Miracle-dz/UniFAR-FTUs huggingface dataset:
Wnt signaling and Dupuytren's disease.

BACKGROUND Dupuytren's disease is a benign fibromatosis of the hands and fingers that leads to flexion contractures. We hypothesized that multiple genetic and environmental factors influence susceptibility to this disease and sought to identify susceptibility genes to better understand its pathogenesis. METHODS We conducted a genomewide association study of 960 Dutch persons with Dupuytren's disease and 3117 controls (the discovery set) to test for association between the disease and genetic markers. We tested the 35 single-nucleotide polymorphisms (SNPs) most strongly associated with Dupuytren's disease (P<1×10(-4)) in the discovery set in three additional, independent case series comprising a total of 1365 affected persons and 8445 controls from Germany, the United Kingdom, and The Netherlands. RESULTS Initially, we observed a significant genomewide association between Dupuytren's disease and 8 SNPs at three loci. Tests of replication and joint analysis of all data from 2325 patients with Dupuytren's disease and 11,562 controls yielded an association with 11 SNPs from nine different loci (P<5.0×10(-8)). Six of these loci contain genes known to be involved in the Wnt-signaling pathway: WNT4 (rs7524102) (P=2.8×10(-9); odds ratio, 1.28), SFRP4 (rs16879765) (P=5.6×10(-39); odds ratio, 1.98), WNT2 (rs4730775) (P=3.0×10(-8); odds ratio, 0.83), RSPO2 (rs611744) (P=7.9×10(-15); odds ratio, 0.75), SULF1 (rs2912522) (P=2.0×10(-13); odds ratio, 0.72), and WNT7B (rs6519955) (P=3.2×10(-33); odds ratio, 1.54). CONCLUSIONS This study implicates nine different loci involved in genetic susceptibility to Dupuytren's disease. The fact that six of these nine loci harbor genes encoding proteins in the Wnt-signaling pathway suggests that aberrations in this pathway are key to the process of fibromatosis in Dupuytren's disease.
Estimation of Multicomponent Polynomial-Phase Signals Impinging on a Multisensor Array Using State–Space Modeling

This contribution addresses the problem of estimating the parameters of multicomponent polynomial-phase signals when impinging on a multisensor array. An original approach is proposed based on a state-space modelization of the signal and the application of an extended Kalman filter for the state estimation. The use of a multisensor array allows the exploitation of a spatial information and leads to the consideration of multiple filters with different observation equations. Computer simulations are used to demonstrate the effectiveness of the proposed algorithm
Design and experimental verification of a direct-drive interior PM synchronous machine using a saturable lumped-parameter model

This paper presents the design and experimental verification of a 6 kW interior permanent magnet (IPM) synchronous machine intended for an automotive direct-drive starter/alternator application. The machine was designed using a saturable lumped-parameter magnetic circuit model in combination with a Monte Carlo optimization process that minimized the machine-plus-converter cost. An experimental IPM machine has been constructed based on the resulting design specifications. Laboratory tests have confirmed the accuracy of the analytical models for predicting the q-axis inductance L/sub q/ (including saturation effects) and the torque production characteristics, but discrepancies between the predicted and measured d-axis inductance L/sub d/ were revealed. The impact of these differences on machine performance is discussed, as well as potential adjustments in the IPM analytical model to improve the performance of future machines.
Differentiation of Salmonella strains from the SARA, SARB and SARC reference collections by using three genes PCR-RFLP and the 2100 Agilent Bioanalyzer

Rapid molecular typing methods are important tools in surveillance and outbreak investigations of human Salmonella infections. Here we described the development of a three-genes PCR-RFLP typing method for the differentiation of Salmonella species, subspecies and serovars using the Agilent 2100 Bioanalyzer. The fliC, gnd, and mutS genes were PCR-amplified in 160 Salmonella strains representing the two Salmonella species, six subspecies, and 41 different serovars of S. enterica subspecies enterica. PCR products were individually cut with two different restriction enzymes and the resulting 930 restriction patterns were collected using the Agilent 2100 Bioanalyzer followed by cluster analysis. Both species of Salmonella were differentiated by conventional PCR. All of S. bongori tested were gnd PCR negative due to a mismatch at the 3′-end in one the PCR primers. Salmonella subspecies were differentiated into third-teen homogeneous groups representing each of the six subspecies by cluster analysis of restriction patterns generated from the mutS gene cut with AciI. S. enterica subspecies enterica serovars were further differentiated by the combination of the three target genes and five out the six sets of restriction patterns with a discriminatory power of 0.9725 by cluster analysis. The combined RFLP results of five sets of restriction patterns allowed us to assign each of the 160 strains to one of 128 restriction types. During inoculation studies we were able to identify S. Saintpaul and Typhimurium from 24 h pre-enrichment samples using the described method. The use of fliC, gnd, and mutS PCR-RFLP with the Agilent 2100 Bioanalyzer can provide an accessible and automated alternative method for differentiation of Salmonella pathogens.
Mobile-based food classification for Type-2 Diabetes using nutrient and textual features

Type-2 Diabetes (T2D) is a dreadful disease affecting hundreds of millions of people worldwide, and is linked and worsen by unhealthy lifestyles, especially the poor diet style. However, managing daily diet effectively remains highly challenging for both T2D patients and doctors. In this paper, we proposed, built, and evaluated an effective food classification tool using mobile computing and predictive models to proactively guide T2D patients along their diet selection. This tool provided a comprehensive food database so that patients can conveniently utilize it to record and track their daily diet. More intelligently, the embedded predictive model classified each food item into three classes (e.g., “Choose More Often”, “In Moderate”, and “Choose Less Often”) using its nutrient and textual features. The evaluation results show that it is able to achieve around 93% classification accuracy in the best scenario, which indicates that it is efficient and effective for T2D diet management.
Assisted dictionary learning for FMRI data analysis

Extracting information from functional magnetic resonance images (fMRI) has been a major area of research for more than two decades. The goal of this work is to present a new method for the analysis of fMRI data sets, that is capable to incorporate a priori available information, via an efficient optimization framework. Tests on synthetic data sets demonstrate significant performance gains over existing methods of this kind.
Leapfrog : Enhancing Information Protection in Commodity Applications with Dataflow Control

Commodity applications can pose a serious threat to users’ confidential information when they do not have sufficient security features or are configured improperly. This problem is difficult due to the unavailability of these applications’ source code, which renders the techniques such as compiler-level security enhancement hard to apply. Existing solutions rely on either system-call level control, which is often too coarse-grained, or instruction-level dataflow tracking, which is too expensive to operate online. In this paper, we present a new solution called Leapfrog which retrofits binary executables with mandatory dataflow control. Our technique enables a “patched” application to perform fine-grained dataflow control at a performance penalty which in many cases can be neglected. This is achieved through a novel technique that tracks sensitive data flows only at a small set of program locations: each location uses the program’s internal state and pre-computed conditions to predict the path the data flows will go through and the next location they will reach. As a result, the sensitive data can be followed until they are to be sent out to the Internet, where they are controlled according to security policies. Such dataflow tracking and control is supported by an offline analysis which identifies the execution paths for processing sensitive data and the conditions for the data to propagate along these paths. We further mitigate the coverage concern of this analysis through enforcing a security policy that disallows highly sensitive data to be processed by unknown execution paths without disrupting a program’s operations. Leapfrog works on multithreaded applications and can attach code to an application without functionally altering its executable files. Our evaluations show that our technique effectively protects sensitive information in misconfigured applications and those with security flaws, and also incurs a small runtime overhead.
Presynaptic calcium and serotonin-mediated enhancement of transmitter release at crayfish neuromuscular junction

Fura-2 microfluorometric measurements of calcium concentrations in excitor motor nerve terminals of the first walking leg of crayfish Procambrus clarkii were made during application of serotonin (5-HT) to examine whether changes in residual calcium concentration or calcium influx are responsible for the 5-HT-induced increase in transmitter release at this synapse. Calcium concentrations and EPSPs were monitored simultaneously during 0.5-Hz and moderate-frequency (3–16 Hz) stimulation before, during, and after a 5-min application of 5-HT at concentrations between 5 and 100 microM. We did not observe a significant increase in resting calcium concentration [Ca2+]i in presynaptic terminals during or after application of 5-HT despite simultaneously recorded excitatory junction potential (EJP) enhancement. Injection of the calcium buffer EGTA to concentrations sufficient to reduce significantly the buildup of frequency-dependent facilitation during trains of action potentials did not reduce 5-HT- mediated EJP enhancement. We therefore conclude that 5-HT does not release calcium from intracellular stores to an extent sufficient to overcome calcium buffering and removal systems. Spontaneous release of transmitter quanta was increased by 5-HT to the same extent in normal and increased-osmolarity saline in the absence of measured changes in [Ca2+]i. Thus, changes in residual calcium cannot explain 5-HT-induced synaptic enhancement. Increasing the action-potential-mediated calcium influx by broadening the spike or by increasing extracellular calcium increased evoked transmitter release and increased the accumulation of calcium during trains of action potentials. However, though 5-HT produced a comparable synaptic enhancement, we found no evidence for increased calcium accumulation during trains of action potentials, suggesting that net calcium influx, buffering, and removal are not affected by 5-HT. Therefore, we conclude that 5-HT's action does not result from effects on calcium metabolism and suggest that it acts on the neurosecretory apparatus to increase the effectiveness of calcium for stimulating release. Furthermore, based on its relatively constant effects on facilitated and unfacilitated action-potential-evoked release and spontaneous transmitter release, we conclude that 5-HT actions are independent of the concentration of calcium that is driving release.
ROB: Using Semantic Meaning to Recognize Paraphrases

Paraphrase recognition is the task of identifying whether two pieces of natural language represent similar meanings. This paper describes a system participating in the shared task 1 of SemEval 2015, which is about paraphrase detection and semantic similarity in twitter. Our approach is to exploit semantically meaningful features to detect paraphrases. An existing state-of-the-art model for predicting semantic similarity is adapted to this task. A wide variety of features is used, ranging from different types of models, to lexical overlap and synset overlap. A maximum entropy classifier is then trained on these features. In addition to the detection of paraphrases, a similarity score is also predicted, using the probabilities of the classifier. To improve the results, normalization is used as preprocessing step. Our final system achieves a F1 score of 0.620 (10th out of 18 teams), and a Pearson correlation of 0.515 (6th out of 13 teams).
Efficacy and benefits of virtual mock interviews: Analyzing student perceptions of digital employment preparations

Mock interviews are an evidence-based method of preparing learners for real employment situations. The effect of novel approaches, such as digital, asynchronous and AI-mediated mock interviews have likewise shown beneficial to students but are currently underrepresented in the literature. This study examined students’ satisfaction levels and perceptions related to virtual mock interviews during the Covid-19 pandemic. Findings show that students had a positive experience, insofar as they believed it to be useful in increasing their ability to perform better in a real interview. While differences were noted among multiple variables, the primary factor associated with positive student outcomes seems to be their level of preparedness prior to the simulation.
SNR-dependent waveform processing for improving the robustness of ASR front-end

We introduce a new concept in advancing the noise robustness of a speech recognition front-end. The presented method, called SNR-dependent waveform processing (SWP), exploits SNR variability within a speech period for enhancing the high SNR period portion and attenuating the low SNR period portion in the waveform time domain. In this way, the overall SNR of noisy-speech is increased, and at the same time, the periodicity of voiced speech is enhanced. This approach differs significantly from the well-known speech enhancement techniques, which are mostly frequency domain based, and we use it in this work as a complementary technique to them. In tests with SWP, we present significant clean and noisy speech recognition performance gains using the AURORA 2 database and recognition system as defined by ETSI for the robust frontend standardization process. Moreover, the presented algorithm is very simple and it is attractive also in terms of computational load.
Human Mutation

Funding information Translational Genomics Research Institute, Center for Rare Childhood Disorders, Grant/ Award Number: Private Anonymous Donations; University of Southern California, Grant/Award Number: Internal Funding Abstract We report a likely pathogenic splice‐altering AP4S1 intronic variant in two sisters with progressive spastic paraplegia, global developmental delay, shy character, and foot deformities. Sequencing was completed on whole‐blood messenger RNA (mRNA) and analyzed for gene expression outliers after exome sequencing analysis failed to identify a causative variant. AP4S1 was identified as an outlier and contained a rare homozygous variant located three bases upstream of exon 5 (NC_000014.8(NM_007077.4):c.295−3C>A). Confirmed by additional RNA‐seq, reverse‐transcription polymerase chain reaction, and Sanger sequencing, this variant corresponded with exon 5, including skipping, altered isoform usage, and loss of expression from the canonical isoform 2 (NM_001128126.3). Previously, loss‐of‐function variants within AP4S1 were associated with a quadriplegic cerebral palsy‐6 phenotype, AP‐4 Deficiency Syndrome. In this study, the inclusion of mRNA‐seq allowed for the identification of a previously missed splice‐altering variant, and thereby expands the mutational spectrum of AP‐4 Deficiency Syndrome to include impacts to some tissue‐dependent isoforms.
On the Robustness of Entropy-Based Similarity Measures in Evaluation of Subcategorization Acquisition Systems

Some statistical learning systems are evaluated using measures of distributional similarity. To deal with the problem of zero events in the distributions under comparison, smoothing is frequently performed before similarity measures are applied. Smoothing alters the information in the original distribution, and may add noise to the results. Here, we investigate the sensitivity of entropy-based similarity measures to noise from uninformative smoothing. Our experiments with two subcategorization acquisition systems show that similarity measures vary in their robustness. While some are led astray by noise from smoothing, others are more resilient.
Fibroblast growth factor‐4 induces vascular permeability, angiogenesis, and arteriogenesis in a rabbit hind limb ischemia model

Previous studies have shown that fibroblast growth factor (FGF)‐1, FGF‐2, and FGF‐5 induce therapeutic angiogenesis. Here, we investigated the potential of FGF‐4 for therapeutic neovascularization in comparison to vascular endothelial growth factor (VEGF), using adenoviral gene transfer in a novel rabbit hind limb ischemia model, with ischemia restricted to the calf. Magnetic resonance imaging and a modified Miles assay showed that both AdFGF‐4 and AdVEGF given intramuscularly (i.m.) resulted in increases in vascular permeability and edema in transduced muscles 6 days after the gene transfer. In contrast, recombinant FGF‐4 protein injected in the rabbit skin did not induce acute vascular permeability. Injections (i.m.) of AdFGF‐4 and AdVEGF, but not intra‐arterially administered AdVEGF, increased collateral growth, popliteal blood flow, and muscle perfusion compared with controls. The angiogenesis response consisted mainly of the enlargement of pre‐existing vessels rather than an increase in capillary density. Adenoviral FGF‐4 overexpression up‐regulated endogenous VEGF, which may explain many of the effects thought to be specific for VEGF such as the increase in vascular permeability. This study demonstrates for the first time that FGF‐4 induces vascular permeability, therapeutic angiogenesis, and arteriogenesis comparable to that of VEGF and could be useful for the treatment of peripheral vascular disease.
建構一個以共時與歷時語言研究為導向的歷史語料庫 (Historical Corpora for Synchronic and Diachronic Linguistics Studies)

The Academia Sinica Ancient Chinese Corpus is designed for linguistic research. The corpus contains ancient texts that are selected because of their usefulness in grammatical and lexical studies, as well as an inspection program with keyword searching, statistics, and collocation functions. The corpus is divided into three subcorpora according to stages of grammatical developments, thus both synchronic and diachronic studies can be performed on them. Their current sizes are as follows: * E-Mail: Weipc@pluto.ihp. sinica.tw + ** 131
A General Population-Genetic Model for the Production by Population Structure of Spurious Genotype–Phenotype Associations in Discrete, Admixed or Spatially Distributed Populations

In linkage disequilibrium mapping of genetic variants causally associated with phenotypes, spurious associations can potentially be generated by any of a variety of types of population structure. However, mathematical theory of the production of spurious associations has largely been restricted to population structure models that involve the sampling of individuals from a collection of discrete subpopulations. Here, we introduce a general model of spurious association in structured populations, appropriate whether the population structure involves discrete groups, admixture among such groups, or continuous variation across space. Under the assumptions of the model, we find that a single common principle—applicable to both the discrete and admixed settings as well as to spatial populations—gives a necessary and sufficient condition for the occurrence of spurious associations. Using a mathematical connection between the discrete and admixed cases, we show that in admixed populations, spurious associations are less severe than in corresponding mixtures of discrete subpopulations, especially when the variance of admixture across individuals is small. This observation, together with the results of simulations that examine the relative influences of various model parameters, has important implications for the design and analysis of genetic association studies in structured populations.
Advanced Sensor Network Software Deployment using Application-level Quality Goals

If we are to deploy sensor applications in a realistic business context, we must provide innovative middleware services to control and enforce required system behavior. Sensor application developers typically reason about required system behavior in terms of high-level quality goals. Due to the extreme dynamism, scale and unreliability of wireless sensor networks, managing these goals using contemporary software management techniques without support for high-level quality goals quickly becomes challenging. This paper presents QARI, a middleware service which addresses these management challenges by offering a simple yet flexible way to define, enforce, and maintain high-level quality goals for software deployment in wireless sensor networks. We have evaluated QARI using the LooCI component model on two sensor node platforms; results confirm that QARI enables quality aware software deployment for a single application as well as multiple applications, and even in the presence of node failure and mobility.
Time series predictor of Ku band rain attenuation over an Earth–space path at a tropical location

A channel model is proposed to predict the time series of Ku band rain attenuation during a rain event at a tropical location. The model is based on considering the Gaussian distribution of the conditional occurrence of rain attenuation with a particular value of the attenuation occurring before. The mean (μ) and standard deviation (σ) for the distribution are modeled with the experimental data. The measured attenuation at a particular time instant is used to obtain μ and σ and to predict the attenuation after certain interval. The channel model has tested well giving the predicted attenuation that agrees with the measured value with a mean error within 15% higher than 1 dB. Validity of the model is also tested with the first‐order and second‐order statistics of attenuation occurrence, on a long‐term basis. The method can also be applied even if attenuation measurements are missing for certain period of time with increased error. Copyright © 2011 John Wiley & Sons, Ltd.
Exploiting the Rule Structure for Decision Making within the Independent Choice Logic

This paper introduces the independent choice logic, and in particular the "single agent with nature" instance of the independent choice logic, namely ICLDT. This is a logical framework for decision making uncertainty that extends both logic programming and stochastic models such as influence diagrams. This paper shows how the representation of a decision problem within the independent choice logic can be exploited to cut down the combinatorics of dynamic programming. One of the main problems with influence diagram evaluation techniques is the need to optimise a decision for all values of the 'parents' of a decision variable. In this paper we show how the rule based nature of the ICLDT can be exploited so that we only make distinctions in the values of the information available for a decision that will make a difference to utility.
Characteristics of aldehydes: concentrations, sources, and exposures for indoor and outdoor residential microenvironments.

Simultaneous indoor and outdoor measurements of aldehydes were made at 6 residential houses located in a suburban New Jersey area during the summer of 1992. Each house was measured for six days and controlled for ventilation and gas combustion conditions during the study. Formaldehyde, acetaldehyde, and seven other aldehydes were identified in the residential air. The study presents the first measurements of nine aldehyde species in both indoor and outdoor air. The total concentrations of the nine aldehydes were 19.12±10.88 ppb outdoors and 62.57±21.75 ppb indoors. Formaldehyde was the most abundant aldehyde