Miracle-dz/UniFAR-FTUs huggingface dataset:
ALDH2 and ADH1 Genetic Polymorphisms May Contribute to the Risk of Gastric Cancer: A Meta-Analysis

Aim We conducted a meta-analysis of case-control studies to determine whether ALDH2, ADH1 and ADH2 genetic polymorphisms contribute to the pathogenesis of gastric cancer. Methods The PubMed, CISCOM, CINAHL, Web of Science, Google Scholar, EBSCO, Cochrane Library, and CBM databases were searched for relevant articles published before November 1st, 2013 without any language restrictions. Meta-analysis was conducted using the STATA 12.0 software. We calculated crude odds ratios (ORs) with their 95% confidence intervals (95%CI) to evaluate their relationships under five genetic models. Seven case-control studies with a total of 2,563 gastric cancer patients and 4,192 healthy controls met the inclusion criteria. Nine common polymorphisms were evaluated, including rs671, rs16941667 and rs886205 in the ALDH2 gene, rs1230025, rs13123099, rs698 and rs1693482 in the ADH1 gene, and rs1229984 and rs17033 in the ADH2 gene. Results The results of our meta-analysis suggested that ALDH2 genetic polymorphisms might be strongly correlated with an increased risk of gastric cancer (allele model: OR = 1.21, 95%CI: 1.11∼1.32, P<0.001; dominant model: OR = 1.23, 95%CI: 1.09∼1.39, P = 0.001; respectively), especially for rs671 polymorphism. Furthermore, we observed significant associations between ADH1 genetic polymorphisms and an increased risk of gastric cancer (allele model: OR = 1.21, 95%CI: 1.08∼1.36, P = 0.001; dominant model: OR = 10.52, 95%CI: 3.04∼36.41, P<0.001; respectively), especially for rs1230025 polymorphism. Nevertheless, no positive relationships were found between ADH2 genetic polymorphisms and gastric cancer risk (all P>0.05). Conclusion The current meta-analysis suggests that ALDH2 and ADH1 genetic polymorphisms may play crucial roles in the pathogenesis of gastric cancer. However, ADH2 genetic polymorphisms may not be important dominants of susceptibility to gastric cancer.
Compound eye images super-resolution reconstruction based on Q-shift dual-tree complex wavelet transform

An image super-resolution reconstruction method based on Q-shift dual-tree complex wavelet transformation was proposed in this paper. After low resolution image sequence registration, the proposed method introduced MAP framework to complex wavelet domain. It used a generalized Gaussian wavelet prior model to provide accurate descriptions of the statistical distribution of complex wavelet coefficients in image data to obtain more directions of details. And then this method offered a new adaptive regularization technique and used the half-quadratic regularization iteration algorithm to determine the best regularization parameter and update image reconstruction simultaneity. This method offers good reconstruction results and global convergence. Experiment results indicate that without adding complexity, the result of this method is better than those of Joint MAP method and wavelet domain method by PSNR and subjective visual judgment.
SAR Image Segmentation Based on Level Set Approach and {\cal G}_A^0 Model

This paper proposes an image segmentation method for synthetic aperture radar (SAR), exploring statistical properties of SAR data to characterize image regions. We consider {\cal G}_A^0 distribution parameters for SAR image segmentation, combined to the level set framework. The {\cal G}_A^0 distribution belongs to a class of {\cal G} distributions that have been successfully used to model different regions in amplitude SAR images for data modeling purpose. Such statistical data model is fundamental to deriving the energy functional to perform region mapping, which is input into our level set propagation numerical scheme that splits SAR images into homogeneous, heterogeneous, and extremely heterogeneous regions. Moreover, we introduce an assessment procedure based on stochastic distance and the {\cal G}_A^0 model to quantify the robustness and accuracy of our approach. Our results demonstrate the accuracy of the algorithms regarding experiments on synthetic and real SAR data.
Adaptive channel quality feedback for LTE

In Long Term Evolution (LTE) networks, resource allocation and link adaptation rely on the channel quality indicator (CQI), which is a quantized representation of the signal interference plus noise ratio (SINR). Since CQI is measured by user equipment (UE) and sent to eNodeB via a feedback channel, in time varying channel, feedback delay should be compensated with prediction based on previous knowledge. In this work, several known prediction schemes are studied. Moreover, an adaptive CQI feedback scheme based on the sample autocovariance function is proposed. Simulation results show the proposed prediction scheme outperforms the other known prediction schemes in terms of overall prediction accuracy and system throughput. In addition, the performance is evaluated for systems both with and without hybrid automatic repeat request (HARQ), to investigate the influence of HARQ.
Local Political Budget Cycles in a Federation: Evidence from West German Cities

This paper analyzes the occurrence of political budget cycles in 604 West German cities between 1975 and 2007. Due to the idiosyncratic timing of state and local elections, the budgetary changes before elections at two tiers of the federalist government can be separately estimated and can also be distinguished from common time effects. Both local and state elections result in pre-election manipulation of the local finances of moderate size. Before both types of elections, we observe an increase in building investments, accompanied by increasing intergovernmental grants for investment purposes but also a halt in the increase of local tax rates. By contrast, elections at the two tiers of the government affect the size of the current budget differently: current revenues and expenditures decrease before local but increase before state elections, suggesting a difference in the tightness of the local budget constraint. The extent of these political budget cycles is more pronounced in municipalities that are politically aligned with the state governments and are politically more contested.
Prosodic phrasing and accentuation in speech production of patients with right hemisphere lesions

While the left hemispheric role in speech production has been extensively studied the role of the right hemisphere is less explored. Therefore, this paper addressed the question to what extend the right hemisphere is involved in prosodic production in an intonational language such as German. In particular, we explored the speech production of three patients with right hemisphere damage and compared these data to that of matched controls. We used a question-answer test of 48 German sentences with different verb-argument structures and varying focus positions. Healthy subjects correctly differentiated between the syntactic conditions and between the wide and narrow focused sentences by correlating prosodic parameters with cues for Intonational Phrasing as well as for accent distribution and accent types. In comparison, patients had difficulties in realizing prominence/accent distribution. Additionally, they seem to have a decreasing capacity for durational control as indicated by missing prefinal lengthening in prosodic domains.
Does the recombination rate affect the efficiency of purifying selection? The yeast genome provides a partial answer.

Much attention has been focused on the effect of recombination on preservation of favorable variants and on reducing the mutational load (Muller 1964; Kondrashov 1993; Barton 1995; Hurst and Peck 1996; West, Lively, and Read 1999). As most amino acid changes are slightly deleterious (Li 1997), their accumulation is expected to depend on the efficiency of purifying selection. Theoretical models have revealed that recombination might prevent the accumulation of such slightly deleterious mutations. This is potentially of importance because it suggests that purifying selection is less effective in genomic regions of low recombination (Hill and Robertson 1966; Carvalho and Clark 1999; Comeron, Kreitman, and Aguade 1999; Comeron and Kreitman 2000; Williams and Hurst 2000). If purifying selection were more effective in regions of high recombination, accounting for within-genome variation, the rate of protein evolution should be lower in order to minimize the harmful effect of mutations on protein function. Furthermore, codon usage bias, which is at least partly influenced by selection, should be more pronounced in these genomic regions. Previous tests have employed this logic and shown that codon usage is slightly more biased under high recombination rates in Drosophila melanogaster (Comeron, Kreitman, and Aguade 1999). Two other studies (Carvalho and Clark 1999; Comeron and Kreitman 2000) have confirmed a negative relationship between intron size and recombination rate in the same organism (although the two groups give different interpretations of their results). One possible shortcoming of these works is that they did not control for variation in gene expression level. Some experimental works on yeast suggest that genomic regions with high transcription rates are also more prone to the initiation of meiotic doublestrand break (DSB) events (Gerton et al. 2000), and there are also some hints that this feature might be a general feature of eukaryotic organisms (e.g., Jones et al. 1997). As codon usage bias (e.g., Duret and Mouchiroud 1999) and intron size (Vinogradov 2001) are affected by differences in gene expression level in numerous model organisms, the conclusions drawn from the association of these parameters with recombination rate should be treated with caution. Yeast is currently a unique tool with which to address all of these issues owing to the availability of re-
Fuzzy sets in nonparametric Bayes regression

A simple Bayesian approach to nonparametric regression is described using fuzzy sets and membership functions. Membership functions are interpreted as likelihood functions for the unknown regression function, so that with the help of a reference prior they can be transformed to prior density functions. The unknown regression function is decomposed into wavelets and a hierarchical Bayesian approach is employed for making inferences on the resulting wavelet coefficients.
Does the Emperor Have Financial Crisis Clothes? Reflections on the Legal Basis of the European Banking Authority

The European Union institutional package launched in response to the financial crisis used Article 114 TFEU as its legal basis. The author explores the legal basis for one of the European Supervisory Authorities recently established – the European Banking Authority (EBA). The use of Article 114 TFEU, the main Treaty basis used to harmonise laws in order to further the internal market, as the foundation for the EBA, is considered in detail. A paradox of contemporary EU institutional law is assessed here, considering whether on the one hand, the EBA is functionally both too narrow and too broad as a matter of law, while on the other hand, it may prove to be central to restoring confidence in EU regulatory powers, rendering it ‘too big to fail,’ despite its slender foundations in Article 114 TFEU.
Conceptual Design of Carbon Steels to Support Heavy Crude Refinement Using Neural Network Modeling and Evolutionary Optimization

The oil industries in the entire World and particularly in Mexico, have been taking an important relevance. There are two major challenges in this industry. The first one is the exploration and utilization of crude oil in deep sea, the second one is the scarce of light crude, the actual production report an increment of heavy crude, generating corrosion steel in the extraction and refinement processes. This paper presents an intelligent system to design conceptual steels considering its properties, taking into account some properties of oil crude and the temperature of the refinement process. The results provide information to choice the correct steel for every refinement phase.
A lightweight processing for conversion of whispering voice into normal speech

Analyzing the whispering voice signal, it is considered to be as understandable as normal speech when heard at proximity and can also carry prosodic information. The study introduced here is centered on developing a signal processing application, not a signal recognizing application, where the whispering (non-speaking voice or talk without vocal fold activation) signal is converted to pseudo-real voice signal similar to normal speech. The method proposed in this study tries to achieve its objective without relying on any specific hardware device, making use of the whispering voice signal's own properties and information. Finally, validation of the proposed method has been conduced and the results analyzed.
IMAGE INPAINTING USING TEXTURE SYNTHESIS AND OBTAIN HIGH RESOLUTION IMAGES FOR PROFESSIONAL RESTORATIONS

Inpainting is the process of filling the missing regions in an image. The main aim of this paper is to fill the missing areas using examplar based inpainting and to recover the missing areas and improve the quality of the image using super resolution algorithm. The performance of the algorithm is evaluated using PSNR. The damaged image is first down sampled. Then the image is inpainted a number of times using examplar based approach. The inpainted images are combined using loopy belief propagation. The image is finally up sampled and the image quality is improved using a super resolution algorithm.
Use of Magnetic Folate-Dextran-Retinoic Acid Micelles for Dual Targeting of Doxorubicin in Breast Cancer

Amphiphilic copolymer of folate-conjugated dextran/retinoic acid (FA/DEX-RA) was self-assembled into micelles by direct dissolution method. Magnetic iron oxide nanoparticles (MNPs) coated with oleic acid (OA) were prepared by hydrothermal method and encapsulated within the micelles. Doxorubicin HCl was loaded in the magnetic micelles. The characteristics of the magnetic micelles were determined by Fourier transform infrared (FT-IR) spectroscopy, thermogravimetric analysis (TGA), transmission electron microscopy (TEM), and vibrating sample magnetometer (VSM). The crystalline state of OA-coated MNPs and their heat capacity were analyzed by X-ray diffraction (XRD) and differential scanning calorimetry (DSC) methods, respectively. The iron content of magnetic micelles was determined using inductively coupled plasma optical emission spectrometry (ICP-OES). Bovine serum albumin (BSA) was used to test the protein binding of magnetic micelles. The cytotoxicity of doxorubicin loaded magnetic micelles was studied on MCF-7 and MDA-MB-468 cells using MTT assay and their quantitative cellular uptake by fluorimetry method. TEM results showed the MNPs in the hydrophobic core of the micelles. TGA results confirmed the presence of OA and FA/DEX-RA copolymer on the surface of MNPs and micelles, respectively. The magnetic micelles showed no significant protein bonding and reduced the IC50 of the drug to about 10 times lower than the free drug.
Learning Auditable Features from Signals Using Unsupervised Temporal Projection

To effectively analyze and design cyberphysical systems (CPS), designers today have to combat the data deluge problem, i.e., the burden of processing intractably large amounts of data produced by complex models and experiments. In this work, we utilize monotonic Parametric Signal Temporal Logic (PSTL) to design features for unsupervised classification of time series data. This enables using off-the-shelf machine learning tools to automatically cluster similar traces with respect to a given PSTL formula. We demonstrate how this technique produces simple and interpetable formulas that are amenable to analysis and understanding using a few representative examples. We illustrate this with a number of case studies related to automotive engine testing, highway traffic analysis, and auto-grading massively open online courses.
Emulating Spiking Neural Networks for edge detection on FPGA hardware

Spiking Neural Networks (SNNs) are an emerging computing paradigm that attempt to model the biological functions of the human brain. However, as networks approach the biological scale with significantly large numbers of neurons, software simulations face the problem of scalability and increasing computation times. Thus, numerous researchers have targeted hardware implementations in an attempt to more closely replicate the parallel processing capabilities of biological networks. Reconfigurable hardware is seen as a particularly viable platform for attempting to replicate to some degree the natural plasticity and flexibility of the human brain. This paper presents a scalable FPGA based implementation approach that facilitates the accelerated emulation of large-scale SNNs. The approach is validated using a SNN-based edge detection application where an order of magnitude speed performance increase was observed in comparison to a software equivalent implementation.
Pattern storage in gene-protein networks

In this paper we study the potential of simple linear gene-protein interaction networks to store sparse input-output patterns. This problem bears similarity to the engineering task of reconstructing gene networks from time series of expression data, such as sets of microarrays. This is currently an intensively studied field where some results are directly applicable to our context. The central question in this study concerns the memory capacity of a network of n genes and proteins, which interact according to a simple linear state space model with p external outputs. Here it is assumed that to a certain combination of inputs u* there exists an optimal state of the system x*, i.e. values of the gene expressions and protein levels, that has been attained externally, e.g. through evolutionary learning. Given such a set of m learned optimal input-out patterns, the design question here is to find a sparse and hierarchical network structure for the gene-protein interaction matrix A, and the gene-input coupling matrix B. This problem is formulated as an optimization problem in a linear programming setting. From this formulation it is directly evident that the maximum number of patterns that can be stored is: mmax = n + p – 1. Furthermore, it allows for a numerical analysis, which shows that there are clear scale-invariant phase transitions for the sparsity, i.e. the relative number of zero-elements, in the matrices A and B as the number of patterns m increases. The sparsity in A and B exhibits continuous second-order phase transitions as the number of patterns reaches m1 = p/2 and m2 = 2p/3 respectively. These phase transitions divide the system in three regions with different memory characteristics. In the first region, below m = m1, the system stores patterns by directly connecting the inputs to the outputs, without directly involving the genes and proteins. In the second region, between m = m1 and m = m2, information is preferentially stored in matrix A, and in the third region, above m = m2, there is no clear preference for storing information in either A or B, and their sparsities behave increasingly identical. It is possible to formulate simple scaling rules for the behaviour of the sparsity in A and B versus m, though the exact morphology of these relations is not scale-invariant. Finally, numerical experiments are described that show that the patterns are stable within a certain finite range around the patterns.
Board monitoring and internal control system disclosure in different regulatory environments

Purpose – The purpose of this paper is to investigate two research questions. Is internal control system (ICS) disclosure, as a monitoring mechanism, associated with the characteristics of the board of directors, particularly the audit committee as the main board committee devoted to the effectiveness of ICS? Does the regulatory environment, particularly the regulation on ICS disclosure as an external governance/monitoring mechanism play a role in shaping the relationship between board monitoring and ICS disclosure and, if so, how? Design/methodology/approach – The authors study the ICS disclosure of 149 companies listed in four European financial markets (London, Paris, Frankfurt and Milan), each with its own regulations about ICS disclosure, during a six-year period (2003-2008). Findings – The findings support an inverse association between the extent of ICS disclosure and the proxies for board monitoring. The authors also find a statistically significant negative relationship between board monitoring a.
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use different terminology and task definitions, claim to have different goals, claim to use different rule learning heuristics, and use different means for selecting subsets of induced patterns. This paper contributes a novel understanding of these subareas of data mining by presenting a unified terminology, by explaining the apparent differences between the learning tasks as variants of a unique supervised descriptive rule discovery task and by exploring the apparent differences between the approaches. It also shows that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision. The commonalities (and differences) between the approaches are showcased on a selection of best known variants of CSM, EPM and SD algorithms. The paper also provides a critical survey of existing supervised descriptive rule discovery visualization methods.
Does exercise alter anaerobic threshold in coronary artery disease during beta blockade?

The effect of propranolol on cardiac patients undergoing exercise training is reported to increase exercise tolerance and maximum oxygen uptake (VO2 max) but its effect on anaerobic threshold (AT) is unknown. It was the purpose of this study to determine the role of exercise training with propranolol on AT in patients with coronary artery disease (CAD). Eight men and one woman with significant (CAD) were selected for this study. Each patient completed a maximum treadmill stress test (MTST) following the Bruce protocol on propranolol 40-160 mg/day as a control study. Cardiorespiratory variables were measured at rest and at each stage of the treadmill test. These patients underwent an exercise training programme for 12-16 weeks on the same dose of propranolol. Training sessions were for a minimum of 30-40 minutes, 3 times a week, with training heart rate of 75%-85% of the pretraining peak heart rate. Training heart rate ranged from 98 to 128 beats/min. They were retested with a MTST after the training programme, on the same dose of propranolol. AT was calculated noninvasively by measuring respiratory variables every 30 seconds in relation to work increment. AT was identified by measuring the time course of VE, VCO2, VE/VO2, etc. in relation to incremental work. The mean values of VO2, O2P and % VO2 max at AT before and after training on propanolol were as follows: VO2 = 1.43 L/min +/- . 25 and 1.86 L/min +/- . 44, O2P = 14.35 +/- 2.40 and 18.73 +/- 4.00 ml/beat, % of VO2 max = 68.20 +/- 6.31 and 73.59 +/- 5.84. The mean changes of VO2 O2P, and % of VO2 max were + 0.43 L/min +/- 0.20 (P < .003), + 4.38 +/- 2.55 (P < .003) and +/- 5.07% +/- 4.84 (P < .001). After exercise training on propanolol, the mean peak exercise tolerance time and absolute VO2 max increased by 2.8 min (from 9.0 to 11.8 min) (P < .001) and 22.7% (P < .007), respectively. We conclude that the increase in anaerobic threshold in patients with coronary artery disease may be due to improvement in VO2 max, increased stroke volume, and peripheral O2 extraction.