important role in many real-world applications and attracts much research attention recently. In this paper, we aim to investigate this problem from the perspective of supervised feature extraction. Specifically, we design a novel algorithm dubbed Multi-view Manifold Learning (M) to uncover the latent factors that are capable of distinguishing interesting media data from non-interesting ones. By modelling both geometry preserving criterion and discrimination maximization criterion in a unified framework, M2L learns a common subspace for data from multiple views. The analytical solution of M2L is obtained by solving a generalized eigen-decomposition problem. Experiments on the Predicting Media Interestingness Dataset validate the effectiveness of the proposed method.
Linguistic Corpora for Biomedical Information Extraction May 16 , 2003 1

Work over the last few years in literature data mining for biology has progressed from linguistically unsophisticated models to the adaptation of natural language processing techniques that use full parsers ([6, 9]) and coreference to extract relations that span multiple sentences ([7, 3]) (For an overview, see [4]). However, there has been a lack of annotated corpora that can fuel further work in this direction in the same way that the development of syntactically annotated corpora such as the Penn Treebank ([5]) led to the development of statistical language parsers ([1]). To address this situation, we are developing new linguistic resources in three categories: a large corpus of biomedical text annotated with syntactic structures (Treebank) and shallow semantic structures (”proposition bank” or Propbank); a large set of biomedical abstracts and full-text articles annotated with entities and relations of interest to researchers, such as enzyme inhibition, or mutation/cancer connections (Factbanks); and broad-coverage lexicons and tools for the analysis of biomedical texts. We are also developing and adapting software tools that allow human experts to annotate biomedical texts for entity tagging, as well as for treebanking and propbanking. We are focusing initially on two applications: drug development, in collaboration with researchers in the Knowledge Integration and Discovery Systems group at GlaxoSmithKline, and pediatric oncology, in collaboration with researchers in the eGenome group at Children’s Hospital of Pennsylvania. These applications, worthwhile in their own right, provide excellent test beds for broader research efforts in natural language processing and data integration.
Predictors of Adolescents’ Interest in Stem Majors and Careers

The United States currently faces a shortage of qualified workers in fields related to science, technology, engineering, and math (STEM). The first critical step in preventing the labor shortage in STEM is understanding the factors that guide adolescents toward STEM pursuits. Drawing on Eccles’ expectancy-value theory (EVT), the current study aims to identify factors that are relevant to adolescents’ interest in STEM majors and careers. Data were collected from 629 adolescents (Mage = 16.09). Participants attended a high school in northern California and predominantly identified as Asian American (82% of the sample). Preliminary analyses revealed that adolescent boys had higher STEM self-expectancies than did adolescent girls, whereas there was no gender difference in STEM values. Consistent with expectations, multiple regression demonstrated that STEM self-expectancies and values accounted for a significant amount of variance in participants’ interest in STEM majors and careers. STEM value was an especially strong predictor; adolescents tended to be most interested in STEM pursuits when they were also high in STEM value. Moderation analyses showed that the association between STEM value and interest in STEM majors and careers was stronger for girls than for boys. As a whole, this study’s findings suggest that valuing and enjoying STEM pursuits during high school could be an important antecedent of pursuing a STEM major and a STEM career later in life. PREDICTORS OF ADOLESCENTS’ INTEREST IN STEM MAJORS AND CAREERS 3
Cdc48p interacts with Ufd3p, a WD repeat protein required for ubiquitin‐mediated proteolysis in Saccharomyces cerevisiae.

A library of random 10 residue peptides fused to the N‐terminus of a reporter protein was screened in the yeast Saccharomyces cerevisiae for sequences that can target the reporter for degradation by the N‐end rule pathway, a ubiquitin (Ub)‐dependent proteolytic system that recognizes potential substrates through binding to their destabilizing N‐terminal residues. One of the N‐terminal sequences identified by this screen was used in a second screen for mutants incapable of degrading the corresponding reporter fusion. A mutant thus identified had an abnormally low content of free Ub. This mutant was found to be allelic to a previously isolated mutant in a Ub‐dependent proteolytic system distinct from the N‐end rule pathway. We isolated the gene involved, termed UFD3, which encodes an 80 kDa protein containing tandem repeats of a motif that is present in many eukaryotic proteins and called the WD repeat. Both co‐immunoprecipitation and two‐hybrid assays demonstrated that Ufd3p is an in vivo ligand of Cdc48p, an essential ATPase required for the cell cycle progression and the fusion of endoplasmic reticulum membranes. Further, we showed that, similarly to Ufd3p, Cdc48p is also required for the Ub‐dependent proteolysis of test substrates. The discovery of the Ufd3p–Cdc48p complex and the finding that this complex is a part of the Ub system open up a new direction for studies of the function of Ub in the cell cycle and membrane dynamics.
Mechanistic insights into landscape genetic structure of two tropical amphibians using field‐derived resistance surfaces

Conversion of forests to agriculture often fragments distributions of forest species and can disrupt gene flow. We examined effects of prevalent land uses on genetic connectivity of two amphibian species in northeastern Costa Rica. We incorporated data from field surveys and experiments to develop resistance surfaces that represent local mechanisms hypothesized to modify dispersal success of amphibians, such as habitat‐specific predation and desiccation risk. Because time lags can exist between forest conversion and genetic responses, we evaluated landscape effects using land‐cover data from different time periods. Populations of both species were structured at similar spatial scales but exhibited differing responses to landscape features. Litter frog population differentiation was significantly related to landscape resistances estimated from abundance and experiment data. Model support was highest for experiment‐derived surfaces that represented responses to microclimate variation. Litter frog genetic variation was best explained by contemporary landscape configuration, indicating rapid population response to land‐use change. Poison frog genetic structure was strongly associated with geographic isolation, which explained up to 45% of genetic variation, and long‐standing barriers, such as rivers and mountains. However, there was also partial support for abundance‐ and microclimate response‐derived resistances. Differences in species responses to landscape features may be explained by overriding effects of population size on patterns of differentiation for poison frogs, but not litter frogs. In addition, pastures are likely semi‐permeable to poison frog gene flow because the species is known to use pastures when remnant vegetation is present, but litter frogs do not. Ongoing reforestation efforts will probably increase connectivity in the region by increasing tree cover and reducing area of pastures.
Involutory Automorphisms of Groups of odd Order and Their Fixed Point Groups

Let G be a finite group of odd order with an automorphism θ of order 2. (We use without further reference the fact, established by W. Feit and J. G. Thompson, that all groups of odd order are soluble.) Let Gθ denote the subgroup of G formed by the elements fixed under θ. It is an elementary result that if Gθ = 1 then G is abelian. But if we merely postulate that G θ be cyclic, the structure of G may be considerably more complicated—indeed G may have arbitrarily large soluble length.
OF THE METHODS FOR QUALITATIVE UNDERSTANDING OF A SCENE IMAGE

Ambiguity in nature gets preserved in its captured images also. Preserving it while classifying an image, is a challenging task. A scene image also can belong to multiple categories at a time which makes a task of classification much more difficult and often leads to classification errors. Binary classification fails to capture this ambiguity while classifying the scene image into one of mutually exclusive classes. Fuzzy classifier handles this problem by considering fuzzy membership with nonmutually exclusive class categories. This paper provides a review of the existing methods for qualitative understanding of a natural scene image.
Four-gene expression ratio test for survival in patients undergoing surgery for mesothelioma.

BACKGROUND Malignant pleural mesothelioma has few effective treatments, one being cytoreductive surgery. We previously developed a gene ratio test to predict outcome of malignant pleural mesothelioma patients undergoing surgery. In this study, we investigated the predictive value and technical assay performance of this test in patients with malignant pleural mesothelioma. METHODS Clinical data were obtained prospectively from 120 consecutive patients with malignant pleural mesothelioma who were scheduled for debulking surgery at one institution. Specimens were obtained at surgery or by pleural biopsy examination. Expression data for four genes were collected from tumor specimens, and three ratios of gene expression (TM4SF1/PKM2, TM4SF1/ARHGDIA, and COBLL1/ARHGDIA) were determined by quantitative reverse transcriptase-polymerase chain reaction. Patients were assigned to good or poor outcome groups by the gene ratio test. Survival was estimated by the Kaplan-Meier method and the log-rank test in univariate analyses. A multivariable Cox proportional hazards model was used to control for prognostic factors. Technical robustness was determined by using up to 30 specimens per patient, two biopsy techniques, and two performance sites. All statistical tests were two-sided. RESULTS The test predicted overall survival (P < .001) and cancer-specific survival (P = .007) in univariate analysis and overall survival in multivariable analysis (hazard ratio for death = 2.09, 95% confidence interval [CI] = 1.27 to 3.45, P = .004). The test was reproducible within patients and repeatable between two determinations for specimens with widely varying tumor cell contents. Repeatability between two determinations was 88.5% (95% CI = 84.0% to 92.2%) or, when technically unacceptable test values were excluded, 91.9% (95% CI = 87.4% to 95.1%). Reproducibility between two determinations was 96.1% (95% CI = 86.5% to 99.5%). Combining the gene ratio test and other prognostic factors allowed prospective discrimination between patients at high risk (median survival = 6.9 months, 95% CI = 2.6 to 8.9 months; 3-year survival = 0%) and low risk (median survival = 31.9 months, 95% CI = 21.9 to 41.7 months; 3-year survival = 42%). CONCLUSION The gene ratio test for survival of patients with malignant pleural mesothelioma has robust predictive value and technical assay performance.