dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Do humanitarian workers trust numbersmeaning, do they believe they are reliable and can be used in their daily practices? Investigating the limited use of ACDs by humanitarian workers enables us to better understand the current importance of quantitative data within the humanitarian fieldand it is, in fact, much less developed than what advocates of EBH currently suggest. It is often taken for granted that the introduction of quantification, through the needsystem, has made humanitarian workers more aware of the merits of quantitative data. Yet in reality, the way humanitarian workers use and even consider quantitative materials is still the same since the beginning and always relates to need assessments in one way or another. Promoting better collecting and processing of data for humanitarian workers, as EBH does, only reinforces their accountability towards donors.
Moreover, if they do not clearly understand the benefits of quantitative data beyond the need-system that they denounce, they cannot be expected to incorporate these in their daily practices. Dedicating time to train humanitarian workers to use quantitative data with confidence and full understanding of their meaning, by allowing them to participate in training organised by Do Humanitarian Workers Really Trust Numbers? other NGOs or even by the Centre for Humdata, could be a first step towards improving the situation. Internalising some of the collecting or processing of quantitative data could also be a great way for humanitarian workers to gain data literacy. We realise that this requires resources that humanitarian organisations may not have: further research may thus have to think of organisational factors explaining these difficulties.
Funding The laboratories of GG and SG are supported by the  Lead Agency  grant " Bactria " funded by the  French National Research Agency (ANR ; grant # ANR-21-CE17-0037-01 ) and the  Swiss National Science Foundation  (SNSF; grant # 310030E_205559 ), respectively. The laboratory of GG is supported by the  Recherche Hospitalo-Universitaire RHU-COVIFERON  project under the program ' Investissement d'Avenir ' launched by the  French Government  and implemented by the  Agence Nationale de la Recherche (ANR)  with the reference  ANR-21-RHUS-08  and by the  EU Horizon101057100   UNDINE  project. The laboratory of SG is supported by grants from the  Swiss National Science Foundation (SNSF)  [ 310030_184757 ], and the  Swiss Cancer League/Swiss Cancer Research  [ KFS-4958-02-2020 ].
Measuring the transcriptome-wide effects of aging on murine adipocytes using RNAseq Adipose tissue plays a central role in age-related diseases. While RNAseq protocols exist for many tissues, few data have been generated with this technology to explore gene expression in adipocytes, particularly during aging. Here, we present a protocol to analyze the transcriptional changes that occur in adipose tissue during normal and accelerated aging in mouse models. We describe steps for genotyping, diet control, euthanasia, and dissection. We then detail RNA purification and genome-wide data generation and analysis.
Quality controls of RNAseq data and alignment on the mouse genome For every sample, perform a quality control test on RNAseq raw data with the NGS Core Tools FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Map sequence reads using STAR  4  and remap unmapped reads with Bowtie2 9 using the very sensitive local option. The total mapped reads are finally available in a BAM (Binary Alignment Map) format for raw read counts extraction. Generate read counts with the htseq-count tool of the Python package HTSeq 6 with default parameters to generate an abundance matrix. Only genes with a total read count across selected samples superior to 10 are kept. At last, perform differential analyses using the DESEQ2 7 package of the Bioconductor framework. Select up-and down-regulated genes based on the adjusted p-value and the fold-change information.
Finally, map the Ensembl gene names to the HGNC gene names with biomart R function. 10 te: Since many readers of this protocol might not have a training in advanced bioinformatics, it is also worth mentioning that all these bioinformatic tools (e. g. FastQC, STAR, Bowtie2, HTSeq, DESEQ2 etc. ) can be performed using the open-source web-based Galaxy platform. 11 alaxy has all these tools built-in and using them requires no prior training in coding languages, which makes these tools incredibly accessible to all bench scientists.
RNAseq data interpretation. Analyze gene expression using dedicated R scripts to build volcano plots. Built heatmaps using the online application heatmapper (http://www. heatmapper. ca/). Identify pathways with IPA (Ingenuity Pathway Analysis, Qiagen). Perform Gene set enrichment using the web-based GEne SeT AnaLysis Toolkit (http://www. webgestalt. org/). Predict the proportion of immune cell populations that infiltrate the adipose tissue from bulk RNAseq data with the ImmQuant deconvolution software 8.
LIMITATIONS Like any tissue, the adipose tissue is not a homogeneous assembly of a single cell type. It contains infiltrating immune cells, the number of which increases with age, as well as stem cells. Therefore, the transcriptomic analysis described here, although describing the metabolic changes of adipocytes, also reflects changes in abundance and activity of additional cell types present in minority. To evaluate potential changes in immune cells infiltration, we use a deconvolution software of bulk RNAseq data (ImmQuant 8 ), as mentioned in the step-by-step protocol. We also would like to stress that accelerated aging seen in Dicer 1 mutant is accompanied with reduced epididymal fat depots and an almost complete disappearance of the sub-cutaneous white adipose tissue. This represents an additional difficulty for investigators aiming at analyzing ageing-associated perturbations of adipose tissues.
ACKNOWLEDGMENTS Work in S. B. laboratory is supported by the  Strasbourg's Interdisciplinary Thematic Institute  ( ITI ) for Precision Medicine, TRANSPLANTEX NG, as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  INSERM , funded by  IdEx Unistra  ( ANR-10-IDEX-0002 ) and  SFRI-STRAT'US  ( ANR-20-SFRI-0012 ), the  INSERM UMR_S 1109 , the  University of Strasbourg (IDEX UNISTRA) , the  European regional development fund (European Union )  INTERREG V program  (project  PERSONALIS ), and the  MSD-Avenir  grant AUTOGEN. Technical help from  Cecile Macquin  and the Genomax sequencing facility personnel is acknowledged.
Models' Development In this study, a supervised forward selection procedure was employed to select the models for predicting GITwps in small ruminants. Predictor variables were added or removed from the set of explanatory variables based on the pre-specified p-value, considering their uniform distribution across the range of BW in the dataset, as well as the goodness of fit of the models (i. e. , Akaike's Information Criterion (AIC) with a small sample correction (AICc) and concordance correlation coefficient (CCC)). Predictor variables with p-values smaller or equal to 0. 05, homogeneously distributed across the BW range in the dataset, and with the potential to enhance model fitness were retained and included in the final selected models. The following predictor variables were considered: BW (kg), intake level (i.
e. , ad libitum or restriction levels), sex, breed, species, DM intake level (g/kg BW), NDFI expressed either as diet concentration (NDFI, g/g DMI) or as BW relative intake (NDFI, g/kg BW), physiological state (i. e. , pregnant adult and growing), stage of pregnancy in days, and type of pregnancy (i. e. , single or twin). Additionally, to examine the non-linear relationship between GITwps and the candidate predictor variables, as well as to address residual heteroscedasticity and enhance the predictive ability of models, four linearly selected equations were fitted using the natural logarithm transformed GITwps and the predictor variable BW. The data were fitted to the models using lme the function from the nlme package  [42]  in R (R Core Team), considering the random effect of the study and residual error.
Models' Development The selection of models began by testing BW (kg) as a predictor variable for GITwps (g/kg BW) using all observations in the dataset (n = 750). When suckling and solid diet-fed animals were evaluated together, no relationship (p > 0. 05) was observed between GITwps (g/kg BW) and BW (kg) (Figure  1 ;  [47] ). Consequently, the relationship between the GITwps (g/kg BW) and BW (kg) was modeled separately for suckling animals (n = 36) and solid diet-fed animals (n = 714), as shown in Equations (  1 ) and  (2). Suckling animals presented a unique condition in which the GIT wet pool size (g/kg BW) is not influenced by BW (p = 0. 326; R 2 = 0. 45), Equation (1), and Figure  1. GITwps suckling animals (g⁄kg BW) = 102. 90 (±26. 33) + 3. 92 (±3. 93) × BW (kg) On the other hand, individuals on a solid diet presented a strong, linear, negative relationship (p < 0.
Considering the unique characteristic of our dataset, which consists of individual observations, animals fed restriction levels within the same study had identical ingested diet compositions and were pair-fed. As a result, Equation (3) was re-fit using data solely from individuals fed ad libitum, and the effects of sex, species, intake, and diet composition were incorporated into the re-fitted Equation (3). The sex effect on Equation (3) was no longer significant when it was re-fitted using individuals fed ad libitum only (p = 0. 088). However, species influenced both the intercept and slope of the relationship between GITwps and BW in individuals fed ad libitum only (p ≤ 0. 0166; Table  2 ; Equation (  5 )). Additionally, the inclusion of DMI (g/kg BW) showed statistical significance for sheep and goats (p ≤ 0.
0059; Table  2 ; Equation (  6 )). On the other hand, the effect of NDF intake (g/kg BW) on the GITwps was independent of species, and the GIT content increased by 2. 84 g/kg BW with increasing NDFI (Table  2 ; Equation (  7 )). Moreover, when the concentration of NDFI in the diet (g/g DMI) was added to Equation (3), it eliminated the species effect, suggesting that its effect on the GITwps is similar for both sheep and goats (Table  2 ; Equation (  8 )). Among all the fitted equations, the four selected equations presented in Table  2  showed the potential to accurately and precisely predict GITwps in small ruminants. Considering the unique characteristic of our dataset, which consists of individual observations, animals fed restriction levels within the same study had identical ingested diet compositions and were pair-fed.
Statistical analysis For molecular and microbiological analyses, all experiments were replicated six times (two replicates per experiment for three independent experiments). Data were analyzed using general linear model procedures in Statgraphics centurion V18 software (FRANCESTAT, Neuilly sur Seine, France). For all samples, analyses of variance (ANOVA) were performed to determine the impact of a) D 2 O concentration on bacterial suspension, b) time of exposure to nondeuterated and deuterated bacterial suspension on i) culturable, ii) viable, and iii) total populations of L. innocua. Differences were considered statistically significant at p < 0. 05.
2019 (Rondón et al. , 2022;; Ieva et al. 2022; Arcoverde et al. 2023). This project created a database with phase curves of 30 NEAs using three 1 m class telescopes in Brazil and Italy  (Arcoverde et al. 2023). Their sample includes only one tiny NEA, 2017 DC 38 , with an absolute magnitude H of 24. 22, which was successfully observed at a very small phase angle of 1°. 1. However, its rotation period was not obtained and thus no rotation correction has been performed in the analysis of its phase curve. In this paper, we present the results of multicolor photometry of the tiny NEA 2015 RN 35 over 17 nights in Japan and Chile. The target asteroid 2015 RN 35 was discovered by the Pan-STARRS 1 survey  (Chambers et al. 2016 ) on 2015 September 9. 2015 RN 35 is an Apollo-class NEA, and its absolute magnitude in the V band is 23.
24 in the NASA JPL Small-Body Database (SBDB). 16  The trajectories of 2015 RN 35 were well studied and possible collisions with Earth were discussed  (Petrov et al. 2018). 2015 RN 35 had a close approach in 2022 December: it was observable at phase angles from 30°to 0°. 6 from 2022 December to 2023 January, which is a quite rare opportunity to obtain a well-sampled phase curve of a tiny asteroid. The paper is organized as follows. In Section 2, we summarize our observations and data reduction. The physical properties of 2015 RN 35 are summarized in Section 3. The surface properties of the tiny asteroid 2015 RN 35 and possible exploration by a spacecraft mission are discussed in Section 4.
Observations and Data Reduction We conducted photometric observations at two observatories in Japan and Chile. The observing conditions are summarized in Table  1. The predicted V-band magnitudes, phase angles, distances between 2015 RN 35 and observer, and distances between 2015 RN 35 and the Sun in Table  1  were obtained from NASA JPL HORIZONS 17  using the Python package astroquery  (Ginsburg et al. 2019).
Seimei Telescope We obtained 12 lightcurves of 2015 RN 35 using the TriColor CMOS Camera and Spectrograph (TriCCS) on the 3. 8 m Seimei telescope  (Kurita et al. 2020 ) from 2022 December 23 to 2023 January 21. We simultaneously took three-band images in the Pan-STARRS (g, r, i) and (g, r, z) filters  (Chambers et al. 2016). The field of view is 12. 6 7. 5 ¢ ´¢ with a pixel scale of 0 350 pixel -1. Nonsidereal tracking was performed during the observations of 2015 RN 35. The exposure times were 5 or 60 s according to the brightness of 2015 RN 35. The signal-to-noise ratios of 2015 RN 35 in the data taken in 2023 January are too low to detect 2015 RN 35 in a single exposure. We took multiple images with short exposures rather than a single image with long exposures in our observations in order to avoid having elongated photometric reference stars and also to eliminate cosmic rays.
We performed standard image reduction including bias subtraction, dark subtraction, and flat-fielding. The astrometry of reference sources from the Gaia Data Release 2 was performed using the astrometry. net software  (Lang et al. 2010). For the data taken in 2023 January, we performed stacking of images before photometry to avoid elongations of the images of 2015 RN 35 as shown in the upper panels of Figure  1  (hereinafter referred to as the nonsidereally stacked image). We stacked 20 successive images with exposure times of 60 s. Since a typical readout time of the CMOS sensors on TriCCS is 0. 4 ms, the total  integration time is about 1200 s, which corresponds to one of the characteristic periods of 2015 RN 35 (see Section 3. 1). We also stacked images using the World Coordinate System (WCS) of images corrected with the surrounding sources to suppress the elongations of the images of reference stars as shown in the lower panels of Figure  1  (hereinafter referred to as the sidereally stacked image).
We focused on the two strongest frequencies, f b and f d , based on the powers of periodograms. f d appears to be the first overtone of f b : 2f b ¥ f d. We considered that f b corresponds to a period of 2015 RN 35 , P 1 , since folded lightcurves with f b 1 -are typical double-peaked lightcurves. The uncertainty of P 1 was estimated with the Monte Carlo technique following the previous work  (Beniyama et al. 2022). We obtained 1000 lightcurves by randomly resampling the data assuming each observed data point follows a normal distribution whose standard deviation is a photometric error. We calculated the 1000 periods corresponding to P 1 and derived it with the uncertainty each night as 1149. 7 ± 0. 4 s (December 19), 1149. 6 ± 0. 5 s (December 22), and 1149. 9 ± 0. 5 s (December 26). We adopted the error-weighted average of these three periods, 1149.
2023). It is known that the slope parameters G 1 and G 2 have a tight correlation with the geometric albedo  (Muinonen et al. 2010; Shevchenko et al. 2016). We show typical G 1 and G 2 values of A-, E-, S-, C-and D-types in Figure  10  (  Shevchenko et al. 2016; Mahlke et al. 2021). We also plot G 1 and G 2 of the A-type MBA (246) Asporina derived using the sparse photometric observations from Gaia Data Release 2  (Martikainen et al. 2021). The smaller G 1 and larger G 2 , by definition, mean that the slope of the phase curve is shallower. The slopes of highalbedo asteroids are shallower since the contribution of the shadow-hiding effect decreases as albedo increases (e. g. ,  Belskaya & Shevchenko 2000) , whereas those of low-albedo asteroids are steeper. Thus, the small G 1 and large G 2 of 2015 RN 35 are indicative of a high geometric albedo.
Acknowledgments We sincerely thank  Seitaro Urakawa ,  Miho Kawabata ,  Qiliang Fang ,  Keisuke Isogai ,  Hideyuki Izumiura ,  Kentaro Tanimoto , and  Ren Ikeya  for observing assistance. Special thanks to  Patricio Rojo  for attempting to observe 2015 RN 35 in Chile. We are grateful to  Benoit Carry  and  Eric MacLennan  for the discussions about the latest taxonomy and phase function, respectively. J. B. also acknowledges the contribution of  Sean Marshall  in improving the quality of the manuscript. We are grateful to the anonymous referee for constructive comments during the review process. J. B. would like to express his gratitude to the  Iwadare Scholarship Foundation , the  Public Trust Iwai Hisao Memorial Tokyo Scholarship Fund , the  JEES Mitsubishi Corporation Science Technology Student Scholarship , and  Iue Memorial Foundation  for the grants.
C. A. acknowledges support from the  ANR   ORIGINS  ( ANR-18-CE31-0014 ). This research is partially supported by the  Optical and Infrared Synergetic Telescopes for Education and Research (OISTER)  program funded by the  MEXT of Japan. This work is supported in part by the  JST SPRING , grant No. JPMJSP2108 , and the  UTEC UTokyo Scholarship. This work has been supported by the  Japan Society for the Promotion of Science (JSPS)   grants-in-aid for Scientific Research (KAKENHI)  grant Nos. 21H04491  and  23KJ0640. The authors thank the  TriCCS  developer team (which has been supported by the  JSPS KAKENHI  grant Nos. JP18H05223 ,  JP20H00174 , and  JP20H04736 , and by  NAOJ Joint Development Research). This research is based on data collected by the  TRAPPIST-South  telescope at the  ESO La Silla Observatory.
TRAPPIST is funded by the  Belgian Fund for Scientific Research (Fond National de la Recherche Scientifique, FNRS)  under the grant  PDR T. 0120. 21. E. J. is a F. R. S. -FNRS Senior Research  Associate. The  Pan-STARRS 1 Surveys  (PS1) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the  Pan-STARRS Project Office , the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under grant No.
NNX08AR22G  issued through the  Planetary Science Division  of the  NASA Science Mission Directorate , the  National Science Foundation  grant No. AST-1238877 , the  University of Maryland ,  Eotvos Lorand University (ELTE) , the  Los Alamos National Laboratory , and the  Gordon and Betty Moore Foundation. Part of the data utilized in this publication were obtained and made available by the  MIT-UH-IRTF Joint Campaign  for NEO Reconnaissance. The IRTF is operated by the  University of Hawaii  under Cooperative Agreement no. NCC 5-538  with the  National Aeronautics and Space Administration, Office of Space Science, Planetary Astronomy Program. The MIT component of this work is supported by  NASA  grant  09-NEOO009-0001 , and by the  National Science Foundation  under grants Nos. 0506716  and  0907766.
To drive health innovation that meets the needs of all and democratize healthcare, there is a need to assess the generalization performance of deep learning (DL) algorithms across various distribution shifts to ensure that these algorithms are robust. This retrospective study is, to the best of our knowledge, an original attempt to develop and assess the generalization performance of a DL model for AF events detection from long term beat-to-beat intervals across geography, ages and sexes. The new recurrent DL model, denoted ArNet2, is developed on a large retrospective dataset of 2,147 patients totaling 51,386 h obtained from continuous electrocardiogram (ECG). The model's generalization is evaluated on manually annotated test sets from four centers (USA, Israel, Japan and China) totaling 402 patients.
The model is further validated on a retrospective dataset of 1,825 consecutives Holter recordings from Israel. The model outperforms benchmark state-of-the-art models and generalized well across geography, ages and sexes. For the task of event detection ArNet2 performance was higher for female than male, higher for young adults (less than 61 years old) than other age groups and across geography. Finally, ArNet2 shows better performance for the test sets from the USA and China. The main finding explaining these variations is an impairment in performance in groups with a higher prevalence of atrial flutter (AFL). Our findings on the relative performance of ArNet2 across groups may have clinical implications on the choice of the preferred AF examination method to use relative to the group of interest.
INTRODUCTION Atrial fibrillation (AF) is the most prevalent heart arrhythmia  1, 2. It is associated with a fivefold increase in stroke incidence and a 3. 5-fold mortality risk increase  3. Across geography, ages and sexes AF has been reported to have varying prevalence  1, 2, [4] [5] [6]. Machine learning presents a unique opportunity to provide an accurate automated diagnosis of AF. Yet, these models must demonstrate generalizability to external datasets integrating a range of population samples. We searched PubMed up to May 24th, 2022, for research articles containing the terms "(deep learning or convolutional neural network)" AND "(atrial fibrillation or atrial flutter)" AND "(generalization across geography or generalization across age or generalization across sex)", without any date or language restrictions.
Datasets The University of Virginia dataset (UVAF) from the USA  7, 8  was used to train the DL models. UVAF consists of a dataset of 2147 patients totaling 51,386 h of annotated continuous electrocardiogram (ECG). The model's generalization was then evaluated on manually annotated events in test sets from four centers (USA, Israel, Japan and China) totaling 402 patients. These test datasets are denoted UVAF-test, RBDB-test, SHDB-test, CPSC-test, (Table  1 ). Finally, the new DL model was validated on a retrospective dataset of 1825 consecutives Holter recordings from the Rambam Hospital Holter clinic, Haifa, Israel (denoted RBDB-test2). Overall, the datasets totaled 4,298 ECG Holter recordings and over 99,705 h of continuous data. Annotations for AF and atrial flutter (AFL) were grouped under a single label denoted AF l for the experiments and similar to previous works  [7] [8] [9] [10].