dataesr/scientific-paragraphs-categorization huggingface dataset:
A Monte Carlo simulation (considering the covariance between the two slopes) was also performed in order to assess the potentially best ROC curve. Expecting a 20% incidence of Candida sp positive samples following septic shock, a drop of 50% in mHLA-DR in the ICI patients by day 7 and no signi cant difference between day 0 and day 7 in NICI patients, we estimated that 50 patients with septic shock would be needed assuming a bilateral test, an alpha risk of 0. 05 and a power of 0. 8. A P value of less than 0. 05 was taken as the signi cance level. We used SAS 9. 4 (Sas Institute, NC, USA), R 3. 0. 2 (Vienna, Austria) and GraphPad Prism 6 for all the statistical analyses.
Explants were stored in the BIO-EC survival culture medium at 37°C in 5% CO 2 -humidified air (Fig. 1 ). Half of the culture medium (1 mL) was refreshed on days 1, 2, 5, and 7. This study was conducted on explants derived from discarded abdominoplasty tissue; all subjects provided written informed consent before the use of these tissues. The only subject data obtained included age, race, and sex.
Assessment of Markers of Hydration To assess AQP3 expression, paraffinized sections were stained with a rabbit anti-AQP3 polyclonal antibody and examined at ×40 magnification. To measure GAG content, paraffinized sections were stained by Alcian blue/periodic acid-Schiff for acidic GAGs and examined at ×10 magnification. To visualize HA, paraffinized sections were stained using a diluted, biotinylated antibody against HA binding protein and examined at ×10 magnification. Evaluation of HA staining, AQP3 expression, and GAG content was based on the qualitative microscopic observation of the stained sections. Evaluation of HA was also made by an analysis of images of the stained sections. The staining intensity was measured in these images using CellD data scoring software (CellD, Roquemaure, France) and expressed as a percentage of the preselected skin layer of interest.
Statistics Quantitative data are expressed as mean (SD). For the analysis of staining for HA, comparisons between injected explants and noninjected controls were assessed at each time point using the Student t test, and significance was determined at P <0. 05. All other markers were assessed by a histopathologist via visual evaluation and/ or microscopic observation without statistical analyses. For corneometry measurements, a 2-way analysis of variance was used to assess the impact of the treatment group and time on the hydration measurements. Pairwise comparisons were made using Tukey's multiple comparison test. Significance was determined at P <0. 05.
A strain-based force sensor (Nano17 Titanium SI-32-0. 2, ATI Inc. ) was mounted above a glass surface to measure the contact force vector with a resolution of 1/171 N in all directions. The force data were collected by a data acquisition board (PCIe 6323, NI Inc. ) with a sampling rate of 500 Hz. The noncoated soda lime silicate glass plate (Optifloat TM Clear, Pilkington Deutschland AG) had a thickness of 3 mm and a roughness <10 nm  (Gläser, 1999 ). An optical monochrome image sensor (DCC2340M, Thorlabs Inc. ) with a lens (MVL5M23, Thorlabs Inc. ) was installed below the glass plate to measure the contact area of the fingerpad. The recording frame rate of the camera was set to 10 frames per second. The aperture size of the lens was minimized (f /16) to maximize the depth of focus, providing a larger focused area.
The contact surface was illuminated from below by a light source (KL 2500 LED, SCHOTT AG) shining through a light diffuser attached to the prism surface, yielding an evenly bright background. The fingerpad moisture was separately measured with a capacitive-type moisture sensor (Corneometer R CM 825 w, Courage + Khazaka electronic GmbH) installed next to the contact glass; this sensor measures the moisture value of the outer-most layer of the fingerpad in arbitrary units (a. u. ) between 0 and 130  (Constantin et al. , 2014). Two custom-made LabVIEW programs (Version 2018 18. 0f1) simultaneously collected the real-time data measured by the force sensor and the camera. The software was operated on 64-bit Windows 10 Enterprise 2016 LTSB installed on a computer with an Intel R Core TM i7-7700 CPU and 32 GB RAM.
Data Processing The raw data collected from the force and image sensors were processed to compute the physical variables that we investigate in this study. First, time-stacked fingerprint image data underwent several processing steps for contact area extraction, and the obtained contact area was then synchronized with the recording of the finger-surface contact forces. FIGURE 3 | (a) Procedure for calculating the real finger contact area from a raw image. The camera calibration step corrects the lens distortion. Next, image transformation rectifies the slanted finger image. Two different threshold methods applied in parallel appropriately distinguish the finger contact area from the non-contact area without being confused by (b) condensed moisture around the finger. In such cases, taking (c) the global threshold identifies condensation as part of the contact finger area, but applying a local adaptive threshold removes the condensation area to produce (d) the final image.
Extracting Parameters From the Force and Contact Area Data We extracted key parameters from the measured force and contact area data (see red dots in Figure  4 ) after synchronizing the image and contact force data recordings. In order to extract quantitative measurements that relate to the physical variables under scrutiny, we decomposed the dynamics of pressing and detaching the finger into several qualitatively different time intervals. t 0 is defined as the time the finger starts to contact the plate. The time the normal force reaches 1. 5 N is marked as t 1. t 2 is the time the pressing finger starts to detach by reducing its normal force. The finger pulls off the stationary glass plate and usually generates negative force values for a short duration. This detachment phase occurs between t 3 and t 4 (inset in Figure  4A ), where t 3 is the time when the force is closest to 0 N during the detachment, and t 4 is the first time when the finger is completely detached.
Subject removal Finally, we could not include any of the data collected from one female participant. In most of this individual's trials, the measured z-force did not smoothly converge to zero after detachment. Instead the signal persistently oscillated at a frequency that we suspect to be the resonance frequency of the force sensor and contact platform. We believe too much xforce (median F x,rms = 1. 48 N) was applied to the force sensor in an extremely short time (median t pull = 0. 022 s). These oscillations hindered the calculation of the parameters used to derive the physical variables for this subject, leaving a total of nine participants (two women, seven men).
CONCLUSION In this paper, we investigated the physical variables that affect the perception of light stickiness. A custom-made apparatus recorded 3-DoF forces over time, contact area over time, and the moisture level of the subject's fingerpad before and after each trial. The recorded data yielded sixteen physical variables, including RMS forces in the three directions, impulses in the three directions, and finger detachment rates. Based on the data from a total of 324 trials by nine participants and their corresponding stickiness ratings, we computed Spearman's correlation coefficients both within and across subjects. We found that the finger's pre-detachment pressing time (t hold ), the time taken for the finger to detach (t pull ), and the impulse in the pressing (normal) direction during the finger detachment after the normal force changes sign (I z ) were significantly related to stickiness.
ACKNOWLEDGMENTS The authors thank the  International Max Planck Research School for Intelligent Systems  ( IMPRS-IS ) for supporting SN. We also thank  Bernard Javot  for support in building the apparatus,  Maria Paola Forte  for suggestions about camera calibration, and  Cem Balda Dayan  and  Dongha Tahk  for helping attach the prism to the glass plate using PDMS. Finally, the authors acknowledge open-access fee support from the  Max Planck Society.
We examined the effects of a dihydropyridine calcium channel blocker nilvadipine with anti-inflammatory properties on cognition and cerebrospinal fluid (CSF) biomarkers by baseline Alzheimer's disease (AD) severity. Exploratory analyses were performed on the dataset (n = 497) of a phase III randomized placebo-controlled trial to examine the response to nilvadipine in AD subjects stratified by baseline AD severity into very mild (MMSE ≥ 25), mild  and moderate AD (MMSE < 20). The outcome measures included total and subscale scores of the Alzheimer's Disease Assessment Scale Cognitive 12 (ADAS-Cog 12), the Clinical Dementia Rating Scale sum of boxes (CDR-sb) and the AD composite score (ADCOMS). Cerebrospinal fluid biomarkers Aβ38, Aβ40, Aβ42, neurofilament light chain (NFL), neurogranin, YKL-40, total tau and P181 tau (ptau) were measured in a subset of samples (n = 55).
All study investigators and patients were blinded to the treatment assignment. There were no interim analyses in this trial. The full details of the inclusion and the exclusion criteria are provided elsewhere  (18, 21   (21). A total of 569 subjects were screened for eligibility and 511 were randomized into the trial with 258 were assigned to the placebo group and 253 assigned to the nilvadipine group, of which one dropped out due to blood pressure measurements being out of range, leaving 252 in this group. Of these 510 subjects, 11 were lost to follow-up and 2 withdrew consent, leaving 497 subjects in the modified intention-to-treat (mITT) dataset, see 18 for additional details. Data from subjects in the mITT set were used for these additional exploratory analyses below. At baseline, each subject was randomly assigned to either 8 mg of Nilvadipine or placebo once a day, and each study subject was required to take the capsule orally after breakfast for 78 weeks.
Statistical Analyses General demographic characteristics across AD subgroups by treatment within the modified intent to treat (mITT) dataset were compared using either ANOVA or the Chi-square test, as applicable. Mixed linear model (MLM) regression was used to examine the main effects and the interactions between treatment, time (time points of study visits at 13, 52, and 78 weeks) and AD severity at baseline. As we were interested in the independent contributions of the baseline AD severity and treatment effect over time on the cognitive outcomes, these analyses were also adjusted to account for the confounding effects of gender and ε4 carrier status (coded as those with the presence of ε4, without ε4 and those with no genotype information since not all mITT subjects had APOE genotypes available) and the confounding effects of age at which subjects left education (referred to as "education" hereon).
To account for the treatment effect modification observed in the subgroup analyses, this model also included interactions between time and APOE; treatment and APOE; time, treatment and APOE; time and gender; treatment and gender; and time, treatment and gender. Interactive terms were also included for education and time and for treatment and education to account for education imbalance across AD severity subgroups. All of these variables were considered fixed factors. Subjects and country were treated as random factors. The autoregressive covariance structure was used in these MLM analyses. The outcome variables included change in the total ADAS-Cog 12 scores and change in composite scores from the ADAS-Cog 12 for different cognitive traits, CDR-sb and the ADCOMS. We also applied Principal Component Analysis (PCA) to minimize multicollinearity and achieve dimension-reduction for data on sub-scales from the ADAS-Cog 12 and CDR-sb for all visits.
These composite variables were used as the outcome measures for further analysis by MLM as described above. Post-hoc stratification was performed if the interaction terms for treatment, time and baseline AD severity showed a p-value ≤ 0. 05. Changes in CSF levels of Aβ38, Aβ40, Aβ42, total tau and P181 tau were calculated by subtracting values of the samples collected at the final visit from the baseline visit for each subject. Given the small sample size for the CSF subset, group comparisons using ANOVA were limited to mild (MMSE ≥ 20) and moderate (MMSE < 20) AD severity categories only. P-values ≤ 0. 05 were considered significant and all analyses were conducted using SPSS version 24 (IBM, NY).
Exploratory Analyses for Subgroup Identification The objectives of these exploratory analyses were to identify a subset of subjects who may have responded differentially to nilvadipine intervention and to facilitate hypothesis development for future studies. The exploratory analyses of the NILVAD trial were restricted to the co-primary outcome measures of ADAS-Cog 12 and CDR-sb. Using a data-driven approach, the mild AD group was further stratified by single point increases in the baseline MMSE scores ranging from 20 to 25 and above. From these results, we generated additional AD subcategories where the AD group with baseline MMSE score > 20 from the NILVAD dataset was further stratified into mild AD (MMSE scores from 20 to 24) and very mild AD (MMSE scores ≥ 25). The moderate AD group (baseline MMSE scores of ≤19) was defined as in the original study  (18).
Similarly, baseline AD severity also modified the treatment response to nilvadipine on cognitive decline detected using the total ADAS-Cog 12 score (F = 2. 56, p = 0. 02, Figure  3 ). In posthoc comparisons, very mild AD subjects had a trend for less cognitive decline observed on the ADAS-Cog 12 test (p = 0. 06 at 52 weeks) and moderate AD subjects had a greater decline on nilvadipine treatment (p = 0. 02). There were no differences on the total ADAS-Cog 12 scores between nilvadipine-and placebotreated individuals from the mild AD group (p > 0. 05). There were no differences on the CDR-sb total score with respect to the disease severity and treatment (data not shown). Compared to placebo, nilvadipine treated very mild AD individuals performed better on the ADCOMS over 78-weeks irrespective of the ε4 status or gender (Supplementary Figure  1 ).
DISCUSSION Many clinical trials in combined populations of mild and moderate AD patients have failed to show overall cognitive benefits. Frequently, the same drugs that have failed in combined mild and moderate populations have suggested cognitive benefits for subjects in mild AD  (7) (8) (9) (10) 30) , when the extent of amyloid and tau pathologies are considerably lower than in moderate AD  (31) (32) (33). Exploratory analyses of the NILVAD dataset presented here show similar findings where a lower rate of cognitive decline was only seen in the very mild AD group. This effect was detectable primarily on memory related outcome measures that are affected early in the disease process of AD. In addition, the reduced rate of cognitive decline in this group corresponded with a high ratio of CSF Aβ42/Aβ40 after nilvadipine treatment whereas the worsening of cognition in the moderate AD group corresponded with a low ratio of CSF Aβ42/Aβ40.
CONCLUSION With failures of most AD trials to satisfy efficacy criteria in mixed AD populations, exploratory analyses of existing trial data are justified and necessary to understand lack of efficacy and to identify sub-populations that may have benefited from interventions. The NILVAD trial was designed for the analysis of a mixed mild and moderate AD population and further stratification of the study population into very mild, mild and moderate AD was unplanned and therefore exploratory. As such, these subgroup analyses were underpowered, particularly when considering the confounding effects of gender and APOE. Nevertheless, analyses adjusted for these factors continue to suggest that very mild AD subjects responded positively to nilvadipine on both the ADAS-Cog 12 and the ADCOMS. Furthermore, analyses of the ADAS-Cog 12 subscales demonstrate that beneficial effects on memory and language traits were associated with nilvadipine treatment in very mild and mild AD patients, respectively.
ACKNOWLEDGMENTS The authors thank all participants in the NILVAD consortium as well as all of the patients and caregivers who so generously gave of their time. The authors also thank  Muireann O'Briain ;  Oliver Gupta ; the members of the  Ethics Advisory Board  ( Ursula Collins ,  Mary Donnelly ,  Tony O'Brien  and  Shaun O'Keefe ); the members of the  Safety Advisory Board  ( Paul Aisen ,  Suzanne Hendrix ,  Robin Jacoby , and  Maurice O'Connell ); and the members of the  Data Safety Monitoring Board  ( Bernadette McGuinness ,  John Newell ,  Martin O'Donnell , and  Peter Passmore ). The authors wish to thank all other study partners-Alzheimer Europe and Newsweaver who helped with dissemination and promotion of the trial and GABO:mi who were the project management company for the majority of the trial.
5 (0. 42) 26. 4 (0. 50) 25. 0 (0. 36) 25. 6 (0. 42) 25. 5 (0. 65) 24. 7 (0. 49) *APOE genotyping was available only for a subset of individuals and the age the subjects left education was significantly different across MMSE categories. P < 0. 05. Education information was not available for 6 subjects and time since AD diagnosis was unavailable for 1 subject. Although the mITT dataset was composed of 498 subjects, 1 subject withdrew consent and therefore data on 497 subjects were available for analysis.
Data collected by the GIHSN provide an opportunity to investigate the associations between severe illness, patient characteristics, and influenza virology. In the 2017-2018 season, the GIHSN included 20 coordinating sites in 19 countries on five continents. The GIHSN has reported epidemiological findings from 2012-2013  [5] , 2013-2014  [8] , 2014-2015  [9] , and 2016-2017 influenza seasons  [10]. The studies have shown that all strains of influenza can result in hospitalization, intensive care unit (ICU) admission, or death and that the strains responsible for severe cases vary substantially between seasons, sites, and even within single regions or countries. In addition, these studies have confirmed that influenza can cause serious outcomes not only in older adults, individuals with comorbidities, and pregnant women but also in the wider population, irrespective of age, sex, or comorbidities  [5, 8, 9].
Influenza strains were detected by RT-PCR. An individual patient could have been positive for more than one strain of influenza (Fig. 7 ). Hospitalization during the previous 12 months was associated with a longer hospital stay in influenzapositive admissions aged < 15 years (coefficient, 0. 96 [95% CI, 0. 41-1. 52]). No factors associated with a longer hospital stay were identified in influenzapositive admissions aged ≥65 years. Length of hospital stay did not differ between influenza A and B for any age group. The length of hospital stay also did not differ between A/H1N1pdm09 and A/H3N2 for any age group (data not shown).
Complicated hospitalization was defined as ICU admission, mechanical ventilation, or death. Abbreviations: COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease Fig. 6  Mean length of hospital stay in patients. Abbreviation: CI, confidence interval identify associations between influenza virology/epidemiology, patient characteristics, and severe cases of influenza illness. In the current study, we pooled data from more than 4000 influenza-positive patients at 14 coordinating sites in 13 countries on four continents. Although this provided a large enough dataset to assess many factors potentially associated with complications, the influence of some factors known to be associated with severe influenza, such as human immunodeficiency virus infection and neurological conditions  [4] , could not be assessed because related information was not collected.
Acknowledgments Medical writing was provided by  Dr. Phillip Leventhal  (Evidera). The GIHSN 2017-2018 collaborators include:  Sélilah Amour  ( Hôpital Edouard Herriot, Lyon, France ),  Coulibaly Anderson N'Gattia  ( Institut National d'Hygiène Publique, Treichville, Ivory Coast ),  Victor Baselga Moreno (Foundation for the Promotion of Health and Biomedical Research, Valencia, Spain) ,  Elsa Baumeister  ( National Reference Laboratory for Viral Respiratory Diseases, Buenos Aires, Argentina ),  Jalila Ben Khelil  ( Institut Pasteur de Tunis, Tunis, Tunisia ),  Daria Danilenko  ( Research Institute of Influenza of the Ministry of Healthcare, Saint Petersburg, Russian Federation ),  Javier Diez-Domingo (Foundation for the Promotion of Health and Biomedical Research, Valencia, Spain) ,  Anca Cristina Drăgănescu  ( National Institute for Infectious Diseases "Prof.
Of note, of the three classes of antibiotic most strongly implicated in the recent CDI epidemic (cephalosporins, fluoroquinolones and macrolides), all are very frequently prescribed for respiratory infections  [9]. Some authors have proposed a switch from cephalosporin or fluoroquinolones treatment in favor of penicillin-based regimens in AP in order to decrease the risk of CDI  [9]. There are however few data to date to support this recommendation. In a frail older population hospitalized for AP, we aimed to identify the risk factors of CDI, especially addressing whether one antibiotic regimen was associated with a lower risk of CDI incidence. We also assessed the burden of CDI occurrence in term of hospitalization length, and short and long-term mortality.
Recorded Data For each subject, we recorded demographic, clinical, and biological data including age, gender, residential status, underlying diseases, Charlson comorbidity index (CCI)  [11] , history of hospitalization in the past 6 months, Pneumonia Severity Index (PSI)  [12]. Antibiotic treatment (medication, duration, route of administration), proton-pump inhibitors (PPI) and immunosuppressive therapies co-prescriptions (>5mg prednisone equivalent corticosteroids, chemotherapy or antirejection agents, tumor necrosis factor antagonists) were also recorded, as well as 6 months re-hospitalizations, in hospital death and at 1 year. The study was conducted in accordance with the Declaration of Helsinki and National standards.
Data Analysis We compared 1) patients developing CDI with those without CDI, and 2) patients who were alive with those who were dead. Continuous variables were compared using the Mann-Whitney U test. Categorical variables were compared using the chi-square test and Fisher test when appropriate. Factors associated with CDI incidence were evaluated in univariate and multivariate analysis. Logistic regression analysis was performed to assess the association of CDI on one-year mortality after adjustment on pre-specified prognostic factors (Age, CCI, PSI, acquisition site). Statistical analyses were performed using SPSS 21. 0 software (IBM Corp, Armonk, NY). Statistical significance was defined for P < 0. 05.
Patients' characteristics In total, 92 patients with AP and CDI and 213 AP patients without CDI were included. Demographic data are presented in table I. Patients were of similar age (86 [82-91] vs 85 [81-89] respectively, p = 0. 2) and had a very high rate of comorbidities in the 2 groups, as highlighted by the median CCI  (7 [6-8]  in the 2 groups). AP was acquired in community for nearly 60% of patients, in nursing home for 30% and in hospital for 10% of patients in both groups.