dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA sequencing The DNA fragments obtained after PCR assays were purified using the QIAquick PCR purification kit (QIAGEN) according to the supplier's instructions. Then, the DNA sequence was determined using a conventional Big Dye Terminator cycle sequencing ready reaction kit (Perkin Elmer, Applied Biosystems, Foster City, CA) and an ABI373 Automated DNA Sequence. The comparison of the sequences was made with the reference sequence XM949625 of T. annulata cyt b gene using BLAST (Basic Local Alignment Search Tool). The DNA and predicted amino acid sequences were aligned and compared using the Clustal W program  [18]. The Clustal output was edited and the region of unambiguously aligned sequences was retained for final analysis. Phylogenetic analyses based on a protein sequence of 217 amino acids were carried out using the MEGA 5 software.
Cytochrome b sequence analyses The 1092 bp nucleotide sequences corresponding to the complete cyt b gene sequence were amplified and sequenced. The sequences of the 28 Tunisians clones were compared with the other related cyt b gene sequences in the GenBank database. Nine novel sequences were identified. All data were deposited into GeneBank data base under the following accession numbers: KF732022, KF732023, KF732024, KF732025, KF732026, KF732027, KF732028, KF732029 and KF732030. The alignment of the identified nucleotide sequences with the reference T. annulata cyt b gene (XM949625) showed a specific polymorphism at seventeen positions in the Tunisian clones. The alignment of our amino acid sequences with the reference show that ten among these are non-synonymous mutations, which cause a change in the polypeptide chain of cyt b (Table  2 ).
Discussion The occurrence of T. annulata resistance to buparvaquone, member of hydroxynaphtoquinones, has been described in Tunisia since 1996 by Darghouth et al. (unpublished data). These cases have been detected in a farm where four out of seven cows have succumbed to tropical theileriosis despite early treatment with the conventional doses of buparvaquone. In vivo experimental study confirmed the resistance of T. annulata isolates to buparvaquone  [8]. In an attempt to collect parasites exposed to different degrees of drug pressure and thus to increase the probability to select resistant strains, we sampled a case of treatment failure at different times after treatment. The isolates where next cloned by limiting dilution. This allowed us to recover over 40 clones from three resistant isolates and 14 clones from four sensitive stocks (data not shown).
Acknowledgments We would like to thank  Pr. Lamia Guizani-Tabbane  ( Institut Pasteur de Tunis, Tunisia ) for critical reading of the manuscript. The authors are grateful to  Prof. Mohamed Gharbi  for his helpful comments on the manuscripts. Thanks are also expressed to the staff of " Laboratoire de Parasitologie, École Nationale de Médecine Vétérinaire de Sidi Thabet" and "Laboratoire d'Épidémiologie et d'Écologie Parasitaire, Institut Pasteur de Tunis ".
Transcript quantification by real-time PCR (qPCR) Subsets of 9 cows per group were selected for transcript quantification analyzes (please see Statistical Analyses section for details). Approximately 30 mg of endometrial tissues were submitted to total RNA extraction, using the RNeasy Mini columns kit (Qiagen, Gaithersburg, MD, USA) according to the manufacturer's instructions. To complementary DNA synthesis 1 μg total RNA was treated with DNAse I followed by reverse transcription using a High Capacity cDNA Reverse Transcription Kit (Life Technologies, Carlsbad, CA, USA). Step-One Plus (Life Technologies) with SYBR® Green Chemistry was used for the amplification reactions. Primers were designed based on GenBank Ref-Seq mRNA sequences of target genes, using the Primer Express software, version 3.
0 (Life Technologies). The specificity of the designed primers was compared by Basic Local Alignment Search Tool (BLAST; http://blast. ncbi. nlm. nih. gov). PCR products of the primers designed were submitted for electrophoresis and sequencing. Details of the primers and probes are provided in Table  2. Determination of PCR efficiency and Cq (quantification cycle) values per sample were performed with Lin-RegPCR software (http://linregpcr. nl/) as described by Ramakers et al. [31]. For data normalization, Genorm software was used as described by Vandesompele et al. [32]. Three constitutive genescyclophilin (PPIA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and beta-actin (ACTB)were used as inputs. The normalization factor generated by Genorm was based on the geometric mean of the most stable genes (GAPDH and ACTB).
Statistical analyses The experimental model was used as a paradigm for lower (SF-SCL) or greater (LF-LCL) receptivity and fertility. Adherence to each of the paradigms was measured in each animal after joint evaluation of specific ovarian and endocrine variables, specifically, concentration of P4 at D6, P4 at D6/P4 at D2 ratio, CL size at D7, CL weight, follicle size at D-2, D-1 and D0 and preovulatory follicle size. Animals within each group were ranked according to responses to each variable. The nine top ranked animals of the LF-LCL group and the lowest ranked animals of the SF-SCL group were chosen for biochemical and transcript analysis. Raw data were checked to determine the normality of the residuals by the Shapiro-Wilk test, and Levene's test was used to check for homogeneity of variances.
If necessary, data were transformed by natural logarithms or ranks. Comparisons between the groups were analyzed by one-way ANOVA using the PROC GLM procedure (SAS software, version 9. 2). The P4 concentration was analyzed by split-plot ANOVA, considering the effects of group, day, and their interaction using the PROC MIXED procedure (SAS, Version 9. 2; SAS Institute Inc. , Cary, NC, USA). The data from the biochemical analyses were compared between the groups by one-way ANOVA (STATISTICA 4. 5, StateSoft, Inc. 1993, Tulsa, OK, USA).
Acknowledgements This work was supported by  LFEM  (project # 210 ). The authors thank the administration of the  Pirassununga campus of the University of São Paulo. Financial Support:  São Paulo Research Foundation (Fundação de Amparo à Pesquisa do Estado de São Paulo -FAPESP)  (# 2011/03226-4  and # 2012/ 23532-5 );  National Council for Scientific and Technological Development (CNPq) ; and  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). The English language was reviewed by American Journal Experts (AJE, http://www. aje. com) and posteriorly by English Language Editing (Elsevier, http://webshop. elsevier. com/languageediting).
Clonal hmgb2-deficient parasite populations were obtained by limiting dilution in 20 naive Swiss mice and confirmed by diagnostic PCR and Southern blotting. A probe spanning the 5=-hmgb2 portion was amplified by PCR and digoxigenin-dUTP (DIG) labeled using a DIG random prime labeling kit (Roche Applied Science). Southern blotting was performed after running 4 g of MstI-digested genomic DNA on a 0. 8% agarose gel, with passive transfer to a nitrocellulose membrane, and the DNA was revealed using an anti-DIG antibody coupled to peroxidase (Roche Applied Science). Supplementation with recombinant proteins. We assessed the best concentration of protein capable of restoring ECM in mice according to the data reported for recombinant HMGB1  (54). C57BL/6 mice infected with 10 5 ⌬hmgb2 ANKA pRBCs were injected by i.
The cell pellet was washed twice and analyzed via flow cytometry. Flow cytometric analysis of brain and spleen leukocytes. Spleen and brain cells were stained for fluorescence-activated cell sorter (FACS) analysis according to standard protocols in cold PBS containing 2% fetal calf serum and 0. 01% sodium azide (FACS buffer) with the following antibodies: allophycocyanin (APC)-labeled CD4, fluorescein isothiocyanate (FITC)-labeled anti-CD8␣, FITC-labeled anti-F4/80 antibody, phycoerythrin (PE)-labeled anti-Ly6G antibody, PE-labeled anti-granzyme B antibody, and PE-labeled anti-IFN-␥ antibody. All antibodies were purchased from eBioscience, San Diego, CA. RBCs were eliminated using cell lysis buffer, and cells were washed in FACS buffer. Living cells of the brain and spleen (4 ϫ 10 4 and 10 5 , respectively) were analyzed using a fourcolor FACSCalibur flow cytometer with ProCellQuest software (BD Biosciences, Mountain View, CA).
After washing, 50 l was added to each well, followed by streptavidin-peroxidase. The reaction was revealed by adding orthophenylenediamine plus H 2 O 2. Statistical analysis. Differences in mouse survival were evaluated by the generation of Kaplan-Meier survival plots and log rank analysis. Differences in growth rates (peripheral parasitemia) and parasite loads were analyzed by the Student t test. Kruskal-Wallis analysis was performed to compare means between the 3 or 4 groups of data for hemorrhagic focus numbering, survival curves, BBB disruption evaluation, brain-or spleen-infiltrating T-cell numbering, and transcript expression levels. Dunn's posttest was applied to analyze the effect of pbhmgb2 deletion. P values of Ͻ0. 05 were considered statistically significant for each test. Nucleotide sequence accession numbers.
The sequences of the P. berghei high-mobility-group protein gene pbhmgb2 (PBANKA_071290) and the P. berghei ANKA rRNA-encoding gene berg06_18s are available in PlasmoDB (http://plasmodb. org/plasmo/). Mouse gene sequences are available in NCBI GenBank under the following accession numbers: Mus musculus hypoxanthine guanine phosphoribosyltransferase (Hprt), NM_013556. 2; M. musculus high-mobility-group protein 1 (muhmgb1), NM_010439. 3; M. musculus high-mobility-group protein 2 (muhmgb2), NM_008252. 3; M. musculus tumor necrosis factor alpha (tnf␣), NM_013693. 2; M. musculus interleukin 6 (il-6), NM_031168. 1; M. musculus gamma interferon (ifn␥), NM_008337. 3; M. musculus interleukin 10 (il-10), NM_010548. 2; M. musculus intercellular adhesion molecule 1 (icam1), NM_010493. 2; M. musculus vascular adhesion molecule 1 (vcam1), NM_011693.
ACKNOWLEDGMENTS We thank  Maurel Tefit  for management of the animal house. Nathalie Bernard ,  Sarah Duponchel , and  Nazla Bakary  participated in this work as undergraduate students. We are grateful to  Abiba Doukani  for assistance with the genomic core facility (P3S) and to  Catherine Blanc  and  Bénédicte Hoareau  for their competent cellular contributions to flow cytometry analyses (CyPS) in  Pitié-Salpêtrière, Paris. We also thank  Olivier Silvie  and  Amélie Bigorgne  for motivating discussions and  Ana Ferreira  in the  MPS laboratory  for sharing her expertise on murine models of ECM. We thank the  Centre d'Elevage, de Production et d'Infection des Anophèles (CEPIA) of the Institut Pasteur, Paris, France.
Data collection We assessed 205 postmenopausal patients treated by HFRT for a non-metastatic BC in two regional cancer centres (Nantes and Angers) between June 2004 and June 2012 both after breast conserving surgery (BCS) or mastectomy. For each patient, a file detailed the following items: BC family history, age at menopause, hormone replacement therapy (HRT), comorbidities, type of surgery (lumpectomy/mastectomy), RT modalities (volumes/dose), chemotherapy and/or hormonal therapy. The following histopathological features were also assessed: tumour size, axillary nodal involvement (ANI), histological subtype (ductal, lobular, mixed), SBR (Scarff, Bloom and Richardson) grading, excision quality, presence of vascular or lymphatic emboli, Her-2 oncoprotein over-expression and hormone receptor status (HR).
Two questions inquired about the availability of advanced imaging services. Seventeen questions addressed access and use of POCUS, including frequency of POCUS use, reason for POCUS use, and training in POCUS in medical school. The survey consisted of yes or no questions, multiple-choice questions and open-ended short answer questions. Questionnaire completion time was estimated to be less than ten minutes. Data was collected between April 2013 and August 2013. The online survey (Survey Monkey software) was sent via e-mail to all part-time and full-time emergency staff practicing (n = 206) in the 19 selected rural EDs. To enhance participation, four automated reminder e-mails were sent at two-week intervals. Emergency department chairs were subsequently contacted by phone or e-mail and asked to remind colleagues of the study and to encourage participation.
River discharge Water discharge data come from an open database managed by the National Under-Secretary for Water Resources. Various resource managers contribute to this on-line database, such as the DGI for the Province of Mendoza. Amongst these data, we chose to use the daily discharge in m 3 /s because the dataset is complete from the 1930s or the 1950s (depending on the river) until June 2014. Flows are measured at noon for three rivers and at midnight for the Diamante river. In order to evaluate the natural discharge for each catchment, the gauging stations located just upstream of the first reservoirs (see Fig. 1 ) were chose, that is to say:.
16 Next, we analysed quantitatively how the total amount of water discharged during the high-flow period, i. e. during September-April, is controlled by the snow cover of the previous winter. Thus, the 8-day snow data were summarised in several parameters: annual maximum recorded snow surface area and monthly average snow surface area, for each year separately. The discharge during the September-April period was also averaged. Lastly, the linear relationship between each snow parameter time series and the average discharge was analysed.
Agriculture and human activities in these oases depend on the river discharge, which clearly depends on the snow extent at the beginning of the snowmelt period. We did not find a direct strong link with the maximum snow surface area during winter. This may be due to the persistent cloud cover from June to August that led us to reject a large amount of data within this period. Thus, what is called "maximum snow cover" is in fact the maximum snow cover observable under the constraint of cloud cover. 24 Our results indicate that it is possible to use remote sensing to forecast the average discharge in the September-April period from MOD10A2 images, e. g. from images during the September period (Fig. 6 ). MOD10A2 data are freely available 10 days after acquisition and thus it is possible to anticipate in late September or early October the risk of a water shortage in the following summer.
, 2006). We observed that the high-water period (September-April) discharge is directly related to the snow extent at the beginning of the snowmelt period, i. e. in September and October, as revealed by a correlation of about 0. 8. Moreover, the decreasing trend in the winter snow bed extent from 2001 to 2014 clearly explains the observed decreasing trend in the annual water discharge. Agriculture and human activities in these oases mostly depend on river discharge, which from our results clearly depends on the snow extent. Our research indicates that it is possible to use remote sensing to forecast the average discharge in the September-April period (high-water season) from MOD10A2 images with an average uncertainty of 15%. As MOD10A2 data are freely available ten days after acquisition, it is possible to anticipate in early October the risk of water shortages in the coming summer.
To extract the snow coverage of each watershed at each available date, a Python script was developed to execute automatically the 3 steps represented in Fig. 2. Firstly, it reprojects all the MODIS dataset from the MODIS specific projection to the UTM 19S coordinate system using the PyModis module (http://pymodis. fem-environment. eu/). Then, it vectorises the re-projected images and calculates the area of each coverage type (snow, cloud) using GDAL/OGR (http://www. osgeo. org/gdal_ogr) for each watershed. This script was used to analyse the 674 images available at the time of this study.
TRAJECTORY GENERATION The trajectory planning approach described in  [10]  has been used for the trajectory generation of the Orthoglide 5-axis. Note that the trajectories are defined in the task space, i. e. , the robot workspace, whereas the control loop requires data in the joint space. According the design rules presented in  [8] , the boundaries of the Orthoglide 3-axis workspace are defined in the Cartesian space. Therefore, the direct kinematics is computed at the frequency of the control loop in order to check whether the moving-platform of the robot is within its Cartesian workspace at anytime or not. Here, the system dynamics is decoupled in order to consider five separate actuators moving an equivalent mass and analyze the stability of the system. In the robot control scheme, the dynamic effects have been taken into account in order to improve the classical PID control scheme.
For instance, if the positional error of the end-effector is larger than 3 mm, the machine will shutdown. Another level of security is on the actuators with electrical sensors stroke end. If enabled, neither the PC nor the Dspace card operates, it amounts to the cable security. The robot trajectories are calculated using Matlab and sent to the Dspace card via an optical fiber. In the Dspace card, the inverse Jacobian matrix is used when the robot is close to its workspace boundaries in order to know whether the Cartesian displacements of the end-effector lead to exceeding joint limits or not. A pneumatic compensator is mounted along the vertical axis in order to reduce the gravity effect. This compensator continuously generates an effort equivalent to the weight of the mobile platform, but in the opposite direction.
6 m/s. Figure  17  gives the execution time for both trajectories. We can notice that the time is stable and close to 10 µs. This time includes the computation of the velocity, the direct kinematics and othe control loop. As we have a real-time operating system, we have also data to send to the computer in which we have the Control Desk software able to make data acquisitions. Figure  18  shows the control signal sent to the drive. The variation is between -1 and 1, which amount to the maximum continuous torques that each motor may produce. For an acceleration equal to 1. 3 G, it is 80% of the maximum motor torque for the 3 rd and 4 th motors. As a consequence, we expect to increase the efficiency of the control loop later on by using a part of peak torques t f = t f max(γ V , √ γ A ) (35 t[s] 0 1 2 3 4 5 6 7 8 Δθ1, Δθ2 [rad] ×10 -3 -4 -2 0 2 4 Δθ1 Δθ2 t[s] 0 1 3 5 6 7 Δρ2, Δρ3.
In this paper, a Multi Layer Perceptron (MLP) based Artificial Immune System (AIS) is presented for breast cancer classification. The proposed algorithm integrates clonal selection principle of AIS in MLP learning to reduce its computational costs and accelerate its convergence to a Mean Squared Error Threshold (MSE th ) set by the user. Applied on the Wisconsin Diagnosis Breast Cancer database (WDBC), the results show that combining Artificial Immune Systems and Neural Networks is effective. Indeed, a significant reduction of computation time has been obtained with a slight improvement of classification accuracy.
Several articles have been published presenting new CAD systems, authors in  [2]  proposed an automatic diagnosis system for detecting breast cancer based on Association Rules (AR) and Neural Network, they used AR for reducing the dimension of breast cancer database and the MLP neural network as classifier, the classification accuracies were 95. 6% for four inputs and 97. 4% for eight inputs. In  [3]  three different methods, Optimized Learning Vvector quantization (LVQ), big LVQ, and Artificial Immune Recognition System (AIRS), were applied and the obtained accuracies are 96. 7%, 96. 8%, and data obtained by: 𝑌 𝑘 = 𝑓 ( ∑ 𝑖 𝑊 𝑖. 𝑋 𝑖 ) (2) Where   corresponds to the input vector, and   is the weight vector and  the activation function. Despite its great success, MLPs showed some limitations and problems during training.
II. WISCONSIN DIAGNOSIS BREAST CANCER DATABASE (WDBC) In this study, the Wisconsin Diagnosis Breast Cancer Database is used  [17]. WDBC consists of data from 569 breast fine needle aspirate (FNA) cases containing 32 descriptive features where the two first features correspond to a unique identification number and the diagnosis status (benign or malignant). The rest 30 features are computed from a digitized image of a FNA of a breast mass. They describe characteristics of the cell nuclei present in the image. Samples of FNA images are shown in figure  1. The case distribution includes 357 cases of benign breast changes and 212 cases of malignant breast cancer. The descriptive features are recorded with four significant digits and include:.
A. Normalization Before starting the algorithm, the database is normalized in [0, 1], according to the following equation: 𝑅 = 𝑋 𝑖 -𝑋 𝑚𝑖𝑛 𝑋 𝑚𝑎𝑥 -𝑋 𝑚𝑖𝑛 (3) Where   is the minimum of the data   for all ,   is the maximum of the data   for all , and   is the  ℎ data of data.
B. 2) Results and Discussion Based on tests performed above to set the parameters of the algorithm, the performance of AIS-MLP is studied on WDBC using in Matlab R ⃝ (Matlab R2013a 1. 1. 0. 604. We shared the database into 75% for training and 25% for the test. Table  3  presents the results of five successive runs of MLP and AIS-MLP using different examples for train and test in each run. The average of this five runs is taken as final result. From the table above it is easily noticeable that the proposed approach greatly reduces the convergence time of the MLP to the ( ℎ ) set by the user, with a slight improvement of classification accuracy. The necessary number of iterations to converge is reduced by more than 75%, and execution time is reduced by 50%. This reduction of computational costs is done thanks to the mutation operator that helps the algorithm to converge faster to the desired error by changing the values of the weight of MLP by a random process, and update the weights only if they have better affinity than their original cell.
This paper introduces HybridMR, a novel model for the execution of MapReduce (MR) computation on hybrid computing environment. Using this model, high performance cloud resources and heterogeneous desktop personal computers (PCs) in Internet or Intranet can be integrated to form a hybrid computing environment. Thanks to HybridMR, the computation and storage capability of large scale desktop PCs can be fully utilized to process large scale datasets. HybridMR relies on two innovative solutions to enable such large scale data-intensive computation. The first one is HybridDFS, which is a hybrid distributed file system. HybridDFS features reliable distributed storage that alleviates the volatility of desktop PCs, thanks to fault tolerance and file replication mechanism. The second innovation is a new node priority-based fair scheduling (NPBFS) algorithm has been developed in HybridMR to achieve both data storage balance and job assignment balance by assigning each node a priority through quantifying CPU speed, memory size, and input and output capacity.
A cloud instance (such as Amazon elastic compute cloud (EC2) instance) is a virtual server node, which can be used to run various applications. MapReduce (MR) is a programming model for data intensive application that was first introduced by Google in 2004  [4]  and has attracted a lot of attentions recently. Hadoop is an open-source implementation of MR, which is widely used in Yahoo, Facebook, and Amazon. MR borrows ideas from functional programming, in order to simplify the parallel programming to process massive datasets, where programmers only define map and reduce tasks and do not need to care about complex processes. Now, many providers offer MR computing services using cloud infrastructure in the pay-as-you-go manner such as Amazon, Google, Rackspace, Aliyun, and so on. Recently, there are some other MR implementations that are designed for large scale parallel data processing specialized on desktop grid or volunteer resources in Intranet or Internet such as BitDew-MapReduce  [5] , MOON  [6] , P2P-MapReduce  [7] , GiGi-MR  [8] , VMR  [9] , and so on.