Miracle-dz/UniFAR-FTUs huggingface dataset:
Cornea TCF 4 Triplet Repeat Expansion and Nuclear RNA Foci in Fuchs ’ Endothelial Corneal Dystrophy

Citation: Mootha VV, Hussain I, Cunnusamy K, et al. TCF4 triplet repeat expansion and nuclear RNA foci in Fuchs’ endothelial corneal dystrophy. Invest Ophthalmol Vis Sci. 2015;56:2003–2011. DOI:10.1167/ iovs.14-16222 PURPOSE. Expansion of the intronic CTG18.1 triplet repeat locus within TCF4 contributes significant risk to the development of Fuchs’ endothelial corneal dystrophy (FECD) in Eurasian populations, but the mechanisms by which the expanded repeats result in degeneration of the endothelium have been hitherto unknown. The purpose of this study was to examine FECD endothelial samples for the presence of RNA nuclear foci, the hallmark of toxic RNA, as well as evidence of haploinsufficiency of TCF4.
PLANT BIOPRINTING : NOVEL PERSPECTIVE FOR PLANT BIOTECHNOLOGY

Bioprinting is a technical innovation that has revolutionized tissue engineering. Using conventional printer cartridges filled with cells as well as a suitable scaffold, major advances have been made in the biomedical field, and it is now possible to print skin, bones, blood vessels, and even organs. Unlike animal systems, the application of bioprinting in simple plant tissue cells is still in a nascent phase and has yet to be studied. One major advantage of plants is that all living parts are reprogrammable in the form of totipotent cells. Plant bioprinting may improve scientists’understanding of plant shape and morphogenesis, and could serve for the mass production of desired tissues or plants, or even the production of plant-based biomaterial for industrial uses. This perspectives paper explores these possibilities using knowledge on what is known about bioprinting in other biosystems.
Effect of Betaine Supplementation on Performance Parameters , Betaine-homocysteine S-methyltransferase Gene Expression in Broiler Chickens Consume Drinking Water with Different Total Dissolved Solids

The objective of the current work was to evaluate the effect of drinking water with various levels of total dissolved solids (TDS) and betaine supplementation on the performance and gene expression of betaine-homocysteine S-methyltransferase (BHMT) in broiler chickens. In a completely randomized design with a 3×2 factorial arrangement, chicks were assigned to six treatments with four replicates and 15 chicks per each. The treatments were included of three levels of total dissolved solids (400, 2.000 and 3.500 ppm) and two levels of betaine supplementation (0 and 0.2% of diet). Weight gain decreased and feed conversion ratio and water intake increased as TDS of water increased. Betaine supplementation had no effect on weight gain and water intake during the grower period, but had a significant effect on gain and feed conversion ratio in the finisher period. Mortality rate and excreta moisture content increased as TDS of water increased. Excreta moisture content decreased with betaine supplementation. Gene expression of BHMT decreased significantly with increases in TDS level and betaine supplementation increased its expression (28 folds) as compared with the non-additive group. It was concluded that consumption of drinking water with higher than 2.000 ppm TDS adversely and betaine supplementation positively affect the performance of broiler chickens. The lowest feed conversion ratio was seen in chicks fed 400 and 2.000 ppm TDS with betaine supplementation.
Measuring Corporate Reputation using Sentiment Analysis

In recent years, new digital media have become important for social networking and content sharing. Due to their large diffusion, social media platforms have also both increased the strategic importance of managing corporate reputation and rendered this more difficult. Companies are increasingly apprehensive about information and opinions that can spread through online communities rapidly without any control. While social media platforms increase the power of stakeholders, they also represent a large-scale source of information about feelings, opinions and sentiments of people that allow us to measure and monitor reputation through the analysis of user generated content in real-time. In this paper, we show how social media content can be used to measure the online reputation of a company. Furthermore, we present an open platform that uses a sentiment analysis algorithm on twitter traffic to monitor the real time evolution of company reputation.
Loss of Pax6 Causes Regional Changes in Dll1 Expression in Developing Cerebral Cortex

The transcription factor Pax6 controls multiple aspects of forebrain development. Conditional deletion of Pax6 in embryonic mouse cortex causes increased proliferation of cortical progenitor cells and a concomitant decrease in neural differentiation. Notch signaling regulates the balance between proliferation and differentiation of cortical progenitor cells, suggesting a possible connection between Pax6 and Notch signaling. We investigated how expression of the Notch ligand delta-like 1 (Dll1) is altered by loss of Pax6. Acute cortex-specific deletion of Pax6 resulted in a widespread decrease in the density of Dll1+ cells at embryonic days 12.5 and 13.5 (E12.5 and E13.5). In constitutive loss-of-function mutants, decreases in the densities of Dll1+ cells were more limited both spatially and temporally. Controlled over-expression of Pax6 had no detectable effect on Dll1 expression. The proneural transcription factor Neurog2 is a target of Pax6 that can activate Dll1 expression and we found clear co-expression of Neurog2 and Dll1 in radial glial progenitors, suggesting that Pax6’s effect on Dll1 could be mediated through Neurog2. However, we found no change in Dll1+ cells in Neurog2−/− cortex suggesting either that Neurog2 is not directly involved, or that its loss of function in embryonic cortex can be compensated for.
Transitioning Towards More Equality? Wealth Gender Differences and the Changing Role of Explanatory Factors Over Time

We investigate the explanatory factors that have contributed to changing wealth levels and the gender wealth gap in Germany over the period 2002-2012. In particular, we analyze the role of changes in labor supply, permanent income, portfolio composition, and marital status on wealth accumulation. Using individual level micro data from the German Socio-Economic Panel results show that real mean wealth levels for the working age population have been decreasing for both women and men since 2002 and that the wealth gap has decreased by 13.5% to 30.700€. We show that the increased participation of women in the labor market and their occupational structure had an increasing positive role on women’s wealth accumulation. Making use of the panel dimension in the data and of Oaxaca-Blinder and Firpo, Fortin, Lemieux decompositions, in comparison to previous analyses, a diminishing role of permanent income is observed, due both to a reduction in the gender difference in permanent income and in gender differences in its returns. Overall, the evidence points to more equal wealth accumulation both in terms of characteristics and returns.
Stochastic Modeling for Parameters of Modified Car-Following Model in Area-Based Traffic Flow

The driving behavior in area-based (i.e., non-lane based) traffic is induced by the presence of other individuals in the choice space from the driver’s visual perception area. The driving behavior of a subject vehicle is constrained by the potential leaders and leaders are frequently changed over time. This paper is to determine a stochastic model for a parameter of modified intelligent driver model (MIDM) in area-based traffic (as in developing countries). The parametric and non-parametric distributions are presented to fit the parameters of MIDM. The goodness of fit for each parameter is measured in two different ways such as graphically and statistically. The quantile-quantile (Q-Q) plot is used for a graphical representation of a theoretical distribution to model a parameter and the Kolmogorov-Smirnov (K-S) test is used for a statistical measure of fitness for a parameter with a theoretical distribution. The distributions are performed on a set of estimated parameters of MIDM. The parameters are estimated on the real vehicle trajectory data from India. The fitness of each parameter with a stochastic model is well represented. The results support the applicability of the proposed modeling for parameters of MIDM in area-based traffic flow simulation. Keywords—Area-based traffic, car-following model, microsimulation, stochastic modeling.
Tracking the Sound of Human Affection: EEG Signals Reveal Online Decoding of Socio-Emotional Expression in Human Speech and Voice

This chapter provides a perspective from the latest EEG evidence in how brain signals enlighten the neurophysiological and neurocognitive mechanisms underlying the recog‐ nition of socioemotional expression conveyed in human speech and voice, drawing upon event‐related potentials’ studies (ERPs). Human sound can encode emotional mean‐ ings by different vocal parameters in words, real‐ vs. pseudo‐speeches, and vocaliza ‐ tions. Based on the ERP findings, recent development of the three‐stage model in vocal processing has highlighted initial‐ and late‐stage processing of vocal emotional stimuli. These processes, depending on which ERP components they were mapped onto, can be divided into the acoustic analysis, relevance and motivational processing, fine‐grained meaning analysis/integration/access, and higher‐level social inference, as the unfolding of the time scale. ERP studies on vocal socioemotions, such as happiness, anger, fear, sadness, neutral, sincerity, confidence, and sarcasm in the human voice and speech have employed different experimental paradigms such as crosssplicing, crossmodality priming, oddball, stroop, etc. Moreover, task demand and listener characteristics affect the neural responses underlying the decoding processes, revealing the role of attention deployment and interpersonal sensitivity in the neural decoding of vocal emotional stimuli. Cultural orientation affects our ability to decode emotional meaning in the voice. Neurophysiological patterns were compared between normal and abnormal emotional processing in the vocal expressions, especially in schizophrenia and in congenital amu ‐ sia. Future directions highlight the study on human vocal expression aligning with other nonverbal cues, such as facial and body language, and the need to synchronize listener's brain potentials with other peripheral measures.
NUMERICAL PREDICTION OF EROSION WEAR FOR HYDRAULIC SPOOL VALVE

The flow characteristics of hydraulic spool valve change significantly due to the solid particle erosion. In the present work, a numerical mechanism model is established to predict the material removal rate and the worn profile with erosion. First, Reynolds stress model is used to obtain the fluid phase information. Second, the particle trajectory is solved by a stochastic separated flow model. Third, an erosion formula is selected to calculate the local erosion rate. Then, a time-discrete and spatial-discrete update method is proposed to obtain the worn profile. The worn profiles obtained by model accord well with experiments, and, the shape of the worn profile is not quartercircle, the quarter-circle assumption that always used in the erosion prediction of spool valve can lead to the erroneous result. By comparing with the laboratory experiments and a field case, the present model is well verified.
The role of rifaximin in the chemoprophylaxis of travelers ’ diarrhoea particularly aiming at the first 2 weeks of travel : a meta-analysis of 5 randomized , double-blind , controlled trials

The role of rifaximin in travelers’ diarrhoea (TD) patients remains effective in major researches and needs further exploration. We pursued one meta-analysis to evaluate the efficiency of rifaximin on TD patients. So a systematic search of integrant databases was performed to identify RCTs comparing the use of rifaximin with placebo in TD patients. Results were expressed as risk ratios with accompanying 95% confidence intervals. This meta-analysis was accomplished with the fixed-effect in accordance with the heterogeneity. Then five studies involving 843 patients conformed our criteria. Rifaximin was associated with a reduction in the occurrence rate of classic travelers’ diarrhoea (RR, 0.474; 95% CI, 0.369-0.608), classic travelers’ diarrhoea during the first week (RR, 0.309; 95% CI, 0.196-0.487) and second week (RR, 0.477; 95% CI, 0.315-0.723) of travel. Furthermore during the first week of travel, rifaximin has a statistical better effect which directs the start time and duration of rifaximin use.
Doing Quantitative Field Research in Management Accounting

This chapter provides practical guidance to management accounting researchers on the design and execution of field studies that use quantitative data analysis to test or build theory. We discuss common purposes for conducting field research and provide a brief overview of the accomplishments and failings of recent field research in management accounting. We then turn to the "doing" of quantitative field research, discussing practical considerations related to the role of theory; site election; and data identification, collection and preparation. Finally, we reflect on how field research practices may be amended to address some of the criticisms of prior field research.
Instantaneous Thrust Control of the Linear Switched Reluctance Motors with Segmental Translator

The linear switched reluctance machine (LSRM) has all advantages of rotary switched reluctance machine including simple and rugged structure, absence of magnetic material and windings on translator, high reliability and appropriate performance over a wide range of speed. Like rotary switched reluctance motor with segmental rotor, segmental translator linear switched reluctance motor (STLSRM) has capability to produce higher output power/weight in comparison to the conventional linear switched reluctance motors. Due to high advantages of the STLSRM drive, various control algorithms including current control, model predictive control, direct force control, universal control and force distribution function are investigated for the first time to control the instantaneous thrust of this motor. Applying these algorithms to a typical three-phase STLSRM, simulation results are presented and they are compared together from the force ripple reduction point of view.
Towards near-infrared photosensitization of tungsten trioxide nanostructured films by upconverting nanoparticles

Upconverting materials are currently explored in the field of solar energy conversion in order to extend the light harvesting properties of semiconductors to the near-infrared (NIR) region, their absorption being generally limited to the visible region of the solar spectrum. Here, we propose to photosensitize nanostructured films of tungsten oxide (WO3), a semiconductor widely investigated in photoelectrochemistry, photocatalysis and electrochromics, with NaGdF4:Er3+, Yb3+ upconverting nanoparticles (UCNPs). In order to do so, we fabricate nanocomposite films of WO3 and NaGdF4:Er3+, Yb3+ UCNPs (indicated as UCNP/WO3 films). Current–time measurements show that, under irradiation at λ = 980 nm, a relative increase in current of about 3% with respect to the dark current is observed in the UCNP/WO3 films. The UCNP/WO3 mol% ratio and the temperature of the thermal treatment of the nanocomposite films are both critical to simultaneously achieve photosensitization and charge carrier transport in the UCNP/WO3 films.
BlockLite: A Lightweight Emulator for Public Blockchains

Blockchain is an enabler of many emerging decentralized applications in areas of cryptocurrency, Internet of Things, smart healthcare, among many others. Although various open-source blockchain frameworks are available, the infrastructure is complex enough and difficult for many users to modify or test out new research ideas. To make it worse, many advantages of blockchain systems can be demonstrated only at large scales, e.g., thousands of nodes, which are not always available to researchers. This demo paper presents a lightweight single-node emulator of blockchain systems, namely \mbox{BlockLite}, designed to be executing real proof-of-work workload along with peer-to-peer network communications and hash-based immutability. BlockLite employs a preprocessing approach to avoid the per-node computation overhead at runtime and thus scales to thousands of nodes. Moreover, BlockLite offers an easy-to-use programming interface allowing for a Lego-like customization to the system, e.g. new ad-hoc consensus protocols.
POWVER Technical Report 2017-09 Title : Polynomial-Time Alternating Probabilistic Bisimulation for Interval

Interval Markov decision processes (IMDPs) extend classical MDPs by allowing intervals to be used as transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that relaxes the need of knowing the exact transition probabilities, which are usually difficult to get from real systems. In this paper, we discuss a notion of alternating probabilistic bisimulation to reduce the size of the IMDPs while preserving the probabilistic CTL properties it satisfies from both computational complexity and compositional reasoning perspectives. Our alternating probabilistic bisimulation stands on the competitive way of resolving the IMDP nondeterminism which in turn finds applications in the settings of the controller (parameter) synthesis for uncertain (parallel) probabilistic systems. By using the theory of linear programming, we improve the complexity of computing the bisimulation from the previously known EXPTIME to PTIME. Moreover, we show that the bisimulation for IMDPs is a congruence with respect to two facets of parallelism, namely synchronous product and interleaving. We finally demonstrate the practical effectiveness of our proposed approaches by applying them on several case studies using a prototypical tool.
Comparing Hedging Effectiveness: An Application of the Encompassing Principle

An empirical methodology is developed for statistically testing the hedging effectiveness among competing futures contracts. The presented methodology is based on the encompassing principle, widely used in the forecasting literature, and applied here to minimum variance hedging regressions. Intuitively, the test is based on an alternative futures contract's ability to reduce residual basis risk by offering either diversification or a smaller absolute level of basis risk than a preferred futures contract. The methodology is easily extended to cases involving multiple hedging instruments and general hedge ratio models. Empirical applications suggest that the encompassing methodology can provide information beyond traditional approaches of comparing hedging effectiveness.
A Novel Finite-Control-Set Model Predictive Directive Torque Control Strategy of Permanent Magnet Synchronous Motor with Extended Output

The performance of conventional direct torque control strategy from the viewpoint of flux and torque ripples has been unsatisfactory. Therefore, this study aims to propose a novel finite-control-set model predictive direct torque control strategy with extended output based on two-step prediction. An appropriate pre-selected vector, which is modulated in a specific manner, is selected through a look-up table and then used to optimise the pre-selected voltage vector based on the computing result from the model prediction and output it. The proposed strategy extends the range of the vectors that can be used to enhance the flux and torque control performance and reduce ripples and computational complexity in comparison with the conventional finite-control-set model predictive direct torque control. The feasibility of the proposed method is verified by conducting a verification test using dSPACE and Tyhpoon HIL 402 experimental platform.
2 Optimal Allocation of PMUs considering Binary Integer Linear Programming Method

In this paper, a Dynamic State Estimation (DSE) method with optimal allocation of Phasor Measurement Units (PMU) is formulated to identify bad data in the power system. A Generalized Normal Residual (GNR) method is proposed to detect and identify bad data with optimal PMU and conventional measurements. Apart from all other methods proposed to identify bad data in the system, a novel index to compute a threshold value for each type of measurement is proposed. The index proposed depends on a number of measurements and normalized residuals computed which exactly determines the bad data bus. The measured data formulated with the integration of optimal PMU measurements and conventional measurements are utilized in dynamic state estimation method to measure accurate states and identify measurement errors of the system. Binary Integer Linear Programming (BILP) method modeled with zero injection buses is utilized for optimal allocation of PMUs in the power system. Dynamic State Estimation with and without bad data is compared to show its effectiveness in estimating states of the system. MATLAB Simulations on IEEE 14, 30, 57 and 118 buses are carried out by BILP method to show the effectiveness of the proposed method. AMS Subject Classification: 90C39, 49L20
Detection of Diversified Stego Sources with CNNs

The goal of this article is construction of steganalyzers capable of detecting a variety of embedding algorithms and possibly identifying the steganographic method. Since deep learning today can achieve markedly better performance than other machine learning tools, our detectors are deep residual convolutional neural networks. We explore binary classifiers trained as cover versus all stego, multi-class detectors, and bucket detectors in a feature space obtained as a concatenation of features extracted by networks trained on individual stego algorithms. The accuracy of the detector to identify steganography is compared with dedicated detectors trained for a specific embedding algorithm. While the loss of detection accuracy w.r. t. increasing number of steganographic algorithms increases only slightly as long as the embedding schemes are known, the ability of the detector to generalize to previously unseen steganography remains a challenging task.
Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation

We explore ways of incorporating bilingual dictionaries to enable semi-supervised neural machine translation. Conventional back-translation methods have shown success in leveraging target side monolingual data. However, since the quality of back-translation models is tied to the size of the available parallel corpora, this could adversely impact the synthetically generated sentences in a low resource setting. We propose a simple data augmentation technique to address both this shortcoming. We incorporate widely available bilingual dictionaries that yield word-by-word translations to generate synthetic sentences. This automatically expands the vocabulary of the model while maintaining high quality content. Our method shows an appreciable improvement in performance over strong baselines.