dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The manuscript was written by the European Virus Archive access and benefit-sharing compliance team but would not have been possible without the front-line scientists that built up the European Virus Archive infrastructure and have worked tirelessly over the past year to support the global pandemic response. We welcome the European Virus Archive signatory authors who endorse this publication and its call for a multilateral pathogen genetic resources mechanism tied to a distributed biobanking infrastructure. This publication was supported by the  European Virus Archive - GLOBAL  project that has received funding from the  EU   Horizon 2020 research and innovation programme  (grant agreement number  871029 ). Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
Panel: Examples of SARS-CoV-2 non-monetary benefits shared by the European Virus Archive (EVA) network* Sharing of sequence data (modalities of shared genetic sequence data via databases, such as the Global Initiative on Sharing Avian Influenza Data the International Nucleotide Sequence Database Collaboration, integrated into the COVID-19 platform: • Bilaterally with another country or organisation (reported by three survey respondents). • In a private hub (reported by two survey respondents). • In public databases (reported by 10 survey respondents). Sharing of material (modalities of shared SARS-CoV-2 biological material, such as viruses, proteins, and nucleic acids): • Bilaterally with another country or organisation (reported by six survey respondents). • In closed collaboration (reported by seven survey respondents).
While we observe some expected responses to the diet, most metabolic and molecular parameters affected by HFD are dependent on sex and strain. Our observations indicate that the genetic repertoire of inbred laboratory mouse strains encodes a wide range of metabolic responses to diet-induced obesity (DIO), modelling different levels of metabolic disease severity or associated comorbidities. All metabolic traits, mitochondrial activity, and transcriptome data collected in this study can be explored with an online, interactive interface at www. systems-genetics. org/CC_founders_metabolic. This resource enables researchers to quickly examine the diverse manifestations of DIO in mice and pick an appropriate mouse model for individual obesity-associated diseases.
A cohort of diet-sensitive and diet-resistant strains We examined the effects of HFD on cardio-metabolic traits in males and females of each strain (Figures 1C, 2 and S1). Out of the 9 strains considered, 5 significantly gained weight in females and 4 in males (Figures  1C  and 2A ). These changes did not seem to be caused by increased food intake, although quantification of food intake under HFD was hampered by mice dispersing the food throughout the cage (Figure  S1 ). The A/J and CAST/EiJ strains gained no additional weight upon HFD in either sex. The WSB/EiJ and CAST/EiJ are wild-derived strains known for their resistance to obesity and high percentage of lean mass  (Karunakaran and Clee, 2018) , but our data show that the A/J strain may exhibit a similar resistance. For all strains but the reference strain C57/BL6/J, the weight gain was greater in females than in males (Figures  1C  and 2A ).
ACKNOWLEDGMENTS We thank the members of  J. Auwerx's laboratory  for their help with sacrifices and sample processing and the staff of  EPFL's Center of Phenogenomics (CPG)  for help with phenotyping. We thank all members of  J. Auwerx  and  K. Schoonjans  laboratories for helpful discussions. We thank  Metabiolab  for their help in measuring the activity of mitochondrial complexes. Funding: this project received funding from the  EPFL , the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  ( ERC-AdG-787702 ), the  Swiss National Science Foundation  ( SNSF 31003A_179435 ), and a GRL grant of the  National Research Foundation of Korea  ( NRF 2017K1A1A2013124 ). LJEG was supported by the  European Union 's  Horizon 2020 research and innovation program  through the  Marie Skłodowska-Curie Individual Fellowship ''AmyloAge''  (grant agreement No.
Acknowledgments We would like to thank  P J van Veldhoven  for technical support at the MOVPE reactor,  A Cavalli ,  L Gagliano , and  M Y Swinkels  for nanoimprint preparation,  M J Brett  for valuable discussions, and  G Badawy  for designing the figures. This work was supported by the  Dutch Organization for Scientific Research  ( NWO-VICI 700. 10. 441 ), and the  European Union 's  Horizon 2020 research and innovation program  under grant agreement number  964191  ( Opto Silicon ). Solliance and the province of Noord-Brabant are acknowledged for funding the  TEM  facility. This project has received funding from the  French National Research Agency (ANR)  under the grant  ANR-17-CE030-0014-01  (HEXSIGE). We acknowledge the  ANR  for funding the  NANOMAX ETEM  through the  TEMPOS grant  ( 10-EQPX-0050 )).
Observations We present 491 new NIR spectra of 420 NEOs mainly between 100 m and 3 km in diameter (Figure  1 ). These observations were collected for the most part between 2015 January and 2021 February through the MITHNEOS program, with the exception of 10 spectra acquired earlier (between 2003 and 2013), but inadvertently never published before. Combined with our previously published spectra  (Binzel et al. 2019) , this leads to a data set of NIR spectral measurements for a total of 976 NEOs (many with multiple measurements available) that we analyze in this work. Spectroscopic observations were conducted with the 3 m IRTF located on Maunakea, Hawaii. We used the SpeX NIR spectrograph  (Rayner et al. 2003 ) combined with a 0 8 × 15″ slit in the low-resolution prism mode to measure the spectra over the 0.
7-2. 5 μm wavelength range. Series of spectral images with 120 s exposure time were recorded in an AB beam pattern to allow efficient removal of the sky background by subtracting pairs of AB images. Asteroid observations were alternated with measurements of calibration stars known to be very close spectral analogs to the Sun: Hyades 64 and Landoltʼs (1983) stars  93-101, 98-978, 102-1081, 105-56, 107-684, 107-998, 110-361, 112-1333, 113-276, and 115-271. We typically observe three different stars each night to be able to identify possible outlier measurements and to mitigate spectral variability across the observations by computing a mean stellar spectrum from the three measurements. An indepth analysis of these calibration stars is provided in  Marsset et al. (2020). Data reduction and spectral extraction followed the procedure outlined in  Binzel et al.
(2019). We summarize it briefly here. Reduction of the spectral images was performed with the Image Reduction and Analysis Facility and Interactive Data Language, using the Autospex software tool to automatically write sets of command files  (Rivkin et al. 2005). Reduction steps for the science targets and their corresponding calibration stars included trimming the images, creating a bad pixel map, flat-fielding the images, sky subtracting between AB image pairs, tracing the spectra in both the wavelength and spatial dimensions, co-adding the spectral images, extracting the spectra, performing wavelength calibration, and correcting for air-mass differences between the asteroids and the corresponding solar analogs. Finally, the resulting asteroid spectra were divided by the mean stellar spectra to remove the solar gradient.
All reduced spectra collected through MITHNEOS and information about their observing conditions are made rapidly available to the community by being posted online at http:// smass. mit. edu/minus. html. Thumbnail figures of the new spectral data set are presented in Appendix A (the complete figure set is available only in the online journal). The corresponding list of spectra, including results of our spectral analysis, information about the NEOs's physical and orbital properties, observing conditions, and ER probabilities (Section 3. 3), is provided in Table  7  in Appendix D.
Taxonomic Assignment Taxonomic classification of our new data set was performed as follows. For any spectra for which a thermal tail is detected, a correction to remove the thermal component was applied before classification as described in Section 2. 3. Spectra were then run through the Bus-DeMeo principal component analysis (PCA) online classification tool  (DeMeo et al. 2009)  developed by Stephen M. Slivan and publicly available at http://smass. mit. edu/busdemeoclass. html. We refer the readers to  Tholen (1984) ,  Tholen & Barucci (1989), and Bus (1999)  for explanations of the PCA method applied to asteroid spectroscopy. Spectra that have both visible and NIR data available are typically assigned a unique class through this method. Spectra with only NIR data, which includes the majority of our data, are typically assigned multiple possible classes through the tool, and the data either are degenerate (cannot be assigned a unique class because insufficient wavelength coverage exists) or require visual inspection to identify features that indicate the class.
Albedos The first set of observables needed to debias the compositional distribution of NEOs by use of Equation (  5 ) is the set of albedos of the different classes of objects in our data set. Albedo measurements for these objects were retrieved from the following catalogs and papers:  Delbo et al. (2003) , IRAS (Ryan & Woodward 2010), ExploreNEOs  (Trilling et al. 2010) , AKARI  (Usui et al. 2011) , NEOWISE  (Mainzer et al. 2011; Nugent et al. 2016; Masiero et al. 2017 Masiero et al. , 2018)) , and NEOSurvey  (Trilling et al. 2016). For each object, an average value was calculated whenever multiple measurements were available, weighting by the inverse of the squared uncertainty on the measurements. Our classified spectra (Section 2. 2) were then grouped into a broad range of taxonomic classes, A, B, C, D, Q, S, K, L, V, and X types, and a median albedo was calculated for each class.
26 to 4. 19 au, assuming that the probabilities do not vary significantly between neighboring cells of the model. Our sample also includes three active comets. Two of these objects, 162P/Siding Spring (2004 TU12) and 169P/NEAT (2002 EX12), have orbital properties fully consistent with those of JFCs, while the third one, P/2006 HR30 (Siding Spring), belongs to the family of Halley-type comets. Due to its uniqueness in our data set, P/2006 HR30 was not considered in the rest of our analysis. For each ER, the compositional distribution was derived separately for H < 19 and H > 19, and each magnitude range was debiased independently by use of Equation (  5 ), albedo values from Table  1 , and β slope values from Table  2. The debiased fractional abundances from the two magnitude ranges were then summed, weighted by the number of observed objects in each range, and renormalized to obtain the final biascorrected compositional distribution of the ER.
e. , semimajor axis a < 2. 5 au), 4. 5 km for the middle belt (between the 3:1J and 5:2J MMR, i. e. , 2. 5 < a < 2. 8 au), and 7. 0 km for the outer belt (beyond the 5:2J, i. e. , a > 2. 8 au; Figure  2 ). These values constitute the lower-size boundary of our MBA comparison sample. The upper boundary was chosen to be 3 km larger, in order to obtain a large number of objects in our comparison sample while still being in a range of sizes that is not too different from our NEO data set. Next, we built a representative sample of each source population feeding the ERs. This was achieved by measuring the ranges of initial semimajor axis and inclination values of the test particles ending up in the ERs in the dynamical simulation of Granvik et al. (2018, see their Figure  3 , second panel) and selecting MBAs falling within these ranges of values.
Additional Class Merging The MITHNEOS data set and the SDSS-derived MBA comparison sample are composed of spectroscopic and photometric data collected over two distinct wavelengths ranges: the NIR range (0. 7-2. 5 μm) for MITHNEOS, and the visible range (0. 3-1. 0 μm) for SDSS. Several taxonomic classes of asteroids can only be distinguished in one of these two wavelength ranges. This is the case, in particular, for the C and X complexes that are often indistinguishable in the NIR (see Section 2. 2). Similarly, and region of the belt (left: inner belt; middle: middle belt; right: outer belt). The completeness limit of three regions is determined by fitting a power-law function to the size distribution and measuring the size at which the distribution diverges from the power law. The fitting power-law functions to the full population and to the subset of dark spectral classes are represented by the dashed green and black lines, respectively.
One can therefore reasonable assume that around 80% of Regions are ordered by increasing distance from the Sun. Each percentage indicates the average probability for an NEO in our data set of originating from the source region. A total of 11 objects in our data set were not included in this plot owing to uncertain spectral classification (Section 2. 2). This figure shows the compositional mixing of the NEO population and, therefore, of the asteroid belt at small object sizes, as well as the global trend of increasing dark-to-bright object ratio as a function of ER distance from the Sun. asteroids identified as V type by DeMeo & Carry (2013) are real V types, still implying that V types are ∼4 times less abundant among NEOs compared to the inner belt. Alternatively, the lack of V-type NEOs may stem from the central location of the Vesta family in the inner belt, away from any strong resonance with the giant planets.
Once dehydration completed, samples were incubated ON in 2/3 Dichloromethane (SIGMA-270997) and 1/3 methanol. The next morning, samples were incubated 30 min in 100% dichloromethane before filling the tube with 100% Dibenzylether (DBE; SIGMA-108014). After waiting at least 24 hours, samples were considered cleared and ready for imaging. Imaging. Acquisitions were performed by using an ultramicroscope I with the Inspector Pro software 5. 0. 285. 1 (LaVision BioTec) and a binocular stereomicroscope (MXV10, Olympus) with a 2X objective (MV PLAPO, Olympus) at different magnifications (from 2X to 6. 3X) (See Supplementary Table  1  for imageby-image details). The light sheet was generated by an OBIS laser diode (wavelength 561 or 635 and 785 nm, Coherent Sapphire Laser, LaVision BioTec) and focused using two cylindrical lenses.
Acknowledgements This project was funded by the  Region Ile-De-France  ( EX047007 -SESAME 2019 -4DEye ),  LabEx LIFESENSES  ( ANR-10-LABX-65 ),  Institut Hospitalo-Universitaire FOReSIGHT  ( ANR-18-IAHU-01 ) and  ANR Normather  ( ANR-14-CE16-0023 ). The funding organizations had no role in the design or conduct of this research.
Models: Spinning and thermal dust emission All estimates presented in the following were obtained with the numerical code DustEM 3  (Compiègne et al. 2011). DustEM is a versatile numerical tool that is mainly used to calculate the extinction and thermal emission (polarised or unpolarised) of any grain model. A calculation of the spinning dust emission is also implemented. The specificity of this implementation is that it allows the user to process all the parameters involved in the calculation of the excitation and damping of the grain rotation in a consistent way according to their size and composition. The rate of grain rotation depends on their emission of rovibrational and rotational photons, and on how they interact with the gas. To quantify these interactions, it is necessary to know the charge distribution of the grains and the main parameters describing the state of the gas.
Purification of primary B cells, cell culture, cell cycle assessment, immunophenotyping, cell sorting and protein assays. Procedures and methods were detailed in Supplemental Methods. Cell lines culture and viability assays. Human myeloma cell lines were obtained and cultured as described in  11. Viability was assessed for cells treated with selected compounds: AZD1208 & AZD5991 (Selleckchem). CellTiter-Glo® Luminescent Assay kit (Promega) was used as described by the manufacturer. Results were normalized to the samples without treatment and synergy score were calculated using the Bliss method  12  as described in  13. RNA-Sequencing, ATAC-sequencing and data accessibility. Procedures and analysis methods were described in  5. Raw data are accessible under GEO accession number GSE136990.
The emergence of PIM2 expression during plasmablast commitment To identify genes regulated specifically during the final commitment step, we used our previously described in vitro differentiation model with human naive B cells (NBCs) generating four cell populations on day (D) 6 (Fig. 1A )  14. The RNA-seq data (confirmed by qPCR assays) revealed a striking elevation of PIM2 mRNA expression (but not PIM1 or PIM3 expression) in prePBs (P2/CD23population) and PBs (P1 population), relative to B cells diverted from differentiation (P2/CD23 + and P3 populations) (Fig. 1B  and Fig. S1A ). In an unsupervised analysis, the results of single-cell RT-qPCRs in P2/CD23vs. P2/CD23 + cell populations ranked PIM2 as the most discriminating gene -ahead of PRDM1 (Fig. 1C  and Fig. S1B ). Analysis of our RNA-seq datasets showed that PIM2 was in the top 150 genes characterizing PBs, relative to noncommitted B cell populations (Fig.
S1C )  5. The protein levels correlated perfectly with gene expression levels, with strong PIM2 expression in prePBs and PBs (Fig. 1B ). With regard to human tonsil-derived cell populations, CD19 + IgD -CD38 bright PBs expressed higher levels of PIM2 than other B cell populations (Fig. 1D ). To determine whether PIM2 gene expression is coupled to genomic changes during PC differentiation, we analyzed our previously published datasets  4, 5  and ENCODE annotations (https://www. encodeproject. org). An assay for transposase-accessible chromatin using sequencing (ATAC-seq) showed that the open regions in prePBs and PBs included the PIM2 promoter and an upstream enhancer. This latter overlaps with a region denoted as interacting physically with the PIM2 promoter in the FANTOM5 database  15  and with a super-enhancer described in PBs and MM PCs (Fig.
To confirm these data, we measured REE and plasma 25(OH)D concentrations in a group of 31 healthy men including 15 young (from 20 to 35 years old) and 16 older individuals (over 60 years old). Consistent with the data obtained in old rats, we found a significant and positive correlation between REE adjusted to lean body mass and plasma vitamin D concentration (r = 0. 483; p = 0. 006) (Fig. 1k ). Taken together, our data collected in rats and a group of healthy men points to a tight relationship between plasma vitamin D levels and REE. Vitamin D status affects muscle mitochondrial function. Skeletal muscle is one of the major contributors to REE, mainly due to its metabolic activity and its relative contribution in terms of body mass. To clarify how vitamin D deficiency affected energy expenditure, we investigated muscle mitochondrial function, which is one of the major cellular processes involved in the modulation of energy production.
01) (Fig. 3c ). We found no significant differences for the other mitochondrial proteins whose content was measured (data not shown). Taken together, data from the mice and rat experiments suggest that long-term vitamin D deficiency decreases mitochondrial function and biogenesis through changes in key gene and protein expression. To demonstrate a direct effect of vitamin D status on mitochondrial gene expressions, we also analyzed the mRNA level of proteins involved in mitochondrial biogenesis and function in C2C12 and human primary myotubes treated with 1,25(OH)2 vitamin D3. C2C12 myotubes treated with 10 nM of 1,25(OH)2 vitamin D3 exhibited a strong increase in levels of PGC1α, PGC1β, NRF2, TFAM, PPARα, COXIV, and UCP3 transcripts compared to control cells (+83%, +123%, +40%, +68%, +39% and +297%, respectively) (Table  2 ).
Mitochondrial respiratory chain activity depends on the expression of hundreds of genes encoded in the nuclear and mitochondrial genomes. This implies that coordinated regulation of the cell's two genetic systems is necessary for a correct mitochondrial biogenesis. To explore this hypothesis, we performed a global analysis of genes that are differentially expressed (DEGs) in plantaris muscle of vitamin D-depleted old rats and in C2C12 myotubes according to vitamin D status. Microarray analyses showed that the levels of 1082 mRNAs were significantly changed in plantaris muscle after a 9-month period of vitamin D depletion in old rats. To understand the biological functions of the DEGs in plantaris muscle of vitamin D-depleted old rats, we conducted a functional analysis of all the genes that showed significant changes using the Database for Annotation, Visualization and Integrated Discovery (DAVID) to determine Gene Ontology (GO) term enrichments.
Methods Human trials: impact of vitamin D status (observational study) and vitamin D supplementation (interventional study). The observational study protocol was approved by the local Ethical Review Board of the General Clinical Research Center (GCRC) at Clermont-Ferrand, France, in accordance with the Declaration of Helsinki. Each participant gave written informed consent after being explained the purposes, methodology and potential risks of the study. This trial was registered at clinicaltrials. gov as NCT01066091. Thirty-one healthy male subjects, including 15 young (from 20-35 years old) and 16 older individuals (over 60 years old), with a body mass index (BMI) between 21. 4 and 34. 2 kg/m 2 were included in the study, as previously reported  29, 30. Each subject had a normal blood biochemical profile and was sedentary.
Participants were not taking any medications liable to affect outcome parameters, i. e. , corticosteroids, b-adrenergic blockers, lipid-lowering agents, or anticoagulants. On the day of the experiment, subjects were studied after an overnight fast of at least 10 h. Indirect calorimetry was performed when subjects were awake, in supine position in a quiet room (Deltatrac, Datex, Geneva, Switzerland). Gas samples were collected every minute for 1 h. The data of the first 10 min were not used because this period corresponds to the stabilization of the metabolism of the subjects under the canopy. The means of the O 2 consumption and CO 2 production values obtained over the last 50 min period were used in the analysis. REE was calculated using the Weir equation  31. Blood samples were then collected in EDTA-coated tubes.
Plasma was prepared and stored at -80 °C until 25(OH)D concentration measurement using an ELISA kit (Promokine, Heidelberg, Germany). The interventional study protocol was approved by the Ethics Committee of the institutional review board at Saint Charles Hospital, Beirut, Lebanon, in accordance with the Declaration of Helsinki. This trial was registered at clinicaltrials. gov as NCT02942732. One hundred and fifteen elderly subjects (mean age: 73. 31 ± 2. 05 years) with vitamin deficiency (25(OH)D < 20 ng/mL as per Institute of Medicine (IOM) recommendations) who were pre-sarcopenic (i. e. , skeletal muscle mass/ height 2 = 7. 26 kg/m 2 for males and 5. 45 kg/m 2 for females) were asked to join a 6-month randomized, controlled, double-blind intervention study  27, 28. A written informed consent was signed by all the subjects which was approved by the Ethics Committee of the hospital's review board.