dataesr/scientific-paragraphs-categorization huggingface dataset:
Solute carrier 26 (SLC26) members are conserved membranous proteins that mediate the transport of various anions across the plasma membrane of epithelial cells and constitute important regulators of pH and HCO 3 -concentration. Most SLC26 members were shown to physically interact and cooperate with the cystic fibrosis transmembrane conductance regulator channel (CFTR) in various epithelia, mainly by stimulating its Cl -channel activity. Among SLC26 members, the function of SLC26A3, A6, and A8 were particularly investigated in the male genital tract and the sperm cells. In this review, we will focus on SLC26s contributions to ionic-and pH-dependent processes during sperm post-testicular maturation. We will specify the current knowledge regarding their functions, based on data from the literature generated by means of in vitro and in vivo studies in knock-out mouse models together with genetic studies of infertile patients.
Following a similar strategy, Wedenoja et al. published in 2017, the screening of a cohort of 283 asthenozoospermic men and the identification of the c. 2062 G > C (p. Asp688His) heterozygous variant in SLC26A3, in 3. 2% of the patients  (Wedenoja et al. , 2017). Analysis of the variant's frequency in Exac database indicated that it is enriched in the Finnish population. Furthermore, functional studies showed that the p. Asp688His variant did not impact SLC26A3 intrinsic Cl -/HCO 3 -exchange activity, nor its protein amount. The p. Asp688His variant, which locates to the STAS domain, was able to interact with the CFTR channel but failed to stimulate CFTR Cl -transport activity, in vitro, in Xenopus oocytes. Here again, the variant was identified at the heterozygous level, but in this case, this might be consistent with the fact that SLC26A3 homozygous loss of function induce Chloride Loosing Diarrhea and subfertility, a much more severe phenotype  (Wedenoja et al.
One of the main research gaps to be overtaken concerns the delineation of each SLC26 member contribution, in those processes. In this regard, a comprehensive and comparative analysis of SLC26 cellular and subcellular expression patterns, within the male reproductive tract, is cruelly missing. Hence, except SLC26A8, for which a clear-cut expression pattern is available (i. e. , exclusively sperm specific), the expression pattern of SLC26 members is still unclear and the observed protein localizations, as in the case of SLC26A3, were found to diverge between different laboratories. The development of high-throughput sequencing technologies and single cell analyses has facilitated the access to large public expression datasets and therefore constitutes one asset to further progress in this field.
In particular, few expression databases dedicated to the reproductive tissues have been established: The ReproGenomics viewer  (Darde et al. , 2015 (Darde et al. , , 2019) )  and The Mammalian Reproductive Genetics database. When analyzing three distinct RNAseq datasets from mouse purified germ cells, conflicting results were obtained as compared to the assumed expression pattern of SLC26 members in the sperm cells. Hence while SLC26A6 and A8 transcripts were clearly detected in the mouse germ cells from spermatogonia to spermatid stage  (Gan et al. , 2013; Green et al. , 2018; Lukassen et al. , 2018) , SLC26A3 transcripts were not detected at all. The analyses of the same datasets also exclude any expression of SLC26A1, A4, A5, and A9 in the mouse germ cells while SLC26A2, A7, and A11 were detected.
Similar analyses of epididymal expression dataset, through the Mammalian Reproductive Genetics database, indicated that SLC26A3 transcripts are readily detected in the mouse epididymis, which is coherent with transcript and protein detection in mouse and in human epididymides that were reported by distinct laboratories,  (Hihnala et al. , 2006; El Khouri et al. , 2018). The discrepancy regarding SLC26A3 expression in the germline really question the presence and the function of SLC26A3, if any, in the mouse sperm cells. These conflicting data result from the limited biochemical tools available to analyze SLC26 protein expression, and potentially from antibody cross-reaction with different SLC26 members. Importantly, the situation is very different in humans as analysis of RNAseq data indicated that SLC26A3 transcripts are detected in human differentiating germ cells, as opposed to the mouse germ cells.
This clearly alerts about the risk of generalizing data obtained from one specie to another. The differences in cytoarchitecture and compartmentations of the epididymis ductus between humans and mouse together with the difference in the sperm "status" used in studies (epididymal sperm in mouse vs. ejaculated sperm in human) constitute additional arguments, if required, to prohibit data transfer from one specie to another. Considering the profound epididymal defects reported in the Slc26a3 knock out mouse model, an important connected point is to determine whether the dysfunctions observed in Slc26a3-null sperm when performing in vitro capacitation, could result from defects initially occurring during epididymal maturation. Hence it is highly probable that functional defects occurring within the epididymis could impair sperm "priming" and later prevent proper response to capacitation in the female genital tract.
This point is particularly important to address, considering the uncertain expression of SLC26A3 in the sperm cells. The discrimination between those two intricated processes (epididymal priming and maturation vs. capacitation in the female genital tract) constitutes a requisite if one wants to better define sperm post-testicular maturation events; in the future this may be performed by generating conditional mutant mouse models with gene invalidation restricted to the epididymal epithelium or to the germ cell lineage. Lastly, an important progress concerns the development of pharmacological compounds specifically targeting SLC26 proteins. To date some compounds such as Tenidap, 5099, DOG and PMA, were utilized, in vitro, to inhibit SLC26 protein functions in the sperm cells but their inhibitory mechanisms are sometimes indirect or unknown; in addition, no information is available regarding their selectivity among SLC26 members, which may limit the interpretation of the data and even lead to confusion in specifying the contribution of each member.
FUNDING This work was supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Centre National de la Recherche Scientifique (CNRS) , the  Université de Paris , and the  French National Research Agency  ( Grants :  MUCOFERTIL ANR-12-BSV1-0011-01 ,  MASFLAGELLA ANR-14-CE15-0002-03 , and  DIVERCIL ANR-17-CE13-0023-02 ).
Sampling site and instrumentation Long-term submicron aerosol on-line measurements used in this study were performed from   Pandolfi et al. , 2018). It is located 25 km southwest of Paris city center and is considered as representative of the background air quality of the Paris region  (Haeffelin et al. , 2005; Petit et al. , 2015). Major submicron aerosol chemical species i. e. , OA, nitrate, sulfate, ammonium, and chloride, were measured using quadrupole ACSM. These measurements were achieved continuously, always using the same instrument, during the investigated period. Data was missing only for a few periods corresponding to two field campaigns performed elsewhere (in fall 2012 and March 2013) and to few technical breakdown and maintenance periods. Detailed descriptions of the ACSM measurement principles and basic data analysis are given by  Ng et al.
4 default value was used for the OA relative ion efficiency for the whole dataset  (Canagaratna et al. , 2007). The accuracy of these ACSM measurements and the overall good working conditions of the instrument were verified through the participation to the ACTRIS ACSM intercomparison exercises that took place at SIRTA in November -December 2013 and March -April 2016  (Crenn et al. , 2015; Fröhlich et al. , 2015b , Freney et al. , 2019). Co-located multi-wavelength aethalometer (Magee Scientific) datasets were also available for the purpose of the study, providing complementary information on equivalent Black Carbon (eBC) concentrations and sources. Two aethalometers were used successively:   i. e. , 370, 470, 520, 590, 660, 880  and 950 nm. The detailed descriptions of the AE31 operation at SIRTA and aethalometer data analysis can be found in  Petit et al.
Based on a least-squares algorithm, PMF algorithm aims to iteratively minimize residuals and a fit parameter Q, defined in Eq. (2): When using PMF, it may be difficult to distinguish between factors with similar spectral profiles, especially for ACSM datasets, which are associated with larger uncertainties compared to AMS  (Sun et al. , 2012; Zhang et al. , 2015; Fröhlich et al. , 2015b). The source finder (SoFi) toolkit, implemented with the ME-2 solver  (Paatero, 1999) , has recently been developed by  Canonaco et al. (2013)  to better address this limitation. SoFi provides robust functions which allow to constrain chosen factor profiles and/or timeseries. In particular, the so-called a-value approach makes use of range-defining scalar values (with a values ranging from 0 to 1) in order to better elucidate specific PMF factor(s) profile(s) with a chosen degree of freedom; the highest the a -value the less constrained the OA profile  (Canonaco et al.
Trend analysis The multi-year trends of OA factors obtained from the ME-2 analysis were analyzed using the Mann-Kendall (MK) trend test. The MK test is a nonparametric test for monotonic trend in a timeseries  (Mann, 1945). The MK test is better suitable for nonnormally distributed, censored and missing data, compared to parametric statistical tests, such as the t test. The normality of the mass concentrations of the OA factors was examined by the Shapiro-Wilk normality test  (Shapiro and Wilk, 1965). As a result of the Shapiro-Wilk normality test, all datasets of the mass concentrations of the four OA factors were not normally distributed cases. The MK test associated with Sen's estimator of slope  (Sen, 1968 ) is insensitive to outliers, while it is not appropriate for the chosen dataset with significant seasonality.
(  6 ): 5%67 ( , ) = 8 9 : ; • < (6) where mij is the total number of selected trajectory endpoints (i, j) associated with receptor concentrations of PMF factors higher than the threshold value, and nij is the total number of back trajectory endpoints at each grid cell (i, j). The 75 th percentile of each OA factors during the entire study was used as the threshold value to calculate mij. To reduce uncertainty caused by small nij values for the PSCF modelling, an arbitrary weighting function (wij) was applied using Eq. (  7 )  (Waked et al. , 2014). To minimize the influence of some trajectories on the possible pathways of air mass transport, observed data points associated with low wind speed conditions (WS < 4 m s -1 ) were filtered out. In addition, observed data points at SIRTA during the period with any hourly precipitation events (precipitation > 0 mm) were removed to reduce influence of wet deposition on ambient aerosol concentrations.
(2015b)  were employed as anchor profiles (with a-values ranging from 0 to 0. 4 with steps of 0. 05). PMF solutions with a factor number ranging from 3 to 6 were investigated on ACSM datasets for the winter 2017-2018 period, taken here as an example. In both cases, mass spectra were in good agreement with those reported in the literature. However, the COA and BBOA factors are displaying very similar diel patterns, leading to surprisingly good correlations between these two factors (see Figure  S5 ). It could then be concluded that COA-like aerosols at SIRTA were primarily linked with wood burning emissions and pure cooking aerosols were probably present in too low loadings to be properly quantified within the present study. This the COA factor could not be solely attributed to cooking aerosols (e.
g. ,  Freutel et al. , 2013 , Dall'Osto et al. , 2015). Therefore, the 4-factor solution, including two constrained POA factors (BBOA and HOA) and two unconstrained factors, was chosen here as the "best estimate" for the PMF runs performed over the long-term dataset. A total of 25 seasonal and individual PMF analyses were then conducted using a similar procedure. The seasonal OOA factor mass spectra are presented in Figure  S6 , showing high seasonal consistency for each OA factor. Moreover, as shown in Figure  S7 , the distribution of residuals derived from the these 4-factor solution ME-2 runs was sharply centered around 0, suggesting insignificance of possible unresolved OA factor(s).
Similarly, but in the opposite way, eBCff was found to have a significant decreasing trend, while HOA trend was found to be statistically insignificant (p-value > 0. 05). However, if removing the high concentration peak observed in December (for which an important contribution of wood burning HOA can be expected), the MK p-value is reduced to be 0. 024, which would be indicative of a significant decreasing trend (with a related Sen's slope of 72 ng m -3 per year). However, such trends analysis should be performed on longer datasets for a much better evaluation of the pollution control strategies (both on road transport and residential heating emissions) in the Paris region. MO-OOA shows a significant decreasing trend (p < 0. 05) with a Sen's slope of 204 ng m -3 per year. Considering the overwhelming secondary origin of this factor, this significant decreasing trend may be partially explained by a reduction of anthropogenic VOCs emissions in France over the investigated period  (CITEPA, 2018).
https://doi. org/10. 5194/acp-2019-515 Preprint. Discussion started: 18 June 2019 c Author(s) 2019. CC BY 4. 0 License. up has been designed to minimize positive sampling artefacts but do not prevent from negative ones. Results obtained from these comparisons with filter-based measurements supported the validity of the datasets used in the present study. Co-located measurements of nitric oxide (NO) and nitrogen dioxide (NO2) were performed with a NO2/NO/NOx analyzer (model T200UP, Teledyne API, USA). Data measurements were used for further constrain traffic related OA sources. The meteorological parameters, including meteorological parameters including temperature (T), relative humidity (RH), wind speed (WS), boundary layer height (BLH), and precipitation were obtained from the main SIRTA ground-based meteorological station, (located at about 4 km North-East of the aerosol monitoring site).
know source information as constraints. Conversely, mass spectral profiles of possible OOA factors were left unconstrained. It should be noted that Crippa et al. (2013c) resolved up to 3 different type of OOA factors and/or a marine OA (MOA) factor when combining HR-ToF-AMS and proton-transfer-reaction mass spectrometer (PTR-MS) datasets obtained during a summer and a winter campaign at SIRTA. OOA factor profiles may differ with time, notably due to seasonal variations of a several parameters such as meteorological conditions, photochemistry, atmospheric lifetime, air masses origin, and/or of gaseous precursor origins. In order to better account for such variability, individual PMF analyses were performed on a 3-month basis, i. e. , winter (December-January-February), spring (March-April-May), summer (June-July-August), and fall https://doi.
Other variables: adolescents' characteristics, clinical data, and life habits Data relative to adolescents' characteristics, clinical data, and life habits were considered to better identify factors associated with BED in obese adolescents: demographic data (age, sex), life habits (tobacco consumption: yes/no), social data (social difficulties in the household: yes/no), corpulence (BMI Z-score), age at onset of overweight (age when the BMI curve exceeded the IOTF-25 centile), parental overweight or obesity (neither, 1, or 2 parents overweight or obese), participation in physical activity (< or ≥5 h/week), sports with a club (yes/no), sedentary activity (<5 h/week: low, 5-10 h/week: moderate, >10 h/week high), eating habits (snacking between meals: yes/no, eating large amounts: yes/no). All these data were collected using a digital medical file completed by the referring physician during care management.
Statistical analysis First, the sociodemographic, weight status, and life habits of the adolescents were described. For the description of prevalence, the total score categorization of emotional disorders (BDI / BAI) and personality dimensions (TAS / BIS / JTCI), well-known scales in the literature, were used. For BED, two categories were considered: none vs. moderate and severe symptomatologies. For the association between BED and both emotional disorders and personality dimensions, total scores were used for each test as a continuous variable, except for the JTCI for which there is no total score. Because of the co-linearity of variables regarding physical activity and sports with a club, only data regarding sports with a club was retained. Second, analyses of associations between adolescents' characteristics, life habits, eating habits, and clinical data with BED were determined.
For each characteristic associated with BED (p≤0. 20), multivariate analyses were performed. Third, we replicated our analyses using tests related to emotional disorders (BDI / BAI) and personality dimensions (TAS / BIS / JTCI) separately in a multivariate linear regression analysis. Finally, all psychological tests associated with BED (p<0. 20) were then considered jointly in a single final multivariate linear regression analysis, in order to assess the independent associations of emotional disorders and personality dimensions with BED. A p-value < 0. 05 was considered as statistically significant. Statistical analyses were performed using the SAS version 9. 4 software (SAS Institute Inc. , Cary, NC, USA).
Observers agree that Nollywood generates much revenue but that it is scattered. A great number of videos and dvds are sold in Africa's informal streets markets. More significant is that Nigerian movies are reproduced and resold by unauthorised vendors through various digital media. This complicates the proper management of the industry. Nigeria's National Bureau of Statistics data can only be an estimate of the industry's actual share of gross domestic product (gdp). Many industry specialists now believe that current efforts to work with video streaming companies-such as US-based Netflix (which is seeking to enter international markets), Africa Magic (a Naspers-owned South African Satellite TV affiliate) and Kenya's Buni. tv-to offer movie streaming services at a fee, will expand the industry.
Quality control and scoring of the RAD51-assay To ensure high quality data we applied three stringent inclusion criteria. First, a semiquantitative analysis of the quality of the tumor tissue was performed on a hematoxylin and eosin (H&E) stained serial section of the irradiated tumor slice used for the RAD51-IRIF assay. The tissue quality was scored (score 1-2=poor, 3-4=moderate, 5-6=good) based on the sum of the tissue vitality (1=poor, 2=moderate, 3=good) and tumor percentage (0=<5%, 1=5-20% 2=20-49% 3=≥50%). Samples were excluded when the total tissue quality score was two or less, or when the tumor percentage was <5%. Second, we only included samples for which we were able to score RAD51-IRIF in at least 50 geminin positive cells, defined by complete nuclear staining. Geminin is a cell-cycle marker to identify cells in the S/G2-phase, the cell cycle phases in which HR is active.
Genomic Instability Score The genomic instability score (GIS) was calculated as the number of altered segments superior to 15Mbp and inferior to chromosome arm, and samples were classified in three categories using an unsupervised machine learning (kmeans -python scikit) based on GIS. For details on the analysis, see supplementary methods. The three categories were somatic copy number alteration low (SCNA-low), somatic copy number alteration high (SCNA-high) and somatic copy number alteration extremely high (SCNA-extremely high).
Next generation sequencing (NGS) NGS was performed using FFPE-isolated tumor DNA with a total input of 500-1000ng per sample. The mean tumor cell percentage of the included samples was 68% (range: 30%-90%). An Agilent Sureselect XT HS Custom panel made in SureDesign (Agilent technologies, Santa Clara, California, United States) was used for variant detection with the following HR-genes design: ATM; exons 2-63, BARD1; exons 1-10, BRCA1; exons 1-24, BRCA2; exons 2-27, BRIP1; exons 2-20, CDK12; exons 1-14, CHEK2; exons 2-15, PALB2; exons 1-13, RAD51C; exons 1-9, RAD51D; exons 1-14. Additional genes included in the panel were TP53 (exons 1-12) and CCNE1 (only for amplification detection). For details on the data analysis, see Supplementary Methods. Variants were categorized using the five-tier pathogenicity classification according to.
Statistics Comparison of age between groups was performed using the unpaired t-test. Associations between all categorical variables were tested using a 2-sided Fisher's exact test. A P-value of <0,05 was considered significant. Cohen's kappa coefficient (κ) was used to measure interobserver and intertest agreement. IBM SPSS version 23. 0 (SPSS, Inc. , Chicago, USA) and R (http://r-project. org) were used for statistical analysis.
BRCA-associated genomic scars in the TCGA cohort To validate the occurrence of HR deficiency in an additional EC cohort, we used SCNA data and somatic mutation annotation files (MAF) from the TCGA study to determine the presence of BRCA-like profiles (data available for N=536), Large-Scale State Transitions (data available for N=444), COSMIC signature 3 (data available for N=246) and pathogenic bi-allelic alterations in HR genes (data available for N=541). Since our data showed a clear difference in the presence of HRdeficiency between EEC and non-endometriod EC, we stratified the cohort by histotype (EEC versus NEEC, the latter including both mixed-EC and USC). Both a BRCA-like profile and a high LST-score were significantly more common in NEEC (BRCA-like profile: 41. 2%, LST; 47,7%) compared to the EEC (BRCA-like profile; 8.
The authors acknowledge grant support by  The Dutch Cancer Society KWF-Alpe d'HuZes  (grant EMCR:  2014-7048 ). MG is funded by a  Wellcome Trust   Clinical Training Fellowship. DC is funded by an  Academy or Medical Sciences / Health Foundation   Clinician Scientist Fellowship. This research was also funded by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the  Department of Health.
The ocean observing system remained relatively sparse and regionally-focused until the 1990s  (Gould et al. , 2013). These human pressures also need to be observed and monitored if their influence is to be placed in proper context compared to change driven by climate variability and change. In the decade since OceanObs'09, the use of autonomous in situ platforms has revolutionized the ocean observing system. Yet despite these successes, it remains that many of these data sets are brief relative to the time scales of natural variability (e. g. ,  Jones et al. , 2016)  and trends or long-term cycles. Nevertheless, the data that the present observing system has delivered have been crucial in developing our understanding of the oceanic state, change and their underlying processes and their relation to observed climate variability to the extent that we can offer actionable guidance to users and stewards of the ocean environment.