dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional support was provided through ( AG-023629 ,  AG-15928 ,  AG-20098 , and  AG-027058 ) from the  National Institute on Aging (Bethesda, MD, USA). A full list of principal Cardiovascular Health Study investigators and institutions can be found at http://www. chs-nhlbi. org/pi. htm. Etude sur le vieillissement arte´riel was organised with an agreement between INSERM and  Merck, Sharp and Dohme-Chibret. The  Northern Manhattan Study/The Oral Infections and Vascular Disease Epidemiology Study  is funded by the  National Institute of Neurological Disorders and Stroke  grant ( R37 NS 029993 ) and the Oral Infections,  Carotid Atherosclerosis  and  Stroke Study  by the  National Institute of Dental and Craniofacial Research (Bethesda, MD, USA)  grant ( R01 DE 13094 ). The  Interventionsprojekt zerebrovas-kula¨re Erkrankungen und Demenz im Landkreis Ebersberg Study  was supported by  AOK Bayern.
The  Rotterdam Study  was supported by the  Netherlands Foundation for Scientific Research, ZonMw  ( Vici 918-76-619 ). The Study of Health in Pomerania is part of the  Community Medicine Research network of the University Medicine Greifswald, Germany , funded by the  German Federal State of Mecklenburg-West Pomerania and German Federal Ministry of Education and Research for SHIP (BMBF , grant  01ZZ96030 ,  01ZZ0701 ). The  PROG-IMT  project was funded by the  Deutsche Forschungsgemeinschaft  ( DFG Lo 1569/2-1 ,  DFG Lo 1569/ 2-3 ). Folkert W Asselbergs is supported by  UCL Hospitals NIHR Biomedical Research Centre.
5 MBq) and imaged under isoflurane inhalational anesthesia (1. 8%, 50% air/50% oxygen, 1. 4 L/min) at 2, 6, 24, 48, and 96 h after injection with a small-animal SPECT/CT scanner (U-SPECT4/ CT; MILabs). Image acquisition lasted 30 min for earlier time-points to 90 min for later time-points. Energy windows were set over the 279-keV peak (620%), and a general-purpose rat-and-mouse collimator was used (75 holes of 1. 5-mm diameter, iterative reconstruction ordered-subsets expectation maximization, and no filter [16 subsets, 6 iterations, and a voxel size of 0. 8 mm]). The SPECT resolution with 203 Pb is estimated to be less than 1 mm. Images were analyzed with PMOD Software (PMOD Technologies).
Dose-Range Finding and Acute Toxicity Anti-hCD38/Daratumumab. Acute toxicity (7 and 21 d) was studied after injection of 185, 277. 5, or 370 kBq of 212 Pb-daratumumab in Rag2 2/2 gC 2/2 healthy mice. For these 3 activity levels, no effect was observed on survival (Table  1 ), body weight, blood cell count, or biochemical doses (data not shown), consistent with daratumumab's lack of binding to mCD38. For that reason, toxicity was thus also studied with anti-mCD38. Anti-mCD38. The acute toxicity of 185 and 370 kBq of 212 Pbanti-mCD38 was studied in C57BL/6 and in Rag2 2/2 gC 2/2 healthy mice. Animal behavior and body weight were monitored during 21 d. For both strains, a significant body weight loss (P , 0. 01) was observed after 185 kBq of 212 Pb-anti-mCD38 compared with PBS (Fig. 4 ; Supplemental Fig.
CONCLUSIONS In this study, we report that the combined use of iron chelators, some of which are FDA-approved drugs, and Met analogs synergistically inhibits proliferation of pancreatic and breast cancer cells. Iron chelators previously have been shown to inhibit cancer cell proliferation when combined with conventional cytotoxic chemotherapeutics  [7, 39, 59]. OXPHOS is emerging as an important target in cancer therapy  [60]. The recent data suggest that iron chelators could exacerbate the antiproliferative effect of OXPHOS inhibitors in cancer cells. Here, we show that iron chelators potently sensitize and exacerbate the antiproliferative effects of relatively nontoxic mitochondria-targeted drugs in cancer cells. Although the exact molecular mechanism of action has not been determined, it is conceivable that multiple cellular targets and several mechanisms (e.
Methods The ICCs have been estimated from the data that was generated in the Targeted Malaria Elimination study (TME) with mass drug administrations (MDA) on falciparum malaria in South-East Asia  [12]. Following vector control activities, community-based case management and intensive community engagement, restricted randomization was conducted within village pairs to select 8 villages to receive early MDA and 8 villages as controls. After 12 months the control villages received deferred MDA. The MDA comprised 3 monthly rounds of 3 daily doses of dihydroartemisinin-piperaquine and, except in Cambodia, a single low dose of primaquine. Cross-sectional surveys of the entire population of each village at quarterly intervals using ultrasensitive quantitative PCR (uPCR) were used to detect Plasmodium infections.
Definitions of outcomes Defining a new P. vivax infection from longitudinally collected data is more complicated than P. falciparum as P. vivax infections recur frequently. Recurrences of P. vivax for trials designed around prevalence and incidence of clinical outcomes. Adequately powered trials are critical to estimate the benefit of interventions to malaria in a reliable and reproducible fashion. Trial registration: ClinicalTrials. govNCT01872702. Registered 7 June 2013. Retrospectively registered. https ://clini caltr ials. gov/ct2/show/NCT01 87270 2 Keywords: ICC, Malaria, Prevalence, Incidence, Cluster randomized trial, Sample size, P. falciparum, P. vivax, Bootstrapping infections after treatment of the blood stage infection can be due to recrudescence, relapse or reinfection. The ICCs are based on baseline prevalence of P.
Statistical methodology The outcomes of interest for the ICC estimation are the prevalence and the incidence of P. falciparum and P. vivax infections. A logistic regression model is the most relevant model for prevalence while the Poison model is the most natural model when modeling incidence as the outcome of interest. The basic ICC formula presented above ( ρ = ) refers to a case where two-level hierarchical data are of interest. In practice, often several levels of clustering are available and of interest. The hierarchical structure of the data in the TME study included 4 levels: longitudinal data on infection status (level 1) collected repeatedly for each individual (level 2) who belonged to a village (level 3) which was located in a country (level 4). However, country specific ICCs were estimated because there was considerable heterogeneity in baseline P.
The calculation of ICC as a postestimation estimate from software is provided using the latent variable approach for logistic model. In the logistic model, the underlying logistic error distribution has a constant variance π 2 3 which was used as residual variance when calculating ICC from a logistic model and then the latent ICC is given by ICC = σ 2 α σ 2 α + π 2 3 , where σ 2 α is the between cluster variance for the binary outcome. Goldstein et al. [11]  suggested that the latent variable approach to estimate the ICC is only appropriate when the binary outcome can be an underlying continuous latent variable. However, this is the version of ICC that is readily obtained in most software including Stata. In a Poisson model, the error variance is not constant and depends on the covariates included in the model.
The simulation procedures are detailed in Austin et al. [8]. The simulation-based algorithm for both prevalence and incidence proceeds as follows: 1. Fit a multilevel logistic or Poisson model to an existing dataset. If the dataset is not available, one can use desired parameters estimated from previous studies to generate a dataset that mimics the original study data. 2. Simulate a large number say, M, cluster-level random effects ( α m , m = 1,. , M) from a mixed effect model distribution obtained from the model fitted in step 1. Typically M = 1000; 5000; or 10, 000 simulations but higher numbers of M are also possible. (5000 simulations or more may take days, weeks or months depending on the data and the fitted model. E V (Y m ) = 1 M M m=1 V (Y m ) 8. Thus, the between cluster variance is V Ê(Y m ) while the total variance is V Ê(Y m ) + 1 M M m=1 V (Y m ).
Acknowledgements We thank first and foremost the study communities that kindly agreed to participate in this project. We thank the members of the trials steering committee for their support:  Prof. David Lalloo  (Chair,  Liverpool School of Tropical Medicine, UK) ,  Prof. Decha Tangseefa  ( Thammasat University, Bangkok, Thailand ),  Prof. Mike Parker  ( Ethox Centre at the University of Oxford, UK ), and  Phan Kim Son  (lay member,  Ho Chi Minh City, Vietnam ). We thank the members of the data safety monitoring board- Prof. Ric Price  ( Menzies School of Health Research, Darwin, Australia ),  Prof. Sarah Walker  ( Clinical Trials Unit, Medical Research Council, UK ), and  Prof. Amir Hossain  ( Chittagong Medical College, Bangladesh )-and we thank the medical monitor,  Prof. Susanna J. Dunachie  ( University of Oxford, UK ).
We evaluated the variants using the Alamut Visual 2. 11 software (Interactive Biosoftware, Rouen, France). www. annlabmed. org https://doi. org/10. 3343/alm. 2019. 39. 5. 503 We identified a heterozygous nonsense variant in exon 34 of CHD7: NM_017780. 3:c. 7234G > T (p. Glu2412Ter; Fig. 1A ). We confirmed this finding by Sanger sequencing of exon 34, using PCR primers CHD7-F 5´-GCCAGCCCATATAGCAGTAC-3´ and CHD7-R 5´-AACACAGCCCAGCATCGTGA-3´. This variant was confirmed as de novo as we did not detect it in the DNA of either of his parents (Fig. 1B ). As this novel variant has not been reported before, we deposited it in the CHD7 database (https:// molgenis51. gcc. rug. nl/, last accessed: October 02, 2018). According to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology Standards and Guidelines for the Interpretation of Sequence Variants  [7] , this variant is classified as pathogenic: PVS1 (very strong evidence of pathogenicity because it is a null variant), PS2 (de novo, both paternity and maternity confirmed), PM2 (variant absent in control population), PP3 (deleterious effect confirmed by multiple computational evidence).
2011 ) and more recently genomic prediction  (Cooper et al. 2014; Messina et al. 2018). Here we use the APSIM (Agricultural Production systems SIMulator) modelling platform  (Holzworth et al. 2014)  as a scaffold to produce a quantitative synthesis of maize reproductive biology. APSIM contains crop, soil, management and weather modules structured around an interface engine that is designed to manage the connections among process modules, while performing the mathematical computations for the specified system simulation. The process modules encapsulate the biological knowledge for different crops and soil and atmospheric processes. Recent development of the plant module for sorghum  (Hammer et al. 2010)  facilitates the use of this model for genetic studies. The maize module in APSIM has been updated recently in a similar manner  (Soufizadeh et al.
Numerical verification In order to verify the coupling formulation presented in Section 3 and to evaluate the performance of the proposed hybrid LEE-LPE numerical model, two three-dimensional test cases are examined. The first one considers the propagation of a set of acoustic plane waves in a uniform mean flow while the second one focuses on the sound scattering through a strongly sheared non-isothermal flow. All the computations reported hereafter were performed on a Dell Poweredge R730XD equipped with two Intel Xeon E5-2667 CPUs with 16 cores running at 3. 20 GHz and with 384 GB of memory. The unstructured meshes are prepared using the open source mesh generator Gmsh  [29]. The matrices are assembled using an in-house adaptive p-FEM research code developed in Matlab (the assembly timings are therefore not reported).
Acknowledgments The author gratefully acknowledge the European Commission (EC) for its support through the FP7-PEOPLE-ITN-2011 FLOWAIRS project (Silent Air Flows in transport, buildings and power generation, www. flowairs. eu, grant 289352) and the CRANE project (Community and Ramp Aircraft NoisE, www. crane-eid. eu, grant 606844). The authors would also like to thank Dr. Brian Tester for providing the TURNEX exhaust geometry and mean flow data and acknowledge Dr. Michael Williamschen for suggesting the boundary layer truncation approach.
Flow Cytometry Analysis Tumor, stromal vascular fraction, spleen and tumor-draining lymph nodes were used for flow cytometry analysis. Before labeling, cells were incubated in Fc-blocking (CD16/32 antibody). Cells were labeled with fluorescent antibodies against CD3, CD4, CD8, CD44, CD62L, PD-1, CD11b, CD11c, F4/80, Granzyme B (intracellular staining after fixation and permeabilization). Antibodies were purchased from (Life Technologies, Villebon-sur-Yvette, France), Biolegend (San Diego, CA, USA) and BD Biosciences (Le Pont-De-Claix, France). All data were acquired on a FACS FORTESSA 3 lasers flow cytometer (Becton-Dickinson, Le Pont-De-Claix, France) and analyzed with FlowJo software (Tree Star, Ashland, OR, USA).
Vitamin D Promotes CD8+ T Cell Tumor Infiltration Numerous studies and more recent pre-clinical or clinical data have highlighted the important role of cytotoxic T cells in breast cancer, especially with respect to immune-checkpoint immunotherapies. Therefore, we wondered whether VD had an impact on CD8+ T cells in our model. After tumor dissociation, we performed immunomonitoring to study the different immune populations. We observed a significant increase of tumor infiltrating CD8+ T cell frequency (Figure  2A ). We next studied T cells subsets. We clearly saw a decrease in the naïve T cells (CD62L+) population in TIL CD8+ cells when VD was present. This observation is correlated with an increase of CD44+ T cell subsets (central and effector memory cells; Figure  2B ). To further investigate the activation status of the CD8+ T cells that infiltrated the tumor, we assessed the cytotoxic T cell phenotype by using a specific marker, Granzyme B, and for activated T cell phenotype, PD-1.
ACKNOWLEDGMENTS The authors wish to thank  Hervé Luche  ( Ciphe, Marseille ),  Jean-François Landrier  ( C2VN ,  Marseille ),  Magali Irla  ( CIML, Marseille ) for advice and  Valérie Ferrier-Depraetere  ( Institut Paoli-Calmettes, CRCM ) for critical reading of the manuscript. EK, SOM, and JAN (équipe NACRe # 39 ) thank the  NACRe (Réseau National Alimentation Cancer Recherche) Network  for helpful discussions. We thank  Canceropôle PACA  for continued support the development of the TrGET preclinical assay platform. We are grateful to the  CRCM animal facility  for taking care of the mouse strain colonies and the  CRCM  cytometry platform for FACS analysis.
In addition, the DNA of D. cetonica was subjected to shotgun genome sequencing and used for the BLAST analyses (for details see below). Only hits were considered with amino acid sequence identities ≥37% and BLAST Expect value (E) scores <2e -37 from organisms that had been reported to anaerobically grow with at least one mono-and homo-cyclic aromatic compound. BLAST analyses identified putative genes encoding the subunits of class II BCR genes exclusively in strictly anaerobic Deltaproteobacteria or Firmicutes comprising 13 sulfatereducing bacteria, five Fe(III)-respiring Geobacter species and three fermenting syntrophic bacteria (Fig. 2 , for a complete list of accession numbers, identity  [%] , and E-value scores see Table  S1 ). In most cases, genes with high similarities to all eight structural genes of the class II BCR from G.
Desulfosarcina cetonica genome sequence Desulfosarcina cetonica genome shotgun sequencing resulted in a 7 022 711 bp assembly, including a main 6 568 036 bp scaffold despite significant repeat content (a previous publicly available assembly of the related strain JCM 12296 (ASM131184v1) was fragmented in more than 500 contigs with a contig N50 value of only 28 kb). The genome completion and contamination statistics were 99. 4% (1 marker out of 247 missing) and 2. 1% respectively, as estimated using the CheckM toolkit on the basis of marker genes occurrence patterns  (Parks et al. , 2015). The 7914 coding sequences were extensively annotated in the MicroScope comparative genomics platform  (Medigue et al. , 2017)  and made available for community expertise. The genome sequence is accessible at EBI/NCBI/DDBJ under study identifier PRJEB32445 and sequence accession numbers CABEFZ010000001-CABEFZ010000097.
daltonii and S. gentianae two adjacent bamB genes were identified. The colours refer to functional modules: red, active site BamBC; green, electron-bifurcating BamDE; blue, adaptor module BamF connecting BamDE to electron input/output modules; yellow, NAD(P) + binding electron output module (Fig. 1 ). The accession numbers of all genes are presented in Table  S1. [Color figure can be viewed at wileyonlinelibrary. com] The dependence on high phosphate concentrations has also been observed for electron-bifurcating hydrogenase/heterodisulfide reductase complexes from methanogens  (Setzke et al. , 1994). Using DEAE anion exchange chromatography and sizeexclusion chromatography a 17. 3-fold enrichment of DCO activity from extracts of D. cetonica cells grown with benzoate and sulfate was achieved with a yield of 9% (Table  1 ).
The data were collected from three independent experiments. Total cell numbers (n) used for statistical analysis are indicated for each condition. E Knockdown of the indicated proteins by siRNA in Drp1/OPA1 DKO 293T cells was confirmed by Western blotting analysis. Source data are available online for this figure. shifting the balance of mitochondrial dynamics toward fission that may favor mitochondrial fission. Together, these data indicate that Fis1 can regulate mitochondrial dynamics via different mechanisms directly or indirectly contributing to mitochondrial fragmentation. Molecular mechanisms mediating mitochondrial fission have also become more complex in mammals. Although Drp1-driven mitochondrial division represents the canonical mitochondrial fission machinery, emerging evidence suggests the existence of Drp1-independent fission mechanisms  (Ishihara et al, 2009; Roy et al, 2016; Yamashita et al, 2016) , and additional GTPases, for example, Dyn2 and lysosome-associated Rab7, have been reported to be involved in mitochondrial fission in mammals  (Lee et al, 2016; Wong et al, 2018).
1. Site-directed mutagenesis creating Myc-hFis1 K149/151A , Mfn2 K109A -Myc, and untagged Drp1 Q34A was performed using the QuikChange Lightning Multi Site-Directed Mutagenesis Kit (Agilent). All constructs were verified by sequencing. Generation of Drp1 À/À , hFis1 À/À , and Drp1/OPA1 DKO cell lines Drp1 À/À , hFis1 À/À , and Drp1/OPA1 DKO cell lines were established using the CRISPR/Cas9 gene-editing system  (Ran et al, 2013). The CRISPR/Cas9 vector (Addgene plasmid # 48139) containing a Cas9 nuclease expression cassette and guide RNA cloning cassette was used in this experiment. The Drp1 (Appendix Figs S1A and S2A), hFis1  (Appendix Fig S3A), and OPA1 (Appendix Fig S4A)  guide RNAs were designed using the online CHOPCHOP software (https:// chopchop. rc. fas. harvard. edu/index. php). The experiments were performed as previously described  (Yu et al, 2017).
Acknowledgements The majority of this work was financed by grants from the  Swedish Research Council VR-NT  and additionally by VR-MH and VR-Linné. It was also supported by grants from the  Swedish Cancer Society , the  Cancer Society in Stockholm ,  Karolinska Institutet , and the  Chinese Scholarship Council. We would like to thank the  Knut and Alice Wallenberg Foundation  ( CLICK facility ) for supporting this work and  Dr. Florian Salomons  for helpful advice on live imaging experiments.
Conclusions: We demonstrate that providing targeted therapy to HSCT patients has the potential to identify and treat HSCT patients early and prevent morbidity and mortality due to aggressive nonspecific treatment efforts when rapidly deteriorating. With further validation, this poses a promising alternative to existing supportive care measures. impact on Day +100 survival. In the CUP (N=710) and T-IND (N=1137), VOD/SOS was diagnosed by Baltimore or modified Seattle criteria or biopsy (CUP enrollment also included hemodynamic, ultrasound, or histologic evidence of VOD/SOS). Adults (>18 years) with VOD/SOS post-HSCT who received DF 25 mg/kg/day dosage and had time to dosing data were pooled. The analyses examined (1) initiation of DF before/after Days 1, 2, 3, 4, 7, and 14 after diagnosis (Fisher's exact test) and (  2 ) starting DF on a particular day post-diagnosis: 0, 1, 2, 3, 4, 5, 6, 7, 8À14, and 15 (Cochran-Armitage test for trend).
Study Design and Subjects For this retrospective study, we analyzed data of patients with CF aged greater than or equal to 10 years who had a full CPET meeting prespecified criteria between January 1, 2000, and December 31, 2007, and for whom follow-up information on survival or LTx was available 5 years after CPET. Patients who left their respective CF center earlier than 5 years after CPET without information on LTx or subsequent survival available were not included in the analysis. Ethical approval was obtained from all respective ethical research committees, if required (see online supplement).
In a second step, we assessed the prognostic value of CPET-derived parameters ( _ VO 2 peak, Wpeak, _ VEpeak/MVVpred, _ VE/ _ VO 2 peak, _ VE/ _ VCO 2 peak, oxygen saturation at peak exercise as measured by pulse oximetry (Sp O 2 peak), and the Δ _ VE/Δ _ VCO 2 slope) in addition to known predictors of survival in CF in multivariable models. Cox proportional hazards regression models were performed including FEV 1 (% predicted), BMI (z-score), age at CPET, sex, and the binary coded variables (yes/no) chronic P. aeruginosa colonization and CFRD. Exocrine pancreatic insufficiency was a predefined covariate to be considered for the statistical model. However, because none of the cases (LTx/death) in our dataset was pancreatic sufficient, we were not able to compute effect estimates and 95% CIs.
Results Data from 10 CF centers from Australia, Europe, and North America were included in this study. A flow chart is shown in Figure  1. A total of 433 of 510 CPETs fulfilled at least one of the four predefined criteria for a maximal effort (i. e. , plateauing of _ VO 2 , or respiratory exchange ratio, heart rate, or _ VE exceeding the previously mentioned thresholds) and were, thus, used for the final analysis. Patients with a nonmaximal effort during CPET (n = 77) were younger (13. 7 6 3. 5 yr; P , 0. 001), had comparable FEV 1 (74. 0 6 21. 7% predicted; P = 0. 868), and showed a trend of lower _ VO 2 peak (77. 3 6 18. 6% predicted; P = 0. 066) compared with the final study population. Of 77 excluded patients, eight (10%) died or had an LTx during the entire follow-up period. We noticed differences in deaths/LTx among study centers (see Table  E3 ) and consequently adjusted all models for study center.
Comparable results for CPET variables were found in non-centeradjusted analyses (see Table  E8 ). In an exploratory analysis aiming to assess the short-term prognostic value of CPET-derived parameters and restricted to 2-year follow-up data (n = 433 patients with  11   E9  and E10 ). Results from these short-term models were similar to complete dataset models (see Tables  E5  and E6 ), except for a significant effect of chronic P. aeruginosa colonization on death or LTx (see Tables  E9  and E10 ). When adjusting the models for sex, age at CPET, FEV 1 (% predicted), BMI (z-score), chronic P. aeruginosa colonization, and CFRD the variables _ VO 2 peak, Wpeak,.