Miracle-dz/UniFAR-FTUs huggingface dataset:
Nitrite Infusion in Humans and Nonhuman Primates: Endocrine Effects, Pharmacokinetics, and Tolerance Formation

Background— The recent discovery that nitrite is an intrinsic vasodilator and signaling molecule at near-physiological concentrations has raised the possibility that nitrite contributes to hypoxic vasodilation and to the bioactivity of nitroglycerin and mediates the cardiovascular protective effects of nitrate in the Mediterranean diet. However, important questions of potency, kinetics, mechanism of action, and possible induction of tolerance remain unanswered. Methods and Results— In the present study, we performed biochemical, physiological, and pharmacological studies using nitrite infusion protocols in 20 normal human volunteers and in nonhuman primates to answer these questions, and we specifically tested 3 proposed mechanisms of bioactivation: reduction to nitric oxide by xanthine oxidoreductase, nonenzymatic disproportionation, and reduction by deoxyhemoglobin. We found that (1) nitrite is a relatively potent and fast vasodilator at near-physiological concentrations; (2) nitrite functions as an endocrine reservoir of nitric oxide, producing remote vasodilation during first-pass perfusion of the opposite limb; (3) nitrite is reduced to nitric oxide by intravascular reactions with hemoglobin and with intravascular reductants (ie, ascorbate); (4) inhibition of xanthine oxidoreductase with oxypurinol does not inhibit nitrite-dependent vasodilation but potentiates it; and (5) nitrite does not induce tolerance as observed with the organic nitrates. Conclusions— We propose that nitrite functions as a physiological regulator of vascular function and endocrine nitric oxide homeostasis and suggest that it is an active metabolite of the organic nitrates that can be used therapeutically to bypass enzymatic tolerance.
High prevalence of mutations in peripherin/RDS in autosomal dominant macular dystrophies in a Spanish population

Purpose Mutations in the peripherin/retinal degeneration slow (RDS) gene are a known cause of various types of central retinal dystrophies. The purpose of this study was to determine the prevalence of mutations in the peripherin/RDS gene in Spanish patients with different types of autosomal dominant macular dystrophy. Methods Ophthalmic and electrophysiological examination was performed in patients from 61 unrelated autosomal dominant macular dystrophy (adMD) Spanish families. Screening for mutations in the peripherin/RDS gene by denaturing gradient gel electrophoresis (DGGE) and direct genomic sequencing was performed in index patients and extended to the family when positive. Results We report four novel mutations in peripherin/RDS and a relatively high frequency (23%) of mutations in this gene in families with adMD. Thirteen different mutations were found in fifteen adMD families. Three novel missense, four nonsense and a cis-acting splicing mutation IVS2+2T>C, were found in a Spanish population while five more missense mutations were also reported in other populations. The Arg142Trp and Arg172Trp mutations, present in several populations, were both detected in two independent Spanish families. All the missense mutations produce an amino acid substitution in the second intradiscal loop of the peripherin, while the nonsense mutations presumably generate a truncated protein. Conclusions A high frequency (23%) of mutations in the peripherin/RDS gene was found in a cohort of 61 unrelated patients with various types of autosomal dominant central retinal dystrophies as compared with a low prevalence (1.3%) of mutations in this gene causing retinitis pigmentosa in a Spanish population. Different macular dystrophy phenotypes according to the mutations in peripherin/RDS are shown. However, a limited phenotype variation was observed for these mutations within the family.
Energy-efficient cloud resource management

We propose a resource management framework that reduces energy consumption in cloud data centers. The proposed framework predicts the number of virtual machine requests along with their amounts of CPU and memory resources, provides accurate estimations of the number of needed physical machines, and reduces energy consumption by putting to sleep unneeded physical machines. Our framework is based on real Google traces collected over a 29-day period from a Google cluster containing over 12,500 physical machines. Using this Google data, we show that our proposed framework makes substantial energy savings.
Protective Effect of High-Mobility Group Box 1 Blockade on Acute Liver Failure in Rats

High-mobility group box 1 (HMGB1) is a monocyte-derived inflammatory mediator that is released in some conditions including shock, tissue injury, and endotoxin-induced lethality. In this study, we determined the plasma and hepatic tissue levels of HMGB1 in a drug-induced rat acute liver failure (ALF) model and investigated the effect of HMGB1 blockade on ALF. Adult male Sprague-Dawley rats, weighing 250 to 300 g, were used for this study. d-galactosamine was injected into the penile vein to induce ALF. To determine HMGB1 levels, plasma and hepatic tissue samples were serially collected after the d-galactosamine injection. To test the effect of HMGB1 blockade, anti-HMGB1 polyclonal antibodies or control antibodies were injected into the penile vein right after injection of d-galactosamine. Levels of HMGB1 were increased in plasma and decreased in hepatic tissue after induction of ALF. Immunohistochemical examination for HMGB1 showed that liver from animals with ALF had little staining, whereas normal liver had strong staining in the nuclei. Injection of anti-HMGB1 antibodies resulted in significant suppression of plasma HMGB1 and hepatic enzymes, marked suppression of plasma inflammatory cytokines, marked improvement of histological findings, and significant improvement of survival. The decrease of hepatic HMGB1 was also significantly suppressed in the group injected with anti-HMGB1 antibodies. The present study suggests that in ALF, the liver may release HMGB1 into the plasma, and that neutralizing the released HMGB1 has a protective effect against injury.ABBREVIATIONS-ALF-acute liver failure; HMGB1-high-mobility group box 1; ELISA-enzyme-linked immunosorbent assay; anti-HMGB1 Ab-anti-HMGB1 polyclonal antibodies; control Ab-control IgY antibodies; AST-aspartate aminotransferase; ALT-alanine aminotransferase; LDH-lactic dehydrogenase; IL-interleukin; TNF-tumor necrosis factor
Branching ratios for secondary processes of water ions induced by proton beams in radiation therapy of cancer

New results are reported that can improve proton beam instrumentation in radiation medicine and cancer therapy. The destructive effects of proton beams are primarily based on direct collision ionization and radiolysis of water. Resulting fragments subsequently alter the cell cycle by damaging the DNA with different efficiency. We have therefore computed the water ion and hydronium potential energy surfaces and obtained branching ratios for proton-water processes that further involve low energy electrons, OH radicals, and metastable O/sup +/ ions. In addition, all related data available in the literature are shortly summarized and reviewed. State-selective excitation and electron capture cross sections reported in this paper allow for solving the rate equations of the ion production cascade.
Structure-Leveraged Methods in Breast Cancer Risk Prediction

Predicting breast cancer risk has long been a goal of medical research in the pursuit of precision medicine. The goal of this study is to develop novel penalized methods to improve breast cancer risk prediction by leveraging structure information in electronic health records. We conducted a retrospective case-control study, garnering 49 mammography descriptors and 77 high-frequency/low-penetrance single-nucleotide polymorphisms (SNPs) from an existing personalized medicine data repository. Structured mammography reports and breast imaging features have long been part of a standard electronic health record (EHR), and genetic markers likely will be in the near future. Lasso and its variants are widely used approaches to integrated learning and feature selection, and our methodological contribution is to incorporate the dependence structure among the features into these approaches. More specifically, we propose a new methodology by combining group penalty and [Formula: see text] (1 ≤ p ≤ 2) fusion penalty to improve breast cancer risk prediction, taking into account structure information in mammography descriptors and SNPs. We demonstrate that our method provides benefits that are both statistically significant and potentially significant to people's lives.
Kernel-Based Nonlinear Subspace Method for Pattern Recognition

A new pattern classification method called the Kernel-based Nonlinear Subspace (KNS) method is proposed. It implements a subspace method in a high-dimensional nonlinear space by a nonlinear transformation defined by kernel functions. The Support Vector Machine, a recent popular current research topic, is a nonlinear classification method employing kernel functions and has advanced classification performance. However, as the number of patterns and the number of classes increase, the problem faced is an explosive increase in the computational complexity needed for learning. Conventional subspace methods are effective classifiers of multiple classes and are fast classification techniques. But satisfactory classification performance is not achieved if the pattern distribution is nonlinear or if the dimensionality of the feature space is small compared to the number of classes. The proposed method combines the advantages of both techniques to compensate for each other's deficiencies to realize nonlinear classification of multiple classes with advanced classification performance and low computational complexity. In this paper, we demonstrate the ability to use nonlinear transforms defined by kernel functions to formulate the nonlinear subspace method, evaluate the proposed method from the perspectives of classification performance for nonlinear distributions and multiclass distributions, the stability of the classification performance with respect to parameter variations, and the computational costs needed for learning and classification, and verify the superiority of the proposed method over conventional methods. © 2001 Scripta Technica, Syst Comp Jpn, 33(1): 38–52, 2002
Automatically Generating Gene Summaries from Biomedical Literature

Biologists often need to find information about genes whose function is not described in the genome databases. Currently they must try to search disparate biomedical literature to locate relevant articles, and spend considerable efforts reading the retrieved articles in order to locate the most relevant knowledge about the gene. We describe our software, the first that automatically generates gene summaries from biomedical literature. We present a two-stage summarization method, which involves first retrieving relevant articles and then extracting the most informative sentences from the retrieved articles to generate a structured gene summary. The generated summary explicitly covers multiple aspects of a gene, such as the sequence information, mutant phenotypes, and molecular interaction with other genes. We propose several heuristic approaches to improve the accuracy in both stages. The proposed methods are evaluated using 10 randomly chosen genes from FlyBase and a subset of Medline abstracts about Drosophila. The results show that the precision of the top selected sentences in the 6 aspects is typically about 50-70%, and the generated summaries are quite informative, indicating that our approaches are effective in automatically summarizing literature information about genes. The generated summaries not only are directly useful to biologists but also serve as useful entry points to enable them to quickly digest the retrieved literature articles.
C-338A Polymorphism of the Endothelin-Converting Enzyme (ECE-1) Gene and the Susceptibility to Sporadic Late-Onset Alzheimer’s Disease and Coronary Artery Disease

The human endothelin-converting enzyme (ECE) is involved in β-amyloid synthesis and regulation of the endothelin-1 (ET-1) vasoconstricting peptide. We investigated the distribution of the C-338A polymorphism of the ECE-1b gene in sporadic late-onset Alzheimer’s disease (LOAD) and in coronary artery disease (CAD) to verify its role in the onset of these two complex diseases. Two cohorts of 458 Italian Caucasian LOAD patients and 165 CAD patients were examined for the C-338A polymorphism and compared with respective control samples (260 and 106 subjects, respectively). The A allele was less present in LOAD patients than in controls, but an at limits statistically significant difference was achieved only in subjects aged less than 80 years, where only the AA genotypes appeared to have a protective role against the onset of the sporadic LOAD. For the overall CAD sample the pattern was similar and significant differences were observed only in subjects non carrying the apolipoprotein E (APOE) e*4 allele, where the A allele carrying genotypes had a protective role against the onset of the disease.
Speckle tracking methods for ultrasonic elasticity imaging using short-time correlation

In ultrasound elasticity imaging, strain decorrelation is a major source of error in displacements estimated using correlation techniques. This error can be significantly decreased by reducing the correlation kernel. Additional gains in signal-to-noise ratio (SNR) are possible by filtering the correlation functions prior to displacement estimation. Tradeoffs between spatial resolution and estimate variance are discussed, and estimation in elasticity imaging is compared to traditional time-delay estimation. Simulations and experiments on gel-based phantoms are presented. The results demonstrate that high resolution, high SNR strain estimates can be computed using small correlation kernels (on the order of the autocorrelation width of the ultrasound signal) and correlation filtering.
A Semi-Supervised Approach Based on k-Nearest Neighbor

k-nearest neighbor (k-NN) needs sufficient labeled instances for training in order to get robust performance; otherwise, performance deterioration occurs if training sets are small. In this paper, a novel algorithm, namely ordinal semi-supervised k-NN, is proposed to handle cases with a few labeled training instances. This algorithm consists of two parts: instance ranking and semi-supervised learning (SSL). Using SSL, the performance of k-NN with small training sets can be improved because SSL enlarges the training set by including unlabeled instances with their predicted labels. Instance ranking is used to pick up the unlabeled instances that are included to the training set. It gives priority to the unlabeled instances that are closer to class boundaries because they are more likely to be correctly predicted (these instances are called high confidence prediction instances). Thus, SSL benefits from high confidence prediction instances if they are added into the training set early. Experimental results demonstrate that the proposed algorithm outperforms the semi-supervised k- NN and the conventional k-NN algorithms if the training set is small. Index Terms—Instance ranking; semi-supervised learning; k-nearest neighbor; weighted mean
Increased Risk of Cancer in relation to Gout: A Review of Three Prospective Cohort Studies with 50,358 Subjects

Gout is a common inflammatory disease characterized by acute arthritis and hyperuricemia. A number of epidemiological studies have suggested the critical role of gout in carcinogenesis. The aim of this study was to estimate the association between gout and cancer risk by meta-analysis of all relevant studies published to date. A comprehensive literature search in PubMed and Embase databases from their inception up to July 1, 2014, was performed to identify eligible studies. The strength for relationship between gout and the risk of different cancers was evaluated by calculating pooled relative risks (RRs) with 95% confidence intervals (95% CIs). All analyses were carried out by STATA 12.0 software. Gout patients were at an increased risk of cancer, particularly urological cancers, digestive system cancers, and lung cancer. No such significant association between gout and the risk of breast or brain cancers was observed. Sensitivity analysis did not materially alter the pooled results. Gout is a risk factor of cancer, particularly that of urological cancers, digestive system cancers, and lung cancer. The pooled data further support the hypothesis of a link between gout and carcinogenesis.
The method of quality parameters measurement and evaluating of audio-video data transmission

The paper describes the passive method of measuring the values of the data transmission quality parameters during audio-video calls performed in computer network. It also describes how the computer network can be evaluated in terms of providing an adequate level of service quality during audio-video data transmission. One can find in the paper the computer network testing procedure as well as results of measurements performed in a given test environment with the usage of described method. Presented method not only allows for computer network troubles diagnosis, but also for evaluation of newly build data quality transmission protocol’s mechanisms.
Modeling Emergence of Complexity: the Application of Complex System and Origin of Life Theory to Interactive Art on the Internet

The origin of this paper lies in the fundamental question of how complexity arose in the development of life and how one could construct an artistic interactive system that can model and simulate this emergence of complexity. Based on the idea that interaction and communication between entities of a system are the driving forces for the emergence of higher and more complex structures than its mere parts, we propose to apply principles of Complex System Theory to the creation of an interactive, computer generated and audience participatory artwork on the Internet and to test whether complexity within the system can emerge.
Unique nanopore pattern formation by focused ion beam guided anodization

The fabrication of ordered porous templates in large areas with sophisticated patterns beyond mono-sized pores in hexagonal packing remains a great technical challenge. Conventional anodization cannot overcome this limitation and a new approach needs to be sought. This study is focused on designing pore patterns in square and hexagonal arrangements via focused ion beam lithography with varying interpore distances in order to form organized pore arrays with more sophisticated patterns. The results demonstrate that the small pores from the anodization can be meshed with the larger pores from the focused ion beam lithography and unique, complicated nanopore patterns can be created. Large pore patterns that cross the grain boundaries are also made possible with the guidance of the pores from focused ion beam lithography. A fundamental pore pattern formation mechanism is proposed.
A GSM/EDGE/WCDMA Adaptive Series-LC Matching Network Using RF-MEMS Switches

To preserve link quality of mobile phones, under fluctuating user conditions, an adaptively controlled series-LC matching circuit is presented for multi-band and multi-mode operation. Following a bottom-up approach, we discuss the design of an RF-MEMS unit cell for the construction of a 5-bit switched capacitor array. To reduce dielectric charging of the RF-MEMS devices their average biasing voltage is minimized by applying a bipolar waveform with a small high/low duty-cycle obtained from a high-voltage driver IC. RF-MEMS capacitive switches are applied because of their high linearity, low loss, large tuning range, and easy control in the discrete domain. Application specific RF-MEMS pull-in and pull-out voltage requirements are derived. An impedance phase detector is used to feed mismatch information to an up-down counter providing robust iterative control. The measured MEMS array capacitance tuning ratio is almost a factor 10. Module insertion loss is 0.5 dB at low-band and high-band. Harmonic distortion is less than -85&nbsp;dBc at 35 dBm output power and the EVM, measured in EDGE-mode, is less than 1% at 27 dBm . The adaptively controlled module, connected to a planar inverted-F antenna, shows desired impedance correction. For extreme hand-effects the maximum module impedance correction at 900 MHz is -75jOmega.
A Novel Partial Decode-and-Forward Relaying with Multiple Antennas

This paper proposes a novel partial decode-and-forward (DF) relaying strategy with multiple antennas. In the first phase, the source broadcasts data streams consisting of non-forwarding and forwarding data streams. In the second phase, a relay node forwards only forwarding data streams to a destination node, and a destination node decodes both nonforwarding and forwarding data streams by successive interference cancellation (SIC). We provide an analytical framework of achievable rate and design a linearly combined precoding matrix for rate maximization. Our results show that the proposed partial DF relaying with a linearly combined precoding matrix achieves substantially higher rate than a conventional DF relaying scheme.
Improved power quality opto-couplerless Cuk converter for flickerless LED lighting

This paper presents a single stage isolated Cuk converter for low power light emitting diode (LED) driver systems. An opto-couplerless feedback control using primary-side regulation technology is presented in discontinuous conduction mode (DCM). A complete analysis of the converter is carried out using pulse-frequency modulation technique. The primary focus of this work is to develop a cost effective solution for an LED driver with improved power quality and efficiency. For low power lighting, this work results in a maximum power factor (PF) of 0.99 and minimum total harmonic distortion (THD) in AC mains current of 3.37% over universal AC voltage. The proposed Cuk topology offers high efficiency and constant voltage-constant current (CV-CC) control. Moreover, for high efficiency, coupling capacitors at both sides of the transformer have been optimized to a film capacitor with low dissipation factor and long life as compared to commonly used electrolytic capacitors. A 27 W prototype with a converter efficiency of 88% has been developed to validate the proposed approach.