Miracle-dz/UniFAR-FTUs huggingface dataset:
Long term cyclic heat stress influences physiological responses and blood characteristics in indigenous sheep

The present study was designed to elucidate changes in physiological and blood parameters of indigenous sheep during cyclic heat exposure. Twelve 2-4 year-old sheep with an average body weight of 10.5±0.5 kg were randomly allocated to one of three experimental heat treatments: control (21°C, stall feeding), short term (21°C to 32°C; 32°C for 4 h/d, grazing), and long term (21°C to 32°; 32°C for 8 h/d, grazing). The results have shown that sheep exposed to cyclic heat treatments exhibited higher (P=0.05) rectal temperature, body temperature, and respiratory rate compared to the control group. Cyclic heat treatments significantly decreased heart rate of sheep. There was a sharp reduction in rumination and an increase in time spent by sheep in eating during cyclic heat treatments. Long term heat treatment significantly increased RBC and WBC counts, PCV values, and hemoglobin level than that of the control and short term heat treatments. Similar responses were also observed in plasma glucose, uric acid, aspartate aminotransferase, and blood urea nitrogen levels. These results suggest that short term heat stress is tolerable but long term is physiologically detrimental to them to indigenous sheep.DOI: http://dx.doi. org/10.3329/bjas.v42i2. 18486 Bang. J. Anim. Sci. 2013. 42 (2): 96-100
THE CONTRIBUTION OF GENETIC POLYMORPHISMS OF XENOBIOTIC-METABOLIZING ENZYMES IN THE RISK OF BREAST CANCER DEVELOPMENT IN WOMEN Summary

273 ÎÍÊÎËÎÃÈß • Ò. 15 • 1 4 • 2013 73 Breast cancer (BC) is multifactor disease, which occurrence is considered to be the result of interaction between series of genetic and environmental factors, including geographical, social, industrial, etc. [1]. The basis of tumor process independently from localization of tumor is cells malignant transformation as a result of abnormality cellular cycle regulation and apoptosis depression [2]. Molecular pathogenesis of oncological diseases includes large amount of genetic and epigenetic events, which cause the oncogenes activation and inactivation of tumor suppression genes [3, 4]. At the same time, modifying impact on the functioning of oncosuppressors genes may have such factors, as xenobiotics detoxification enzymes genes, which are responsible for metabolism (activation and inactivation) of wide range endoand exobiotics, including carcinogens [5, 6]. The main enzymes of I phase xenobiotics detoxification are enzymes of cytochrome P450 superfamily. It is expected that the cytochrome P450 gene polymorphism may affect the risk a number malignant neoplasms, including BC. To the system of detoxification is also referred ATP-dependent transporter — P-glycoprotein, which realizes energy-dependent transport of substances in cells. CYP2D6 gene (according to the electronic systematization of genes — ОMIM*124030) is localized on the chromosome 22q13.1. In CYP2D6 gene (product— cytochrome Р450 2D6) mostly in our region is detected allele variant *4 — single nucleotide replacement G1934A (rs3892097) on the boundary between intron 3 and exon 4, presence of which causes the incorrect splicing of mRNA, that results in a reading frame shift, premature completion of the translation and formation of the defective protein product devoid of enzymatic activity [7]. Cytochrome Р450 2С19 gene — CYP2C19 (ОМІМ*124020) is localized on the chromosome 10q24.1-q24.3. Allele variant *2 of CYP2C19 gene (rs4244285), which in literature is marked also as CYP2C19m1 and is typical only for Europeans, is formed by replacement of guanine (G) by adenine (A) in 681 position of 5th exon «wild type» (CYP2C19*1 — G681) and creates aberrant splicing site [8]. Such single nucleotide replacement causes the displacement of mRNA reading frame, starting from 215 amino-acid residue, and untimely creates stop-codon on 20 amino-acid residues earlier that results in truncated, non-functional protein. CYP2C9 gene (OMIM*601130) is localized on the chromosome 10q24 and encodes cytochrome Р450 2С9 (CYP2C9 enzyme). Today are known two allele variants, which are significant for Caucasians population: *2 and *3. Allele *1 is «wild type» and encodes normal protein. Allele *2 contains replacement of С430Т, that causes the replacement of arginine by cysteine in position 144 of amino-acid sequence (R144C, rs1799853). Allele *3 is determined by nucleotide replaceTHE CONTRIBUTION OF GENETIC POLYMORPHISMS OF XENOBIOTIC-METABOLIZING ENZYMES IN THE RISK OF BREAST CANCER DEVELOPMENT IN WOMEN
Drying Temperature and Hornification of Industrial Never-Dried Pinus radiata Pulps. 1. Strength, Optical, and Water Holding Properties

Hornification and changes in properties of bleached Pinus radiata pulps were studied for oven-dried pulps and compared to never-dried pulps. Evaluation of unrefined and PFI-refined pulps showed an increase in strength loss with high drying temperature. The tensile index was reduced by 40 to 55%, the tear index was reduced by 14 to 31%, and the degree of hornification, measured as WRV, increased from 25 to 34% when the drying temperature was increased from 25 °C to 130 °C. The tensile stiffness index, Scott bond, and elongation were reduced, whereas the bulk, opacity, air permeability, and light scattering values increased at high drying temperatures. Neither fiber deformations nor damages were observed to justify such reductions in strength properties. In PFI refining, pulps dried at 130 °C required three times more revolutions than never-dried pulps to develop tensile index until 70 Nm/g. Dried pulps were found to have less capability to hold water into the fibers’ pore structure, as shown by water retention value. Changes in Scott bond, bulk, and water retention value suggested that besides irreversible pore closure and fibril microfibril aggregation, delamination can contribute to the observed strength loss in dried compared to never-dried pulps.
Predicting and managing light in the understory of boreal forests

This paper reviews current information relating to the dynamics of light in northern and boreal forests and discusses factors affecting overstory light transmission, seasonality of light, sunflecks, canopy gaps, and understory development, particularly with regard to tree regeneration. Techniques for measurement of light in forests such as radiometers, photosensitive paper or chemicals, hemispherical canopy photographs, the plant canopy analyzer, or visual estimators of canopy density are each discussed in terms of their accuracy, costs, ease of use, and conditions required during measurement. Predictive models of light transmission based on canopy architecture are also described in terms of their assumptions, accuracy, and input data costs. Lastly the paper discusses the relationship among overstory and understory densities, ground-level light, and "windows of opportunity" for regeneration of trees in the understory following management interventions.
Visualization of and Software for Omnibus Test-Based Change Detected in a Time Series of Polarimetric SAR Data

ABSTRACT Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution and a factorization of this test statistic with associated p-values, change analysis in a time series of multilook polarimetric synthetic aperture radar data in the covariance matrix representation is carried out. The omnibus test statistic and its factorization detect if and when change occurs. Using airborne EMISAR and spaceborne RADARSAT-2 data, this article focuses on change detection based on the p-values, on visualization of change at pixel as well as segment level, and on computer software.
Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm

Classical machine learning, which is at the intersection of artificial intelligence and statistics, investigates and formulates algorithms which can be used to discover patterns in the given data and also make some forecasts based on the given data. Classical machine learning has its quantum part, which is known as quantum machine learning (QML). QML, which is a field of quantum computing, uses some of the quantum mechanical principles and concepts which include superposition, entanglement and quantum adiabatic theorem to assess the data and make some forecasts based on the data. At the present moment, research in QML has taken two main approaches. The first approach involves implementing the computationally expensive subroutines of classical machine learning algorithms on a quantum computer. The second approach concerns using classical machine learning algorithms on a quantum information, to speed up performance of the algorithms. The work presented in this manuscript proposes a quantum support vector algorithm that can be used to forecast solar irradiation. The novelty of this work is in using quantum mechanical principles for application in machine learning. Python programming language was used to simulate the performance of the proposed algorithm on a classical computer. Simulation results that were obtained show the usefulness of this algorithm for predicting solar irradiation.
Theory-based measurement of the saving-investment correlation with an application to Norway

We demonstrate that the correlation of saving and investment is measured best by an error correction model (ECM), because theory implies a cointegrating relation between these variables. The ECM comprises all previous specifications as special cases, which are shown to be potentially misspecified on theoretical grounds. We argue that the correlation can serve to reject the hypothesis of capital immobility, but not the one of capital mobility. Applying the ECM to Norway yields the following findings: First, the ECM outperforms prevailing specifications. Second, we detect structural breaks, which underpins the need for careful diagnostic testing and, third, the correlation's time profile is consistent with other indicators of capital mobility. The Feldstein-Horioka puzzle does not exist for Norway.
GermEval 2015: LexSub -- A Shared Task for German-language Lexical Substitution

Lexical substitution is a task in which participants are given a word in a short context and asked to provide a list of synonyms appropriate for that context. This paper describes GermEval 2015: LexSub, the first shared task for automated lexical substitution on German-language text. We describe the motivation for this task, the evaluation methods, and the manually annotated data set used to train and test the participating systems. Finally, we present an overview and discussion of the participating systems' methodologies, resources, and results.
Inter‐basin exchange and repeated headwater capture across the Sierra Madre Occidental inferred from the phylogeography of Mexican stonerollers

Aim Geomorphic evolution of river basins can shape the structure and diversity of aquatic communities, but understanding the biological significance of basin evolution can be challenging in semi‐arid regions with ephemeral or endorheic conditions and complex drainage configurations such as the Sierra Madre Occidental (SMO) in North America. In this study, we characterized range‐wide patterns of genetic variation in the Mexican stoneroller (Campostoma ornatum) to infer how orogenic and erosional influences on river basin connectivity have given rise to the diverse and largely endemic freshwater communities across the SMO region.
Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis

We propose a novel multilinear dynamical system (MLDS) in a transform domain, named $\mathcal{L}$-MLDS, to model tensor time series. With transformations applied to a tensor data, the latent multidimensional correlations among the frontal slices are built, and thus resulting in the computational independence in the transform domain. This allows the exact separability of the multi-dimensional problem into multiple smaller LDS problems. To estimate the system parameters, we utilize the expectation-maximization (EM) algorithm to determine the parameters of each LDS. Further, $\mathcal{L}$-MLDSs significantly reduce the model parameters and allows parallel processing. Our general $\mathcal{L}$-MLDS model is implemented based on different transforms: discrete Fourier transform, discrete cosine transform and discrete wavelet transform. Due to the nonlinearity of these transformations, $\mathcal{L}$-MLDS is able to capture the nonlinear correlations within the data unlike the MLDS \cite{rogers2013multilinear} which assumes multi-way linear correlations. Using four real datasets, the proposed $\mathcal{L}$-MLDS is shown to achieve much higher prediction accuracy than the state-of-the-art MLDS and LDS with an equal number of parameters under different noise models. In particular, the relative errors are reduced by $50\% \sim 99\%$. Simultaneously, $\mathcal{L}$-MLDS achieves an exponential improvement in the model's training time than MLDS.
Analysis of the Impact of NVivo and EndNote on Academic Research Productivity

The aim of this paper is to analyze the impact of literature review software on researchers. The aim of this study was achieved by analyzing models in terms of perceived usefulness, perceived ease of use, and acceptance level. Collected data were analyzed using WarpPLS 4.0 software. This study used two theoretical frameworks, namely, Technology Acceptance Model and the Training Needs Assessment Model. The study was experimental and was conducted at a public university in South Africa. The results of the study showed that acceptance level has a high impact on research productivity followed by perceived usefulness and perceived ease of use. Keywords—Technology acceptance model, training needs assessment model, literature review software, research productivity.
Using a Non-linear Multilayer Feedforward Neural Network for Ballhandling Control within RoboCup Middle Size League

Soccer playing robots of team Tech United Eindhoven have active ballhandling control in place for four years now. Two drawbacks of the current method are identified: (i) Small changes in hardware or environment cause the need to regularly re-tune the system and (ii) ballhandling dynamics are non-linear, complicating modeling and control design. In this report first steps are made to investigate whether a neural network can do better than this. A non-linear multilayer feedforward neural network was trained to imitate the original, modelbased, controller. By implementing the network with trained weights on the robot itself, performance was validated. In the final section of this report suggestions are made on how to use neural networks within different frameworks for self-learning.
Social Signal Analysis in Criminal Mediation Processes

In the courts of the Generalitat of Catalonia, the criminal mediation processes are common acts for justice purposes. These processes are performed with the main goal of helping both parts involved in an offense to reach an agreement, where the victim can feel partially recompensed and the imputed has the opportunity to apologize and mitigate the associated conviction. A key point in these processes is the role of the mediator, who has the objective of obtaining such agreement that can benefit both parts. In order to achieve this agreement, it is common to perform a set of sessions during several months, sometimes just with one of the parts, and taking into account the considerations of the mediator about that the agreement is possible. Given that the success of a penal mediation is considered as a positive reward to the victim and can help the condemned to allow his earlier social reintegration, it is quite desirable to reach that agreement. Unfortunately, this agreement is not always guaranteed. In this sense, it is of our interest to perform a psychological-methodological study in order to automatically analyze the behavior of the parts involved in the mediation process, where it can then be used to detect their social signals. In the behavioral sense, these social signals are those patterns that appear during the sessions having a high correlation with the success and/or failure of the mediation. This automatic behavior analysis can then be used by the mediator in order to self-analyze his/her behavior during the process and the effect of their social signals in relation to the final agreement, providing a feedback of his/her professional skills. Moreover, the automatic detection of the patterns that correlates the success and failure of the process can be used by the mediator in order to include the required mechanisms to guide the process towards the desired agreement. The high dependence of these ’states of success’ for the conversational processes -or in many other social contextswith the socials signals have been very analyzed in several works [1].
Deep-Space Optical Communications Downlink Budget from Mars: System Parameters

This article describes the elements of a design control table for an optical communications link from a spacecraft in Mars orbit to a ground-based receiving station on Earth. A fixed average laser transmitter power of 5 W transmitted through a 30-cmdiameter near-diffraction-limited telescope is assumed, along with a 10-m-diameter ground receiving antenna. Pulse-position modulation of the laser with direct detection also is assumed. An end-to-end systems analysis is presented to provide the expected signal and background-noise photons as a function of the Earth–Mars range. The signal and noise photons received are treated using an ideal Poisson channel model in order to predict data rates when Mars is close to conjunction. The data rates range from 5 to 40 Mb/s with a large part of the uncertainty owing its origin to variability of the atmosphere. The article also concludes that further work is required in order to narrow the rather wide range of preliminary data rates presented.
Implications of the small spin changes measured for large Jupiter-family comet nuclei

Rotational spin-up due to outgassing of comet nuclei has been identified as a possible mechanism for considerable mass-loss and splitting. We report a search for spin changes for three large Jupiter-family comets (JFCs): 14P/Wolf, 143P/Kowal-Mrkos, and 162P/Siding Spring. None of the three comets has detectable period changes, and we set conservative upper limits of 4.2 (14P), 6.6 (143P) and 25 (162P) minutes per orbit. Comparing these results with all eight other JFCs with measured rotational changes, we deduce that none of the observed large JFCs experiences significant spin changes. This suggests that large comet nuclei are less likely to undergo rotationally-driven splitting, and therefore more likely to survive more perihelion passages than smaller nuclei. We find supporting evidence for this hypothesis in the cumulative size distributions of JFCs and dormant comets, as well as in recent numerical studies of cometary orbital dynamics. We added 143P to the sample of 13 other JFCs with known albedos and phase-function slopes. This sample shows a possible correlation of increasing phase-function slopes for larger geometric albedos. Partly based on findings from recent space missions to JFCs, we hypothesise that this correlation corresponds to an evolutionary trend for JFCs. We propose that newly activated JFCs have larger albedos and steeper phase functions, which gradually decrease due to sublimation-driven erosion. If confirmed, this could be used to analyse surface erosion from ground and to distinguish between dormant comets and asteroids.
A Review on Conversion of Image to Text As Well As Speech Using Edge Detection and Image Segmentation

An image can be defined as a matrix of square pixels arranged in rows and columns. Image processing is a leading technology which enhances raw images received from gadgets such as camera or a mobile phone in normal day-to-day life for various applications. An image to text and speech conversion system can be useful for blind as well as physically challenging people to understand the scenario from the images. Core idea for image to text and speech conversion is to overcome the challenges faced by a blind person in real life. The techniques of image segmentation and edge detection play an important role in implementing this system. We formulate the interaction between image segmentation and object recognition in the framework of Canny algorithm. The system goes through various phases such as preprocessing, feature extraction, object recognition, edge detection, image segmentation and text- to-speech (TTS) conversion. The database of this system consists of huge set of sample images which help to identify similar kind of objects in every different image. The system mainly consists of two main modules such as image-to-text and text-to-speech. An image- to-text module generates text descriptions in natural language based on understanding of image. A text-to-speech module converts natural language into speech synthesis.
Multisummability for some classes of difference equations

This paper concerns difference equations y(x + 1) = G(x, y) where G takes values in C-n and G is meromorphic in x in a neighborhood of infinity in C and holomorphic in a neighborhood of 0 in C-n. It is shown that under certain conditions on the linear part of G, formal power series solutions in x(-1/p),p is an element of N, are multisummable. Moreover, it is shown that formal solutions may always be lifted to holomorphic solutions in upper and lower halfplanes, but in general these solutions are not uniquely determined by the formal solutions.
The Role of Massively Multi-Task and Weak Supervision in Software 2.0

Over the last several years, machine learning models have reached new levels of empirical performance across a broad range of domains. Driven both by accuracy improvements and deployment advantages, many organizations have begun to shift to learning-centered software stacks—a new mode that has been called Software 2.0 . This approach holds the promise of radically accelerating the construction, maintenance, and deployment of software systems, and opens up a broad research agenda around changes to hardware, systems, and interaction models. However, these approaches require one critical and often prohibitively expensive ingredient: labeled training data. We outline a vision for a Software 2.0 lifecycle centered around the idea that labeling training data can be the primary interface to Software 2.0 systems. In our envisioned approach, Software 2.0 stacks are programmed using weak supervision —i.e. noisier, programmatically-generated training data—which is specified at various levels of declarative abstraction and precision, and then combined using unsupervised statistical techniques. The codebase for Software 2.0 is also radically different: we envision labels for tens or hundreds of different tasks across an organization combined in a massively multitask central model, leading to amortization of labeling costs and new models of software reuse and development. Finally, we envision Software 2.0 stacks deployed by using collected training labels to supervise commodity model architectures over different servable feature sets. We outline the components of this lifecycle, and provide an interim report on Snorkel, our prototype Software 2.0 system, based on our experiences working on problems ranging from ad fraud to medical diagnostics with some of the world’s largest organizations.
Functional performance after hip resurfacing or total hip replacement : A comparative assessment with non-operated subjects

a Orthopedics A, Roger-Salengro Hospital, Lille Regional University Hospital Center, 2, avenue Oscar-Lambret, 59037 Lille cedex, France b Orthopedic Dept, Charles-Nicolle Hospital, Félix-Dévé-Ward, Rouen Regional University Hospital Center, 1, rue de Germont, 76031 Rouen, France c Medicine and Sports Center, Lille-2 Medical School, Lille Nord-de-France University, 59045 Lille cedex, France d Orthopedics C, Roger-Salengro Hospital, Lille Regional University Hospital Center, 2, avenue Oscar-Lambret, 59037 Lille cedex, France e Rouen CETAPS, Sports Science and Physical Education Faculty, boulevard Siegfried, 76821 Mont-Saint-Aignan cedex, France