328,141 research outputs found
Fighting with the Sparsity of Synonymy Dictionaries
Graph-based synset induction methods, such as MaxMax and Watset, induce
synsets by performing a global clustering of a synonymy graph. However, such
methods are sensitive to the structure of the input synonymy graph: sparseness
of the input dictionary can substantially reduce the quality of the extracted
synsets. In this paper, we propose two different approaches designed to
alleviate the incompleteness of the input dictionaries. The first one performs
a pre-processing of the graph by adding missing edges, while the second one
performs a post-processing by merging similar synset clusters. We evaluate
these approaches on two datasets for the Russian language and discuss their
impact on the performance of synset induction methods. Finally, we perform an
extensive error analysis of each approach and discuss prominent alternative
methods for coping with the problem of the sparsity of the synonymy
dictionaries.Comment: In Proceedings of the 6th Conference on Analysis of Images, Social
Networks, and Texts (AIST'2017): Springer Lecture Notes in Computer Science
(LNCS
Regulated Inositol‐Requiring Protein 1‐Dependent Decay as a Mechanism of Corin RNA and Protein Deficiency in Advanced Human Systolic Heart Failure
BACKGROUND: The compensatory actions of the endogenous natriuretic peptide system require adequate processing of natriuretic peptide pro‐hormones into biologically active, carboxyl‐terminal fragments. Natriuretic peptide pro‐peptide processing is accomplished by corin, a transmembrane serine protease expressed by cardiomyocytes. Brain natriuretic peptide (BNP) processing is inadequate in advanced heart failure and is independently associated with adverse outcomes; however, the molecular mechanisms causing impaired BNP processing are not understood. We hypothesized that the development of endoplasmic reticulum stress in cardiomyocytes in advanced heart failure triggers inositol‐requiring protein 1 (IRE1)‐dependent corin mRNA decay, which would favor a molecular substrate favoring impaired natriuretic peptide pro‐peptide processing. METHODS AND RESULTS: Two independent samples of hearts obtained from patients with advanced heart failure at transplant demonstrated that corin RNA was reduced as Atrial natriuretic peptide (ANP)/BNP RNA increased. Increases in spliced X‐box protein 1, a marker for IRE1‐endoribonuclease activity, were associated with decreased corin RNA. Moreover, ≈50% of the hearts demonstrated significant reductions in corin RNA and protein as compared to the nonfailing control sample. In vitro experiments demonstrated that induction of endoplasmic reticulum stress in cultured cardiomyocytes with thapsigargin activated IRE1s endoribonuclease activity and time‐dependent reductions in corin mRNA. In HL‐1 cells, overexpression of IRE1 activated IRE1 endoribonuclease activity and caused corin mRNA decay, whereas IRE1‐RNA interference with shRNA attenuated corin mRNA decay after induction of endoplasmic reticulum stress with thapsigargin. Pre‐treatment of cells with Actinomycin D to inhibit transcription did not alter the magnitude or time course of thapsigargin‐induced corin mRNA decline, supporting the hypothesis that this was the result of IRE1‐mediated corin mRNA degradation. CONCLUSIONS: These data support the hypothesis that endoplasmic reticulum stress‐mediated, IRE1‐dependent targeted corin mRNA decay is a mechanism leading to corin mRNA resulting in corresponding corin protein deficiency may contribute to the pathophysiology of impaired natriuretic peptide pro‐hormone processing in humans processing in humans with advanced systolic heart failure
DATA-DRIVEN TECHNIQUES FOR DIAGNOSING BEARING DEFECTS IN INDUCTION MOTORS
Induction motors are frequently used in many automated systems as a major driving force, and thus, their reliable performances are of predominant concerns. Induction motors are subject to different types of faults and an early detection of faults can reduce maintenance costs and prevent unscheduled downtime. Motor faults are generally related to three components: the stator, the rotor and/or the bearings. This study focuses on the fault diagnosis of the bearings, which is the major reason for failures in induction motors. Data-driven fault diagnosis systems usually include a classification model which is supported by an efficient pre-processing unit. Various classifiers, which aim to diagnose multiple bearing defects (i.e., ball, inner race and outer race defects of different diameters), require well-processed data. The pre-processing tasks plays a vital role for extracting informative features from the vibration signal, reducing the dimensionality of the features and selecting the best features from the feature pool. Once the vibration signal is perfectly analyzed and a proper feature subset is created, then fault classifiers can be trained. However, classification task can be difficult if the training dataset is not balanced. Induction motors usually operate under healthy condition (than faulty situation), thus the monitored vibration samples relate to the normal state of the system expected to be more than the samples of the faulty state. Here, in this work, this challenge is also considered so that the classification model needs to deal with class imbalance problem
The Impact of Rumination Induction on IQ Performance
Performance deficits on cognitive tasks have been demonstrated consistently in depressed and anxious individuals. Processing efficiency theory asserts that these deficits might be accounted for by task-irrelevant processes, including the negative impact of rumination. This study was designed to better understand the relationship between cognitive deficits and depression by creating a ruminative state in healthy control subjects to determine if they would exhibit performance deficits similar to those observed in patients with depression. Specifically, the effect of rumination induction on select subtests of the Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV) was examined. Participants were college students with no current depression, anxiety, ADHD, or substance abuse disorders, who also are not currently taking psychoactive medications or receiving psychotherapy. Participants were randomized to a rumination or distraction condition and administered subtests from the WAIS-IV hypothesized to be most affected by rumination and depression. Controlling for test, math, and trait anxiety, as well as pre-experimental rumination, individuals in the rumination condition performed more poorly on one subtest within the processing speed domain, as well as on the Processing Speed Index score. These results support the processing efficiency model of cognitive deficits in depression, suggesting that rumination induction interferes in the efficient completion of mental tasks. Future research can build on these findings by studying this model in a clinical population and by continuing to improve the effectiveness of mood induction tasks for research on the effects of this widespread and significantly impairing disorder
The NF45/NF90 Heterodimer Contributes to the Biogenesis of 60S Ribosomal Subunits and Influences Nucleolar Morphology
The interleukin enhancer binding factors ILF2 (NF45) and ILF3 (NF90/NF110) have been implicated in various cellular pathways, such as transcription, microRNA (miRNA) processing, DNA repair, and translation, in mammalian cells. Using tandem affinity purification, we identified human NF45 and NF90 as components of precursors to 60S (pre-60S) ribosomal subunits. NF45 and NF90 are enriched in nucleoli and cosediment with pre-60S ribosomal particles in density gradient analysis. We show that association of the NF45/NF90 heterodimer with pre-60S ribosomal particles requires the double-stranded RNA binding domains of NF90, while depletion of NF45 and NF90 by RNA interference leads to a defect in 60S biogenesis. Nucleoli of cells depleted of NF45 and NF90 have altered morphology and display a characteristic spherical shape. These effects are not due to impaired rRNA transcription or processing of the precursors to 28S rRNA. Consistent with a role of the NF45/NF90 heterodimer in nucleolar steps of 60S subunit biogenesis, downregulation of NF45 and NF90 leads to a p53 response, accompanied by induction of the cyclin-dependent kinase inhibitor p21/CIP1, which can be counteracted by depletion of RPL11. Together, these data indicate that NF45 and NF90 are novel higher-eukaryote-specific factors required for the maturation of 60S ribosomal subunits
Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data
With the support of Internet of Things (IoT) devices, it is possible to
acquire data from degradation phenomena and design data-driven models to
perform anomaly detection in industrial equipment. This approach not only
identifies potential anomalies but can also serve as a first step toward
building predictive maintenance policies. In this work, we demonstrate a novel
anomaly detection system on induction motors used in pumps, compressors, fans,
and other industrial machines. This work evaluates a combination of
pre-processing techniques and machine learning (ML) models with a low
computational cost. We use a combination of pre-processing techniques such as
Fast Fourier Transform (FFT), Wavelet Transform (WT), and binning, which are
well-known approaches for extracting features from raw data. We also aim to
guarantee an optimal balance between multiple conflicting parameters, such as
anomaly detection rate, false positive rate, and inference speed of the
solution. To this end, multiobjective optimization and analysis are performed
on the evaluated models. Pareto-optimal solutions are presented to select which
models have the best results regarding classification metrics and computational
effort. Differently from most works in this field that use publicly available
datasets to validate their models, we propose an end-to-end solution combining
low-cost and readily available IoT sensors. The approach is validated by
acquiring a custom dataset from induction motors. Also, we fuse vibration,
temperature, and noise data from these sensors as the input to the proposed ML
model. Therefore, we aim to propose a methodology general enough to be applied
in different industrial contexts in the future
HIV-1 Infection Causes a Down-Regulation of Genes Involved in Ribosome Biogenesis
HIV-1 preferentially infects CD4+ T cells, causing fundamental changes that eventually lead to the release of new viral particles and cell death. To investigate in detail alterations in the transcriptome of the CD4+ T cells upon viral infection, we sequenced polyadenylated RNA isolated from Jurkat cells infected or not with HIV-1. We found a marked global alteration of gene expression following infection, with an overall trend toward induction of genes, indicating widespread modification of the host biology. Annotation and pathway analysis of the most deregulated genes showed that viral infection produces a down-regulation of genes associated with the nucleolus, in particular those implicated in regulating the different steps of ribosome biogenesis, such as ribosomal RNA (rRNA) transcription, pre-rRNA processing, and ribosome maturation. The impact of HIV-1 infection on genes involved in ribosome biogenesis was further validated in primary CD4+ T cells. Moreover, we provided evidence by Northern Blot experiments, that host pre-rRNA processing in Jurkat cells might be perturbed during HIV-1 infection, thus strengthening the hypothesis of a crosstalk between nucleolar functions and viral pathogenesis
Pre-processing for noise detection in gene expression classification data
Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.São Paulo State Research Foundation (FAPESP)CNP
Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE
The single-phase liquid argon time projection chamber (LArTPC) provides a
large amount of detailed information in the form of fine-grained drifted
ionization charge from particle traces. To fully utilize this information, the
deposited charge must be accurately extracted from the raw digitized waveforms
via a robust signal processing chain. Enabled by the ultra-low noise levels
associated with cryogenic electronics in the MicroBooNE detector, the precise
extraction of ionization charge from the induction wire planes in a
single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event
display images, and quantitatively demonstrated via waveform-level and
track-level metrics. Improved performance of induction plane calorimetry is
demonstrated through the agreement of extracted ionization charge measurements
across different wire planes for various event topologies. In addition to the
comprehensive waveform-level comparison of data and simulation, a calibration
of the cryogenic electronics response is presented and solutions to various
MicroBooNE-specific TPC issues are discussed. This work presents an important
improvement in LArTPC signal processing, the foundation of reconstruction and
therefore physics analyses in MicroBooNE.Comment: 54 pages, 36 figures; the first part of this work can be found at
arXiv:1802.0870
Artificial neural network prediction of weld distortion rectification using a travelling induction coil
An experimental investigation has been carried out to determine the applicability of an induction heating process with a travelling induction coil for the rectification of angular welding distortion. The results obtained from experimentation have been used to create artificial neural network models with the ability to predict the welding induced distortion and the distortion rectification achieved using a travelling induction coil. The experimental results have shown the ability to reduce the angular distortion for 8 mm and 10 mm thick DH36 steel plate and effectively eliminate the distortion on 6 mm thick plate. Results for 6 mm plate also show the existence of a critical induction coil travel speed at which maximum corrective bending occurs. Artificial neural networks have demonstrated the ability to predict the final distortion of the plate after both welding and induction heating. The models have also been used as a tool to determine the optimum speed to minimise the resulting distortion of steel plate after being subjected to both welding and induction heating processes
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