Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary Acid Load and Potassium Intake Associate with Blood Pressure and Hypertension Prevalence in a Representative Sample of the German Adult Population

Diets rich in fruits and vegetables, like the Dietary Approaches to Stop Hypertension (DASH)-diet, are usually characterized by high potassium intake and reduced dietary acid load, and have been shown to reduce blood pressure (BP). However, the relevance of potential renal acid load (PRAL) for BP has not been compared with the relevance to BP of urinary biomarker (K-urine)- and dietary food frequency questionnaire (K-FFQ)-based estimates of potassium intake in a general adult population sample. For 6788 participants (aged 18–79 years) of the representative German Health-Interview and Examination Survey for Adults (DEGS1), associations of PRAL, K-urine, and K-FFQ with BP and hypertension prevalence were cross-sectionally examined in multivariable linear and logistic regression models. PRAL was significantly associated with higher systolic BP (p = 0.0002) and higher hypertension prevalence (Odds ratio [OR] high vs. low PRAL = 1.45, p = 0.0004) in models adjusted for age, sex, body mass index (BMI), estimated sodium intake, kidney function, relevant medication, and further important covariates. Higher estimates of K-FFQ and K-urine were related to lower systolic BP (p = 0.04 and p < 0.0001) and lower hypertension prevalence (OR = 0.82, p = 0.04 and OR = 0.77, p = 0.02) as well as a lower diastolic BP (p = 0.03 and p = 0.0003). Our results show, for the first time in a comparative analysis of a large representative population sample, significant relationships of BP and hypertension prevalence with questionnaire- and biomarker-based estimates of potassium intake and with an estimate of dietary acid load.
Better Initialization Heuristics for Order-based Bayesian Network Structure Learning

An effective approach for learning Bayesian network structures is to perform a greedy search on the space of variable orderings using a restricted space of parent sets. Typically, the search is initialized with a randomly generated ordering. This can lead to poor local optima and hurt the performance of the method. In this article we develop informed heuristics for generating initial solutions to order-based structure learning search. Experiments with a large collection of real-world data sets demonstrate that our heuristics increase the quality of the solutions found with a negligible overhead.
Cognitive random stepped frequency radar with sparse recovery

Random stepped frequency (RSF) radar, which transmits random-frequency pulses, can suppress the range ambiguity, improve convert detection, and possess excellent electronic counter-countermeasures (ECCM) ability [1]. In this paper, we apply a sparse recovery method to estimate the range and Doppler of targets. We also propose a cognitive mechanism for RSF radar to further enhance the performance of the sparse recovery method. The carrier frequencies of transmitted pulses are adaptively designed in response to the observed circumstance. We investigate the criterion to design carrier frequencies, and efficient methods are then devised. Simulation results demonstrate that the adaptive frequency-design mechanism significantly improves the performance of target reconstruction in comparison with the nonadaptive mechanism.
Conservative management of hyperplastic and multicentric nephroblastomatosis.

PURPOSE The treatment of hyperplastic nephroblastomatosis remains controversial. We report the advantages of conservative management of hyperplastic and multicentric nephroblastomatosis associated with unilateral Wilms tumor (WT). MATERIALS AND METHODS During the last 10 years 48 children with unilateral WT were consecutively treated at our 2 institutions. Children with multiple solid renal masses on imaging were treated with 2-drug chemotherapy until disappearance of the lesions. Stabilization or progression of the lesions despite chemotherapy, as well as heterogeneity of the lesions on imaging, prompted nephron sparing surgery (NSS). RESULTS Three female infants (12, 13 and 20 months old, respectively) presented with multiple solid renal tumors at imaging. Despite chemotherapy, small and unilateral WT developed in 2 cases of hyperplastic nephroblastomatosis, which was excised. One of these infants subsequently presented with a small contralateral metachronous WT, which was excised. Both infants are disease-free with 2 normal kidneys at followup of 6 and 2 years, respectively. The third infant, who presented with unilateral multicentric WT and unilateral hyperplastic nephroblastomatosis nodules, was successfully treated with preoperative chemotherapy and enucleation of 5 tumors. Subsequently, nephrectomy was performed at another institution because the abnormal kidney outline due to NSS was misinterpreted as a recurrence of WT. She was lost to followup. CONCLUSIONS Hyperplastic and multicentric nephroblastomatosis is not a rare lesion and is most often associated, either initially or subsequently, with WT. In some infants with multiple solid renal masses on imaging chemotherapy and for developing WT NSS may safely allow maximum sparing of the parenchyma of both kidneys.
Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity

Imposing sparse + group-sparse superposition structures in high-dimensional parameter estimation is known to provide flexible regularization that is more realistic for many real-world problems. For example, such a superposition enables partially-shared support sets in multi-task learning, thereby striking the right balance between parameter overlap across tasks and task specificity. Existing theoretical results on estimation consistency, however, are problematic as they require too stringent an assumption: the incoherence between sparse and group-sparse superposed components. In this paper, we fill the gap between the practical success and suboptimal analysis of sparse + group-sparse models, by providing the first consistency results that do not require unrealistic assumptions. We also study non-convex counterparts of sparse + groupsparse models. Interestingly, we show that these are guaranteed to recover the true support set under much milder conditions and with smaller sample size than convex models, which might be critical in practical applications as illustrated by our experiments.
Linkage Disequilibrium (LD) Analysis in Alfalfa (Medicago sativa L.) Populations Spreading in Different Geographies of the World

Kultur yoncasi ( Medicago sativa L.), islah calismalari sonucunda Medicago sativa tur kompleksinden gelistirilmistir. Son zamanlarda, yonca bitkisindeki calismalarin molekuler markorler temelinde DNA polimorfizmlerini belirlemeye ve bu polimorfizmlerle iliskili olan fenotipler arasinda baglanti kurmaya odaklandigi bilinmektedir. Ozellikle genotipler ve fenotipler arasindaki iliskiler, Baglanti Analizleri ve Iliski Haritalama calismalari seklinde degerlendirilmektedir. Bilgiler temelinde bu calismada; 70 populasyon icin 31 SSR markoru kullanilarak Baglanti Esitsizligi (Linkage Disequilibrium, LD) analizi de gerceklestirilmistir. Baglanti Esitsizligi analizi icin; 31 markorden 23 tanesinin yonca’nin 8 kromozomu uzerindeki fiziksel mesafeleri belirlenmistir. 8 kromozom uzerinde 103 lokustan olusan mesafeler Medicago truncatula genomu temel alinarak saptanmistir. Degerlendirilen markorler icin; bu populasyonlarda onemli (p<0,0001) bir Baglanti Esitsizligi (LD) degeri gozlenmemistir.
Colloid deposition dynamics in flow-through porous media: role of electrolyte concentration.

The role of solution ionic strength and counterion valence in controlling the dynamic (transient) behavior of colloid deposition in granular porous media has been investigated. Packed-bed column experiments were conducted using two different suspensions of positively charged colloidal latex particles and negatively charged glass bead collectors. Variations in the concentration of an indifferent, 1 :1 electrolyte solution are used to demonstrate an inverse logarithmic relationship between the average area of collector surface excluded (blocked) by deposited colloidal particles and solution ionic strength. Both electrolyte concentration and type influence the dynamics of particle deposition in porous media. A transition from declining deposition rate (blocking) to increasing deposition rate (ripening) is observed when the concentration of an electrolyte containing divalent counterions is gradually increased. Results from experiments involving irreversible, monolayer particle deposition are used to determine values of the maximum attainable surface coverage for spherical collectors. These values are unique to the collector geometry, flow conditions, and particle sizes used in the experiments. Implications of the results to colloidal transport and mobilization in subsurface aquatic environments are discussed.
Coping with Unseen Data Conditions: Investigating Neural Net Architectures, Robust Features, and Information Fusion for Robust Speech Recognition

The introduction of deep neural networks has significantly improved automatic speech recognition performance. For realworld use, automatic speech recognition systems must cope with varying background conditions and unseen acoustic data. This work investigates the performance of traditional deep neural networks under varying acoustic conditions and evaluates their performance with speech recorded under realistic background conditions that are mismatched with respect to the training data. We explore using robust acoustic features, articulatory features, and traditional baseline features against both in-domain microphone channel-matched and channel-mismatched conditions as well as out-of-domain data recorded using farand near-microphone setups containing both background noise and reverberation distortions. We investigate feature-combination techniques, both outside and inside the neural network, and explore neural-network-level combination at the output decision level. Results from this study indicate that robust features can significantly improve deep neural network performance under mismatched, noisy conditions, and that using multiple features reduces speech recognition error rates. Further, we observed that fusing multiple feature sets at the convolutional layer feature-map level was more effective than performing fusion at the input feature level or at the neural-network output decision level.
An Improved Approach for Multi-Task Feature Image Classification using Hybrid GA-SIFT

Here in this paper an efficient technique for the Image Classification is proposed using Optimization of SIFT Algorithm by Genetic Algorithm. The Proposed Procedure implemented here is used for the Classification of Single Task as well as Multiple Task Features from the Image and classification is done. The Experimental results achieved on numerous datasets such as MIR Flickr, NUS Datasets shows the recital of the planned methodology. The algorithm provides High Precision and recall rate as well as more number of features extracted from the image with High Accuracy.
New algorithm for recommender systems based on singular value decomposition method

Matrix factorization is one of the most favorable techniques based on model-based recommender systems. Matrix factorization approaches are superior than other algorithm of collaborative filtering for investigating sparsity data problem. In this paper, we develop a recommendation algorithm based on this idea that unknown ratings are affected from information which are extracted from available ratings, so, data need to preprocessing, since ratings of this dataset have categorical type, therefore, first impute suitable value to missing values for example replacing zero value of data with user median, item median, total median of ratings then, SVD approach is implemented on preprocessing data and predict rating of MovieLens dataset. New method is compared with simple SVD and normalize SVD methods on original data. Proposed methods are evaluated with three metrics: RMSE1, RE2, MAE3. We show that our work is efficient and significantly outperforms simple SVD and normalize SVD.
An efficient interleaved high step-up converter with winding-cross-coupled inductor and common active clamp for photovoltaic applications

This paper presents a high-efficiency and high-step up Non isolated interleaved dc-dc converter with a boost converter as clamp circuit. In the proposed converter, the coupled-inductor boost converters are interleaved. A boost converter is used to clamp the voltage stresses of all the switches in the interleaved converter, caused by the leakage inductances present in the practical coupled inductors, to a low voltage level. The leakage energies of the interleaved converters are collected in a clamp capacitor and recycled to the output by the boost converter. The proposed converter achieves high efficiency because of the recycling of the leakage energies to the output, reduction of the switch voltage stress, mitigation of the output diode's reverse recovery problem, and interleaving of the converters. Detailed analysis and design of the proposed converter are carried out. A simulation of the proposed converter with the PSPICE software is done, and its results are presented for validation of design.
Constructing verification models of nonlinear Simulink systems via syntactic hybridization

In this paper, we present a methodology that facilitates the integration of formal verification techniques into model-based design. The focus is on set-based reachability analysis and on control systems that are described by hybrid dynamics and nonlinear components. Starting with a standard simulation model, e.g. in MATLAB/Simulink, we transform it into an equivalent verification model, formally a network of hybrid automata. This verification model complies with the SX format, which is a formalism used by several reachability tools. A major obstacle encountered is that highly scalable reachability algorithms and tools exist for piecewise affine (PWA) dynamical models, but not for nonlinear ones. To obtain PWA over-approximations of nonlinear dynamics, we use an abstraction method known as hybridization. Hybridization consists in partitioning the state-space into a set of domains and for each domain approximating the nonlinear dynamics by simpler ones. Nondeterministic inputs are added to account for the abstraction error. Existing hybridization procedures operate on the composed (flattened) system, so the number of partitions is exponential in the number of variables. This quickly leads to intractably large models, even for small systems. To mitigate this problem, we decompose the original dynamics and carry out the state-space partitioning and PWA approximation on the components. The number of partitions in each PWA component is at most quadratic in the abstraction error, thus largely avoiding an explosion in the number of partitions. Since the SX format can handle templates, several components may share the same abstraction. The result is a highly compact model that retains the modular structure of the original simulation model. If only a small subset of the partitions is reachable, the bottleneck of having excessively large PWA models can be avoided by composing the model on-the-fly during the reachability analysis.
Face and mouth localization of viseme

Face and mouth localization are the vital phase for visual speech recognition. These tasks refer to the detection of the face and mouth region within the viseme images. The main problem in face and mouth localization is the constraints on the image such as rotation of the images and color of the homogeneity intensity within the images and some parts of the object images cannot be detected. This paper investigated face and mouth localization of viseme images using the enhanced Viola-Jones algorithm as to ensure the face and mouth are accurately detected. The enhanced Viola-Jones algorithm experimented on 80 viseme images from the recorded video data of 500 Bahasa Melayu common words as the test images which include female and male adult with no speech problem. The proposed method also showed a promising result with the high acceptance rate. For future work, further research may focus on overcoming the detection of similar color intensity and illumination problems within the images. The future research also may focus on mouth segmentation and temporal extraction of the mouth features.
Nuclear medicine imaging of diabetic foot infection: results of meta-analysis

Background and aimOsteomyelitis of the foot is the most commonly encountered complication in diabetic patients. Nuclear medicine techniques are usually complementary to radiology in the diagnosis of foot infections; they play an important role in various clinical situations. The aim of this study was to develop a practical guideline to describe the radiopharmaceuticals to be used for different clinical conditions and different aims in diabetic foot infection. MethodsIn this study, we reviewed 57 papers (published between 1982 and 2004; 50 original papers and seven reviews) that described the imaging of the diabetic foot and examined a total of 2889 lesions. We performed data analysis to establish which imaging technique could be used as a ‘gold standard’ to diagnose infection, evaluate the extent of disease and monitor the efficacy of therapy. Results and conclusionWe provide a guideline to assist in the selection of the optimal radiopharmaceuticals for different clinical conditions and different aims.
Activated Notch1 Can Transiently Substitute for EBNA2 in the Maintenance of Proliferation of LMP1-Expressing Immortalized B Cells

ABSTRACT Epstein-Barr virus (EBV) nuclear antigen 2 (EBNA2) and latent membrane protein 1 (LMP1) are essential for immortalization of human B cells by EBV. EBNA2 and activated Notch transactivate genes by interacting with the cellular transcription factor RBP-Jκ/CBF1. Therefore, EBNA2 can be regarded as a functional homologue of activated Notch. We have shown previously that the intracellular domain of Notch1 (Notch1-IC) is able to transactivate EBNA2-regulated viral promoters and to induce phenotypic changes in B cells similar to those caused by EBNA2. Here we investigated whether Notch1-IC can substitute for EBNA2 in the maintenance of B-cell proliferation. Using an EBV-immortalized lymphoblastoid cell line in which EBNA2 function can be regulated by estrogen, we demonstrate that murine Notch1-IC, in the absence of functional EBNA2, is unable to maintain LMP1 expression and to maintain cell proliferation. However, in a lymphoblastoid cell line expressing LMP1 independently of EBNA2, murine Notch1-IC can transiently maintain proliferation after EBNA2 inactivation. After 4 days, cell numbers do not increase further, and cells in the G2 phase of the cell cycle start to die. In contrast to EBNA2, murine Notch1-IC is unable to upregulate the expression of the c-myc gene in these cells.
Ontology Hierarchy Self Generation using Algebraic Multi-Grid (AMG)

In Computer Science an ontology is a standardized representation of knowledge as a set of concepts within a domain, and the relationships between those concepts. This can be described as a graph or a network. In this paper we claim that the topology itself of the ontology contains significant semantics. Then we discuss the possibility of revealing a hierarchy of element-class from the network itself of an abstract ontology. We will use the Algebraic-Multi-Grid (AMG) concepts and tools for the Laplace equation defined on this network.
When “N of 2” is not enough: integrating statistical and functional data in gene discovery

The expanding use of genomic sequencing promises to improve clinical diagnostics and to drive the discovery of new disease genes. Candidate genes are increasingly being identified through recurrent cases (e.g., two or more independent cases [“N of 2”] in which variants are present in the same gene). These second case hits provide statistical evidence of an association, which may then be combined with functional validation or familial segregation studies to bolster the evidence that a gene is truly causal. Here, we discuss how to integrate different forms of functional evidence with human genetics case and segregation data to improve the significance of new disease–gene associations.
Prevalence and antimicrobial susceptibility of Ureaplasma urealyticum and Mycoplasma hominis in Chinese women with genital infectious diseases.

BACKGROUND Previous studies have confirmed that the presence of Ureaplasma urealyticum (UU) and Mycoplasma hominis (MH) increases the risks of various diseases including genital infections in women. Hence, the surveillance policies for the prevalence and antimicrobial susceptibility of UU and MH are important for both the prevention and the treatment of the diseases associated with genital mycoplasmas. AIM The objective of this study is to investigate the characteristics of UU and MH prevalence and its antimicrobial susceptibility in Chinese women with genital infection. METHODS By using commercial mycoplasma strips, we investigated the incidence and antimicrobial resistance of UU and MH in 3306 Chinese women with genital infection between January 2005 and December 2009 in Changzhou China. RESULTS (1) The overall positive incidence of genital mycoplasmas was 62.16%. The most common pattern was UU monoinfection (46.52%), the UU-MH coinfection pattern ranked second (13.91%) and MH monoinfection was lowest (1.71%). According to annual analysis, MH infection revealed an increasing trend between 2005 and 2009. However, a significantly higher infection rate by genital mycoplasmas was found in young women (age range: 16-35 years). (2) Overall, MH susceptibility rates remained high only to doxycycline (DOX), minocycline (MIN) and josamycin (JOS), while UU had high susceptibility rates only to DOX, MIN and clarithromycin (CLA). The resistance rates of UU-MH-mixed isolates to most of drugs were significantly higher than those of UU- or MH-single isolates. CONCLUSIONS High infection rates and severe drug resistances of genital mycoplasmas were found in Chinese women with genital infections. The laboratory screening and antimicrobial susceptibility testing for genital mycoplasmas is vital to treat the infection.
Cancer of the anal canal: treatment with chemotherapy and low-dose radiation therapy.

PURPOSE To determine the efficacy of a standardized protocol of chemotherapy and low-dose radiation therapy in treatment of patients with anal canal cancer. MATERIALS AND METHODS Forty-two consecutive patients with anal canal cancer were treated with 15 fractions of 30-Gy photon beam radiation therapy administered anteroposterior-posteroanterior in conjunction with chemotherapy with 5-fluorouracil and mitomycin C. Survival analysis was performed with the lifetest procedure. RESULTS In patients with stage T1 and T2 tumors, 26 of 29 (90%) were free of disease after chemotherapy and radiation therapy and had no recurrent tumors. In patients with stage T3 and T4 tumors, five of 13 (38%) were free of disease after therapy and had no recurrences. CONCLUSION This therapy is effective for epidermoid cancers of the anal canal that are smaller than 5 cm regardless of nodal status. Tumors larger than this or that invade adjacent structures are not adequately controlled with this protocol.