dataesr/scientific-paragraphs-categorization huggingface dataset:
miR-183 Appeared as a Main miRNA Involved in Progression Toward Aggressiveness and Malignancy Previous transcriptomic analysis using Codelink microarrays had been performed on 12 out of the 26 PRL tumors (Table  1 , tumors with *) and had revealed the deregulation of specific genes involved in the control of cell cycle and proliferation and interconnected in a pathway specifically up-regulated in aggressive tumors  (6, 9). This increased proliferation specifically in aggressive tumors is consistent with the clinical data, the Ki-67 and p53 labeling and the classification recently proposed  (7). For the purpose of simplification, herein, we will refer to this pathway as the "aggressive pathway" (Figure  1 ). In order to identify miRNA potentially involved in tumoral progression toward aggressive and malignant phenotype, we used the following criteria: (1) miRNA targets (predicted by databases and already published targets) should be linked to the "aggressive pathway"; (2) targets mRNA expression should be inversely correlated to miRNA expression at mRNA level; (3) significant correlation should be observed between miRNA expression and  the main molecular markers of cell cycle taking into account in the tumor grading (i.
e. , Ki-67 and p53). Two databases (TargetScan and microRNA. org) were used in order to identify known and predicted targets of the 11 deregulated miRNA and lead to the identification of 541 mRNA targets. Among these, only 22 genes presented a variation of expression in aggressive vs. non-aggressive tumors that was inversely correlated to their associated miRNA. Using Ingenuity Pathway Analysis Software on these 22 genes, we found that nine of these genes were linked to the "aggressive pathway" and were predicted to be regulated by four miRNAs (miR-183, miR-340*, miR98, and miR-744) (Table  3 ). Finally, we assessed the correlation between the four miRNA expression and Ki-67 and p53 labeling in the 26 PRL tumors and it appeared that only miR-183 showed a significant inverse correlation with both cell cycle markers (miR-183:Ki-67 r = -0.
miR-183 Overexpression Decreases Cell Proliferation The criteria used to identify candidate miRNAs involved in aggressiveness were strongly linked to proliferation as it is the main difference between non-aggressive and aggressive PRL tumors. Thus, we assessed the effect of miR-183 overexpression on HeLa and ZR-75-1 cell lines. We performed growth curves on three independent experiments overexpressing miR-183 in each cell line. As shown in Figure  4A , miR-183 overexpression in HeLa and ZR-75-1 cell lines led to a significant slowdown in cell growth. Moreover, evaluation of the distribution of cell cycle phases at 120 h after miR-183 overexpression, using FACS analysis with propidium iodide labeling, showed that S phase tended to be delayed, which could explain the decrease of proliferation rate (Figure  4B ), this was confirmed by a BrdU incorporation assay (data not shown).
Acknowledgments This work was supported by  Institut National de la Santé et de la Recherche Médicale (INSERM). This work was supported by the  Ligue Contre le Cancer (comités Puy de Dôme and Rhône-Alpes) , Grant to Pr. JL. This work was supported by the  Région Rhône-Alpes (France). This work was supported by the  Programme Hospitalier de Recherche Clinique National  (Hypopronos no. [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43 ]. Dr. MR received a Ph. D. fellowship from the  Ministère de l'Enseignement Supérieur et de la Recherche of France. FACS facilities were kindly provided by  Pr. C. Dumontet ,  E. Cros-Perrial , and  A. Evesque  from the  "Anticorps Anti-cancer" Lab in Centre de Recherche en Cancérologie de Lyon. Tissues were provided by  Neurobiotech Bank  and we kindly thank  Prof.
3 -1. 86 0. 015 No probe hsa-miR-574-3p MI0003581 4 38869653-38869748 [+] p14 -1. 84 0. 047 -2. 12 0. 0337 hsa-miR-331-3p MI0000812 12 95702196-95702289 [+] q22 -1. 83 0. 012 -1. 87 0. 0143 hsa-miR-1280 MI0006437 3 128081008-128081101 [+] q21. 3 -1. 57 0. 035 No probe a Accession number from MirBase. b FC, fold change of miRNA expression in A tumors compared to NA tumors. c p-Value from Student's test.
Germ-line Mutations and Heteroplasmy Are Key Features A key question is whether mutations associated with GBM are genetically inherited events (germ-line) or sporadic somatic (non-germ-line) events. Analysis of a subset of 33 GBM samples with corresponding matched bloods in the combined cohort allowed us to determine whether the mutations were germ-line or somatic. Set A mtDNAs (n ¼ 10) contained 193 mutations relative to the rCRS, whereas Set B (n ¼ 32) contained 174 mutations (Fig. 1A ), and the average heteroplasmy of Set A mtDNAs was lower than Set B (60%+39 vs 97%+9, respectively; Fig. 1B  and D ). The differences between the datasets were also evident at the individual patient level, where the new data display. 4-fold increase in the average number of mutations observed per sample (Set A was 52+13 vs 12+6 in Set B; Fig.
1C ). The ratio of germ-line to somatic mutations is roughly consistent between the datasets ( 21:1 for Set A and 35:1 for Set B), although this is 2 -2. 5-fold higher than that previously documented for GBM studies that have focused on nuclear DNA. 16 wenty-five mutations that cause nonsynonymous amino acid substitutions in complex III and IV proteins were found in the combined GBM mtDNA dataset (Set A + B; Table  1 ). Thirty-two percent (8/25) have no known disease association (Set A: C9469T, T9252C, A6692del, C6237A, T15479C, A15326G, G15048A, and C14766T); 40% (10/25) were observed in GBM previously (Set B: A15218G, T15453C, G15500A, T15693C, A15758G, G6267A, G6619A, G9655A, A9667G, and G9966A); 16% (4/25) were found in both Sets A and B at very similar heteroplasmy despite the differences in sample types and methodologies used to obtain the mutational data in each case (A14793G, T14798C, C15452A, and G9477A); and 56% of the mutations (14/25) had other disease associations (A14793G, T14798C, A15218G, G15257A, C15452A, T15693C, A15758G, G15803A, G6267A, G9300A, G9477A, G9655A, A9667G, and G9966A) (Supplementary Table  S5 ).
5E ). The pocket has evolved to make intimate contacts to the ubiquinone to action binding, redox reaction, and dissociation. In order to model the effect of GBM mutations on binding, we first used an in silico docking approach with the wild-type pocket and the natural substrate. We were able to simulate the precise docking of ubiquinone as observed in the crystal structure (Protein Data Bank identification [PDB ID] 1NTZ 34 ) (Fig. 5F  and Supplementary Fig. S5 ). We then modeled the effect of mutation F18L on this interaction and revealed that the structure of the binding cavity is opened up by the loss of a bulky aromatic group leading to alternative ubiquinone binding modes (Fig. 5G  and H ). It is likely that these additional modes are non-optimal for the crucial balance of association and dissociation following the redox event and are likely to influence the activity of complex III.
38 0 33. 7 1 Somatic No 2 Functional M271Ter (A6692del) 2. 38 ND 8 ND No 1 Functional MT-CO3 W16R (T9252C) 2. 38 0 8. 6 1 ND No 4 Functional (mt-co3) A32T (G9300A) 2. 38 0. 47 (blood) 100 2 ND No 0 Nonfunctional T88I (C9469T) 4. 76 0. 06 (ns) 99. 2 2 Inherited No 0 Nonfunctional V91I (G9477A) 4. 76 4. 41 (blood) 98. 55+1. 45 2 Inherited No 0 Nonfunctional S150N (G9655A) 2. 38 0. 01 (ns) 73. 8 2 Somatic No 4 Functional N154S (A9667G) 2. 38 0. 75 (blood) 92. 6 2 Inherited No 0 Nonfunctional V254I (G9966A) 2. 38 0. 67 (blood, buccal) 96. 8 2 Inherited No 0 Nonfunctional Abbreviations: ND, not determined; ns, not specified; K-means group-mutations were stratified into 2 groups based on 3 variables: %GBM ¼ prevalence of mutations in the GBM cohort, %HmtDB ¼ prevalence of mutations found in the human mitochondrial database, and %Het ¼ heteroplasmy.
Secondly, many catchments are impacted by direct anthropogenic influences, such as water withdrawal or hydroelectricity production, which may create artificial trends in addition to climate trends. Lastly, ensuring the homogeneity of streamflow measurements over several decades is difficult: rating curves may change or gauging stations may be relocated, thus creating spurious trends in the time series  (Lang et al. , 2010). Specific testing procedures  (Kundzewicz and Robson, 2000; Parey et al. , 2007; Renard et al. , 2008)  and thoroughly-reviewed datasets of undisturbed catchments  (Hannah et al. , 2011; Burn et al. , 2012; Whitfield et al. , 2012)  are required to face these challenges.
Observed trends in hydro-climatic variables in the Alps Many studies have investigated the existence of trends in cryospheric variables over the world. Chapter 4 of the IPCC report (2013) provides an overview of these studies. We focus here on reviewing trend analyses for hydro-climatic variables in the Alpine region. Stewart (2009)  presents an overview of the changes in snowpack and snowmelt-related flows in the Alps and in other mountainous areas of the world. Focusing on the Alps, an overall decrease in snow cover is observed during the 20 th century for low-and mid-elevations. Trends are less significant at higher elevations, and snowpack even increased due to higher precipitation totals. Auer et al. (2007)  and  Brunetti et al. (2006; 2009)  describe an analysis of several climate variables over the Alpine region based on the HISTALP dataset.
Objectives The objective of this paper is to assess the existence of trends affecting hydrologic regimes in the Alps. The analysis described herein is unique in several respects. Firstly, it uses an extended and thoroughly-reviewed dataset of daily streamflow series covering most of the Alpine region. Secondly, it uses hydrologic indices adapted to the peculiarities of snowmeltinfluenced catchments, and describing low, medium and high flows. Lastly, it operates at both local and regional scales, enabling a comprehensive assessment of the detected trends consistency. The paper is organized as follows. Section 2 describes the dataset of daily streamflow series and its properties. Section 3 describes the methods used to analyze the evolution of hydrologic regimes, and more precisely the definition of hydrologic indices and the statistical setup for trend detection.
Daily streamflow series and associated catchments The dataset used in this paper was gathered within the EU Alpine Space Programme project AdaptAlp, and is therefore named after it. The AdaptAlp dataset contains daily streamflow series for snowmelt-influenced catchments located in the Alps. As described in  Bard et al. (2012) , the strategy used to gather these series involves extensive quality checks in order to meet the following requirements: (a) the gauging station has been active over a period of at least 40 years; (b) the station controls a largely "undisturbed" catchment where direct anthropogenic influences can be neglected; (c) the daily streamflow series is free from any major non-homogeneity due to measurement issues. Data quality has been assessed through a first round of analysis investigating changes affecting low, medium and high flows over the whole available period for each gauging station.
In particular, step-change tests  (Pettitt 1979)  were used to highlight suspicious stations. For example a significant step change occurring on the same date for many hydrologic indices may be indicative of a measurement inhomogeneity (e. g. due to station relocation, change in the measurement sensor or method, etc. ). The results from this first round of analysis were discussed with the data producers. Following this discussion, stations were excluded from the dataset whenever a specific cause for the detected change could be identified (typically, station relocation, building of some hydraulic structure influencing the river flow, etc. ). Some of the stations were judged appropriate only for a specific flow range. As an example, some stations are only usable for high flow analyses because measurement issues and/or minor direct influences compromise their suitability for low flows.
Such long series are unfortunately scarcely available for streamflow data: while long series of water stages do exist, the same is not true for rating curves that estimate the stage-discharge relationship, thereby reducing the availability of reliable streamflow data. At the time of writing of this paper, 169 time series from the Adaptalp dataset have been made available through the Global Runoff Database Center 2. The station elevations range mostly between 400 and 1200 m. a. s. l. (Fig. 1c ). The dataset comprises catchments of varied size, the majority of which have an area between 100 and 1000 km² (Fig. 1d ). Around twenty catchments have a significant part of their area covered by glacier, however precise quantification was not available for all of them. Lastly, Figure  1f  shows the data availability, and suggests that the period 1961-2005 provides the best trade-off for analyzing as many stations as possible over a 40 years long common period.
Hydrologic regimes and hydro-climatic regions Although all catchments in the AdaptAlp dataset are influenced by snowmelt, they still span a significant diversity of hydrologic regimes. The catchments are clustered into homogeneous hydrologic regimes to allow regime-specific analyses. Nine regimes are defined as presented in Figure  2 ; they range from pure glacial and snowmelt regimes to mixed snowmelt-rainfall regimes. The regimes clustering is performed by applying the Kohonen algorithm  (Wehrens and Buydens, 2007)  to the inter-annual monthly streamflow (standardized by the inter-annual mean) computed for each station. The Kohonen algorithm was chosen because it imposes continuity between clusters, thus mimicking the continuous shift from glacial regimes to mixed snowmelt-rainfall regimes.
Field significance When applying a statistical test to a large number of series with a 10% error level, a detection rate of about 10% of significant trends is expected, even in absence of any change in the series. Therefore, at-site testing is complemented by an evaluation of field significance, which answers the following question: what is the minimum number of significant at-site trends ensuring that these trends are not due to chance? The Bootstrap procedure proposed by  Douglas et al. (2000) , specifically designed to account for the spatial correlation within a dataset, is used for this purpose. Field significance is evaluated for each hydrologic regime, and for the whole dataset.
Analysis of long series over the period 1925-2005 In order to assess the stability of detected trends over different time periods, the at-site analysis is repeated for two  40-year periods: 1925-1964 and 1965-2005. Only 22 series (mapped in Fig. 3 ) are long enough to cover the extended period 1925-2005, thus precluding an analysis of the regional consistency at the scale of hydro-climatic regions. Figure  11  shows the results of the at-site trend analysis. Generally, the results over the long period 1925-2005 are similar to the main results observed for the whole dataset over the 1961-2005 period described previously. Overall, lower percentiles are the most impacted by significant decreasing trends, whereas higher percentiles are found significantly increasing for snowmelt-rainfall 1 regimes.
Interpreting the results of trend tests Meaningful trend detection in climate variables requires a good-quality dataset. For this reason, extensive efforts have been made to use a consistent non-influenced streamflow dataset in the AdaptAlp project. However, spurious trends might also be detected if unrealistic assumptions are made by the test. The detection of a significant trend does not prove beyond doubt that a trend actually exists in the data: firstly because of the error level, secondly because what might be rejected is one of the hypotheses used to build the test rather than the null hypothesis. The main questionable assumption relates to the treatment of autocorrelation. Indeed, the local Mann-Kendall test used in this paper assumes that autocorrelation, if present, follows a simple first-order autoregressive structure (AR(1)).
Conclusion The objective of this paper was to assess the existence of trends in the hydrologic regime of Alpine rivers. This was carried out using a dataset of 177 daily streamflow series. Local and regional statistical tests were applied to various hydrologic indices describing low, medium and snowmelt-related high flows. The main findings can be summarized as follows:  Winter droughts: o Severity tends to decrease for all regimes except snowmelt-rainfall regimes, with drought durations decreasing by an average of 25 days over 1961-2005 and volume deficits decreasing by an average of 47%. o For the drought "end" a slight shift is detected toward an earlier occurrence for all regimes except snowmelt-rainfall regimes, with an average trend magnitude of 16 days. o Mixed snowmelt-rainfall regimes in the Southeastern Alps (mostly Slovenian stations) show an opposite evolution: seasonality seems to be shifted toward later occurrences, for the drought "start", "center" and "end" by 22 days on average.
Acknowledgements We would like to thank all data owners for providing the hydrologic data used in this study:  Bundesministerium für Land und Forstwirtschaft, Umwelt und Wasserwirtschaft (BMLFUW) ,  Hydrographisches Zentralbüro ,  French Ministry of Ecology, Sustainable Development and Energy , through the  HYDRO database, Electricité de France (EDF) ,  Compagnie Nationale du Rhône (CNR) ,  Bayerisches Landesamt für Umwelt (LfU) ,  Landesanstalt für Umwelt, Messungen und Naturschutz Baden-Württemberg (LUBW), Agenzia Regionale per la Protezione Ambientale (ARPA) Piemonte, ARPA Lombardia, ARPA Veneto, ARPA Friuli Venezia Giulia, ARPA Valle d'Aosta, Ufficio Idrografico della Provincia Autonoma di Bolzano-Alto Adige, Environmental Agency of the Republic of Slovenia , and  Federal Office for the Environment, Hydrology Division.
Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated "direct" measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well.
, 2013). Numerous techniques exist to compute atrophy from longitudinal MRI scans of the brain, including  (Freeborough and Fox, 1997; Holland and Dale, 2011; Hua et al. , 2013; Reuter et al. , 2012). Many of these methods have been applied to the same set of large, publicly available cohorts, and in some cases, comparisons between different methods have been performed, primarily by comparing the effect sizes of each method through the estimation of sample sizes needed to statistically power a hypothetical clinical trial. For the most part, direct comparisons between multiple techniques using exactly the same dataset have not yet been performed, and such comparisons have rarely been carried out in a blinded fashion. Another major issue inherent to longitudinal studies of atrophy is the lack of ground truth.
In an attempt to address this,  Fox et al. (2011)  proposed desirable characteristics that atrophy measurement techniques should exhibit, including: symmetry, transitivity, comparisons with results from manual measurements, comparison with more established techniques  (Schott et al. , 2006) , and reproducibility of measurements over short time intervals. For these purposes, we created the MIRIAD atrophy challenge, where we provided a common dataset of serial MRI data that could be used for a comparison of atrophy measurement techniques. We chose the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset  (Malone et al. , 2013)  consisting of 708 T1volumetric scans acquired from 46 patients with a clinical diagnosis of AD and 23 controls scanned on multiple occasions over a variety of different intervals on the same 1.
5 T scanner, and by the same radiographer; these data have since been made an open-access resource. 3 articular features of this dataset include the availability of shortinterval followup scans, 2 and 6 weeks from the initial baseline scan, allowing for thorough investigation into repeatability of atrophy measurements; and a means of assessing within-day reproducibility as most subjects had 2 viable back-to-back scans at 3 of the time points. In previous studies, these scans have been used to perform atrophy simulation modelling  (Camara et al. , 2008)  and to estimate sample sizes for clinical trials using both manual and automated techniques (reviewed in  Malone et al. , 2013) , but aside from one paper comparing results between BSI and SIENA  (Smith et al. , 2007) , little work has been done using this dataset to directly compare different techniques.
Data Full details about the MIRIAD dataset, including how to obtain the data, can be found in  Malone et al. (2013). In brief, the dataset consists of scans from 46 patients fulfilling National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) working group criteria  (McKhann et al. , 1984)  for probable AD and 23 age-matched, healthy elderly controls scanned multiple times over a follow-up period of up to 2 years, with subjects being scanned twice in one session at some visits. The basic demographics of the MIRIAD subjects can be found in Table  1. All scanning was done on a single 1. 5-T scanner (GE Signa, GE Medical Systems, Milwaukee, Wisconsin) from 2000 to 2003. Volumetric T1-weighted images were acquired with an IR-FSPGR (inversion recovery prepared fast spoiled gradient recalled) sequence, field of view 24 cm, 256 × 256 matrix, 124 1.
MIRIAD atrophy challenge The MIRIAD atrophy challenge was announced as part of the MICCAI 2012 Workshop on Novel Imaging Biomarkers in Alzheimer's Disease (NIBAD12)  (Wang et al. , 2012). Participants requested a login and then downloaded data from a dedicated XNAT (eXtensible Neuroimaging Archive Toolkit)  (Marcus et al. , 2007)  server, which contained all the image data in NIFTI (Neuroimaging Informatics Technology Initiative) format. For each subject, only one of the baseline scans, henceforth referred to as the identified baseline, was identified to the participants. The rest of the temporal ordering was blinded to the participants. This blinding was performed to ensure that no added optimization could be performed when doing change measures on back-to-back or short interval scans. Participants submitted volumetric measures for every dataset in the challenge, as well as change measurements between each time point combination, both in the "forward" direction (e.