dataesr/scientific-paragraphs-categorization huggingface dataset:
After PBS washes, sections were incubated with biotinylated secondary and ABC kit Vectastain PK-6100 from Vector Labs. DAB (DAKO) was used to visualize the reaction, followed by counterstaining with hematoxylin. After immunostaining, the sections were scanned at 20× magnification at standardized settings using a Nanozoomer (Hamamatsu) by a single investigator who was not informed of the clinical characteristics. The whole slide image was initially acquired in the NDPI File Format and further semiquantitatively analyzed by open-source software QuPath v0. 2. 0-m4  (50). The software was run on a standard offthe-shelf laptop computer. The H-score (or "histo" score) of B7-H4, CD8α, PD-L1, STT3A, AMFR, and phospho-eIF2α (Ser51) staining in tumor cells were evaluated by TMA algorithms. H-score is assigned using the following formula: H-score = 3x% of strongly staining cells + 2x% of moderately staining cells + 1x% of weakly staining cells, giving a score range of 0-300.
Immunofluorescence Cells grown on coverslip were fixed with 4% paraformaldehyde in PBS for 10 minutes. The membrane staining of the indicated proteins was performed without membrane permeabilization by 0. 1% Triton X-100 unless otherwise stated. The indicated antibodies were incubated with cells 4°C overnight. After three washes with PBSB, cells were incubated with Alexa 488 anti-rabbit/594-anti-mouse IgG diluted in PBSB for 1 hour. Cells were washed, mounted with Ultra Cruz DAPI containing mounting medium (Santa Cruz Biotechnology), viewed, and photographed under Nikon A1 (Nikon) with the software of NIS-Elements. The fluorescence of doxorubicin alone was also checked.
Structure Modeling and Docking Analysis Structural models for B7-H4 Ig-like V-type 1 and V-type 2 domains were generated by comparative/homology modeling, searching the Swiss-Model server. Protein-protein docking simulations for AMFR RING domain (PDB: 2LXH) and the modeled B7-H4 structure were performed using the ZDOCK server. Docking poses were ranked on the basis of the ZDOCK 3. 0. 2's scoring function and the top 100 binding poses were clustered using K-means clustering with a cutoff RMSD of 5 Å into five clusters. Docking poses with the highest scores in each cluster were selected to conduct two independent 120-ns unbiased MD simulations to further evaluate the stability of the predicted binding poses. NAMD package and CHARMM36m force field were used in MD simulations.
Statistical Analysis Unless specified, results were expressed as mean ± SEM. Experiments were performed at least twice unless otherwise specified. Statistical analyses were performed using SPSS, GraphPad Prism (version 8, GraphPad Software), or R software (version 3. 3. 3). The significance of the differences in the assays was analyzed by Student t test or one-or two-way ANOVA, followed by Tukey multiple comparisons test. Comparison of survival curves or tumor-free curve was performed using Log-rank (Mantel-Cox) test. Pearson correlation or Spearman correlation was used to study the correlation between two molecules. A value of P < 0. 05 was considered significant.
Acknowledgments We are grateful to  Drs. Wade Harper  and  Jianping Jin  for kindly providing the TAP purification vector. Ziyi Fu participated in part of the plasmid construction and provided experimental help. We appreciate the proteomic core at the  Feinberg School of Medicine Northwestern University  for Mass Spectrometry Analysis and post data analysis (NCI CCSG  P30 CA060553  and  P41 GM108569 ). This work was also supported by the  Northwestern University Pathology Core Facility ,  RHLCCC Flow Cytometry Facility, Image CORE, Animal Resources Facility , and a  Cancer Center Support Grant  ( NCI CA060553 ). We thank all members of the  Wan, Bahar, Kepp, Hung, and Kroemer laboratories  for their helpful discussion. We also acknowledge  Dr. Qiang Hu  at  Roswell Park Comprehensive Cancer Center  for kindly helping with bioinformatics analysis.
We are thankful to  Monopar Therapeutics  for kindly providing us camsirubicin. This work was supported by the  Northwestern University   Zell scholar  fund and part of the effort of  Y. Wan  is covered by  NIH   R01CA202963. Support from the  NIH  grants  P01 DK096990  and  P41 GM103712  is gratefully acknowledged by  I. Bahar. G. Kroemer  is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR)-Projets blancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases ;  AMMICa US23/CNRS UMS3655 ;  Association pour la recherche sur le cancer (ARC) ;  Association "Le Cancer du Sein , Parlons-en!";  Cancéropôle Ile-de-France ;  Chancelerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Project Oncobiome ;  Fondation Carrefour ;  Institut National du Cancer ;  Inserm ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); the  RHU Torino Lumière ; the  Seerave Foundation ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and the  SIRIC Cancer Research and Personalized Medicine (CARPEM).
There were no comparisons made between treatment groups. For efficacy variables, data were reported as observed. Patients who received rescue therapy at week 16 were imputed as nonresponders (NRI) between weeks 16 and 24. As secondary analyses, missing data were imputed using modified NRI (mNRI) for categorical outcomes or modified baseline observation carried forward (mBOCF) for continuous efficacy variables at all other timepoints. Safety analyses were performed on the All Ixekizumab Exposure Safety Population (AIESP; IXE Q4W, IXE Q2W), which included all patients who received at least one dose of ixekizumab at any time during the 156-week study period, with baseline defined as the time of first exposure to ixekizumab. Safety results were summarized as the frequency and incidence rate (IR) per 100 patient-years (PY) of exposure to ixekizumab, using the entire duration of exposure for the treatment period and expressed as a number of unique patients within a particular category of event.
All patients provided written informed consent prior to receiving investigational product or undergoing study procedures. SPIRIT-P2 is registered at ClinicalTrials. gov (NCT02349295). Data Availability. Eli Lilly and Company provides access to all individual participant data collected during the trial, after anonymization, with the exception of pharmacokinetic or genetic data. Data are available to request 6 months after the indication studied has been approved in the US and EU and after primary publication acceptance, whichever is later. No expiration date of data requests is currently set once data are made available. Access is provided after a proposal has been approved by an independent review committee identified for this purpose and after receipt of a signed data sharing agreement. Data and documents, including the study protocol, statistical analysis plan, clinical study report, and blank or annotated case report forms, will be provided in a secure data sharing environment.
In Japan, serosurveys indicated the presence of anti-C. burnetii Ig in 16. 5% of human serum samples collected between 1978 and 1991 (77, 78). Since 1999, 7 to 46 Q fever cases were reported annually (79-82). In the Arabian Peninsula, the presence of C. burnetii in humans was reported in 1968, and a recent serological analysis detected C. burnetii Ig in 35. 2% of patients with pyrexia of undetermined cause (83-85). In Qatar, Q fever data are rare, yet a seroprevalence of 2. 1% was found in US soldiers deployed in this country (86). On the Australian/Oceanian continent, since the first description of Q fever in 1937, the disease has continued to be endemic in Australia (87). Between 1977 and 1994, 202 to 860 cases were reported annually (incidence 3. 11-4. 99 cases/100 kI/y), despite a vaccine is recommended to farmers since 1989 (88-91).
FUNDING This work was supported by the  French Government  under the  Investissements d'avenir' (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  (reference: Méditerranée Infection  10-IAHU-03 ). IO was supported by a grant from the  Caisse Nationale de Sécurité Sociale de Djibouti (Social security, Djibouti). The images are available under a Creative Commons CCBY 3. 0 license.
5-s duty cycle and 4 Hz mass spectometry 1 spectra rate followed by tandem mass spectrometry scans at 8-20 Hz dependent on precursor intensity for the remainder of the cycle. Mass spectometry spectra were acquired between a mass range of 200 and 2,000 m/z. Peptide fragmentation was performed using collision-induced dissociation. Raw files consisting of highresolution tandem mass spectrometry spectra from the Bruker Impact II instrument were processed with MaxQuant (version 1. 5. 8. 3) for feature detection and protein identification using the Andromeda search engine  (Cox et al. , 2011). Extracted peak lists were searched against the UniProtKB/Swiss-Prot Mus musculus database (October 2016) and a separate reverse decoy database to empirically assess the false discovery rate (FDR) using strict trypsin specificity allowing up to two missed cleavages.
Statistics In arthritis experiments, differences in clinical score between the groups were assessed using two-way ANOVA for repeated measurements, with Bonferroni post-test correction to compare differences between the groups. For other experiments, differences between groups were determined using unpaired Student's t tests, as indicated. All analyses were performed using GraphPad Prism version 7 (GraphPad Software). Data are shown as means ± SEM unless stated otherwise. Levels of statistical significance are expressed as P values: *, P < 0. 05, **, P < 0. 01, and ***, P < 0. 001.
Data and materials availability The accession number for the RNASeq reported in this paper is GEO accession no. GSE128909. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  (Vizcaíno et al. , 2016)  with the dataset identifier PXD012794. Online Supplemental material Fig. S1  shows the generation and validation of Gr/fr mice. Fig. S2  shows flow-cytometric analyses of GM-CSF in human NK cells and T cell subsets from RA SF and peripheral blood of healthy donors. Fig. S3  shows induction of CIS in human myeloid cells by GM-CSF stimulation, the myeloid compartment of CIS -/-mice, and prolonged p38 MAPK and SAPK/JNK signaling in CIS -/- neutrophils in response to GM-CSF. Fig. S4  shows an overview of the RNASeq analysis of WT and CIS -/-neutrophils at various times after GM-CSF stimulation.
contains a list of reagents and software used in this study. andMedical Research Council Early Career Fellowship (1088703),  a National Breast Cancer Foundation Fellowship (PF-15-008), and grant awarded through the Priority-Driven Collaborative Cancer Research Scheme (1120725) and funded by Cure Cancer Australia Foundation with the assistance of Cancer Australia. N. D. Huntington is a National Health and Medical Research Council CDF2 Fellow (1124788), a recipient of a Melanoma Research Grant from the Harry J. Lloyd Charitable Trust, a Melanoma Research Alliance Young Investigator Award, a Tour De Cure research grant, an equipment grant from the Ian Potter Foundation, and a CLIP grant from the Cancer Research Institute. W. S. Alexander is a National Health and Medical Research Council Senior Principal Research Fellow (1058344).
Acknowledgments We thank all the members of the Wicks and  Huntington Laboratories  for discussion, comments, and advice on this project, The  Walter and Eliza Hall Institute of Medical Research Bioservices  for mouse breeding, maintenance, and technical support, and  Jeanette Rientjes  ( Gene Recombineering Facility, Monash University ),  Janelle Lochland ,  Liz Viney , and  Fiona Waters  for assistance in the generation of Csf2 fl/fl mice. We thank  Prof. Marc Pellegrini  (The Walter and  Eliza Hall Institute of Medical Research, Parkville, Australia)  for providing IFN-γ -/- mice and  Prof. Burkhard Becher  ( Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland ) for Csf2rb fl/fl mice. This work is supported by grants from the  National Health and Medical Research Council of Australia  ( 1113577  to  W.
1G1-3  and Video 7 ). Together with locomotion statistics, engorgement data provides a high-level description of the behavioral trajectory. To assess the capability of the biteOscope to characterize the behavior of different species of mosquito, we performed experiments with the two most important vectors of arboviral diseases (Ae. aegypti and Ae. albopictus) and two dominant malaria vectors ( An. stephensi and An. coluzzii, formerly known as Anopheles gambiae M molecular form). Figures 2 and S2 show locomotion and feeding statistics for the four species. All species land readily on the bite substrate and undertake exploratory bouts leading to full engorgement in 18%, 7%, 4%, and 14% of all trajectories and 46%, 22%, 10%, and 31% of all > 10 second trajectories, for Ae. aegypti, Ae. albopictus, An.
Discussion The biteOscope provides an alternative for current methods using human subjects or mice to study mosquito blood feeding. The elimination of the need for a human subject opens new avenues of research, blood feeding studies with pathogen infected mosquitoes, enabling precise surface manipulations and characterization of the associated behavior, and facilitates the use of high-resolution imaging and machine learning based image analysis. Through these innovations, the biteOscope increases experimental throughput and expands the type of experiments that can be performed and measurements that can be made. We used the biteOscope to describe behavioral patterns of 4 medically relevant mosquito species and anticipate that such datasets will provide a useful 'behavioral baseline' for future studies quantifying the effect of a mosquito's physiology on blood feeding behavior.
Computational tools All image processing and downstream analysis code was written in Python 3 and is available from Github (https://github. com/felixhol). Raw images were background subtracted, thresholded, and subjected to a series of morphological operations to yield binary images representing mosquito bodies of which the center of mass was determined using SciPy  (Virtanen et al. , 2019). The Crocker-Grier algorithm  (Crocker and Grier, 1996)  was used to link the obtained coordinates belonging to an individual mosquito in time using trackPy  (Allan et al. , 2016). The obtained tracking data is used to select all images that make up a single behavioral trajectory (e. g. landing, exploration, feeding, and take off) and store cropped image sequences centered on the focal mosquito. Cropped images are used to determine a mosquito's shape at each timepoint of a trajectory to subsequently infer engorgement status by computationally removing all appendages and fitting an active contour model (using OpenCV  (Bradski, 2000) ) to the remaining body shape.
The DeepLabCut framework  (Mathis et al. , 2018)  was used to train a convolutional neural network (ResNet architecture) to detect the most distal part of the 6 legs, the abdominal tip, the center of the abdomen, the head, the tip of the proboscis, and for anophelines the tip of the maxillary palps. Due to their similar appearance, Ae. aegypti and Ae. albopictus can be analyzed using the same network, while a second network was trained for An. stephensi and An. coluzzii. Approximately 350 images were used to train the Aedes dataset, while approximately 300 images were used for the Anopheles dataset yielding an accuracy of 10 pixels (250 micrometer) and 8 pixels micrometers) in a 4. 3 x 4. 3 cm field of view, respectively. Cropped image sequences (described bioRxiv preprint 11 of 18 above) were used for inference.
Acknowledgments We thank  Haripriya Vaidehi Narayanan ,  Shailabh Kumar ,  William Gilpin ,  Hongquan Li ,  Leslie Vosshall ,  Veronica Jove , and all members of the  Prakash and Lambrechts labs  for valuable discussions; and  Catherine Lallemand ,  Sylvain Golba , and  Patricia Baldacci  for help with the rearing of mosquitoes. An. stephensi strain Sda500 and An. coluzzii strain N'Gousso were provided by the  Centre de Production et Infection d'Anopheles of Institut Pasteur; Ae. aegypti strain Liverpool was provided by  Leslie Vosshall  ( Rockefeller University ); Ae. aegypti strain D2S3 (NR-45838) was provided by  Centers for Disease Control and Prevention for distribution by BEI Resources, NIAID, NIH. FJHH was supported by a  Rubicon fellowship for the Netherlands Foundation for Scientific Research , a  Career Award at the Scientific Interface  from the  Burroughs Wellcome Fund , and a  Marie Curie Fellowship  from the  European Union.
L. L. is supported by the  French Agence Nationale de la Recherche  (grants  ANR-16-CE35-0004-01  and  ANR-18-CE35-0003-01 ), and the  French Government 's  Investissement d' Avenir program Laboratoire d ' Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID ). MP was supported by  NIH   DP2-AI124336   New Innovator Award and USAID Challenges: Zika and Future Threats Award.
burnetii genome: Cox2, 5, 6, 18, 20, 22, 37, 51, 56, and 57  (5). We identified a new MST genotype (MST65) by using web-based MST database (http://ifr48. timone. univmrs. fr/MST_Coxiella/mst) (Figure  , panel A ). The patient was given oral doxycycline (100 mg 2×/d) and hydroxychloroquine (200 mg 3×/d) for >24 months  (9). A convalescence-phase serum sample obtained after 6 months of treatment was positive for C. burnetii: phase I IgG titer 1:800, phase I IgM titer 0, and phase II IgG titer 1:800, phase II IgM 0. Our preliminary data show that physicians in Greece are not familiar with Q fever because most of the suspected cases were in patients without bovine contact. A limitation of our study was that culture was not performed because of the absence of a Biosafety Level 3 laboratory. The fact that we did not provide diagnoses of classic, acute Q fever showed that C.
The American continent was the last to be occupied by modern humans, and native populations bear the marks of recent expansions, bottlenecks, natural selection, and population substructure. Here we investigate how this demographic history has shaped genetic variation at the strongly selected HLA loci. In order to disentangle the relative contributions of selection and demography process, we assembled a dataset with genome-wide microsatellites and HLA-A, -B, -C, and -DRB1 typing data for a set of 424 Native American individuals. We find that demographic history explains a sizeable fraction of HLA variation, both within and among populations. A striking feature of HLA variation in the Americas is the existence of alleles which are present in the continent but either absent or very rare elsewhere in the world.
g. , Eskimos and Na-Dene populations)  [3, [7] [8] [9]. Some studies have also detected a Polynesian genetic component in extinct Native South Americans  [10, 11] , as well as an Austromelanesian genetic component in contemporary  [7]  and ancient  [12]  South Americans. Although the hypothesis of direct contribution of these groups by marine routes was proposed a while ago  [13] , bioanthropological studies suggest that at the end of the Pleistocene the paleoamerican populations had greater morphological diversity shared with common ancestors of South Asia and Oceania, and that these characteristics may have been retained in some populations or individuals  [14, 15]. This recent progress in our knowledge regarding Native American history and demography relies on studies with genomic datasets.
Jointly, these factors posed new selective challenges to the populations which reached the continent. Second, the founding of the Americas likely involved an extreme bottleneck, causing a marked reduction in diversity with respect to other world regions  [19, [24] [25] [26] [27] , highlighting the importance of analyzing the relative intensity of selection and drift in shaping extant Native American HLA diversity. Finally, for more than two decades we have known that Native Americans exhibit HLA alleles which are present exclusively in this continent, and are not found in any other region  [28, 29] , raising the question as to whether this pattern is a consequence of selection favoring locally advantageous alleles or a consequence of intense genetic drift. In order to disentangle the relative contributions of demography and selection on specific loci of interest it is essential to have a large dataset, which includes markers that document the demographic history of the sampled populations.
In the present study we take such an approach, using HLA sequence data and a genome-wide microsatellites dataset from a panel of 424 Native American individuals. We use the microsatellites as a demographic control and evaluate the differences between variation at these neutral markers and that of the HLA genes. Specifically, we examine how well the putatively neutral microsatellites predict variation and differentiation at the strongly selected HLA genes. We also systematically survey the geographic distribution of HLA alleles, with particular emphasis on those that are restricted to the American continent, so as to investigate the relative importance of selection and drift on patterns of geographic variation.
HLA typing To identify the class I HLA-A,-B,-C alleles carried by each individual, PCR primers were used to amplify each locus as previously described  [30]. HLA typing was carried out on DNA samples previously extracted for the study of Wang et al. [18]. Applied Biosystems Big Dye terminator chemistry and sequencing primers were used to obtain the sequences of both strands of exons 2 and 3. Exon 2 of the class II HLA-DRB1 alleles were amplified and sequenced using the AlleleSEQR class II kit (Abbott Molecular Inc, Des Plaines, IL). Additional in-house PCR and sequencing primers were added when needed to obtain resolution. Reaction products were identified with Applied Biosystems 3730xl DNA analyzer (PE Applied Biosystems, Foster City, CA) and sequence interpretation used Assign software (Conexio Genomics, Applecross, Western Australia).
For subjects with multiple possible class I genotypes, either allele specific sequencing primers or allele specific PCR amplification were used to identify the specific allele combination present. Interpretation of alternative genotypes (i. e. allele pairs) used the IPD-IMGT/HLA Database (database release 2. 19. 0, October 2007). In-house primer sequences are available at https://genevol. ib. usp. br/software/. Alternative alleles identical in exons 2 and 3 (class I) or exon 2 (DRB1) were not resolved. Unresolved alleles of this type that differ in two-field names, i. e. , encode allelic products that vary in amino acid sequence outside of the antigen binding site, are indicated by the use of a "g" following the name of the lowest numbered allele in the group. For example, A � 02:01:01G includes alleles A � 02:01:01:01, A � 02:09, A � 02:43N, A � 02:66 as well as synonymous alleles A � 02:01:01:02L and A � 02:01:08.