dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study was funded by Meat and Livestock Australia. References Kamiya Y, Kamiya M, Tanaka M. 2006. West Japan Journal of Animal Science. 49: 25-30. Anderson S 2017. European Society of Veterinary and Comparative Nutrition, Cirencester UK. Acknowledgements This research was supported by the grants from the National Key Research and Development Program of China (2016YFD0500500) and China Agriculture Research System (CARS-37). Acknowledgements We thank the  Inra Phase division  for funding proteomics assay and  APIS-GENE  for funding I. Molnar. Acknowledgements This research was supported by grants from  WAAPP  and the  SELMET Research Unit.
References Silanikove N and Koluman N 2015. Small Ruminant Research 123,  27-34. Acknowledgements This study was supported by  PTUPT  Grant  2018  from  RISTEKDIKTI and Universitas Gadjah Mada. Acknowledgements to  Universidad del Tolima  and  Universidad de Caldas  by facilitating this research work. Acknowledgements This project was generously funded by the  Research Deanship at the Jordan University of Science and Technology. Acknowledgements Funded by the  Ministry of Economy and Competitiveness of Spain  (Project  AGL2013-44061-R ). Acknowledgements This study was funded by the  Economy and Competitiveness Spanish Ministry  ( AGL2017-86938 ). References Belanche ,  A ,  J. M. Palma-Hidalgo ,  E. Jimenez ,  Martín-García A. I , and  D. R. Yáñez-Ruiz. 2018. Proceedings  ISNH Clauss , M.
,  I. Hume , and  J. Hummel. 2010. Animal 4,  979-992. Yáñez-Ruiz DR ,  Abecia  L and Newbold  CJ 2015. Frontiers in Microbiology 6, 1133 Acknowledgements We acknowledge  Alister Hawksford ,  Australian Government Bureau of Meteorology. Acknowledgements The research project was funded by  SLU EkoForsk. References Lammers BP ,  Buckmaster DR  and  Heinrichs   AJ 1996. Journal of Dairy Science 79,  922-928. Acknowledgements This research was funded by a grant from the  Pennsylvania Department of Agriculture. Acknowledgements The authors acknowledge  O. Troquier  and the staff of the  INRA experimental farm  'Herbipôle' for taking care of the animals, and the  INRA-PHASE Division  for funding the study. Acknowledgements The Bred Well Fed Well workshops are supported by producer levies via  Meat and Livestock Australia  and  Australian Wool Innovation.
Materials & methods Meta-analyses  (Sauvant et al. , 2008)  were applied on published data compiled from the INRA Flora database  (Vernet and Ortigues-Marty, 2006) , which gathers published results on net portal appearance of nutrients in ruminants. The FOM of diets was calculated according to  INRA 1978 (FOM_1978), INRA 2007 (FOM_2007)  and INRA 2018 (FOM_2018) using the systoolweb. fr application  (Chapoutot et al. , 2015). The FLORA database is totally independent from databases used to establish the INRA 2018 feeding system. Relationships between NPA of total VFA and the three expressions of the FOM were studied by intra-experiment covariate models.
This research was supported by the french CASDAR project CAPHERB N°5546. References Bibby J and Toutenburg H 1977. Whiley, London Delagarde R and Lamberton P 2015. Applied Animal Behaviour Science 165, 25-32. Visa S, Ramsay B, Ralescu A and Van der Knaap E 2011. Proc. 22 th Cognitive Science Conference, Cincinnati, Ohio, US.
Patient selection and data collection All consecutive patients admitted to the ICU with a diagnosis of septic shock of abdominal origin from February 2010 to May 2013 were enrolled. In all cases, the source was controlled by surgery. Septic shock was defined according to the criteria of the International Sepsis Definitions Conference  [19]. Patients younger than 18 years of age, pregnant patients, and patients with aplasia or immunosuppressive disease (e. g. , HIV) or receiving immunosuppressive therapy (i. e. , chemotherapy, chronic used of steroids, autoimmune disease treatment) were excluded from the study. None of the patients received steroids during their ICU stay. Note that these patients were included in another previously published study (86 of 130 patients)  [20]. The following demographic and clinical data were collected: age, sex, comorbidities (Charlson comorbidity index score)  [21] , Simplified Acute Physiology Score II  [22] , Sequential Organ Failure Assessment score  [23]  at day 1, length of ICU stay, ICU mortality, post-ICU discharge mortality, and 1-year survival.
Laboratory procedures Data collection was performed within (1) 24 h of the onset of shock, (2) on the day of ICU discharge, and (3) during an ambulatory care visit 12 to 18 months after ICU discharge. The biomarker analyses were blinded. The plasma levels of IL-6, IL-7, IL-10, IL-17, IFN-γ and TNFα were measured using sandwich immunoassay methods with commercially available electrochemiluminescent detection systems, plates and reagents (V-PLEX human cytokine 30-Plex kits (Meso-Scale Discovery (MSD), Gaithersburg, USA), as per the manufacturer's instructions. Briefly, 50 μL of plasma was loaded per well in the MSD plates. The plates were analyzed using the SECTOR Imager 2400. Plasma Trp and Kyn levels were measured using high-performance liquid chromatography, as described by Kema et al. [24]  and Fujigaki et al.
BACKGROUND: Diet quality is associated with brain aging outcomes. However, few studies have explored in humans the brain structures potentially affected by long-term diet quality. We examined whether cumulative average of the Alternative Healthy Eating Index 2010 (AHEI-2010) score during adult life (an 11-year exposure period) is associated with hippocampal volume. METHODS: Analyses were based on data from 459 participants of the Whitehall II imaging sub-study (mean age [standard deviation] (SD) = 59. 6 [5. 3] years in 2002-2004, 19. 2% women). Multimodal magnetic resonance imaging examination was performed at the end of follow-up (2015-2016). Structural images were acquired using a high-resolution 3-dimensional T1-weighted sequence and processed with Functional Magnetic Resonance Imaging of the Brain Software Library (FSL) tools.
Magnetic Resonance Imaging Acquisition and Processing and Assessment of Hippocampal Volume in 2015-2016 Multimodal magnetic resonance imaging (MRI) scans were acquired at the Oxford Centre for Functional MRI of the Brain (FMRIB Centre) using a 3-tesla MRI scanner (MAG-NETOM Verio; Siemens Healthineers, Erlangen, Germany) with a 32-channel head coil. Details of the imaging protocol and the analysis pipelines have been published previously. 17 In short, structural images were acquired using a high-resolution 3-dimensional T1-weighted sequence: repetition time = 2530 ms, echo time = 7. 37 ms, flip angle = 78 degrees, field of view = 256 mm, and voxel dimensions = 1. 0 mm isotropic. MRI data processing and analysis was performed using FSL tools (FMRIB Software Library; FMRIB, Oxford, UK). Structural, T1-weighted images were processed using fsl_anat (FMRIB).
Second, linear regression models described above were repeated for each AHEI-2010 component to identify the key components of the AHEI-2010 associated with hippocampal volumes. To further examine the contribution of each AHEI-2010 components to the overall AHEI-2010hippocampal volumes association, we computed for each component (component i), a modified AHEI-2010 score based on the total AHEI-2010 score without the component i (modified AHEI-2010 score i = total AHEI-2010 score À score of the component i). All component scores and modified AHEI-2010 scores were standardized by using z scores. Analyses were conducted using SAS software, version 9. 4 (SAS Institute, Cary, NC).
Participants' Descriptive Data Of the 550 Whitehall II imaging sub-study participants, 459 were included in the main analyses. The selection of participants is detailed in the online supplementary material (Supplementary Figure  1 ). Excluded participants and those included did not substantially differ in any of the reported characteristics (data available upon request). Characteristics of the 459 participants are presented in Table  1. Distribution of cumulative average AHEI-2010 score according to the characteristics of participants is also detailed in Table  1. Means of AHEI-2010 score increased with age. A significantly lower mean AHEI-2010 score (ie, less healthy diet) was found in white participants compared with nonwhite participants and in smokers compared with former and nonsmokers.
Dietary Components and Hippocampal Volume We further examined which of the 11 components of the AHEI-2010 were most strongly associated with hippocampal volume. Linear regression models were performed to examine the association of cumulative average score of each AHEI-2010 component with total and lateral hippocampal volumes. In an analysis adjusted for sex, age, and total energy intake (Table  3 ), alcohol consumption was associated with larger hippocampal volumes (total, right, and left), and fruit and red and processed meat components were associated with left hippocampal volume. Only the association between the alcohol component and hippocampal volume persisted in fully adjusted models (Figure  2 ). *MRI data processing and analysis used FSL tools (FMRIB Software Library, Oxford, UK). Structural, T1-weighted images were processed using fsl_anat (FMRIB).
Dietary data were collected using a semi-quantitative FFQ. This method is less precise than those based on weighted records, but it nevertheless covers a range of specific foods and is feasible for largescale cohort studies such as ours. The validity of FFQs has been criticized 36 but appears to be reasonable in assessing associations of nutrients and food consumption with outcomes, at least in the UK context. 37, 38 We have shown, for example, that nutrient intakes estimated by the FFQ method are correlated with biomarker concentrations and intake estimates from the 7-day diary. Although the FFQ is open to measurement errors common to all self-reported dietary assessments,  39  it remains one of the main methods in analytical epidemiological studies. 36 Indeed, many of the current dietary recommendations and policies to reduce disease burden (eg, obesity, type II diabetes, and cardiovascular disease) rely on evidence from studies using an FFQ.
Availability of Data and Material The datasets used, analyzed, or both during the current study are available from the corresponding author upon reasonable request. *Requests for reprints should be addressed to Tasnime Akbaraly, M ecanismes Mol eculaires dans les D emences Neurod eg en eratives, Universit e Montpellier, Place Eug ene Bataillon CC105, 34095 Montpellier cedex 5.
Effects of the Arg378Cys mutation on protein function. Both wild-type and Arg378Cys recombinant proteins formed stable complexes with their target proteases C1s and kallikrein, as indicated by SDS-PAGE (data not shown). Kinetics of inhibition analysis were set up under pseudo-first-order conditions, as summarized in Supplementary Table  1. Progress curves for rC1-INH with C1s and kallikrein were determined (Fig. 6a.
Genetic studies. Genomic DNA was isolated from peripheral white blood cells and analysed by direct sequencing on an ABI Prism 310 Genetic Analyzer. All SERPING1 (Ref. Seq NG_009625. 1) exons and intron/ exon boundaries were analysed in order to investigate the entire gene coding sequence. Total RNA was obtained from whole blood with the QIAamp RNA Blood Midi Kit (Qiagen, Hilden, Germany), according to the manufacturer's protocol. Real-time RT-PCR was performed with SYBR Green I using C1-INH-specific primers. Data were analysed with the relative quantification method and normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression.
Complex Reactions were performed at 25 °C in 50 mM Tris, 10 mM Na 2 HPO 4 , 150 mM NaCl, 0. 1% Tween pH 8. Buffer, inhibitor, and substrate were mixed in a 2-ml cuvette and reactions were initiated by addition of a fixed amount of protease. Product accumulation was continuously recorded by Cary 4E spectrophotometer (Varian, Inc) at A 405nm (A). A typical progress curve experiment consisted of 6 assays (1 zero and 5 nonzero concentrations of inhibitor). The progress curve data were fitted by nonlinear regression analysis to the equation describing the mechanism of slow binding inhibition (Equation  1 )  62. k obs , the observed first order inhibition rate constant in the presence of substrate, v 0 , the zero time value for the velocity of substrate hydrolysis, and v s , the steady state velocity at completion of the reaction, were obtained.
25 μl samples at the protein concentration of 2 mg/ml were loaded with a flow rate of 0. 5 ml/min and the elution monitored at 280 nm. To test C1-INH oligomerization, prior to gel-filtration, samples were incubated at 55 °C and 70 °C for 1 hour. Molecular modelling. The model of the mutant was built on the basis of 1M6Q (Protein Data Bank accession number), an homology model of the serpinic domain of C1-INH  68. To build the dimer we used Rosetta online Server (ROSIE) to perform a local docking of two mutated molecules  69. Among 1000 poses we chose the structure with the shortest distance between the cysteines. Then we performed a refinement of the structure allowing for loop adjustment using MODELLER v. 9. 16  70. A sulfur bridge patch was imposed between the mutated cysteine residues. We added two complex glycans (a total of three are bound to the serpinic domain of C1-INH, http://www.
The second molecule has been drawn nearly transparent for clarity purposes. We must consider that the disulphide bridge does not necessarily occur between native-like structures as reported in the picture. The coordinates used are from pdb entry 1M6Q for C1-INH as active serpin (homology model of the native C1-INH serpin domain), 1K1L for free protease, and 1EZY for serpin-protease complex. Figures were produced usingChimera 72.
Acknowledgements The authors are indebted to  Dr. Ineke Boss  and  Dr. C. Erik Hack  for scientific and technical support with Pichia pastoris, to  Françoise Csopaki  for skilful technical assistance, to  Dr. Lorena Maggioni  for the analysis of complement parameters. We wish to thank  Drs Alberto López-Lera  and  Margarita López-Trascasa  for providing plasma samples of the homozygous subject,  Dr. Diana Wouters  for providing mAbs KOK12 and KII,  Dr. Daniel Elenius Madsen  and  Dr. Yaseelan Palarasah  for providing mAb  12-27-15 ,  Dr. Alessandro Gori  for providing C1-INH C-terminal peptide,  Prof. Alberto Clivio  for producing chicken antibodies against C1-INH and for his helpful discussions. This work was supported by  CARIPLO FOUNDATION  [grant number  2013-0967 ] coordinated by  Dr.
Statistical analysis Continuous data with approximately normal distributions (including all amino acids) are presented as mean ± SD; those with skewed distributions are presented as median (with interquartile range). Categorical data are presented as n (%). Pearson correlations were used to explore associations of the amino acids with each other. Associations of amino acids with classical risk factors were investigated across quarters of the distribution of each amino acid. Cox regression models were fitted using the STSELPRE procedure for case-cohort analyses (StataCorp, College Station, TX, USA). Models estimated HRs for a 1 SD increase in each amino acid with each of the endpoints. Two models, with different potential confounding variables, were fitted for each amino acid/outcome combination: model 1 with age, sex, region and randomised treatment; model 2 with, additionally, a prior macrovascular complication of diabetes (myocardial infarction, stroke, hospital admission for a transient ischaemic attack or for unstable angina, coronary or peripheral revascularisation, or amputation secondary to peripheral vascular disease), duration of diabetes, current smoking, systolic blood pressure, BMI, urinary ACR, eGFR, HbA 1c , plasma glucose, total cholesterol, HDL-cholesterol, triacylglycerols, aspirin or other antiplatelet agent, statin or other lipidlowering agent, β-blocker, ACE inhibitor or angiotensin receptor blocker, metformin use, history of heart failure, participation in moderate and/or vigorous exercise for >15 min at For the random subcohort, the ability of amino acids to discriminate between those who will and those who will not go on to suffer each of the three adverse outcomes were estimated, in the context of model 2, using c statistics for 5 year risk, accounting for censoring.
86 [95% CI 0. 76, 0. 98]). The association between tyrosine and renal impairment was further investigated by assessing the HRs across tertiles of eGFR and ACR. There was no evidence of interaction by eGFR or ACR (data not shown). In contrast, several amino acids were associated with allcause mortality. Phenylalanine was positively associated with risk of mortality, while glutamine, leucine, alanine, histidine and valine were all inversely associated with risk of mortality in model 1 (ESM Table  10 ). After adjustment for classical risk ) but the positive association of phenylalanine was attenuated to the null (Fig. 2c  and ESM Table  10 ). A sensitivity analysis using samples from individuals with complete data gave similar results (ESM Table  11 ). There was no evidence of a randomised treatment interaction in any model (data not shown).
Funding The biomarker work in the present study was funded by the  Chest Heart and Stroke Association Scotland  ( R13/A149 ) and by the  Glasgow Molecular Pathology NODE , which is funded by The  Medical Research Council and The Engineering and Physical Sciences Research Council  ( MR/N005813/1 ). The ADVANCE trial (ClinicalTrials. govregistration no. NCT00145925 ) was funded by the  National Health and Medical Research Council (NHMRC) of Australia  (project grant  ID 211086  and program grant IDs  358395  and  571281 ) and by  Servier. PWu is supported by the  Academy of Finland  ( 312476  and  312477 ) and the  Novo Nordisk Foundation. MAK was supported by the  Sigrid Juselius Foundation , Finland. MAK works in a Unit that is supported by.
An extra 2× magnification was obtained by a magnifying lens in the light path. GFP+ OSNs were visualized under fluorescent illumination. Superimposition of the fluorescent and bright-field images allowed identification of the fluorescent cells under bright field, which directed the recording pipettes. Electrophysiological recordings were controlled by an EPC-10 USB amplifier and with Patchmaster software (HEKA Electronic, Germany). Perforated patch clamp was performed on the dendritic knobs by including 260 μM nystatin in the recording pipette, which was filled with the following solution (in mM): KCl 70, KOH 53, methanesulfonic acid 30, EGTA 5, HEPES 10, sucrose 70; pH 7. 2 (KOH) and 310 mOsm. The junction potential was ~9 mV and was corrected in all experiments offline. Under voltage-clamp mode, the signals were filtered at 10 kHz followed by 2.
Data were analyzed using Fitmaster (HEKA). Maximum amplitude of the response and kinetics characteristics were measured. In these recording conditions, only OSNs that fit certain criteria can be recorded: the OSN must have a dendritic knob that is standing out from the surface of the preparation and is therefore available to be reached by the electrode. Such OSNs happen to bear cilia. As a consequence, all recorded OSNs can be considered as mature. In both strains, the number of OSNs fitting these criteria was low: ~20 for each ex vivo preparation, depending on the age and the success of the dissection.
Site selection and description This retrospective, observational time series study examined daily or monthly data on weather, water quality, and acute gastroenteritis in France and Spain. Three locations were selected as a nested sample based on health data availability from earlier studies involving 20  (Loret et al. , 2016)  and five  (Setty et al. , 2017)  Suez-operated utilities, respectively (Table  1 ). Each location was served by a single drinking water treatment facility (Table  1 ) that implemented an ISO 22000-certified WSP during the study period (Table  2 ). The three locations have different climates and water sources, warranting individual models. Raw water is tested prior to treatment at all sites to inform treatment practices, such as coagulant dosage. In addition, location 5 often switches from surface to groundwater sources, which are less vulnerable to nonpoint source polluted runoff, during heavy rain events.
Data compilation Exposure and control variables of interest were identified via literature review (Table  3 ). Air temperature can affect multiple modes of disease transmission. Hydrology (including precipitation and river flow) helps to describe transport processes for pathogens, such as nonpoint source pollution of surface waters. Frequently measured water quality parameters relevant to pathogen survival included temperature, turbidity (particle content), UV absorption (an indicator of organic matter), and residual free chlorine used for disinfection. Finished water flow may be a proxy for drinking water consumption. Finally, the amount and percentage contribution of surface water is relevant for discerning variable exposure to surface versus groundwater sources at location 5, which may be mixed or used in isolation.
Several parameters were excluded because data from a substantial proportion of days were missing, including manually measured water quality indicators (e. g. , heterotrophic plate counts in finished water at location 1, free chlorine in the distribution system at location 5), and raw water turbidity and daily flow of finished water at location 3. For higher resolution datasets (e. g. , sensor readings taken multiple times per day), daily (locations 1 and 3) or monthly (location 5) averages were generated. Daily or monthly minima were extracted for free chlorine, and daily or monthly maxima for turbidity, to capture suspected high-risk conditions. Values below lower detection limits were set to zero, and values above upper detection limits were set to the upper detection limit. Missing data were left as missing.
Data analysis was conducted using SAS 9. 4. Parameters were plotted over time to visualize trends, and descriptive statistics were calculated. Correlation tests identified multicollinearity among variables, as well as variables highly correlated with the outcome measure. For daily data from locations 1 and 3, univariable Poisson regression models were run to test lag times of up to 15 days for each exposure variable based on existing studies  (Beaudeau et al. , 2014b (Beaudeau et al. , , 2012;; De Roos et al. , 2017). For monthly data from location 5, a one-month lag time was considered, incorporating about 30-60 days prior to case reporting (depending on when the case occurred within the month). The most appropriate time lag (s) were identified for each parameter, taking into account expected transit time, pathogen latency, reporting delays, consistent direction of effect, and statistical significance over multiple days.
Thus, splines are a discretionary addition to the linear terms for continuous variables only. ln(Cases/Population) = β 0 + β i * Controls + spline(Controls) + β j * Exposures + spline(Exposures) (1) Models were fit separately to the full datasets at each location based on correlation tests, the univariable model results, parameter significance after introduction of controls, and plausibility based on existing literature regarding exposure pathways. Potential interaction and higher order terms were considered, and two alternative models were fit by dropping non-significant or multicollinear terms. The final model for each location was selected to minimize overall deviance. Splines were included by default for continuous exposure variables, and retained especially when significant nonlinearity was identified.