dataesr/scientific-paragraphs-categorization huggingface dataset:
We must consider all aspects that control the distribution of sediment and the ETDC cycle, including biological physical and chemical processes. In particular, the role of microbial assemblages is now recognised as a significant factor deserving greater attention across disciplines. Funding information JAH was supported by NERC award, COHBED (NE/1027223/1). DMP received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) and from the Extended Evolutionary Synthesis Research Program funded by the John Templeton Foundation and by NERC awards NE/J015644/ 1and NE/N016009/1 -CBESS, and Blue Coast. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions. Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4.
In human, we analyzed the database of a cohort on CDI in acute flares of inflammatory bowel disease (IBD). As PSC-IBD patients were under UDCA treatment, we compared PSC-IBD patients to IBD patients without PSC. Results: In vitro, UDCA inhibited germination and growth of C. difficile at 0. 05 and 0. 1%, competing with 0. 1% TCA (with 0. 1%: 0. 05% ± 0. 05% colony forming unit versus 100% ± 0%, P < 0. 0001). In hamsters, UDCA reached high levels only when administered with antibiotics (43. 5% UDCA at D5). Without antibiotics, UDCA was in small amount in feces (max. 4. 28%), probably because of UDCA transformation into LCA by gut microbiota. During infectious challenge, mortality was similar in animals treated or not with UDCA (62. 5%, n = 5/8, P = 0. 78). UDCA percentage was high, similar and with the same kinetics in dead and surviving hamsters.
CDI in IBD Patients With or Without UDCA We used the database of a cohort studying CDI in acute flares of IBD  (Sokol et al. , 2017). All consecutive patients hospitalized for a flare in Saint-Antoine Hospital (Paris, France) from September 2012 to May 2014 were prospectively tested for CDI, defined by a clinical picture compatible and a microbiological evidence of toxin-producing C. difficile in stools. As PSC-IBD patients were under UDCA treatment, we compared PSC-IBD patients to IBD patients without PSC, thus untreated.
Statistical Analysis Data were reported as means ± standard error of the mean (SEM). OD 600 and CFU were compared with oneway ANOVA; growth, germination and BA curves with twoway ANOVA. BA between surviving and dead hamsters were compared with Student t-test. Hamsters' survival was evaluated with Kaplan-Meier method and compared with log-rank test. Statistical tests were performed using Prism version 6. 00d (GraphPad Software, La Jolla, CA, United States). Statistical significance was set at a P-value of < 0. 05.
We also analyzed the rate of complications connected with surgical approach. Long-term results of treatment were evaluated after 2 years from prosthesis implantation. A good outcome was considered as low scrotal position of the prosthesis, size of the prosthesis comparable to healthy, contralateral testis, and tidy post-operative scar (Figs. 3  and 4 ). A bad outcome included the prosthesis displaced in the direction of the groin, partial unveiling, or prolapse of the prosthesis. Statistical analysis of complications was performed using the commercial software Excel Statistica Statsoft 8. 0. The categorical variables were presented as counts and percentage. The categorical variables were compared using a chi-square test with Yates amendment. A P-value of <. 05 was considered statistically significant.
Discussion , 16]  Our study consists of 290 boys after primary testicular prosthesis implantation. ] Injury and neoplastic disease are infrequent indications for the implantation of testicular prostheses. [15, 18, 20]  Our data are in line with the above-cited findings. Patients with disorders of sex differentiation are rare recipients of testicular prostheses. [1] [2] [3] [4]  In our study, there were no boys with such disorders. Lattimer and Puranik et al independently introduced in 1973 the most popular prostheses made of silicone sheath and silicone gel filling (elastomer). [5, 21] Other prostheses were made of solid silicone, silicone sheath with normal saline filling, or polyurethane sheath with silicone gel filling. [5, 10] Martín-Crespo Izquierdo et al proposed multiple injections of hyaluronic acid gel inside a scrotal sac, which played the role of an expander.
| RESULTS Thirteen physicians were interviewed for the study, of which seven were male and six were female. Eleven of the 13 had worked as a physician for more than 10 years. The median number of years working in the health care facility in which they were recruited was 7 years (Table  1 ). Analysis of the data yielded 14 determinants (ie, subcategories) functioning as barriers (B) and/or facilitators (F) to patient participation of potential relevance for patient safety (Table  2 ). These 14 determinants were mapped to five types of major determinants (ie, categories) of patient participation that could contribute to improved patient safety: physicians' capability to involve patients in their care (C in COM-B); patients' capability to become involved in their care, as perceived by the physicians (C); physicians' opportunity to achieve patient participation in their care (O); physicians' motivation to involve patients in their care (M); and patients' motivation to become involved in their care, as perceived by the physicians (M).
RNA isolation and quantitative PCR Total RNA was extracted from SH-SY5Y cells after 0, 30, 60 and 90 min of treatment with 30 μM 5-HIAA, using the Nucleospin RNA L protocol (Macherey-Nagel, Düren, Germany). This protocol included a treatment of isolated RNA by DNase I. Integrity and purity of RNA was checked by spectrophotometry. Reverse transcription was performed with 1 μg RNA using Biorad iScript® cDNA synthesis kit. q-PCR was performed in an iCycler thermal cycler (Biorad, Hercules, CA, USA) using SYBR Green dye (iQ SYBR green Supermix, Biorad). For each sample, the reaction mix was a make-up of 320 nM forward primer (F), 320 nM reverse primer (R), 200 nM probe, and 4 μL cDNA template in a total reaction volume of 20 μl  [3]. Using the iCycler iQ optical system software (version 3. 1, Bio-Rad), a standard curve based on successive cDNA dilutions was performed and was used to calculate starting quantities.
Detection was carried out by using a chemiluminescence detection kit (Clarity Western ECL substrate, Biorad). After washing steps, signals were detected with ChemiDoc MP (Biorad). The relative intensity of bands was densitometrically determined by Image J software 1. 46r (NIH, USA). For statistical analysis, all data from 3 independent experiments were expressed as the ratio to optical density values of the corresponding β-actin control  [53]. The values were expressed as a percentage of the control group arbitrarily set at 100%. The statistical analysis was done with a Student t-test.
Then the membranes were washed 3 times in wash buffer, and were put in a solution of Streptavidin-HRP for ½ hour under agitation at RT. After a further 3 washes, the membranes were covered with a Chemi-reagent mix for 1 min before detection of the signal with ChemiDoc MP (Biorad). The relative intensity of bands was densitometrically determined by Image J software 1. 46r (NIH, USA).
Neprilysin activity assay SH-SY5Y cells were harvested and washed with PBS after 0, 30, 60 and 90 min of treatment with 30 μM 5-HIAA (Sigma-Aldrich). The cells were then sonicated in iced Tris buffer (50 mM Tris-HCl, pH 7. 4) and store at -80 °C. Measurement of neprilysin activity was performed according to the technical details given by the manufacturer for the fluorescent SensoLyte 520 kit (AnaSpec Inc. , Fremont, CA, US). Briefly, 100 μg of total protein were placed in the wells of a non-binding 96-well plate (Corning) before adding the substrate working solution. The reagents were mixed by shaking the plate gently for 30 s and the fluorescence signal was immediately measured at Ex/Em = 490 nm/520 nm continuously and the data were recorded every 5 min for 1 h. The initial reaction velocity was determined by the slope of the linear portion of the data plot and the results were expressed in percentage by reference to control condition.
, Fremont, CA, US). Briefly, 100 μg of total protein were placed in the wells of a non-binding 96-well plate (Corning) before adding the substrate working solution. The reagents were mixed by shaking the plate gently for 30 s and the fluorescence signal was immediately measured at Ex/Em = 490 nm/520 nm continuously and the data were recorded every 5 min for 1 h. The initial reaction velocity was determined by the slope of the linear portion of the data plot and the results were expressed in percentage by reference to control condition.
Acknowledgements This work was supported by grants from  Institut National de la Santé et de la Recherche Médicale  ( INSERM, France ) and  Université de Strasbourg (France). Additional support was provided by the  NeuroRhine Consortium  coordinated by AGMN and funded by  INTERREG IV Program (European Fund for Regional Development) in the Upper Rhine Region  and the Offensive Science Call 2012. Authors would like to thank  Dr. Martine Schmitt  for the chemical advices.
g. The Sample Array Tube Handler (Thermo Scientific, San José, CA, USA)  [67]. Such solution decreases the amount of sample needed and its loss, limits human-dependent pre-analytical errors and simplifies identification and storage system. The other component of a proper identification system is labeling. There are numerous solutions like 1D, 2D barcodes, tubes with laser-etched barcodes or radio-frequency identification based labels (RFID)  [68]. Regardless of labeling system typeit must be unique for each sample and its aliquot. It also should be compatible with IT system o f a Biobank to ensure fast access to the sample (decreasing thawing of other samples while searching). Unifying labeling with SPREC results in a straightforward and sharable database of a Biobank collection  [69]. -pre-.
IT Systems An efficient bioinformatics in biobanks is a crucial matter. IT systems have to serve databases in a real-time, easy access and user-friendly manner. Additionally, privacy protection and anonymization components cannot be neglected (see 3. 5. Ethical and legal issues subchapter). The enormous number of digital, sensitive data requires optimized both hardware and software. There has been developed international standards for data obtaining and processing to enable compatible data sharing. International organizations such as NCI, ISBER, caHUB, OECD, EC-JRC are providers of recommendation useful in harmonizing every branch of BBs' functionality  [70]. But for the neatness, a consistent database should meet the standards for the international compatibility. The issue is essential in any case of cooperation, which increases the   -80 °C for years  [26, 60]  extent and significance of a project and brings profits for the shareholders.
Record Constitution Numerous partners provide ready-to-implement sets of data, distinguishing every piece of information is helpful for further research. NCRI has published such document, the sets of tables comprise the list, which divides information about the patient and collected samples. Each table is extended with a list of particular records. Document available at: (http://ccb. ncri. org. uk/wp-content/uploads/2014/03/CCB-Data-Standard-v1. pdf; date of access: 27. 07. 2017). Also, BRISQ has a resourceful and worldwide compatible protocol for preparation a novel and up-to-date database in biobank  [72]. BBMRI (Biobanking and Biomolecular Resources Research Infrastructure) created in 2016 MIABIS 2. 0 (Minimum Information About BIobank data Sharing)comprehensive and detailed document describing guidelines for sharing data between biobanks within networks.
Workflow and Online Tools for Biobanking The wide range of data, such as: demographic, ethnical, medical, environmental, genetic and other  [74]  collected from the patient trigger a need for the development of complex IT systems. But for the clinical data, there are sets of biological information, which also must be input to the IT system, stored and processed -thus appeared complicated tasks. Holistic IT solutions, compatible with hospital and pathology units are the optimal solution. Many researchers and companies took on the evolution of bioinformatics applications such as MIBBI, MIAME, SysMOSEEK, openBIS, Gaggle-BRM, MIMAS, XperimentR, ISA tools, BASE, LabKey  [75]. One of the most interesting approaches was implemented in XTENS, which consists of a web portal, an internal database, and a data grid storage element  [75].
molecularmedicineireland. Ireland (MMI) Standardised Biobanking procedures 2- ie/wp-content/uploads/2 Part II: Clinical standard operating 0- 015/09/MMIGuidelinesforStan procedures 1- dardisedBiobanking_FINAL160610 Part III: Laboratory standard operating 0. pdf procedures Organization for OECD Guidelines Part I. Guidelines on Human Biobanks and https://www. oecd. org/sti/biotech/ Economic on Human Biobanks Genetic Research Databases 2- 44054609. pdf Cooperation & and Genetic Research Part II. Annotations 0- Development Databases 0- 9.
A custom-written C++ program based on the HapticAPI (Moog FCS Control Systems) computed the ball kinematics and controlled the virtual display as well as the force feedback. of the task in the virtual environment: the robotic manipulandum provided haptic feedback of the mechanical interaction with the object, while the behavior of the system was displayed online on the back-projection screen. The physical model used the distances shown on the figure, while the distances displayed on the screen were multiplied by a factor of 4 for visibility. The cup displayed was 7. 5 times smaller than the physical arc determined by the length d of the pendulum. B: A participant using the HapticMaster to interact with the simulated cup-andball system. The position of the cup was controlled by the position of the end-effector of the robot.
Data Processing: For all kinematic and result variables, only the data between t = 20 s and t = 40 s of each trial were analyzed to eliminate transients at the beginning and end of the trial. As the experimental data were compared with model simulations described below, trials that significantly deviated from periodicity needed to be excluded as the model assumed periodicity. Hence, when the standard deviation of the oscillation frequency exceeded 10% of its mean, the trial was excluded as this indicated significant deviation from the instructed periodic movements. Similarly, a trial was excluded if the mean cart excursion was smaller than 12 cm or larger than 21 cm, as it did not satisfy the instructed excursion (15 to 18 cm), even allowing an additional 3 cm of tolerance. These relatively stringent inclusion criteria were adopted in post-processing only to enable meaningful comparison with the simulation study reported below (the simulation assumed constant movement frequency within a given amplitude range).
They were not success/failure criteria for the participants. One participant's majority of trials did not satisfy these criteria and his entire data were eliminated from subsequent analysis. From the remaining 450 trials of 9 participants, only 17 trials did not meet these criteria. These 17 trials were not at the beginning of the experiment, but distributed across early and late trials. This indicated that the task did not require practice, and performing with periodicity was not a challenge per se. The data processing and analyses were performed with MATLAB® (The Mathworks Inc. , Natick, MA) and Gnumeric. The numerical values of the interaction force and predictability estimates for each experimental trial were computed with Matlab from the experimental trajectories. Mutual information was calculated with the Matlab MIToolbox-2.
Simulation of Result Variables and Hypothesis Testing: As for the behavioral experiment, the simulation tested the hypotheses by evaluating the result variables root mean squared interaction force RMSF (Eq. 2) and mutual information MI between the cart kinematics and the interaction force (Eq. 3). To obtain the space of all executions spanned by execution variables f, K and B forward dynamics simulation of the coupled model were run to generate the profiles of the cup kinematics φ(t) and the interaction force Finter(t). Using Matlab-Simulink, the simulation time was 45 s, but only data from 20 ≤ t ≤ 40 s were analyzed to eliminate transients. The two result variables MI and RMSF were then calculated with Matlab as for the experimental data. These results then served to test Hypotheses 1 and 2.
To evaluate Hypothesis 3 (exploit resonance), a frequency response analysis of the coupled model was conducted in Matlab. Due to the nonlinearity of the coupled cart-and-pendulum plus human hand system, classic frequency response tools could not be used. However, the system could be linearized assuming small pendulum angles. Although this approximation was not valid for all frequencies, the linear analysis allowed further insight into the behavior of the system. In the frequency response analysis, only one of the execution variables, the movement frequency f, was varied, while the hand stiffness K and damping B were fixed to typical values: one corresponding to the mean values of K and B adopted by participants in the low-frequency group, and the other to the mean values in the high-frequency group (see Appendix B for the identification procedure of experimental values of K and B).
Such unpredictable dynamics poses significant challenge to any internal model guiding the goal-directed manipulation. Using both behavioral data and numerical analysis of the cart-and-pendulum system coupled to a model of hand impedance, we tested three hypotheses: humans minimize the interaction force required to move the system (Hypothesis 1); alternatively, they maximize predictability of the system behavior (Hypothesis 1); and/or they exploit the resonance structure of the system (Hypothesis 3). Interaction force between hand and cart was quantified by its root mean squared value. Predictability was operationalized by the mutual information between the kinematics of the cart and the interaction force. Exploiting resonance was tested by comparing the chosen frequencies with the resonance structure of the system.
Grants Dagmar Sternad was supported by the  National Institutes of Health   R01-HD087089 ,  R01-HD081346 , and  R21-DC013095  and the  National Science Foundation   NSF-NRI 1637854  and  NSF-EAGER-1548514. Neville Hogan  was supported by  NIH   R01-HD087089 ,  NSF-NRI 1637814 ,  NSF-EAGER-1548501  and by the  Eric P. and Evelyn E. Newman fund. Pauline Maurice  was supported in part by the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant Agreement No. 731540.
2008). A previous study identified similarities between the C-terminal domain of Zip2 and the XPF domain  (Macaisne et al. 2008). Consistent with this finding, we could predict with 97% confidence (probability HHpred) the whole three-dimensional (3D) structure of the XPF domain of Zip2 by using the solved structure of the XPF domain of human FANCM as a template (Protein Data Bank 4bxo) (Fig. 4A ;  Coulthard et al. 2013). To investigate the role of the Zip2 XPF domain during meiotic recombination, we generated a truncated form of Zip2, Zip2ΔXPF, lacking the ERCC4-(HhH) 2 structure. Strains expressing Zip2ΔXPF showed a delay in meiotic progression and reduced sporulation efficiency and spore viability (Fig. 4B-D ). Western blot analysis indicated that the meiotic phenotypes observed were not due to reduced levels of Zip2ΔXPF (Supplemental Fig.
1) and K. lactis (XP_454656. 1), respectively, indicating that it diverged rapidly during the evolution of yeasts, significantly more so than Zip2. Besides, no homologs in species more distantly related than K. lactis could be identified even when using sensitive tools for remote homology detection such as HHblits  (Remmert et al. 2011). We also searched for Spo16 homologs in several fully sequenced species of the Ascomycete clade. When the profile of S. cerevisiae Spo16 was queried with HHsearch  (Söding 2005)  against the database of profiles built for all protein sequences in each of these Ascomycete species, no match had a probability >32%, and no candidate Spo16 ortholog could be reliably identified (Supplemental Table  S3 ). Nevertheless, in each species, at least one of the 10 best matches could be assigned to some members of the XPF-ERCC1 superfamily, supporting the possibility that Spo16 might be a highly diverged version of the ERCC1-like domain (Supplemental Table  S4 ).
We therefore explored whether the Spo16 sequence could be aligned with a structure of known members of the XPF-ERCC1 superfamily. Through weak profile-profile alignment relationships and secondary structure similarity analysis, the Spo16 sequence could be aligned to a reference structure belonging to the ERCC1 family (Supplemental Fig. S6 ). The structure of the FANCM-FAAP24 complex was used as a template to create a model of Spo16 in complex with the Zip2 XPF domain using the Rosetta CM program  (Song et al. 2013). Interestingly, the regions of Spo16 that could be matched with the best agreement of secondary structure prediction from the profile-profile alignment correspond to those interacting with the XPF-like model of Zip2 (Fig. 5C ; Supplemental Fig. S6A, B ). Upstream of these regions, the sequence alignment and the secondary structure prediction do not ideally match with the structural template and may adopt a different conformation.
Single-end reads were extended to a size of 150 bp (the estimated size of our sonicated chromatin preparations). Aligned and extended reads were then normalized using a custom script as follows: For normalization for the untagged control, the top and bottom outliers of the distribution (Q3 + 1. 5IQR and Q1 -1. 5IQR, respectively) were removed. The average read number per position (coverage) of the remaining reads of the untagged control was then normalized to 1 (scaled untagged control). Similarly, top and bottom outliers of the distribution were removed from the average read number in the tagged samples. Average coverage for the remaining reads was then calculated for the tagged and untagged control at the same positions. This was used to compute the immunoprecipitate/untagged control ratio.
Acknowledgments We thank  Franz Klein  for the  V5-Red1  strain, and  Jean-Baptiste Charbonnier  for the pKL and pUCDM plasmids. We thank the  Next-Generation Sequencing  and Recombinant Proteins platforms and the discovery platform and informatics group at EDyP. We thank  Mathilde Grelon  and  Wayne Crismani  for critical reading of the manuscript. We thank  Dr. M. Levikova  for purified Dna2. This work was supported by  Institut Curie ,  Centre Nationnal de la Recherche Scientifique, Labex DEEP (Development, Epigenesis, Epigenetics , and life-time  Potential ;  ANR-11-LBX-0044 ; part of the  IDEX Idex PSL  [ ANR-10-IDEX-0001-02 PSL] ),  ANR-12-BSV6-0009 , and the  Fondation ARC ,  ANR-11-PDOC-0011  to J. G. ; the  Fond d'Intervention of the University Grenoble Alpes  to J. G. ; and the  European Union   FP7 Marie Curie Action "Career Integration Grant " ( 304003 ) to J.