Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterozygous PINK1 p.G411S increases risk of Parkinson’s disease via a dominant-negative mechanism

See Gandhi and Plun-Favreau (doi:10.1093/aww320) for a scientific commentary on this article. It has been postulated that heterozygous mutations in recessive Parkinson’s genes may increase the risk of developing the disease. In particular, the PTEN-induced putative kinase 1 (PINK1) p.G411S (c.1231G>A, rs45478900) mutation has been reported in families with dominant inheritance patterns of Parkinson’s disease, suggesting that it might confer a sizeable disease risk when present on only one allele. We examined families with PINK1 p.G411S and conducted a genetic association study with 2560 patients with Parkinson’s disease and 2145 control subjects. Heterozygous PINK1 p.G411S mutations markedly increased Parkinson’s disease risk (odds ratio = 2.92, P = 0.032); significance remained when supplementing with results from previous studies on 4437 additional subjects (odds ratio = 2.89, P = 0.027). We analysed primary human skin fibroblasts and induced neurons from heterozygous PINK1 p.G411S carriers compared to PINK1 p.Q456X heterozygotes and PINK1 wild-type controls under endogenous conditions. While cells from PINK1 p.Q456X heterozygotes showed reduced levels of PINK1 protein and decreased initial kinase activity upon mitochondrial damage, stress-response was largely unaffected over time, as expected for a recessive loss-of-function mutation. By contrast, PINK1 p.G411S heterozygotes showed no decrease of PINK1 protein levels but a sustained, significant reduction in kinase activity. Molecular modelling and dynamics simulations as well as multiple functional assays revealed that the p.G411S mutation interferes with ubiquitin phosphorylation by wild-type PINK1 in a heterodimeric complex. This impairs the protective functions of the PINK1/parkin-mediated mitochondrial quality control. Based on genetic and clinical evaluation as well as functional and structural characterization, we established p.G411S as a rare genetic risk factor with a relatively large effect size conferred by a partial dominant-negative function phenotype.
Incremental / decremental SVM for function approximation

Training a support vector regression (SVR) resumes to the process of migrating the vectors in and out of the support set along with modifying the associated thresholds. This paper gives a complete overview of all the boundary conditions implied by vector migration through the process. The process is similar to that of training a SVM, though the process of incrementing / decrementing of vectors into / out of the solution does not coincide with the increase / decrease of the associated threshold. The analysis shows the details of incremental and decremental procedures used to train the SVR. Vectors with duplicate contribution are also considered. The migration of vectors among sets on decreasing the regularization parameter C is particularly given attention. Eventually, experimental data show the possibility of modifying this parameter on a large scale, varying it from complete training (overfitting) to a calibrated value, to tune up the approximation performance of the regression.
Software risk management

Modern software development faces many challenges nowadays. In order to avoiding, minimizing, and monitoring the risks and their impact, it is important to identify, estimate and evaluate the risks. The study addresses software risk management in software development area. An approach to analysis, structuring, and usage is proposed for the information technology (IT) projects in the risk management context. It is based on the study of the existing theoretical knowledge and practices and examples of better management. The author provides recommendations on software risk management and gives a brief overview of the automated software tools.
Aggregation analysis in empirical multivariate dynamic models *

The paper analyses the aggregation problem when the micro relations consist of multivariate specifications. We focus on a generalization of the model selection criterion proposed in the previous literature for discriminating between aggregate and disaggregate models. In addition, we extend the model selection problem in forecasting aggregate variables out of sample. Both the stationary and cointegrated VAR processes are considered. The paper also introduces a new test of perfect aggregation in the multivariate environment. The properties of the test are analysed by Monte Carlo simulation. The results suggest adopting a correction to reduce the small sample bias of the test.
An Efficient Software Clone Detection System based on the Textual Comparison of Dynamic Methods and Metrics Computation

clone is a kind of software recycling that has a greater influence on the maintenance of huge software systems. The business services are provided by web applications that employ a combination of page design and language code scripting. Usually, code redundancy in applications result from copy and paste practices called code clones. Searching for software clones is the key objective of this research. The clone identification procedure which is introduced in this paper is a hybrid approach that depends on template conversion and metrics comparison. There are four phases involved in the proposed scheme, namely, input and pre-processing, template conversion, metrics computation and clone type detection. File integration, elimination of white noise and statement normalization are the steps involved in the pre-processing stage. The metrics that are calculated includes numerous lines of code, arguments, looping statements, return statements and conditional statements. The pairs that have identical characteristics during textual evaluation are termed as the clones. This method of clone detection seems to be less complex with better accuracy and efficiency in contrast to other existing methods. The performance analysis is made against the prevailing systems to show efficiency improvement obtained through this method. The implementations are carried out with the help of JAVA. Keywordsdetection, Template conversion, Metrics computation, File integration, Normalization.
Non‐Farm Employment and Incomes in Rural Cambodia

Rural households in Cambodia derive income from various sources. On average, non‐farm income accounts for more than 60 per cent of total household income. However, the average masks the substantial heterogeneity of non‐farm employment. We account for this heterogeneity and find significant differences in non‐farm participation and incomes across segments of the income distribution. The poor and the less well‐educated participate less in the non‐farm sector, and when they do work in the non‐farm sector, they work in low‐paid jobs and earn lower incomes. Accounting for endogeneity and sample selection issues, we conduct an empirical enquiry of the determinants of participation in non‐farm activities and of non‐farm incomes. As expected, we find that education plays a major role in accessing more remunerative non‐farm employment. Interestingly, we do not find evidence that women, ethnic minorities, or the land‐poor are disadvantaged in access to the non‐farm sector. Geographical location plays a role in access to and income from non‐farm employment, indicating the importance of local context.
Low complexity greedy detection method with generalized multicarrier index keying OFDM

Multicarrier Index Keying (MCIK) is a recently developed technique that modulates subcarriers but also indices of the subcarriers. In this paper a novel low-complexity detection scheme of subcarrier indices is proposed for an MCIK system and addresses a substantial reduction in complexity over the optimal maximum likelihood (ML) detection. For the performance evaluation, a closed-form expression for the pairwise error probability (PEP) of an active subcarrier index, and a tight approximation of the average PEP of multiple subcarrier indices are derived in closed-form. The theoretical outcomes are validated using simulations, at a difference of less than 0.1dB. Compared to the optimal ML, the proposed detection achieves a substantial reduction in complexity with small loss in error performance (≤ 0.6dB).
A 18 mW CT ΔΣ modulator with 25 MHz bandwidth for next generation wireless applications

The design of a wideband low-power continuous-time (CT) delta-sigma modulator is presented in this paper. A modified feed-forward architecture is proposed to realize the low-power loop filter as well as cancel the out-of-band peaking in the signal transfer function. Several high-speed low-power design techniques are used in the circuits. The modulator achieves 60 dB dynamic range (DR) within 20 MHz signal bandwidth, and 55 dB DR within 25 MHz signal bandwidth. Clocked at 400 MHz, the modulator consumes only 10 mA current from a 1.8 V supply.
Dimensionality reduction for interactive data visualization via a Geo-Desic approach

This work presents a dimensionality reduction (DR) framework that enables users to perform either the selection or mixture of DR methods by means of an interactive model, here named Geo-Desic approach. Such a model consists of linear combination of kernel-based representations of DR methods, wherein the corresponding coefficients are related to coordinated latitude and longitude inside of the world map. By incorporating the Geo-Desic approach within an interface, the combination may be made easily and intuitively by users -even non-expert ones- fulfilling their criteria and needs, by just picking up points from the map. Experimental results demonstrates the usability and ability of DR methods representation of proposed approach.
Modulating Dynamic Models for Lip Motion Generation

Generation of natural human motion is one of key techniques for multimodal dialogue systems with a human-like avatar. In particular, natural and expressive lip motion synthesis is necessary to make conversation between a user and an avatar richer. However, such expressive lip motion is often difficult to be generated automatically because it can be changed depending on phonemic context and prosody. To address this difficulty, we introduce a novel motion generation method on the basis of the modulation of a set of dynamic models learned from neutral motion data. As a suitable model for lip motion generation, we adopt a hybrid dynamical system, which consists of linear dynamical systems for each motion unit and a symbolic automaton for switching between these units. We show that, from the viewpoint of control theory, it is possible to modulate linear dynamical systems for various types of motion. Early results demonstrate the applicability of the proposed method using lip motion synthesis for simple phoneme sequences.
Financial Risk Management, Firm Structure & Valuation, an Empirical Analysis.

This paper investigates if empirical evidence on the rm structure, its market value and its corporate hedging decisions support the theoretical model proposed by Boyer, Boyer and Garcia (2005). The database is composed by rms of the US S&P500 in 2004, market and accounting data are collected from 1993 to 2004, then hedging and managerial shareholding data are extracted from 2004 annual reports. Our results show empirical relations between the rm market value, its beta and the risk free rate. Moreover, we have created an estimator for the exibility of the rms activities. The Flexibility Factor is validated as a new empirical determinant of operational risk hedging decisions. Finally, for the explanation of the use of derivatives, we provide evidence that signi cant variables change according to the type of risk hedged. Keywords: Flexibility, Financial Risk Management, Firm Value, Project Portfolio Management 1Master Student, Universite de Montreal. E-mail: david.de. angelis@umontreal.ca 2 I would like to thank particularly Rene Garcia for his advice and his nancial support during my Master. I am also grateful to Marcel and Martin Boyer for helpful comments, Julien Picault for suggestions as discussant and Catherine Gendron for excellent research assistance. 3Hydro-Quebec Professor of risk management and mathematical nance, Universite de Montreal, and Fellow of CIRANO and CIREQ. E-mail: rene.garcia@umontreal.ca
Barriers to growth

Why have Latin American economies lagged countries with similar ability to adopt technologies and similar preferences for market goods? Four economists document this “persistent economic stagnation” and analyze it through a neoclassical growth model. Capital, labor and total factor productivity are potential explanations, according to this model, and the economists narrow it to the last: TFP, “the efficiency with which an economy uses its capital and labor services. ” Why have Latin American countries been persistently inefficient? Because of barriers to competition, suggest the economists, who show that when barriers were eliminated in Latin America, productivity and output climbed significantly.
Mobility Based Data Collection Algorithm for Wireless Sensor Networks

With the aim of prolonging network lifetime, many energy efficient algorithms and data dissemination protocols have been proposed for wireless sensor networks (WSNs) recently. Since sensor nodes close to sink have to bear more traffic burden to forward data, they will quickly deplete their energy which leads to energy hole and network partition problem. Adding mobility into sensor networks brings in new opportunities to improve network performance in terms of energy consumption, network lifetime, latency etc. In this paper, our Mobility based Data Collection Algorithm (MDCA) for Wireless Sensor Networks is proposed. Multiple mobile sinks are utilized, instead of one static sink, to move back and forth along predetermined paths to collect data. One of the predetermined paths is the diameter of the circle area and the other two are fixed on arc lines. The mobile sinks only sojourn at some fixed points to collect data from relevant areas. The effectiveness of our proposed algorithm is demonstrated through extensive simulation results.
Super-Twisting Differentiator-Based High Order Sliding Mode Voltage Control Design for DC-DC Buck Converters

This paper aims to focus on the smooth output of DC-DC buck converters in wireless power transfer systems under input perturbations and load disturbances using the high-order sliding mode controller (HOSM) and HOSM with super-twisting differentiator (HOSM + STD). The proposed control approach needs only measurement of converter output voltage. Theoretical analysis and design procedures, as well as the super-twisting differentiator of the proposed controller are presented in detail with the prescribed convergence law of high-order sliding modes. Comparisons of both simulation and experimental results among conventional proportional-integral (PI) control, traditional sliding mode control (SMC), HOSM and HOSM + STD under various test conditions such as steady state, input voltage perturbations and output load disturbances, are presented and discussed. The results demonstrate and validate the effectiveness and robustness of the proposed control method.
Parafoveal Retinal Vascular Response to Pattern Visual Stimulation Assessed with OCT Angiography

We used optical coherence tomography (OCT) angiography with a high-speed swept-source OCT system to investigate retinal blood flow changes induced by visual stimulation with a reversing checkerboard pattern. The split-spectrum amplitude-decorrelation angiography (SSADA) algorithm was used to quantify blood flow as measured with parafoveal flow index (PFI), which is proportional to the density of blood vessels and the velocity of blood flow in the parafoveal region of the macula. PFI measurements were taken in 15 second intervals during a 4 minute period consisting of 1 minute of baseline, 2 minutes with an 8 Hz reversing checkerboard pattern stimulation, and 1 minute without stimulation. PFI measurements increased 6.1±4.7% (p = .001) during the first minute of stimulation, with the most significant increase in PFI occurring 30 seconds into stimulation (p<0.001). These results suggest that pattern stimulation induces a change to retinal blood flow that can be reliably measured with OCT angiography.
Nanomedicines for cancer therapy: state-of-the-art and limitations to pre-clinical studies that hinder future developments

The ability to efficiently deliver a drug or gene to a tumor site is dependent on a wide range of factors including circulation time, interactions with the mononuclear phagocyte system, extravasation from circulation at the tumor site, targeting strategy, release from the delivery vehicle, and uptake in cancer cells. Nanotechnology provides the possibility of creating delivery systems where the design constraints are decoupled, allowing new approaches for reducing the unwanted side effects of systemic delivery, increasing tumor accumulation, and improving efficacy. The physico-chemical properties of nanoparticle-based delivery platforms introduce additional complexity associated with pharmacokinetics, tumor accumulation, and biodistribution. To assess the impact of nanoparticle-based delivery systems, we first review the design strategies and pharmacokinetics of FDA-approved nanomedicines. Next we review nanomedicines under development, summarizing the range of nanoparticle platforms, strategies for targeting, and pharmacokinetics. We show how the lack of uniformity in preclinical trials prevents systematic comparison and hence limits advances in the field.
Relative Functional Comparison of Neural and Non-Neural Approaches for Syllable Segmentation in Devnagari TTS System.

This paper presents methods for automatic speech signal segmentation using neural network. Speech signal segmentation is carried out to form syllables. Syllable is a common unit for concatenative TTS systems. Concatenative TTS being using speech segments of recorded speech is natural as compare to Formant or Articulatory TTS systems. This TTS stores small segments of speech and join them together to form new word. This helps to generate more number of words based on very small database. As manual segmentation is very time consuming and it has certain limitation on naturalness, some neural network models are used to improve naturalness of resulting segments in speech synthesis. The proposed work explains how neural network approaches like Maxnet, K-means outweighs in performance than traditional non neural approaches like slope detection and simulated annealing. About more than 90% accuracy is achieved with neural network models for syllable segmentation which resulted in naturalness improvement of Marathi TTS
One-click white balance using human skin reflectance

Existing methods for white balancing photographs tend to rely on skilled interaction from the user, which is prohibitive for most amateur photographers. We propose a minimal interaction system for white balancing photographs that contain humans. Many of the pictures taken by amateur photographers fall into this category. Our system matches a user-selected patch of skin in a photograph to an entry in a skin reflectance function database. The estimate of the illuminant that emerges from the skin matching can be used to white balance the photograph, allowing users to compensate for biased illumination in an image with a single click. We compare the quality of our results to output from three other low-interaction methods, including commercial approaches such as Google Picasa's one-click relighting [19], a whitepoint-based algorithm [16], and Ebner's localized gray-world algorithm [7]. The comparisons indicate that our approach offers several advantages for amateur photographers.
NONLINEAR TIME SERIES MODELLING : ORDER IDENTIFICATION AND WAVELET FILTERING Abbreviated title : Nonlinear time series modelling

In this paper we discuss an approach for modelling nonlinear time series data based on wavelet smoothing. The technique involves decomposing the series into two components a deterministic component which when extracted by wavelet ̄ltering leaves a random component which can be easily modelled using well known linear time series modelling techniques or by a simple diagonal pure bilinear model discussed in this paper. The two components are then combined to describe the series. We also discuss how patterns present in the third order cumulants of the diagonal pure bilinear time series model can be used to identify the order of the model. Simulated data and real time series examples are used to illustrate the techniques.
Pedagogical effects of service-learning in a human exceptionality course: A comparison of two approaches

Service-learning. as a pedagogical approach for increasing social responsibility in students. has increasingly gained acceptance in higher education. With its emphasis on reflection and reciprocity. service-learning combines content-area learning with authentic community-based experiences in order to enhance understanding of the course content and to promote civic responsibility. Although several studies have investigated the effects of service-learning at the college level, few studies have specifically focused on the use of service-learning in special education. The purpose ofthls study was to examine the service-learning component ofan undergraduate human exceptionality course. One section ofthe course utilized an unlimited choice (UC) approach. in which participants were instructed to design and implement a service-learning project on their own. The second course section utilized a limited choice (LC) approach in which participants chose among three service options that had been prearranged by the instructors. Acomparative case study design was used in which multiple sources ofdata were analyzed in order to (a) develop an understanding ofthe service-learning experiences of participants engaged in the UC and LC projects, (b) identify similarities and/or differences in the service-learning experiences between sections, (c) identify differences in participants' perceptions of the benefits of the service-learning experience, and (d) determine if any pedagogical advantages or disadvantages resulted from the use ofeither • approach in the domains of course content and citizenship, or in the affective, behavioral, and cognitive dimensions of learning. The findings suggest that most participants in each section engaged in quality service-learning projects. The data also suggest that the service experience did contribute to their understanding of the course content and citizenship roles, and had some influence on the affective. behavioral. and cognitive dimensions of learning. Although similarities in experiences were identified, several differences were also identified between sections and within sections that may have affected participants' perceptions of the benefits of the experience. Overall, participants in the UC section perceived a greater benefit of the service experience than did participants from the LC section. Based on the findings of this study, recommendations for future practice were developed.
TECHNOLOGY Web Crawlers and Search Engines

In large distributed hypertext system like the Worl d-Wide Web; users find resources by following hypertext links. As the size of the system increase s the users must traverse increasingly more links t o find what they are looking for, until precise navigation becomes i mpractical. The WebCrawler is a tool that solves th ese problems by indexing and automatically navigating the Web. T his paper describes the basic definition of web and search engine and we have also explored the web crawler with its types.