Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal resource allocation for smart-grid powered MIMO broadcast channels

A novel framework is proposed for optimal resource management in multi-input multi-output (MIMO) downlink systems with smart-grid capabilities. Practical models are put forth to account for the stochastic renewable energy sources (RES), dynamic energy prices, as well as random wireless channels. Capitalizing on these models, a resource allocation task is formulated as an optimization problem that aims at maximizing the weighted sum-rate of MIMO broadcast channels. In addition to storage units accommodating the RES variability, a budget constraint is imposed for the worst-case energy transaction cost. Leveraging the information-theoretic uplink-downlink duality and Lagrange dual based subgradient method, an efficient algorithm is tailored to obtain the optimal strategy.
Comprehensive Sports Psychological Services for the Junior "A" Hockey Leagues in Canada

The Canadian Junior “A” Hockey League includes ten leagues that span across Canada. Junior hockey players face challenges during the developmental years that can have far-reaching implications. Most of these teams do not have access to sport psychologists, or high school, college or university counselors. There is a need for junior “A” hockey players to have access to positive and informed psychological services to enhance performance and their overall wellbeing. Currently there is no comprehensive sport psychology program in place for developing young athletes in hockey or any other sport that we know of. In this article, a brief literature review is provided with regard to sport psychology and sport counselling. Recommendations for increasing sport psychological services—for enhancing performance as well as overall wellbeing for individual athletes, teams, and the league—within junior “A” hockey, and the evaluation of services are also discussed.
Structural Analysis in Multi-Relational Social Networks

Modern social networks often consist of multiple relations among individuals. Understanding the structure of such multi-relational network is essential. In sociology, one way of structural analysis is to identify different positions and roles using blockmodels. In this paper, we generalize stochastic blockmodels to Generalized Stochastic Blockmodels (GSBM) for performing positional and role analysis on multi-relational networks. Our GSBM generalizes many different kinds of Multivariate Probability Distribution Function (MVPDF) to model different kinds of multi-relational networks. In particular, we propose to use multivariate Poisson distribution for multi-relational social networks. Our experiments show that GSBM is able to identify the structures for both synthetic and real world network data. These structures can further be used for predicting relationships between individuals.
Tumor classification by partial least squares using microarray gene expression data

MOTIVATION One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. The use of microarrays allows simultaneous monitoring of thousands of genes expressions per sample. This ability to measure gene expression en masse has resulted in data with the number of variables p(genes) far exceeding the number of samples N. Standard statistical methodologies in classification and prediction do not work well or even at all when N < p. Modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. RESULTS We propose a novel analysis procedure for classifying (predicting) human tumor samples based on microarray gene expressions. This procedure involves dimension reduction using Partial Least Squares (PLS) and classification using Logistic Discrimination (LD) and Quadratic Discriminant Analysis (QDA). We compare PLS to the well known dimension reduction method of Principal Components Analysis (PCA). Under many circumstances PLS proves superior; we illustrate a condition when PCA particularly fails to predict well relative to PLS. The proposed methods were applied to five different microarray data sets involving various human tumor samples: (1) normal versus ovarian tumor; (2) Acute Myeloid Leukemia (AML) versus Acute Lymphoblastic Leukemia (ALL); (3) Diffuse Large B-cell Lymphoma (DLBCLL) versus B-cell Chronic Lymphocytic Leukemia (BCLL); (4) normal versus colon tumor; and (5) Non-Small-Cell-Lung-Carcinoma (NSCLC) versus renal samples. Stability of classification results and methods were further assessed by re-randomization studies.
Acridone Derivative 8a Induces Oxidative Stress-Mediated Apoptosis in CCRF-CEM Leukemia Cells: Application of Metabolomics in Mechanistic Studies of Antitumor Agents

A new acridone derivative, 2-aminoacetamido-10-(3, 5-dimethoxy)-benzyl-9(10H)-acridone hydrochloride (named 8a) synthesized in our lab shows potent antitumor activity, but the mechanism of action remains unclear. Herein, we report the use of an UPLC/Q-TOF MS metabolomic approach to study the effects of three compounds with structures optimized step-by-step, 9(10H)-acridone (A), 10-(3,5-dimethoxy)benzyl-9(10H)-acridone (I), and 8a, on CCRF-CEM leukemia cells and to shed new light on the probable antitumor mechanism of 8a. Acquired data were processed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify potential biomarkers. Comparing 8a-treated CCRF-CEM leukemia cells with vehicle control (DMSO), 23 distinct metabolites involved in five metabolic pathways were identified. Metabolites from glutathione (GSH) and glycerophospholipid metabolism were investigated in detail, and results showed that GSH level and the reduced/oxidized glutathione (GSH/GSSG) ratio were significantly decreased in 8a-treated cells, while L-cysteinyl-glycine (L-Cys-Gly) and glutamate were greatly increased. In glycerophospholipid metabolism, cell membrane components phosphatidylcholines (PCs) were decreased in 8a-treated cells, while the oxidative products lysophosphatidylcholines (LPCs) were significantly increased. We further found that in 8a-treated cells, the reactive oxygen species (ROS) and lipid peroxidation product malondialdehyde (MDA) were notably increased, accompanied with decrease of mitochondrial transmembrane potential, release of cytochrome C and activation of caspase-3. Taken together our results suggest that the acridone derivative 8a induces oxidative stress-mediated apoptosis in CCRF-CEM leukemia cells. The UPLC/Q-TOF MS based metabolomic approach provides novel insights into the mechanistic studies of antitumor drugs from a point distinct from traditional biological investigations.
Unsupervised local color correction for coarsely registered images

The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time.
Forecasting Financial Distress by the Semiparametric Logistic Scoring

The aim of this paper is to compare the forecasting financial distress models named the logistic mixed scoring and the semi parametric logistic scoring. Altman scoring variables are used to predict financial situation of Tunisian firms. First, the mixed logistic scoring is applied in order to detect inobservable heterogeneity. Then, the semi parametric logistic scoring is established by the careful examination of the functional relationship between the explanatory variables and the probability of the distress. The proposed semi parametric model has a better discriminatory power and a predictive performance.
Changes in the Expression of Biofilm-Associated Surface Proteins in Staphylococcus aureus Food-Environmental Isolates Subjected to Sublethal Concentrations of Disinfectants

Sublethal concentrations (sub-MICs) of certain disinfectants are no longer effective in removing biofilms from abiotic surfaces and can even promote the formation of biofilms. Bacterial cells can probably adapt to these low concentrations of disinfectants and defend themselves by way of biofilm formation. In this paper, we report on three Staphylococcus aureus biofilm formers (strong B+++, moderate B++, and weak B+) that were cultivated with sub-MICs of commonly used disinfectants, ethanol or chloramine T, and quantified using Syto9 green fluorogenic nucleic acid stain. We demonstrate that 1.25–2.5% ethanol and 2500 μg/mL chloramine T significantly enhanced S. aureus biofilm formation. To visualize differences in biofilm compactness between S. aureus biofilms in control medium, 1.25% ethanol, or 2500 μg/mL chloramine T, scanning electron microscopy was used. To describe changes in abundance of surface-exposed proteins in ethanol- or chloramine T-treated biofilms, surface proteins were prepared using a novel trypsin shaving approach and quantified after dimethyl labeling by LC-LTQ/Orbitrap MS. Our data show that some proteins with adhesive functions and others with cell maintenance functions and virulence factor EsxA were significantly upregulated by both treatments. In contrast, immunoglobulin-binding protein A was significantly downregulated for both disinfectants. Significant differences were observed in the effect of the two disinfectants on the expression of surface proteins including some adhesins, foldase protein PrsA, and two virulence factors.
End-to-end reliability- and delay-aware scheduling with slot sharing for wireless sensor networks

Applications that require the stringent reliability with delay constraint such as industrial process automation and patient monitoring have been emerged as new area of applications of wireless sensor networks (WSNs). This paper presents a per-flow reliability- and delay-aware scheduling algorithm for industrial WSNs. The proposed method adds time slots for retransmission to each link in order to satisfy required reliability. Furthermore, by sharing retransmission slots among different flows, the proposed method achieves the required reliability and forwarding delay while suppressing increase in the number of allocated slots. Through simulation-based evaluations, we show that the proposed method can reduce the number of slots allocated to neighbor links of a sink by up to 70% compared with conventional methods, which contributes to shorten the slotframe size, while satisfying the requirements of end-to-end reliability and forwarding delay.
Complementation of Essential Yeast GPI Mannosyltransferase Mutations Suggests a Novel Specificity for Certain Trypanosoma and Plasmodium PigB Proteins

The glycosylphosphatidylinositol (GPI) anchor is an essential glycolipid that tethers certain eukaryotic proteins to the cell surface. The core structure of the GPI anchor is remarkably well conserved across evolution and consists of NH2-CH2-CH2-PO4-6Manα1,2Manα1,6Manα1,4-GlcNα1,6-myo-inositol-PO4-lipid. The glycan portion of this structure may be modified with various side-branching sugars or other compounds that are heterogeneous and differ from organism to organism. One such modification is an α(1,2)-linked fourth mannose (Man-IV) that is side-branched to the third mannose (Man-III) of the trimannosyl core. In fungi and mammals, addition of Man-III and Man-IV occurs by two distinct Family 22 α(1,2)-mannosyltransferases, Gpi10/PigB and Smp3/PigZ, respectively. However, in the five protozoan parasite genomes we examined, no genes encoding Smp3/PigZ proteins were observed, despite reports of tetramannosyl-GPI structures (Man4-GPIs) being produced by some parasites. In this study, we tested the hypothesis that the Gpi10/PigB proteins produced by protozoan parasites have the ability to add both Man-III and Man-IV to GPI precursors. We used yeast genetics to test the in vivo specificity of Gpi10/PigB proteins from several Plasmodium and Trypanosoma species by examining their ability to restore viability to Saccharomyces cerevisiae strains harboring lethal defects in Man-III (gpi10Δ) or Man-IV (smp3Δ) addition to GPI precursor lipids. We demonstrate that genes encoding PigB enzymes from T. cruzi, T. congolense and P. falciparum are each capable of separately complementing essential gpi10Δ and smp3Δ mutations, while PIGB genes from T. vivax and T. brucei only complement gpi10Δ. Additionally, we show the ability of T. cruzi PIGB to robustly complement a gpi10Δ/smp3Δ double mutant. Our data suggest that certain Plasmodium and Trypanosoma PigB mannosyltransferases can transfer more than one mannose to GPI precursors in vivo, and suggest a novel biosynthetic mechanism by which Man4-GPIs may be synthesized in these organisms.
Proton-Decoupled Carbon-13 NMR Spectroscopy in a Lyotropic Chiral Nematic Solvent as an Analytical Tool for the Measurement of the Enantiomeric Excess†

Organic solutions of poly-γ-(benzyl-l-glutamate) (PBLG) generate a sufficient differential ordering effect (DOE) to discriminate enantiomers using proton decoupled carbon-13 NMR in natural abundance. Discrimination between enantiomers is observed through the carbon-13 chemical shift anisotropy (CSA) differences. This method is successfully applied to a large number of chiral molecules including a case of axial chirality and offers the advantage that no labeling or chemical modification of molecules is needed. In most cases, the chemical shift differences are large enough to measure the enantiomeric excess with accuracy. We show that this new tool is an attractive and powerful alternative to the existing enantiomeric analytical techniques.
Human papillomavirus oncoproteins and apoptosis (Review)

The aim of this study was to review the literature and identify the association between human papillomavirus (HPV) oncoproteins and apoptosis. HPV-associated apoptosis may be primarily blocked by a number of oncoproteins, including E5, E6 and E7. E5 protein protects cells from tumor necrosis factor-associated apoptosis; the oncoprotein E6 predominantly inhibits apoptosis through the p53 pathway; and oncoprotein E7 is involved in apoptosis activation and inhibition. In addition, HPV oncoproteins are involved in activating or repressing the transcription of E6/E7. In conclusion, HPV oncoproteins, including E5, E6 and E7 protein, may interfere with apoptosis via certain regulatory principles.
FMRI unmixing via properly adjusted dictionary learning

The mapping of the functional networks within the brain is a major step towards a deeper understanding of the the brain function. It involves the blind source separation of obtained fMRI data, usually performed via independent component analysis (ICA). Recently, there is an increased interest for alternatives to ICA for data-driven fMRI unmixing and notably good results have been attained with Dictionary Learning (DL) - based analysis. In this paper, the K-SVD DL method is appropriately adjusted in order to cope with the special properties characterizing the fMRI data.
Algorithm and implementation of joint trajectory planning for virtual robot's legs

A complete planning mechanism was established to improve efficiency of trajectory planning of biped robot. The kinematic model of robot's legs was built by D-H method. Trajectory planning for robot's legs was finished based on complete kinematic model of the biped robot. The stability of the robot was improved by adding multiple geometrical constraints. Rotation angles of each joint were obtained by solving equation sets based on the planned trajectory equations. The 3D model of biped robot was built by the software of 3DS MAX. The servo interface between servo drivers and the virtual motion control software was created which was programmed by software of Visual Studio. Experiment results indicate that the mechanism can promote working efficiency greatly as well as having good control performance.
Effects of Multiple Sedentary Days on Metabolic Risk Factors in Free-Living Conditions: Lessons Learned and Future Recommendations

Background: Recent experimental studies in adults have demonstrated that interruptions to prolonged sitting have beneficial effects on metabolic risk factors in adults, compared to prolonged sitting. We explored the hypothesis that multiple consecutive days of predominantly prolonged sedentary time may have an unfavorable effect on the postprandial response of C-peptide, glucose, and triglycerides in free-living healthy young men. Methods: In this explorative pilot study, healthy young men (n = 7; 18–23 years) consumed standardized mixed meals at 1 and 5 h during two experimental laboratory-sitting days, with 6 days of predominantly prolonged sedentary time in between. Serum and plasma samples were obtained hourly from 0 to 8 h for measurement of glucose, C-peptide, and triglycerides. Participant's sedentary time was monitored using an accelerometer during the prolonged sedentary days as well as during 6 normal days prior to the first laboratory day. Differences in postprandial levels were assessed using generalized estimating equations analysis. Due to the explorative nature of this study and the small sample size, p-value was set at <0.10. Results: Overall, when expressed as % of wear time, sedentary time was 5% higher during the 6 prolonged sedentary days, which was not significantly different compared to the 6 normal days (n = 4). Following 6 prolonged sedentary days, postprandial levels of C-peptide were significantly higher than at baseline (B = 0.11; 90%CI = [0.002; 0.22]; n = 7). Postprandial levels of glucose and triglycerides were not significantly different between the 2 laboratory days. Conclusions: Due to the relatively high sedentary time at baseline, participants were unable to increase their sedentary time substantially. Nevertheless, postprandial C-peptide levels were slightly higher after 6 prolonged sedentary days than after 6 normal days.
Effectiveness of UI Noncompliance Detection

Abstract: This article reports on the different methods of investigation employed to detect noncompliance with UI regulations, and their relative effectiveness. It considers a previous evaluation of the Unemployment Insurance program for which a national random sample of UI claimants was selected from the active claimload and referred to Investigation and Control Officers for in-depth enhanced investigations. Results of these investigations, along with details of the specific control and investigation activities, were reported for analysis. A variety of investigation methods were generally combined, which meant that these were intercorrelated, making it difficult to apply standard regression analysis. Instead, principal components method is used for grouping of variables, and these are then entered into a logit regression model.
COllaborative Virtual ENvironments: Experiments on Small Group Behaviour in the COVEN Project

This paper describes work being undertaken by the multi-partner European Union funded project COVEN (COllaborative Virtual ENvironments). COVEN is developing collaborative virtual environment (CVE) systems and technologies in order to support a group of travel rehearsal applications. One of the major themes of the project is the evaluation of CVE applications through extensive network trials, usability evaluations and in depth case-controlled experiments. This paper concentrates upon an experimental study where groups of three strangers meet in a CVE to carry out a simple collaborative task. The experiment was designed to investigate issues about group behaviour such as the relationship between emergent leadership and computational resources, presence of being in a place, and the co-presence, the sense of togetherness, amongst the participants. This experiment and others undertaken by the project highlight a number of issues to do with participant representation, interaction style and system implementation that need to be addressed in future CVE development.
The generative self-organizing map: a probabilistic generalization of Kohonen's SOM

We present a variational Expectation-Maximization algorithm to learn proba- bilistic mixture models. The algorithm is similar to Kohonen's Self-Organizing Map algorithm and can be applied on any mixture model for which we can find a standard Expectation Maximization algorithm. We maximize the variational free- energy which sums data log-likelihood and Kullback-Leibler divergence between the neighborhood function and the posterior distribution on the components, given data. We illustrate the algorithm with an application on word clustering.
Political Transformations and Public Finances: Europe, 1650-1913

How did today's rich states first establish modern fiscal systems? To answer this question, Political Transformations and Public Finances by Mark Dincecco examines the evolution of political regimes and public finances in Europe over the long term. The book argues that the emergence of efficient fiscal institutions was the result of two fundamental political transformations that resolved long-standing problems of fiscal fragmentation and absolutism. States gained tax force through fiscal centralization and restricted ruler power through parliamentary limits, which enabled them to gather large tax revenues and channel funds toward public services with positive economic benefits. Using a novel combination of descriptive, case study and statistical methods, the book pursues this argument through a systematic investigation of a new panel database that spans eleven countries and four centuries. The book's findings are significant for our understanding of economic history and have important consequences for current policy debates.
Daily global solar radiation forecasting over a desert area using NAR neural networks comparison with conventional methods

This paper presents a solar radiation forecasting method using nonlinear autoregressive neural networks (NAR). NAR predicts a clearness index that is used to forecast global solar radiations. The NAR model is based on the feed forward multilayer perception model with two inputs and one output. Data of three years (2012-2014) of global solar radiation time-series for Ghardaïa site (desert area), south Algeria have been used to develop the model. A comparison with Box-Jenkins (ARMA) method was done, and the proposed approach was found to be more efficient and accurate. The forecasted values are compared with the measured data and the accuracy of the models is judged based on the statistical analysis such as root mean square error (RMSE) and his normalized value (nRMSE), mean bias error (MBE) and his normalized value (nMBE) and the mean percentage error (MPE). The obtained results showed an improvement of the NAR model over ARMA in term of mean absolute error (MPE) of 23.89% and a decrease in RMSE values of about 15.50% while the coefficient correlation was found to be 0.91.