dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction The study will attempt to predict dengue incidence over time using entomological and immunological indices based on repeated (monthly) field collections. This will be done in two ways: by predicting the risk of a future outbreak, and by estimating associations between dengue incidence and the indices. Predict the risk of a future outbreak within a week or within a longer lead time According to the Ministry of Public Health, an outbreak is defined as the number of cases per week exceeding the median number of cases during the last 3-5 years. We will have data on the stated indices 1 week every month, as opposed to every week, so approximately one quarter of the dengue case series data will be able to be used in this analysis. Using logistic regression, we will develop a prediction rule for the outbreak status (i.
e. , outbreak or not) in a given week based on data on the indices and on climate, in the previous week or earlier. Climate variables will include rainfall amount and frequency and ambient maximum and minimum temperatures. We will also consider other variables related to housing type and socioeconomic status at the spatial level to be predicted. The aim of the analysis will be to obtain a rule with a high negative predictive value, i. e. , with most of the negative predictions being borne out, and with few outbreaks being missed. We will also calculate other operating characteristics such as sensitivity and specificity. We will concentrate on trying to predict outbreaks from one week to the next, i. e. , with a lag of 1 week, but will also assess rules for lags up to 4 weeks. The accuracy of this prediction rule will be assessed by developing it on the majority of the data as a "training" dataset, then evaluating it on the remainder of the data as a "test" dataset.
This reduces the tendency to overestimate the accuracy of prediction when the evaluation is done on the same dataset from which the rule was developed. Estimate associations between dengue incidence and the indices For this method, we will use Poisson regression and/or time series methods (e. g. , autoregressive integrated moving average (ARIMA)) to relate the number of cases per week to our study indices and to climate. Again, the cases in one week will be modeled as a function of data from the previous week or earlier. The associations will be measured in terms of rate ratios or similar coefficients. This analysis will be done using both the incidence in the public health surveillance system and the incidence data from the current study. Associations identified in this analysis may be statistically significant but not of large enough magnitude to enable prediction of outbreak status in the previous section.
Dissemination policy and access to data Results from this trial will be published in open access, peer-reviewed journals. The presentation of the final results of this trial will follow the CONSORT 2010 statement and the extension to cluster-randomized trials  [71]  and, if needed, extensions on non-pharmacological interventions and pragmatic designs  [73, 74]. This study protocol followed the recommendations of items to address in a clinical trial protocol (Additional file 1)and the minimum trial registration information of WHO (Additional file 2), in addition to what was registered in the primary ISRCTN registry. Access to the trial dataset will be made available upon publication of results. Access to data will also be archived and made available through the Norwegian University of Life Sciences and the Norwegian Centre for Research Data (http:// www.
The IgG immune response to Nterm-34 kDa salivary peptide is not expected to last for more than 15-30 days; hence, it represents a relevant temporal biomarker to assess recent relative exposure of humans to Aedes bites  [84]. This peptide was used successfully as a short-time indicator to evaluate vector control interventions against Aedes exposure in Réunion Island  [86]. In this trial, DENV detection in adults and pupae will be assessed in relation to the number of recent and subsequent confirmed dengue incidents in humans in the same locality. Human exposure to Aedes bites measured by IgG antibody response will be examined for correlation with dengue cases. Data will be analyzed to include socioeconomic factors and influence of environmental and seasonal fluctuations, such as rainfall, relative humidity, and ambient temperature.
These parameters and specific measures have so far not been fully integrated in epidemiological risk assessments and epidemic forecasting. The permanent staff of local public health departments, sub-district hospitals, and VHVs will collect data for all listed outcomes, thereby minimizing involvement of full-time trial project staff. This is intended, as much as possible, to allow national authorities to emulate the project procedures in follow-on surveillance and intervention activities or adoption of these methods into routine vector control program activities. The exception to this is molecularbased assay confirmation of DENV in human blood and mosquitoes and human antibody response to Ae. aegypti salivary peptides; this testing will be conducted by project staff.
The hygromycin B resistance cassette was amplified by PCR from vector pGL4. 14 (Promega), subcloned into pGEM â -T Easy and inserted into pMRPS5WT-IRES-eGFP and pMRPS5V336Y-IRES-eGFP using MluI-specific restriction sites. For generation of stable transfectants, HEK293 cells were transfected with vectors pMRPS5 V336Y-IRES-eGFP and pMRPS5WT-IRES-eGFP using TurboFect (Thermo Fisher) according to the manufacturer's instructions. Cells were cultured in DMEM supplemented with 10% FBS and propagated under hygromycin B (100 lg/ml) selection for 5-7 weeks. GFP-expressing colonies were picked for further characterization. GFP fluorescence was analyzed by flow cytometry using the BD FACSCanto II (BD Biosciences) and the FlowJo data analysis software (Tree Star Inc. ).
2 nM Plasma Membrane Permeabilizer (PMP) reagent (Seahorse Bioscience). The experiment started in a coupled state in the presence of succinate, pyruvate, and malate (state 2, basal respiration). State 3 was initiated following the injection of 55 ll of 40 mM ADP (4 mM final). State 4o was induced by addition of 62 ll of 25 lg/ml oligomycin (3 lg/ml final). State 3 uncoupled (3u) was assessed after injection of 68 ll 40 lM FCCP (4 lM final) before shutting down mitochondrial respiration by injecting 76 ll of 40 lM and 20 lM (4 lM and 2 lM final) of antimycin A and rotenone, respectively. Drugs were purchased from Seahorse Bioscience. Data were extracted from the Seahorse XF-24 software, and bioenergetic parameters were calculated according to the manufacturer's guidelines. Cellular growth was monitored using Alamar Blue (Life Technologies).
RNA sequencing and data analysis RNA sequencing (RNA-seq) was performed at the UZH/ETH Functional Genomics Center Zurich (FGCZ) according to the Illumina RNA sequencing protocol. For transcriptome analysis, four clones of Mrps5 V336Y and six clones of Mrps5 wild-type transfected cells were compared. RNA was extracted using TRIzol. The quality of the isolated RNA was determined using a Qubit â (1. 0) Fluorometer (Life Technologies) and a Bioanalyzer 2100 (Agilent). Only those samples with a 260 nm/280 nm ratio between 1. 8 and 2. 1 and a 28S/18S ratio within 1. 5-2 were further processed. The TruSeq Stranded mRNA Sample Prep Kit (Illumina) was used in the succeeding steps. Briefly, total RNA samples (100-1,000 ng) were ribosome-depleted and then reverse-transcribed into doublestranded cDNA with actinomycin added during first-strand synthesis.
The quality of the reads was assessed using FastQC (Babraham Bioinformatics), and potential contaminations were evaluated with FastQ Screen (Babraham Bioinformatics) using bowtie2 v. 2. 1. 0 default parameters  [59]. Quantification of gene expression was performed using the RSEM package (version 1. 2. 18)  [60]  mapping against the ensembl 75 annotations derived from the human genome assembly GRCh37 and mouse genome assembly GRCm37. Genes not present (< 10 counts per gene) in at least 50% of samples from one condition were discarded from further analyses. Differential gene expression analysis between sample groups of interest was performed using the R/bioconductor package edgeR  [61]. To evaluate functional activities, differentially expressed genes were mapped to known biological ontologies based on the GO project using gene annotation tool Enrichr  [62, 63].
IntelliCage system The IntelliCage apparatus (TSE Systems) was placed in a polycarbonate cage (20. 5 cm high, 58 × 40 cm top, 55 × 37. 5 cm bottom, Techniplast, 2000P) and accommodated up to 16 mice  [66]. A food rack provided standard mouse chow ad libitum. Woodchip bedding as well as four standard red shelters were provided. Four triangular conditioning chambers (15 × 15 × 21 cm) were fitted in the cage corners. Two drinking bottles were accessible in each of them behind motorized doors. Visits, nosepokes, and licks were recorded and assigned to individual mice through a RFID antenna at the chamber entrance. A PC running IntelliCage Plus software (TSE Systems) collected the data and controlled the experiments. Testing began by free adaptation (7 days) providing access to all bottles with doors open at all times.
Video tracking During open field and water-maze tests, on the elevated O-maze, and during the light-dark transition test, the mice were videotracked using a Noldus EthoVision 3. 00 system (Noldus Information Technology). The system recorded position, object area, and the status of defined event recorder keys on the keyboard. Raw data were then transferred to public domain software Wintrack 2. 4 (www. dpwolfer. ch/wintrack) for further analysis.
Acoustic startle response The SR-LAB TM SDI startle response system (San Diego Instruments) was controlled by proprietary SR-LAB software. The cylindrical animal enclosure (28 × 89 mm) was made of clear Perspex, rested on a plate with a motion sensor and was enclosed in a sound-attenuated ventilated cabinet. The loudspeaker was located above the animal and produced white noise pulses. To record a startle response profile, 66 trials were presented in total after a habituation time of 300 s. Nine different sound levels (dB) used were as follows: 64, 68, 72, 76, 80, 90, 100, 110, and 120. Each stimulus was 40 ms and presented 6 times in pseudorandom order such that each sound level occurred once within a block of nine trials. The series began and ended with six presentations each of the 120 dB stimulus.
Statistical analysis Data from biochemical assays were analyzed with Prism 5. 0 software using unpaired Student's t-test to determine the significance of difference unless otherwise specified. Data from behavioral studies were analyzed using an ANOVA model with genotype (Mrps5 V338Y/V338Y , Mrps5 WT/WT ) and age cohort (9 months, 19 months) as between-subject factors. Withinsubject factors were added as needed to explore the dependence of genotype effects on place, time, or stimulus. Interactions were further explored by pairwise t-tests or by splitting the ANOVA model, as appropriate. Variables with strongly skewed distributions or strong correlations between variances and group means were subjected to Box-Cox transformation before statistical analysis. The false discovery rate (FDR) control procedure of Hochberg was applied to groups of conceptually related variables within single tests to correct significance thresholds for multiple comparisons.
Acknowledgements David P. Wolfer  is a member of the  Neuroscience Center Zurich (ZNZ)  and the  Zurich Center for Integrative Human Physiology (ZIHP). We are indebted to  Diarmaid Hughes  and  Douglas Huseby ,  Uppsala University (Sweden) , for providing recombinant E. coli strains. We thank the  Institute of Medical Microbiology  and the  University of Zurich  for continuous support.
Acknowledgments: This work has been supported by the project  ELI-Extreme Light Infrastructure-phase 2  ( CZ. 02. 1. 01 /from  European Regional Development Fund , by the  Ministry of Education, Youth and Sports of the Czech Republic  (project No. LQ1606 ) and by the projects "advanced research using high intensity laser produced photons and particles" ( CZ. 02. 1. 01/0. 0/0. 0/16_019/0000789 ) and high field initiative ( CZ. 02. 1. 01/0. 0/0. 0/15_003/0000449 )from  European Regional Development Fund.
We further provide a plausible mathematical reasoning for the existence of these co-activations along with diffusion kernels by linearizing a variant of reaction-diffusion model and extending it to generate FC. Moreover, we also describe a succinct multiple kernel learning (MKL) procedure to retrieve these co-activations by formulating it as an optimization formulation. MKL techniques are well explored in the machine learning community  30, 31. Our proposed model while retaining the parsimony of a simple linear approach, proposes a novel learning scheme for optimizing the best-fitting kernels for SC-to-FC mapping. Our detailed empirical results demonstrate the validity of the proposed model on a larger dataset.
Acknowledgements DR is supported by the  Ramalingaswami Fellowship  ( BT/RLF/Re-entry/07/2014 ) from  Department of Biotechnology (DBT) ,  Ministry of Science & Technology, Government of India. We acknowledge the efforts of an honors student,  Joyneel Misra , at  IIIT-Hyderabad  who assisted the authors in the mathematical development of the model, improving figures and their captions, and proofreading the manuscript.
µ-Tomography Observation The CT scan is used to perform micro tomography analysis on the same material, 0 degree position 2, to obtain orientation tensor evolution along the thickness of the sample. Target is to illustrate and quantify the skincore effect of such samples. The calculation of the orientation tensor and its components are well described in  [1] [2] [3] ,  [7] [8] [9] [10]. For simplicity purpose, only the two components of the second order orientation tensor (a11 and a22) are plotted in Fig. 2  [1], wherein direction 1 represents the injection direction and 2 is the one perpendicular to the injection direction as shown in Fig. 1. The motivation of presenting this data is to get reliable data that can be compared to the outputs of our image analysis.
, 2009b; Tramblay et al. , 2010 Tramblay et al. , , 2012;; Li et al. , 2012; Massari et al. , 2014a Massari et al. , , b, 2015;; Grillakis et al. , 2016). The dependence of catchment responses to initial soil moisture is also observed by  Marchi et al. (2010)  in a dataset comprising data for 25 flood events in 60 basins across Europe and on this study's site by  Huza et al. (2014). This relationship is characterized by high nonlinearity and threshold effects  (Zehe et al. , 2005; Huza et al. , 2014). There is no consent on the importance of initial soil moisture during extreme events. Wood et al. (1990)  conclude that catchment characteristics are important only for flood events with a low return period (up to ca. 10 years), whereas rainfall characteristics dominate those with a higher return period.
On the other hand this finding is rejected in analyses of historic flash floods  (Gaume et al. , 2004; Borga et al. , 2007; Le Lay and Saulnier, 2007)  or flash flood databases  (Marchi et al. , 2010)  whose authors conclude that soil moisture plays an important role in the hydrological response, also under extreme conditions. The aim of this study is to assess how soil moisture controls the hydrological response in a flash-flood-prone area in southern France. The study is conducted in the two nested catchments of the Claduègne (43 km 2 ) and Gazel (3. 4 km 2 ), Ardèche, France. Thanks to an exceptionally good database, it is possible to obtain reliable estimates of the two catchments' initial soil moisture states for several rain events and to quantify the hydrological response with the event-based runoff coefficient.
To this end, the spatiotemporal heterogeneity is assessed to obtain reliable estimates for mean initial soil moisture at the catchment scale as well as its uncertainty. Other studies results suggest a dependence of φ ev on initial soil moisture (e. g. ,  Merz et al. , 2006; Blume et al. , 2007; Merz and Blöschl, 2009; Norbiato et al. , 2009). However, most of these studies use indirect information such as remote sensing data, antecedent precipitation indices, initial baseflow, continuous soil moisture accounting models or the ratio of actual evaporation to precipitation. These approaches offer many advantages, such as the global availability of remote sensing data and the easier acquisition of these data (e. g. ,  Brocca et al. , 2009c). Numerous studies found good agreement of soil moisture data obtained from in situ measurements and remote sensing (e.
g. ,  Brocca et al. , 2009c Brocca et al. , , 2013;; Huza et al. , 2014). Nonetheless, case studies are important to confirm the results obtained with indirect data as well as the results from modeling exercises and to thoroughly understand the hydrologic functioning of local sites. At this study's site the impact of θ ini on φ ev was already considered by  Huza et al. (2014). However, these authors used soil moisture data obtained from ASCAT satellite data which is fitted to in situ measurements of topsoil moisture that were conducted on grasslands only. They considered five rain events only, so this relation could not be quantified unambiguously. Thus, this study's novelty is to analyze the relation between φ ev and θ ini when both are obtained from a comprehensive, high-resolution data set allowing the assessment of the uncertainty of the two variables.
The temporal resolution is 15-20 min  (Nord et al. , 2017). The sensors in the vineyards were installed between two vine plants in a row, which is a compro- mise between feasibility and representativeness of soil moisture in the vineyards, which is heterogeneous due to transpiration. The accuracy and the range of the probe as provided by the manufacturer are ±3 vol % and 0-57 vol %. The data are available from June 2013-November 2014 in the dataset (OHMCV, 2013) presented in  Nord et al. (2017). In addition, following forecasts of heavy rain events, onalert measurements of soil moisture in 0-5 cm depth were conducted at 11 plots within the Gazel catchment with a hand-held FDR soil moisture sensor (Delta-T SM200). The accuracy and the range of the probe are ±3 vol % and 0-50 vol %. The plots comprised four vineyards, five grasslands, one fallow and one cultivated field.
Precipitation P Rainfall data were obtained from the HPiconet rain gauge network at a resolution of 1 min. The network consists of 19 tipping bucket rainfall gauges with a sampling surface of 1000 cm 2 and a resolution of 0. 2 mm, out of which 12 are located in the Claduègne catchment or its close vicinity  (OHMCV, 2010; Nord et al. , 2017, Fig. 1).
Additional data Spatial data used for this study include a digital elevation model with a resolution of 5 m  (Nord, 2015)  and the Ardèche soil database by the French National Institute for Agricultural Research, Bureau de Recherches Géologiques et Minières and the French Department of Agriculture  (Braud 2015; Nord et al. , 2017). Furthermore, a 0. 5 m resolution land use map of the Claduègne catchment based on QuickBird satellite images is available  (Andrieu, 2015). Data on soil properties such as porosity, texture and saturated hydraulic conductivity were obtained during a measurement campaign in 2012 by  Braud and Vandervaere (2015). Electrical conductivity (EC) of streamflow is continuously measured at the outlets of both catchments (OHMCV, 2011), and measurements of EC of overland flow from two runoff and erosion plots in a vineyard in the south of the Gazel catchment are available  (OHMCV, 2009; Cea et al.
Precipitation data processing The catchment mean hyetographs for both catchments are calculated from the HPiconet rain gauge data with the method of Thiessen polygons. Rain events are separated by using a threshold of 12 h without precipitation being recorded at any rain gauge. The onset of an event was defined as the first time rain occurred after a dry period of at least 12 h, the end as the last time with rain being recorded by at least one rain gauge before the next dry period. The threshold of 12 h provides a good compromise between having a high number of events and excluding two separate events that are not independent from each other. Averaged catchment rainfall is then summed over the whole period of the rain event to calculate cumulative event precipitation P cum. Furthermore, mean rain intensity I µ over the whole event as well as maximum rain intensity I max at 2, 10, 20, 30 and 60 min are calculated using the averaged catchment rainfall.
Hysep filters (HS1-HS3) Three further filtering approaches are implemented in the Unites Stated Geological Survey's (USGS's) HySep program  (Sloto and Crouse, 1996). It is based on finding minima in the discharge time series. The minima are determined either within fixed (HS1) or sliding (HS2) intervals or with a local minima algorithm (HS3). The interval width is adjusted according to  Gonzales et al. (2009). It is applied using the R code of the USGS (2015). 3 Results.
Acknowledgements. This work was funded by the  French National Research Agency (ANR)  via the  FloodScale  project under contract no. ANR 2011 BS56 027 , which contributes to the  HyMeX program. OHM-CV is supported by the  Institut National des Sciences de l'Univers (INSU/CNRS) , the  French Ministry for Education and Research, the Environment Research Cluster of the Rhône-Alpes Region , the  Observatoire des Sciences de l'Univers de Grenoble (OSUG)  and the  SOERE Réseau des Bassins Versants  ( Alliance Allenvi ) and belongs to the  OZCAR Reasearch Infrastructure. Magdalena Uber  received financial support from  German Academic Exchange Service (DAAD). The authors want to thank  Brice Boudevillain ,  Sahar Hachani ,  Simon Gérard  and  Cindy Nicoud  for their help during field work and  Luca Brocca ,  Hongkai Gao  and two anonymous referees for their comments on earlier versions of this paper that were very valuable to improve this paper.
Among other parameters, the algorithm computes the shift in time required to optimally align a subjects' biomarker trajectories with these population curves. This time shift serves as a disease progression score and it was used as a quantitative trait in a discovery genome-wide association study with n = 944 subjects from the Alzheimer's Disease Neuroimaging Initiative database diagnosed as Alzheimer's disease, mild cognitive impairment or healthy at the time of imaging. We identified a genome-wide significant locus implicating LCORL (rs6850306, chromosome 4; P = 1. 03 Â 10 À8 ). The top variant rs6850306 was found to act as an expression quantitative trait locus for LCORL in brain tissue. The clinical role of rs6850306 in conversion from healthy ageing to mild cognitive impairment or Alzheimer's disease was further validated in an independent cohort comprising healthy, older subjects from the National Alzheimer's Coordinating Center database.
Introduction Alzheimer's disease is the most common cause of dementia, affecting 46. 8 million people worldwide  (Prince et al. , 2015). The pathophysiology of Alzheimer's disease and its genetic drivers have been widely studied in recent years. The "4 allele of the apolipoprotein E gene (APOE4) is the strongest known common genetic risk factor for sporadic late-onset Alzheimer's disease  (Saunders et al. , 1993). Further genetic risk factors have been identified through genome-wide association studies (GWAS) in case-control datasets, with recent studies comprising 74 000 subjects  (Lambert et al. , 2013). However, case-control studies in a disorder where the prodromal stage spans decades neglect the fact that many participants labelled as controls may be future cases and therefore reduces statistical power of the study.