gebhart/patrepeval huggingface dataset:
Partial purification and characterization of two distinct protein kinases that differentially phosphorylate the carboxyl-terminal domain of RNA polymerase subunit IIa. (1993)

RNA polymerase II is a multisubunit enzyme composed of two large subunits of molecular weight in excess of 100,000 and a collection of 8-10 smaller subunits. The largest subunit, designated IIa, contains at its carboxyl terminus a highly repetitive domain consisting of tandem repeats of the consensus sequence Tyr-Ser-Pro-Thr-Ser-Pro-Ser. Extensive phosphorylation within this COOH-terminal domain (CTD) gives rise to subunit IIo which has a markedly reduced mobility in SDS-polyacrylamide gel electrophoresis (PAGE) relative to subunit IIa. Recent evidence suggests that RNA polymerase IIA, containing an unphosphorylated CTD, is involved in preinitiation complex assembly, whereas RNA polymerase IIO is involved in elongation. Consequently, CTD phosphorylation is thought to occur after RNA polymerase II has bound to the promoter by a protein kinase that stably associates with the preinitiation complex. We present here the partial purification and characterization of two distinct CTD kinases from a HeLa cell transcription extract. These CTD kinases, designated CTDK1 and CTDK2, are fractionated by chromatography on Mono Q. CTDK1 catalyzes the incorporation of approximately 33 pmol of phosphate/pmol of calf thymus RNA polymerase subunit IIa, almost exclusively on serine. CTDK2 catalyzes the incorporation of approximately 50 pmol of phosphate/pmol of calf thymus subunit IIa, predominantly on serine; appreciable phosphate transfer onto threonine is also observed. Phosphorylation by CTDK2, but not CTDK1, results in a complete mobility shift in SDS-PAGE of subunit IIa to the position of IIo. CTDK1 can utilize ATP, dATP, or GTP as phosphate donor, whereas CTDK2 can utilize only ATP or dATP. The apparent Km for ATP is 30 microM for CTDK1 and 60 microM for CTDK2. CTDK1 and CTDK2 also differ in their protein substrate specificity. CTDK1 phosphorylates casein whereas CTDK2 does not. Neither kinase phosphorylates phosvitin or histone H1 to an appreciable extent. CTDK1 and CTDK2 do not appear to be related to cdc2 kinases as determined by their inability to phosphorylate H1 and their failure to react with antibodies directed against the cdc2 kinase. These results establish that a partially fractionated HeLa transcription extract contains two distinct CTD kinases that differ in their nucleotide requirements and in their patterns of CTD phosphorylation.
Hyperlipidemia and Insulin Resistance Are Induced by Protease Inhibitors Independent of Changes in Body Composition in Patients With HIV Infection (2000)

Although protease inhibitor (PI) therapy has improved the clinical status of patients with HIV infection, concerns have arisen that such treatment may have deleterious effects on glucose control, lipid metabolism, and body fat distribution. To determine whether initiation of PI therapy uniquely affects glucose and lipid metabolism, we analyzed paired data in HIV-infected patients before and after beginning antiretroviral therapy that included a PI (PI; N = 20) or lamivudine (3TC) but no PI (3TC; N = 9); and a control group on stable regimens that included neither of these agents (CONT: N = 12). Measurements included fasting glucose; insulin; triglycerides; total, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol; HIV RNA; CD4+ lymphocytes; weight; and total and regional body composition. Neither weight nor total or regional fat content changed significantly in any group during the period of observation. Nonetheless, in patients beginning PI therapy, there were significant increases in glucose (+9+/-3 mg/dl; p = .0136), insulin (+12.2+/-4.9 U/ml; p = .023), triglycerides (+53+/-17 mg/dl; p = .0069), and total and LDL cholesterol (+32+/-11 and +18+/-5 mg/dl; p = .0082 and .0026, respectively). None of these parameters changed significantly in the 3TC or CONT groups. The PI and 3TC groups experienced comparable increases in CD4+ lymphocytes, suggesting that the observed metabolic effects may be associated with PIs uniquely, rather than improvement in clinical status. However, it is also possible that the metabolic effects of PIs are associated with more effective viral suppression, because a greater proportion of patients in the PI group achieved undetectable levels of virus. We conclude that changes in glucose and lipid metabolism are induced by PI therapy in the absence of significant changes in weight or fat distribution. Longer periods of follow-up will be required to determine the clinical significance of these changes. However, at the moment, the risks associated with these metabolic effects do not appear to outweigh improvements in survival seen with PI therapy.
Effect of Intravitreal Administration of Somatostatin and sst<sub>2</sub>Analogs on AMPA-Induced Neurotoxicity in Rat Retina (2008)

These results demonstrate for the first time that SRIF and the sst(2) analogs, administered intravitreally, protect the retina from excitotoxicity. Further studies are essential to ascertain the therapeutic relevance of these results.
Noninvasive fluorescence imaging of small animals (2013)

Noninvasive in vivo fluorescence imaging of small animals as a method in preclinical research has developed considerably in recent years, and is used widely across a variety of disciplines such as oncology and infectious disease research. It provides a means of detecting a fluorescent signal within a living animal reflecting specific, mostly disease-related, processes, such as parts of the host immune response, inflammation, cancer growth or presence of pathogens. As well as offering many advantages as a standalone technique, it can also be highly complementary to other imaging modalities. This review discusses aspects of light distribution in animal tissue and the implications on in vivo imaging; the most widely used imaging techniques including planar and tomographic imaging; advantages and challenges of the techniques; fluorescent contrast agents and some examples of applications. Rather than in detail reviewing studies using in vivo fluorescence imaging, we focus on the principles and practicalities of the method itself, so that the reader can apply these to their own research question.
3GPP: Standardization of Femtocells - TI Journals (2012)

Abstract: Cellular system operators have identified the critical need for standardization of femtocell devices and their associated interfaces into the operators’ core networks as a critical requirement for the success of femtocell products. Driven by this demand, the UMTS/UTRAN standards community has been undertaking a large-scale and comprehensive effort to specify such standards for femtocell devices and systems that are based on the UMTS/HSDPA/HSUPA family of radio access technologies. This article describes those standardization activities and provides an overview for the femtocell system architecture that has been developed within 3GPP.
A Survey on Railway Assets: A Potential Domain for Big Data (2019)

The railway is an infrastructure insistent industry in which each railroad is operated and maintained on the basis of a significant quantity of data and information gathered from various sources. This paper examines the big data technologies to maintain and enhance the rail industry. Currently, a huge quantity of rail data is being collected at numerous sources in different countries. However, the available data is not effectively utilized with the traditionally existing techniques and technologies. Thus the significant information remains buried under a large number of gigabytes. The paper discusses, 5V's concept of big data (velocity, volume, veracity, variety, and value) in the railroads monitoring system. Also addresses the different available monitoring systems for rail track defects, application of visual data analysis in railroads for passenger flow with a case study of Shanghai metro network, optimization in big data with a critical dissection of Metaheuristics method and how big technologies leads to big challenges in the real-time railway system.
Joint time-scale and TDOA estimation: analysis and fast approximation (2005)

Relative motion (rm) between a signal source and a receiver causes a time scaling of the signal arriving at the receiver. When estimating the time-difference-of-arrival (TDOA) of a signal at two receivers, time scaling, when not properly accounted for, can introduce a bias that could dominate the estimation errors. Segmentization processing cannot reduce this bias. Following the derivation of the formulae for bias and mean-square errors of TDOA estimation under rm, this paper moves on to the joint estimation of TDOA and time scale. It proposes an iterative search for the maximization of the cross-ambiguity function (CAF), which is also the maximum likelihood function for additive Gaussian bandlimited white noise disturbance. In addition, a quadratic Lagrange interpolator is also proposed to obtain the initial parameter values for the iterative search, which can increase the chance of converging to the global minimum solution. It is necessary to time scale a digital sequence by a noninteger in the maximization process. For an N-point sequence, this operation, which first interpolates the samples by sinc functions and then resamples, is in the order O(N/sup 2/). Noting that the magnitude of the sinc function decreases rapidly from its peak, this paper uses a fast approximation (FA) method that applies only five sinc coefficients for the interpolation, reducing the computation to O(N). Simulation results have corroborated that the maximization of the CAF does provide estimates that reach the Cramer-Rao lower bound and that the degradation in accuracy is negligible when FA is applied.
A Neuroprotective Brain-penetrating Endopeptidase Fusion Protein Ameliorates Alzheimer Disease Pathology and Restores Neurogenesis (2014)

Alzheimer disease (AD) is characterized by widespread neurodegeneration throughout the association cortex and limbic system, deposition of amyloid-β peptide (Aβ) in the neuropil and around the blood vessels, and formation of neurofibrillary tangles. The endopeptidase neprilysin has been successfully used to reduce the accumulation of Aβ following intracranial viral vector delivery or ex vivo manipulated intracranial delivery. These therapies have relied on direct injections into the brain, whereas a clinically desirable therapy would involve i.v. infusion of a recombinant enzyme. We previously characterized a recombinant neprilysin that contained a 38-amino acid brain-targeting domain. Recombinant cell lines have been generated expressing this brain-targeted enzyme (ASN12). In this report, we characterize the ASN12 recombinant protein for pharmacology in a mouse as well as efficacy in two APPtg mouse models of AD. The recombinant ASN12 transited to the brain with a t½ of 24 h and accumulated to 1.7% of injected dose at 24 h following i.v. delivery. We examined pharmacodynamics in the tg2576 APPtg mouse with the prion promoter APP695 SWE mutation and in the Line41 mThy1 APP751 mutation mouse. Treatment of either APPtg mouse resulted in reduced Aβ, increased neuronal synapses, and improved learning and memory. In addition, the Line41 APPtg mice showed increased levels of C-terminal neuropeptide Y fragments and increased neurogenesis. These results suggest that the recombinant brain-targeted neprilysin, ASN12, may be an effective treatment for AD and warrant further investigation in clinical trials.
Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems (2019)

The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge focuses on audio tagging, sound event detection and spatial localisation. DCASE 2019 consists of five tasks: 1) acoustic scene classification, 2) audio tagging with noisy labels and minimal supervision, 3) sound event localisation and detection, 4) sound event detection in domestic environments, and 5) urban sound tagging. In this paper, we propose generic cross-task baseline systems based on convolutional neural networks (CNNs). The motivation is to investigate the performance of a variety of models across several audio recognition tasks without exploiting the specific characteristics of the tasks. We looked at CNNs with 5, 9, and 13 layers, and found that the optimal architecture is task-dependent. For the systems we considered, we found that the 9-layer CNN with average pooling after convolutional layers is a good model for a majority of the DCASE 2019 tasks.
Continuous speech recognition using multilayer perceptrons with hidden Markov models (2002)

A phoneme based, speaker-dependent continuous-speech recognition system embedding a multilayer perceptron (MLP) (i.e. a feedforward artificial neural network) into a hidden Markov model (HMM) approach is described. Contextual information from a sliding window on the input frames is used to improve frame or phoneme classification performance over the corresponding performance for simple maximum-likelihood probabilities, or even maximum a posteriori (MAP) probabilities which are estimated without the benefit of context. Performance for a simple discrete density HMM system appears to be somewhat better when MLP methods are used to estimate the probabilities.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
A study of the characteristics of white noise using the empirical mode decomposition method (2004)

Based on numerical experiments on white noise using the empirical mode decomposition (EMD) method, we find empirically that the EMD is effectively a dyadic filter, the intrinsic mode function (IMF) components are all normally distributed, and the Fourier spectra of the IMF components are all identical and cover the same area on a semi–logarithmic period scale. Expanding from these empirical findings, we further deduce that the product of the energy density of IMF and its corresponding averaged period is a constant, and that the energy–density function is chi–squared distributed. Furthermore, we derive the energy–density spread function of the IMF components. Through these results, we establish a method of assigning statistical significance of information content for IMF components from any noisy data. Southern Oscillation Index data are used to illustrate the methodology developed here.
The use of lipid emulsions for the iv administration of a new water soluble polyene antibiotic, SPK-843 (2002)

Venous tolerance of a new water soluble polyene antibiotic, SPK-843, in 5% glucose solution for infusion is low in laboratory animals. The use of Intralipid 10% emulsion was therefore proposed, in which the antibiotic remained chemically stable for at least 2 h in a mildly acid or nearly neutral environment and at concentrations of 0.1-0.5 mg/mL, producing no alterations in the emulsion structure. Tolerance was assessed through repeated infusions in the ear marginal vein of rabbits and was found much more satisfactory than the tolerance observed when the vehicle used was 5% glucose solution. The study of the effect of some variables (concentration, volume infused, dose per kg) on venous toxicity offered the possibility to plan optimal administration conditions of presumed therapeutic doses.
γδ T cells in Behçet’s disease (BD) and recurrent aphthous stomatitis (RAS) (1999)

The immunopathogenesis of BD is believed to be T cell-mediated. The objective of this study was to characterize the activation stage and cytokine profile of peripheral blood lymphocytes (PBL), with particular emphasis on gammadelta T cells. Venous blood was collected from 20 patients with BD, and for comparison, from 11 patients with RAS and from 15 healthy controls. Both the expression of activation markers (CD25, CD29, CD40 ligand, CD69 and HLA-DR) on freshly isolated PBL and T cell subsets, and the expression of intracellular cytokines (IL-4, IL-10, interferon-gamma (IFN-gamma) and tumour necrosis factor-alpha (TNF-alpha)) on mitogen-stimulated PBL and T cell subsets were analysed by double immunofluorescent staining and flow cytometry. Significantly decreased proportion of alphabeta T cells and increased proportion of gammadelta T cells, CD56+ cells and CD8+ gammadelta T cells were found in BD patients compared with healthy controls. This was also seen to a lesser extent in patients with RAS. Furthermore, in BD a significantly increased proportion of the gammadelta T cell population expressed CD69 and high levels of CD29 and were induced to produce IFN-gamma and TNF-alpha compared with healthy controls. In contrast, an increased percentage of gammadelta T cells from RAS patients was induced to produce IFN-gamma, but not TNF-alpha. These results indicate that in BD, activated gammadelta T cells, capable of producing IFN-gamma and TNF-alpha, are present in peripheral blood, suggesting that gammadelta T cells are dynamic and may be regulating immunopathogenic events.
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data (2020)

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable <i>Convolutional Neural Network</i> (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.
A gel-ceramic multi-layer electrolyte for long-life lithium sulfur batteries (2015)

A newly designed gel-ceramic multi-layer electrolyte has been used as the separator and electrolyte for lithium sulfur (Li-S) batteries. The Li-S cells, free of the shuttle effect, exhibit superior electrochemical performance. With almost no self-discharge, the cell demonstrates an initial discharge specific capacity of up to 725 mA h g(-1) and remains at 700 mA h g(-1) after 300 cycles at the C/2 rate.
Chemistry for Positron Emission Tomography: Recent Advances in <sup>11</sup>C‐, <sup>18</sup>F‐, <sup>13</sup>N‐, and <sup>15</sup>O‐Labeling Reactions (2018)

Positron emission tomography (PET) is a molecular imaging technology that provides quantitative information about function and metabolism in biological processes in vivo for disease diagnosis and therapy assessment. The broad application and rapid advances of PET has led to an increased demand for new radiochemical methods to synthesize highly specific molecules bearing positron-emitting radionuclides. This Review provides an overview of commonly used labeling reactions through examples of clinically relevant PET tracers and highlights the most recent developments and breakthroughs over the past decade, with a focus on <sup>11</sup> C, <sup>18</sup> F, <sup>13</sup> N, and <sup>15</sup> O.
Transformer Transducer: One Model Unifying Streaming and Non-streaming Speech Recognition (2020)

In this paper we present a Transformer-Transducer model architecture and a training technique to unify streaming and non-streaming speech recognition models into one model. The model is composed of a stack of transformer layers for audio encoding with no lookahead or right context and an additional stack of transformer layers on top trained with variable right context. In inference time, the context length for the variable context layers can be changed to trade off the latency and the accuracy of the model. We also show that we can run this model in a Y-model architecture with the top layers running in parallel in low latency and high latency modes. This allows us to have streaming speech recognition results with limited latency and delayed speech recognition results with large improvements in accuracy (20% relative improvement for voice-search task). We show that with limited right context (1-2 seconds of audio) and small additional latency (50-100 milliseconds) at the end of decoding, we can achieve similar accuracy with models using unlimited audio right context. We also present optimizations for audio and label encoders to speed up the inference in streaming and non-streaming speech decoding.
Genome-Wide Analysis of the Dof Transcription Factor Gene Family Reveals Soybean-Specific Duplicable and Functional Characteristics (2013)

The Dof domain protein family is a classic plant-specific zinc-finger transcription factor family involved in a variety of biological processes. There is great diversity in the number of Dof genes in different plants. However, there are only very limited reports on the characterization of Dof transcription factors in soybean (Glycine max). In the present study, 78 putative Dof genes were identified from the whole-genome sequence of soybean. The predicted GmDof genes were non-randomly distributed within and across 19 out of 20 chromosomes and 97.4% (38 pairs) were preferentially retained duplicate paralogous genes located in duplicated regions of the genome. Soybean-specific segmental duplications contributed significantly to the expansion of the soybean Dof gene family. These Dof proteins were phylogenetically clustered into nine distinct subgroups among which the gene structure and motif compositions were considerably conserved. Comparative phylogenetic analysis of these Dof proteins revealed four major groups, similar to those reported for Arabidopsis and rice. Most of the GmDofs showed specific expression patterns based on RNA-seq data analyses. The expression patterns of some duplicate genes were partially redundant while others showed functional diversity, suggesting the occurrence of sub-functionalization during subsequent evolution. Comprehensive expression profile analysis also provided insights into the soybean-specific functional divergence among members of the Dof gene family. Cis-regulatory element analysis of these GmDof genes suggested diverse functions associated with different processes. Taken together, our results provide useful information for the functional characterization of soybean Dof genes by combining phylogenetic analysis with global gene-expression profiling.