dataesr/scientific-paragraphs-categorization huggingface dataset:
We describe here the identification of two novel HLA-C alleles, now named HLA-C*08:258 and HLA-C*12:374, identified in two recipients waiting for renal transplantation. For the first HLA typing, full-length amplification of the HLA-C gene, sequencing libraries and indexed libraries were generated using the NGSgo kit (GendX, Utrecht, Netherlands) on Illumina Miseq (San Diego, CA). For further confirmation of the new alleles, we sequenced subsequently using NanoTYPE™ based on the Nanopore technology  1   The nucleotide sequences of the exons 1 to 8 of these new alleles were submitted to the GenBank database (Accession No. OP676255 and OP747514) and to the IPD-IMGT/HLA Database 2 (Submission No. HWS10063198 and HWS10063358). The names have been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System 3 in November 2022.
Safety analyses included all randomly assigned patients who received at least one dose of study treatment. This study is registered with ClinicalTrials. gov, NCT03529110. Findings Between July 20, 2018, and June 23, 2020, 699 patients were screened for eligibility, 524 of whom were enrolled and randomly assigned to receive trastuzumab deruxtecan (n=261) or trastuzumab emtansine (n=263). Median duration of study follow-up was 28•4 months (IQR 22•1-32•9) with trastuzumab deruxtecan and 26•5 months (14•5-31•3) with trastuzumab emtansine. Median progression-free survival by blinded independent central review was 28•8 months (95% CI 22•4-37•9) with trastuzumab deruxtecan and 6•8 months (5•6-8•2) with trastuzumab emtansine (hazard ratio [HR] 0•33 [95% CI 0•26-0•43]; nominal p<0•0001). Median overall survival was not reached (95% CI 40•5 months-not estimable), with 72 (28%) overall survival events, in the trastuzumab deruxtecan group and was not reached (34•0 months-not estimable), with 97 (37%) overall survival events, in the trastuzumab emtansine group (HR 0•64 [95% CI 0•47-0•87]; p=0•0037).
Safety analyses were performed on the safety analysis set, which included all randomly assigned patients who received at least one dose of study treatment, either trastuzumab deruxtecan or trastuzumab emtansine. If there was no death reported for a patient before the data cutoff analysis, overall survival was censored at the last contact date at which the patient was known to be alive. Overall survival was compared between the two treatment groups using a stratified log-rank test. The treatment effect HR and its 95% CIs were estimated using a Cox proportional hazards regression model stratified by the randomisation stratification factors as recorded by the interactive web or voice response system. The median survival time and the two-sided 95% CIs for the median were calculated using the Brookmeyer and Crowley method for each treatment group.
The prespecified second interim analysis of overall survival was planned with 153 events (information fraction of 61%), with a p value boundary to reach statistical significance of 0•008. The p value was recalculated based on the actual overall survival events at the data cutoff. Progression-free survival analyses were performed using the same approach as described for overall survival. The interim analysis of progression-free survival was performed based on the data cutoff of May 21, 2021, with 245 blinded independent central review events, and was reported previously. 14 The statistical analysis plan was included in the protocol and has been previously published. 14 ecause progression-free survival demonstrated statistical significance at the progressionfree survival interim analysis, overall survival was compared between trastuzumab deruxtecan and trastuzumab emtansine for the May 21, 2021, data cutoff, but the p value and α level for the expression was not statistically significant (the p value efficacy boundary was 0•0003 and the actual p value observed was 0•0072).
14 At the second overall survival interim analysis data cutoff of July 25, 2022, 169 overall survival events were observed and the p value boundary for statistical significance was 0•013. Given that the primary endpoint of progression-free survival by blinded indepen dent central review was met at the interim analysis for progression-free survival, no further hypothesis testing on the primary endpoint was required; the p values for progression-free survival and other efficacy endpoints, except for overall survival, at this second interim analysis for overall survival were nominal. Sample size calculation was conducted using EAST version 6. 4 software and all statistical analyses were performed using SAS version 9. 3 or higher. This study is registered with ClinicalTrials. gov, NCT03529110.
Results Between July 20, 2018, and June 23, 2020, 699 patients were screened for eligibility, 524 of whom were enrolled and randomly assigned to receive trastuzumab deruxtecan (n=261) or trastuzumab emtansine (n=263; appendix p 14). As of the data cutoff (July 25, 2022), 75 (29%) patients in the trastuzumab deruxtecan group and 18 (7%) patients in the trastuzumab emtansine group remained on treatment. Demographic and baseline characteristics were similar between the two treatment groups (table  1 ). Median duration of study follow-up was 28•4 months (IQR  A consistent overall survival benefit in favour of trastuzumab deruxtecan over trastuzumab emtansine was observed across the subgroups analysed (figure  2 ). A consistent progression-free survival benefit continued to be observed with trastuzumab deruxtecan over trastuzumab emtansine across the subgroups analysed (appendix p 15).
Exposure-adjusted incidence rates were measured to account for differences in treatment duration between trastuzumab deruxtecan and trastuzumab emtansine and were lower in the trastuzumab deruxtecan group than the trastuzumab emtansine group, consistent with the earlier safety update analysis of DESTINY-Breast03. 25 These results reinforce the established favourable benefit-risk profile of trastuzumab deruxtecan over trastuzumab emtansine. Interstitial lung disease or pneumonitis remains an identified risk in patients treated with trastuzumab deruxtecan. With longer follow-up and increased treatment exposure, the rate of adjudicated drug-related interstitial lung disease or pneumonitis cases with trastuzumab deruxtecan increased from 11% in the interim analysis of progression-free survival (data cutoff May 21, 2021) to 15% in the present second overall survival interim analysis (data cutoff July 25, 2022); however, all new cases were low grade (grade 1 or 2).
Acknowledgments We thank the patients who participated in this trial, as well as their families and caregivers; the staff and investigators at all the trial sites; and  Cindy M Rigby ,  Rachel Hood , and  Andre Wang , of  ApotheCom , for editorial and medical writing assistance, which was funded by  Daiichi Sankyo. The trial was funded by  Daiichi Sankyo  and  AstraZeneca.
Acknowledgements The authors would like to thank the  ANR (SPIROQUEST ,  n°19-CE05-0024  and  EVOLUTION n°22-CE07-0041 ) for financial support of this project, the  Agence de l'Environnement et de la Maitrise de l'Energie (ADEME)  for PhD grand (CB, EcoElec Project). We would like to thank  GENCI  ( N°AD0100805032R1 ) and the  Inorganic Theoretical Chemistry (CTI) Team at ISCR  for generous allocation of computational time,  Bruno Lafitte  (ADEME), and the  CRMPO (Rennes)  for mass analysis. The authors thank  Dr J. F. Bergamini  ( Rennes ) for the TOC material.
Upon infection of a new cell, the envelope is stripped off, and the nucleocapsid delivers the rcDNA to the cell nucleus for conversion into a stable covalently closed circular (ccc) DNA episomal minichromosome from which new viral transcripts, including pgRNA, are produced, completing the cycle. Hepadnaviruses were long known to occur in a few mammals (genus orthohepadnavirus; type member HBV) and birds (genus avihepadnavirus; type member duck HBV [DHBV]), with hepatocytes as primary replication site. However, bioinformatic screens of genomic and transcriptomic databases  5  revealed the presence of HBV-like virus sequences in numerous other vertebrates, including amphibians, reptiles, and fishes (belonging to the genera herpetohepadnavirus, metahepadnavirus, and parahepadnavirus)  1, 6, 7  , all sharing a common genome organization with ortho-and avihepadnaviruses.
Sample preparation of African cichlid nackednavirus capsid protein ACNDV Cp was expressed in E. coli and spontaneously assembled particles were purified by sedimentation in sucrose gradients in TRIS-HCl buffer at pH 7. 5 similar as in refs. 34, 45 , and ref. 19. To assess pH dependent alterations, purified ACNDV capsids were dialyzed against acetate buffer, pH 5. 5. All samples were sedimented into magic-angle spinning (MAS) rotors  35, 36  using an ultracentrifuge and home-built filling-tools  63. For expression of the ACNDV Cp, the HBV Cp coding sequence in plasmid pRSF-T7-HBcopt  18  was replaced by a synthetic DNA string encoding the 174 aa ACNDV Cp sequence (Genbank ID: GenBank: AZP02123. 1) with an extra glycine residue immediately following the start Met residue (for cloning reasons) using an E.
Most homogeneous fractions were selected based on SDS-PAGE and negative stain transmission electron microscopy (TEM) as shown in Fig. S1a-c. Selected fractions were concentrated in a 100 kDa cutoff concentrator and washed with HEPES buffer (50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), 5 mM DTT, pH adjusted to 7. 5) until the sucrose content was only about 0. 3%. Filled NMR rotors were stored at 4 °C. Resulting data is shown in Figs. S1a-c  and S3a. (b) Low pH screening: As additional purification step size exclusion chromatography was performed with a Sephacryl S400 resin. The column was equilibrated with TRIS lysis buffer (50 mM TRIS, 50 mM NaCl, 5 mM EDTA, 5 mM DTT, pH adjusted to 7. 5). Fractions of the sucrose gradient purification containing protein were concentrated in a 100 kDa cutoff concentrator to a volume of 6-7 mL.
The assignment is deposited in the BMRB databank under accession number 51506. Proton-detected spectra of fully protonated 13 C-15 N labeled ACNDV capsid protein at pH 7. 5 were recorded in NMR rotors with 0. 7 mm diameter at 100 kHz MAS on an Avance III Bruker wide-bore 850 MHz spectrometer. The sample temperature was determined to 19-22 °C63 and chemical shifts were referenced to DSS. For the assignment of NH amide and HA proton resonances, 2D spectra (hNH, hCH) and 3D spectra (hNCAH, hCANH, hCAcoNH, hNcoCAH) were recorded (Tables S1, S2, and S3)  67. The already known resonances from carbon-detection experiments were used for verification of the assignment and for identification of proton resonances where the connection in the sequence was difficult to assess.
Cryo-EM data collection and image processing Vitrified grids of the capsid at pH 7. 5 were imaged with a Titan Krios electron microscope (Thermo Scientific) operated at 300 kV using a K2 direct electron detector (Gatan) in counting mode behind an energy filter. The energy filter slit width was set to 20 eV. 8999 movies were recorded with the EPU data acquisition software at a defocus ranging between -0. 5 μm and -2 μm at 130'000x magnification (pixel size 1. 07 Å/pixel) with an electron dose of 76 e -/Å 2 fractionated over 40 frames. After drift correction and dose weighting with MotionCorr 68 , particles were picked with crYOLO 1. 7. 6  69  and defocus was determined with Gctf  70. Micrographs of the capsid at pH 7. 5 were manually selected based on the quality of the power spectrum and micrographs recorded with defocus values smaller than -0.
5 μm were removed. 375,654 particles were selected with crYOLO. Further image processing was carried out in Relion 3. 1  71. Particles were extracted without binning (box size: 432 pixels, pixel size: 1. 084 Å/pixel) and subjected to 3D classification into 10 classes without application of symmetry, using an initial model generated in Relion. One class (138,158 particles) displayed the features expected of a viral capsid with icosahedral symmetry. This class was subjected to a second round of 3D classification into 10 classes with application of icosahedral symmetry, with 81,855 particles in the selected class. The second 3D classification was performed over 100 iterations and particles that changed class more than 40 times were excluded from the dataset, resulting in 70,868 particles. Refinement with application of icosahedral symmetry resulted in a map with an overall resolution of 3.
77 s and fractionated over 30 frames. A total number of 1515 micrographs were acquired for sample 1 (pH 7. 5), 4023 for sample 2 (pH 5. 5), and 2384 for sample 3 (pH shift from pH 5. 5 back to pH 7. 5). Image processing was carried out in CryoSPARC  72. Micrograph movies were drift corrected and averaged using cryoSPARC patch motion correction with default settings. Defocus was determined using cryoSPARC Patch CTF estimation with default settings. Capsids were selected manually from 10 micrographs of sample 1 and the resulting particle positions were used to train a neural network with Topaz 73 in order to pick particles with high accuracy. All capsids were selected independently of the shape and size during manual picking to achieve a representative image on the particle population. The same neural network model was used for picking particles from all three datasets.
Picking identified 146,077 particles in dataset 1, 346,287 particles in dataset 2, and 257,179 particles in dataset 3. Particles were extracted with binning (box size 512 pixels to 96 pixels) and subjected to 2D classification into 50 classes in cryoSPARC. After a first round of classification, classes that were empty or showed only fragments of a capsid molecule were removed. The relatively low number of around 10% of all particles removed during the first round of 2D classifications (Sample 1: 10,709 particles rejected, 135,368 particles accepted; sample 2: 38,274 particles rejected, 308,013 particles accepted; sample 3: 24,036 particles rejected, 233,143 particles accepted) attests to the high quality of neural network picking. The remaining classes were subjected to a second 2D classification into 50 classes The particle acceptance rate for the samples 1 and 3 (both at pH7.
For the refinement procedure a cryo-EM density map of the entire capsid (Fig. 2e , 3. 9 Å resolution, FSC cutoff at 0. 143) was used. Validation parameters from MolProbity  [81] [82] [83]  for the final models at pH 7. 5 and at pH 5. 5 are given in Table  S4  and details of the fitted models are shown in Fig. S9. N-terminal and spike residues, as well as hydrogen atoms were only removed after structure refinement. The structures were submitted to the Protein Data Bank. In this publication, protein structures are visualized with UCSF ChimeraX  80.
Acknowledgements We thank  Riccardo Cadalbert  and  Simon Widler  for their effort in teaching cell culture handling and for their great support with protein expression. Simona Rodighiero ,  Sebastian Tacke , and  Cecilia Bebeacua , former or present members of ScopeM at ETH Zurich, were involved in measurements with the transmission electron microscopes, for which we are very grateful. Further, we would like to thank  Pavel Afanasyev , member of the Cryo-EM Knowledge hub, at  ETH Zurich and Miroslav Peterek of ScopeM at ETH Zurich  for their support in cryo-EM data analysis. We also thank  Marc André Leibundgut  from the  Institute of Molecular Biology and Biophysics at ETH Zurich  for his help in structure refinement. This research was supported by an  ERC Advanced Grant  (grant number  741863 , Faster to B.
H. M. ) and by the  Swiss National Science Foundation  (grant number  200020_159707  and  200020_188711  to B. H. M. ) and by the  French Agence Nationale de Recherche  ( ANR-14-CE09-0024B ) and the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) within the  Université de Lyon program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) to A. B.
Data availability Chemical shifts of  13  C,  15  N, and 1 H atoms of ACNDV Cp at pH 7. 5 were deposited in the Biological Magnetic Resonance Data Bank under accession number 51506. Cryo-EM maps of ACNDV capsids at pH 7. 5 and at pH 5. 5 were deposited in the Electron Microscopy Data Bank under accession numbers EMD-15295 (pH 7. 5) and EMD-16371 (pH 5. 5) and molecular capsid structures were submitted to the Protein Data Bank under accession numbers 8AAC (pH 7. 5) and 8C0O, (pH 5. 5). Two-and 3-dimensional NMR spectra are available upon request. Source data are provided with this paper.
9 3. 4-6. 3 3. 4-6. 7 3. 4-6. 4 Refinement Initial model used de novo de novo (PDB code) Model resolution (Å) 3. 9 4. 2 a FSC threshold 0. 5 0. 5 Map sharpening B factor (Å 2 ) -190 -280 Model composition Non-hydrogen atoms 198,540 (1103 per chain) 197,460 (1097 per chain) Protein residues 137 per chain 136 per chain B factors (Å 2 ) Protein 68. 73 55. 67 R. m. s. deviations Bond lengths (Å) 0. 008 0. 005 Bond angles (°) 1. 06 1. 144 Validation MolProbity score 0. 89 1. 24 Clashscore 1. 47 2. 04 Poor rotamers (%) 0 0. 9 Ramachandran plot Favored (%) 98. 02 96. 05 Allowed (%) 1. 98 3. 95 Disallowed (%) 0 0 aThe model resolution is given for the stable core (residues 2-65, 75-135 chain A, 2-65, 75-134 chain B, 2-64, 76-132 chain C). When flexible parts at the tip of the spikes and at the N-termini are included in the overall map-model resolution estimate is 7.
1 ; Supplementary Table  3 ). In order to prioritize potential candidate genes in the GWAS dataset, we first used a gene-based VEGAS to conduct analysis of the GWAS data. A total of 1180 genes were identified in the top 5% of the VEGAS-prioritized genes, including three genes (APCDD1, DSP, and PTP4A3) shared with the 41 protein-coding DE gene list. We then employed GWAB to boost the GWAS results and obtain prioritized genes utilizing network information  [31]. GWAB reprioritizes GWAS genes by boosting "not quite significant" genes that are near other more significant genes in the network. This method reprioritizes genes but does not provide an association statistic for each gene, nor does it provide weights for edges indicating the functional interaction of genes. We identified a total of 1119 genes in the top 5% of the GWAB-prioritized gene list, including four genes (FLNC, LEF1, MBP, and PRKD1) that were shared with the DE gene list (n = 41).
The top 5% prioritized genes obtained by VEGAS and GWAB are listed in Supplementary Table  9. We performed network propagation of 41 DE genes using the GIANT brain interactome database  [37]  to construct a 500proximal gene network with 25 020 edges (Fig. 2a-c ). We further boosted the network to a 2000-proximal gene network with 157 688 edges (Fig. 2d ). Out of 41 DE genes, 34 were present in both networks. For the GWAB-prioritized genes, 73 and 241 were detected in the top 500-and top 2000-gene networks, respectively. For the VEGAS-prioritized genes, 36 and 118 were detected in the top 500-and top 2000-gene networks, respectively. The details and genes included in the two networks are presented in Supplementary Table  10. Next, we examined the significance of the gene overlap between the DE-derived top 500-or top 2000-proximal gene networks and the 1119 GWABprioritized genes.
Also, genetic variation in iPSCs seems to be mainly driven by genetic background in individual donors rather by the induced reprogramming process  [42] [43] [44]. Moreover, though incorporating network information substantially increased the power, this must be interpreted in the light of the limitations of the datasets which may not be complete. Extended multi-omics data would provide further support. Lastly, our results are all correlative and the biology not rigorously tested. There are limited reports with which to compare our results. Recently, studies of lithium response in BD have examined gene expression but utilized different tissues or different comparisons (BD vs controls; BD LR vs BD NR; Li-exposure vs non-exposure). The majority of studies utilized lymphocytes  [36, [45] [46] [47] , whose expression pattern is of limited relevance to brain; while few studies utilized either iPSC-derived neurons  [18, 48]  or postmortem brains  [49].
ACKNOWLEDGEMENTS We thank the patients who participated. The study was primarily supported by a grant to JRK from the  NIMH  ( U01 MH92758 ) as part of the  Pharmacogenomics Research Network (PGRN)  and a grant from the  Department of Veterans Affairs. VN was supported by the  UCSD Fellowship in Biological Psychiatry and Neuroscience  ( T32MH018399 ). KMF is supported by a grant from the  National Center for Advancing Translational Sciences , part of the  NIH , as part of support for the  UCSD CTSA  ( UL1TR001442 ). FHG and MCM are supported by  U19MH106434 , part of the  National Cooperative Reprogrammed Cell Research Groups (NCRCRG)  to  Study Mental Illness. FHG is also supported by grants from  AHA-Allen Initiative in Brain Health and Cognitive Impairment Award made jointly  through the  American Heart Association and The Paul G.
Research Work Collection Methodology As previously foretold, this study is based on a specific dataset of aeronautical bearings related to diagnostic and prognostic studies. When the gathered research articles are processed on the same dataset/topic, this is highly valuable in this context. By comparing identical endeavors using the same criteria, it becomes much simpler to trace developments in contributions in a given topic and disclose its gaps. It should be emphasized that this work used data from the Politecnico di Torino bearing test bench, which is open to the public at  [7]. As a result, we followed its citations until the end of July 2023 and discovered that it is receiving a lot of attention, with a total of 70 citations as clarified by the Scopus citation monitoring database. The majority of citations are explicitly dedicated to the application, rather than only making a passing reference to it.
Analysis Criteria We needed to create a strong criterion for analyzing the gathered works to guarantee that research gaps were identified and to support the robustness of and need for our suggested technique. Such criterion had to meet previous challenges and condition monitoring needs. The bearing dataset introduces massive sets of bearing tests, the "variable speed and load set" and the "endurance set". The first one includes 13 subsets of different operating conditions and 7 records of operating modes, including healthy and unhealthy operating modes (further details will be revealed in the data description section). It is mainly dedicated to diagnosis studies solving a multi-class classification problem with imbalanced data with higher levels of volume (i. e. , 46,592,000 samples × 6 channels 46, 592, 000 samples × 6 channels), velocity, and variety.
Related-Work Analysis The study in  [9]  uses a collection of real-world cases, some of which include the previously mentioned aeronautical bearing dataset  [7]. To identify sensitive features, minimize noise, and prevent the loss of important information, the authors suggested a multi-scale slip-averaging method based on sensitive multi-scale symbol dynamic entropy. This is an important breakthrough in feature engineering. The data are randomly divided into training and test sets using a 50% ratio, and classification is performed using a support vector machine. Three-fold cross-validation is used for SVM evaluation and tuning. While the endurance test associated with prognosis was not addressed, the challenges raised here are closely connected to diagnosis. Recall, precision, specificity, and F1 score were the four measures employed in the evaluation.