dataesr/scientific-paragraphs-categorization huggingface dataset:
subtilis, in terms of number of impacted genes. Finally, we explore how the most complex regulation is used to trigger major system responses to a wide range of environmental conditions. Additionally, we introduce the use of ARs to reconcile our proposed network with available gene expression data. We show how this methodology is able to fill gaps and correct inconsistencies in the regulatory network. The reconciliation process allowed us to expand our knowledge of B. subtilis regulation by adding new stimuli to genes and regulons, by adjusting stimuli on existing genes, and by adjust regulons based on inconsistent expression patterns. We were also able to provide clues for putative gene function assignments for genes with unknown functions. All AR data were integrated into the PubSEED 5  , and they can be used as part of the annotation curation tools available in that framework.
A web resource was also created showing the relationship between the ARs and the expression data used for their computation in B. subtilis. This resource is available for the public and can be used to conduct analysis of the ARs and expression data sets beyond the objectives of the work described in this manuscript. During the reconciliation process, we were able to match many ARs to the same regulatory mechanisms we found in our manually curated network. This fact highlights the convenience of using ARs to study the regulatory network of an organism without a huge effort put into initial manual curation.
threeplusone. com/)  29  with the default parameters, the classic colour scheme and the unit frequency being plotted as 'probability'. The data set supporting the results of this article is available on ArrayExpress repository under the accession E-MTAB-4097. BP sequence analysis. The online tools SVM-BPfinder 20 (http://regulatorygenomics. upf. edu/Software/SVM_BP/) and the Human Splicing Finder 30 (http://www. umd. be/ HSF/) were used to predict the BPs. The SVM_BP code was altered to allow for BP six base pairs from the 3 0 ss by setting minidist3ss ¼ 6 in svm_getfeat. py. Minigene constructs. For each selected candidate gene alternative AG 0 -centred sequence of B200 nucleotides was PCR amplified from the genomic DNA of HEK293T cells using Phusion Hot Start II High Fidelity DNA Polymerase (Thermo Fisher Scientific).
All constructs were verified by DNA sequencing. Primers used for generating the mutated SF3B1 are: (forward) 5 0 -CTGGTGGATGAGCAGCAGGAGGTCAGAA CCATCTCTGC-3 0 and (reverse) 5 0 -GCAGAGATGGTTCTGACCTCCTGCTG CTCATCCACCAG-3 0. BP mutant constructs. Mutations of potential BP in TMEM14C and ENOSF1 ExonTrap constructs were introduced using QuikChange II Site Directed Mutagenesis Kit (Stratagene) and verified by DNA sequencing. The primer sequences used to generate the mutations are provided in Supplementary Table  3. Cell culture and transfection. Mel202 cell line was purchased from the European Searchable Tumour Line Database (Tubingen University, Germany) and MP41 (derived at Institut Curie and described in ref. 31) UM cell lines were cultured in RPMI-1640 supplemented with 10% fetal bovine serum.
The membrane was then incubated at room temperature for 1 h with either goat anti-rabbit or goat anti-mouse Odyssey secondary antibodies coupled to a 700 or 800 nm. Immunolabelled proteins were detected using the Odyssey Infrared Imaging System (Li-cor). Quantifications were performed using the ImageJ Software. b-Actin immunoblotting was used to quantify and normalize results. Co-immunoprecipitation. HEK293T cells were co-transfected with pcDNA3. 11. -Myc-U2AF2, kindly given by Edwin Chan  32  , and either pCMV-3tag-1A-SF3B1 WT or pCMV-3tag-1A-SF3B1 K700M. After 48 h, proteins immunoprecipitated by anti-Flag gel affinity (A2220, Sigma) were separated by SDS-polyacrylamide gel electrophoresis and probed in a western blot assay with anti-U2AF2 and anti-U2AF1 antibody (Supplementary Fig. 8 ).
Fragment analysis by capillary electrophoresis. Minigene fragments were amplified by RT-PCR using a 5' FAM-forward primer and reverse-specific primers (Supplementary Table  2 ). One microlitre of RT-PCR product was added to 18. 5 ml of deionized formamide and 0. 5 ml HD400 marker (Applied Biosystems). The mixture was then denatured 3 min at 95 °C, immediately put on ice, and separated using an ABI 3130xl Genetic Analyzer. The data were analysed using GeneMarker software (SoftGenetics).
Acknowledgements We thank  Drs Edwin Chan ,  Olivier Bernard ,  Veronique de Dalle ,  Raphael Margueron ,  David Gentien ,  Lisa Golmard ,  Olivier Kosmider  and  Professor Michaela Fontenay  for providing key reagents and  Drs Didier Auboeuf ,  Stephan Vagner  and  Sophie Bonnal  and  Juan Valcarcel  for helpful discussion and reviewing the manuscript. This work was supported by  INSERM , the  French National Cancer Institute (INCa)  and the  Programme Incitative et Cooperatif 'Melanome uveal'  from  Institut Curie. S. A. is supported by the  INCa  grant ' MeluGene '.
Background Antibiotic resistance is a constantly evolving phenomenon and a threat to infection and disease control; it complicates patient management and treatment strategy and prolongs hospital stays. Nowadays, this international public health problem is recognised as one of the scourges of the 21st century  [1]. Several studies have sought to estimate the morbidity and mortality of infections due to multidrug-resistant bacteria (MDRB). A joint report from the European Centre for Disease Prevention and Control (ECDC) and the European Medecines Agency (EMEA), published in 2009 and based on data from 2007  [2] , estimated at approximately 386,000 the annual number of infections due to MDRB in Europe that year, including 42,500 cases (11%) of bloodstream infections. The number of deaths associated with these infections was estimated at more than 25,000.
Number of infections due to MDRB To select the most frequently diagnosed infectious body sites for each of these MDRB, data from the 2012 point prevalence survey on healthcare-associated infections and antimicrobial use in French hospitals (2012 PPS)  [7]  were used. This survey, conducted every five years on a single day in nearly all French healthcare facilities collects standardised data about nosocomial infections. In 2012, data were collected for 91% of French hospital beds. Based on this data, the following infectious body sites were selected, as there were the most frequent infections in the 2012 PPS: for all MDRB: invasive infections (bacteria isolated from blood or cerebrospinal fluid), urinary tract infections, skin and soft tissue infections, and surgical site infections; for all MDRB excluding GRE: respiratory tract infections; for MRSA only: bone and joint infections (does not include surgical site infections); for GRE only: gastrointestinal tract infections (gastro-intestinal and intra-abdominal infections).
The number of invasive infections for each MDRB was estimated from data transmitted by France to EARS-Net  [5]. This European network collects resistance data of bacterial strains isolated from invasive infections (bacteria isolated from blood or cerebrospinal fluid). As duplicate isolates for the same patients had already been eliminated during data collection, the number of incident cases of invasive infections due to MDRB occurring each year in the network was directly estimated from the number of resistant strains isolated. For instance, there were 1005 strains of MRSA reported in EARS-Net for France in 2012, so the number of incident cases of invasive infections due to MRSA in 2012 in the French EARS network was estimated as 1005. The number of incident cases of invasive infections was then corrected by taking into account the coverage of the EARS-Net network for France, estimated from the number of inpatient hospital days (HD) reported by hospitals whose laboratories had transmitted data to the network (secondary and tertiary care hospitals).
The list of these hospitals was obtained from the Epibac network  [8]  and the total number of HD from the 2012 Health Facilities Annual Statistics (SAE) report  [9]. The EAR-S-Net coverage was estimated by dividing the number of HD in the participating hospitals (N 1 ) by the total number of HD in all French secondary and tertiary care hospitals that same year provided by the same source (N 2 ). For instance, the number of cases of invasive infections due to MRSA in 2012 for France was estimated as 5574 (n 1 ). The incidence of patients with an invasive infection was also expressed as the number of cases per 1000 HD. In order to estimate the number of cases with infections at other body sites, the ratio between the number of cases with infections at a given body site and the number of cases of invasive infections was calculated for each MDRB and each body site using data from the 2012 PPS.
For instance, to evaluate the number of cases of urinary tract infections (UTI) due to MRSA in our study, we first calculated a specific ratio between the number of UTI due to MRSA reported in the 2012 PPS (n = 103) divided by the number of invasive infections due to MRSA reported in the 2012 PPS (n = 105). The specific ratio for UTI due to MRSA was 103/105 = 0. 98. We then multiplied the number of cases with invasive infections due to MRSA estimated in our study based on EARS-Net data (n 1 = 5574) by this ratio (0. 98), to estimate the number of cases with urinary tract infections due to MRSA in France (n 11 = 5468). The same approach was used for other body sites and MDRB. In order to have intervals of plausibility around these estimates, we also estimated these ratios from a non-systematic review of the literature targeting French and European publications when available.
Number of infections due to MDRB The number of infections due to MDRB was estimated at approximately 158,000 in France in 2012, for an incidence of 1. 83 per 1,000 HD. Data from the literature enabled us to estimate a plausibility interval for the annual number of cases from 127,000 to 245,000 (incidence: 1. 48 to 2. 85 per 1,000 HD). Most cases were infections caused by MRSA (33%; plausibility interval: 28 to 38%), 3GC-R E. coli (32%; 26 to 32%), CR P. aeruginosa (23%; 23 to 29%) or 3GC-R K. pneumoniae (10%; 6 to 10%). The other infections due No. MRSA respiratory infections/No. MRSA invasive infections 2. 37  [7]  1. 25  [11]  2. 50  [12]  No. MRSA skin and soft tissue infections and surgical site infections/No. MRSA invasive infections 4. 17  [7]  4. 90  [12]  5. 25  [11]  No. MRSA bone and joint infections/No.
These results underestimate the morbidity and mortality of infections due to antibiotic-resistant bacteria in France. Although our study covers a large panel of MDRB and the most frequent body sites, it does not include all bacteria resistant to antibiotics and all infection sites. For example, as only the most frequent infections in the 2012 PPS data were selected for each MDRB, gastro-intestinal tract infections due to Enterobacteriacae were not taken into account in our study. We did not take into account patients with more than one MDRB. However, this bias in underestimating MDRB infections should be very limited. Indeed, according to data from the 2012 French PPS, only 39 patients among a total of 15,180 infected patients (0. 2%) had two or more different MDRB (data not shown). Furthermore, the extrapolation of EARS-Net data to France was calculated on the number of inpatient hospital days reported only by French secondary and tertiary care hospitals, as the laboratories participating in the EARS-Net network are located only in such institutions.
This study considered only a fraction of all bloodstream infections, by correcting their incidence reported by EARS-Net by a factor derived from national prevalence studies to take into account only those of nosocomial origin, e. g. , only 65. 6% of MRSA bloodstream infections and 58. 9% of 3GC-R E. coli bloodstream infections (ECDC, unpublished data). Moreover, the panels of infections and MDRB species considered in the present study were more complete than those of the European study, which did not include three MDRB from our panel (CR K. pneumoniae, Acinetobacter spp. and GR Enterococcus faecalis) and one infection site (bone and joint infections for MRSA). Another major difference between the two studies is related to the correction of the EARS-Net data to estimate the number of cases for the entire country.
Aims: To investigate age-related persistence with bisphosphonates (BIS) in women with breast cancer (BC) and bone metastases. Methods: We included a dataset of 1541 patients diagnosed with BC and bone metastases and initially treated with BIS between 1994 and 2013. The primary outcome measure was the age-related rate of BIS discontinuation within 12 months after treatment initiation. Therapy discontinuation was defined as a period of at least 90 days without treatment. A multivariate Cox regression model was created to determine the influence of age on the risk of discontinuation. Health insurance coverage (private/statutory), type of care (gynecological/general), region (West/East Germany), depression, chemotherapy, hormone therapy, pain medication, antidepressants, and the number of co-medications were included as covariates.
Database The Disease Analyzer database (IMS HEALTH) compiles drug prescriptions, diagnoses, basic medical and demographic data obtained directly and in anonymous format from computer systems used in the practices of general practitioners  [13]. Diagnoses (ICD-10), prescriptions (Anatomical Therapeutic Chemical (ATC) Classification System) and the quality of reported data have been monitored by IMS based on a number of criteria (e. g. , completeness of documentation, linkage between diagnoses and prescriptions). In Germany, the sampling methods used for the selection of physicians' practices were appropriate to obtain a representative database of general and gynecological practices  [13]. Prescription statistics for several drugs were very similar to data available from pharmaceutical prescription reports  [13].
Study population Overall, the database included 2067 general practices and 397 gynecological practices reporting to IMS HEALTH on a continuous basis during the study period. First-time cancer-related BIS prescriptions (ATC: M03B4) from January 1994 until December 2013 in subjects diagnosed with bone metastases (ICD 10: C795) following breast cancer diagnosis (ICD 10: C50) were defined as the index dates; the latest follow-up date was identified as April 2014. Patients with a follow-up time of less than 365 days prior to the index date were excluded. This exclusion was necessary to ensure correct identification of treatment initiation. Further inclusion criteria comprised the following: age of over 18 years at the index date and no diagnosis of other tumors in the time between first BC diagnosis and first bone metastasis diagnosis.
Study outcome The main outcome measure was BIS treatment discontinuation rate within one year after the index date. Treatment discontinuation of a specific BIS therapy was defined as a period of 90 days devoid of this or an alternative BIS therapy within that time frame. Persistence is defined as the time from the beginning of therapy to therapy discontinuation. A longitudinal dataset of medication supply was established for each individual patient and non-persistence with one of the study drugs (i. e. zoledronate, ibandronate, clodronate and pamidronate) was calculated. As part of this process, the number of days of drug supply was calculated on the basis of the quantity and dosage information associated with each prescription record. All patients were monitored for a duration of at least three months to one year from their index date in order to identify treatment discontinuation.
Covariates Demographic data included age, health insurance coverage (private or statutory), gynecologist care, and practice region (East versus West Germany). Co-diagnoses of depression were determined based on primary care diagnoses (ICD-10 codes: F32, F33). Furthermore, the pre-or co-treatment was defined based on Anatomical Therapeutic Chemical Classification (ATC) for chemotherapy (L01, L02), endocrine treatment (tamoxifen or aromatase inhibitors), pain medication (ATC: N02A, N02B, M01A), and antidepressants (ATC: N06A). Finally, the number of co-medications was defined as the maximum number of different drugs a patient was prescribed to take in a single day.
Acknowledgments This work has been supported by the  French National Research Agency  ( Grant reference ' ' ANR-08-MIE-007 ''), the  Agencia Nacional de Promocion Cientifica y Tecnológica of Argentina  (Grant reference " PICT2008-0035 ") and the  Algerian Ministry MESRS  (Grant reference "CNEPRUB02020130062''). The authors are truly grateful to the anonymous referees for their valuable comments and suggestions, which greatly improved the paper.
Q6 After an inaugural multidisciplinary meeting of PCA researchers  [11] , latterly formalized as the Alzheimer's Association's International Society to Advance Alzheimer's Research and Treatment (ISTAART) Professional Interest Area in Atypical AD and Associated Syndromes, an international collaborative group was established to assess genetic risk factors for PCA. Researchers identified individuals with PCA, in whom a deoxyribo nucleic acid (DNA) sample was available. Patients who the referring physician had diagnosed with AD, had multidomain cognitive impairment fulfilling criteria for AD dementia, and had one or both of two published criteria for PCA, as proposed by Tang-Wai  [5]  and Mendez  [12]  (Table  1 ) were included. Additional data collected included gender, age at disease onset, age at death (where applicable), and whether there was molecular (cerebrospinal fluid or amyloid PET using locally defined ranges) evidence or pathologic confirmation of underlying AD pathology.
Exploratory GWAS As several different array platforms were used in the study, only 210,670 SNPs were genotyped across all samples. In this set, there was little evidence of inflation in the association test statistic (l 5 1. 05). Only the proxy for the APOE E4 AD-risk allele, rs2075650, achieved genome-wide significance. We went on to analyze 5. 9-M SNPs in the imputed data set. Aside from chromosome 19 (APOE locus), three loci on chromosomes 7, 2, and 6 respectively were of interest (Fig. 1 ). rs2525776 on chromosome 7, upstream of SEMA3C (OR 3. 3 [2. 1-5. 1], P 5 1. 4 ! 10 28 , P GC 5 4 ! 10 28 ; Fig. 2C ); rs76854344 on chromosome 2, upstream of CNTNAP5, (OR 1. 9 [1. 5-2. 3], P 5 8. 0 ! 10 210 , P GC 5 2 ! 10 29 ; Fig. 2D ), and rs72907046 on chromosome 6, downstream of FAM46A (OR 3. 2 [2.
Funding support for the genotyping of the MyCode cohort was provided by a Geisinger Clinic operating funds and an award from the Clinic Research Fund. The data sets used for the analyses described in this article were obtained from dbGaP at http://www. ncbi. nlm. nih. gov/gap through dbGaP accession number phs000381. v1. p1. This study also makes use of the data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www. wtccc. org. uk. Funding for the project was provided by the Wellcome Trust under award 076113.
Acknowledgments We acknowledge the support of the  Alzheimer's Association 's International Society to Advance Alzheimer's Research and Treatment (ISTAART) ,  Alzheimer's Research UK , the  UK Alzheimer's Society  and the  Raena Frankel-Pollen Fund. This work was supported by the  NIHR UCL/UCLH Biomedical Research Centre , the  NIHR Queen Square Dementia Biomedical Research Unit , the  Wolfson Foundation , and the  UK Medical Research Council. The  Dementia Research Centre  is an  Alzheimer's Research UK Coordinating Center. N. C. F. and M. N. R. are NIHR senior investigators. J. T. B. receives funding from the  UK Alzheimer's Society. This work was also supported by the  Medical Research Council Dementias Platform UK  ( MR/L023784/1  and  MR/009076/1 );  Alzheimer's Research UK  ( Senior Research Fellowship  to S.
L. M. ). D. G. and E. S. are supported by  Italian Ministry of Health  ( Ricerca Corrente ). W. M. v. d. F. , P. S. receive funding from  PERADES-JPND. E. L. receives funding from  Stichting Dioraphte. Case ascertainment in Australia was funded by grants from the  National Health and Medical Research Council of Australia  ( NHMRC program  grant # 1037746 ) and the  Australian Research Council Centre of Excellence in Cognition and its Disorders Memory Node  (# CE110001021 ); G. H. is a NHMRC senior principal research fellow (# 1079679 ). We would like to thank the participants in the Framingham, Geisinger, WTCCC, and KORA studies used as controls in this article. The  Framingham Heart Study  is conducted and supported by the  National Heart, Lung, and Blood Institute (NHLBI)  in collaboration with  Boston University  (Contract No.
INTRODUCTION H ydrologists strive to provide reliable operational river forecasts that facilitate effective water management and emergency flood protection. Shifting institutional resources and growing complexitysuch as an increasing number of data sources and forecasting models, and demand for new forecast products-creates pressure to reshape hydrologists' involvement with forecast production. Increased automation is one way to increase efficiency, accelerate information generation, and broaden the capacity of forecast centers. Automation in this context means the delegation of tasks (normally carried out by a person) to a computer. This may be performed within the context of a decision-support system where the person and machine interact during forecast production. Automation enables implementation of advanced techniques that may be inconsistent or incompatible with the traditional manual forecasting paradigm.
Weather modelers routinely objectively assimilate tens of millions of four-dimensional observations per day into models with 10 8 degrees of freedom. Furthermore, automatically processed satellite image and weather radar products are widely available to forecasters and the public. While imperfect models are used to translate raw sensor values into public products, there is no expectation that a person would be editing those data graphics. 2. Use well-designed forecasting interfaces. Some studies of meteorological automation focuses on workstations, the primary tool for forecast creation. 55 Meteorology has pioneered the development of Interactive Forecast Preparation software, such as the Graphical Forecast Editor. 56 Here, instead of manually crafting narrative and products, the forecaster interactively edits a set of NWP forecast grids and products are automatically generated from the result.
Working within the model space often aids in preserving the physical consistency of all aspects of the forecasts, including other sites downstream. The research community has not adequately demonstrated practical methods of objective data assimilation that performs to forecaster expectations. 3 As a result, automatic data assimilation is very rare in operational flood forecasting, even though hydrological models are simpler and datasets are much smaller than their meteorological counterparts. The solution may lay with automatic data assimilation, manual postadjustment of local features on the hydrograph, automated algorithms to maintain internal consistency, and automated checking that the forecaster is not over-adjusting. This may be similar to decision support systems in weather forecasting where the expert is 'Over-the-loop' and the expert and machine work together to ensure the physical realism of the edited products.
ACKNOWLEDGMENTS Thanks are given to  Albert Mannes ,  Michael Cranston ,  Andrew Preece ,  Lionel Berthet ,  Jeff Perkins ,  Peter May ,  Robin Webb ,  Roger Deslandes ,  Bodo Zeschke , and  Dasarath Jayasuriya  for their reviews of this article and the Bureau technical report that led to the article's development. We are also appreciative of  Tanya Smith 's editorial improvements.