dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance / Computation cost trade-off The Protein Data Bank (PDB) is the most populated database for the protein structures. As of May 2020, more than 160,0 0 0 structures have been deposited and more than 11,0 0 0 new structures are deposited every year. Furthermore, the size of the proteins deposited is growing as the performance of experimental protein structure resolution methods are improving, and the number of multi-domain proteins follows this trend. The ability to screen such a large database in a reasonable time and with acceptable performances is therefore a challenge. The two main steps of the shape comparison are the computation of a descriptor for each object, and the comparison between two descriptors. Depending on the algorithm, the descriptor computation times are ranging from 8 (3DZ) to 112 (HAPT1) seconds for descriptors computed on a CPU, and 45 ms for the descriptors computed on a GPU (GraphCNN).
The comparison between two descriptors are in the order of millisecond or below, except for the 3DZD and 3DZM descriptor comparisons which are in the range of 0. 22-0. 5 s on a GPU. Regarding the learning-based methods, the computation times are ranging from 20 min to 1 week. Carefully assessing the performance / computational cost ratio is therefore required if one aims to screen a large database as the computation cost may prove prohibitive for large-scale screening projects.
Conclusion In the present work, we have presented a dataset of shapes from multi-domain proteins. Six groups, among which three used machine learning approaches in their respective work-flows, submitted 15 sets of results. The performances were assessed at the protein and species levels of the SCOPe database. Shape retrieval methods displayed high-quality results at the protein level. We observed a significant decrease in the performances of all the methods at the species level. These results indicate that comparing multi-domains proteins based on their shapes only remains challenging, especially for closely related proteins. It could be of interest to compare shape-retrieval methods to the reference methods used in the structural biology community. Protein structures in the PDB are highly heterogeneous; missing (i.
Acknowledgments Yuxu Peng  was supported by the Young teachers growth plan project (  2019QJCZ014  ) funded by  Changsha University of Science & Technology. Stelios Mylonas ,  Apostolos Axenopoulos  and  Petros Daras  were supported by the  ATXN1-MED15   PPI  project funded by the  GSRT -Hellenic Foundation for Research and Innovation. Matthieu Montes  and Florent Langenfeld were supported by the  European Research Council Executive Agency  under the research grant number  640283.
Fatal Encephalitis Caused by Cristoli Virus, an Emerging Orthobunyavirus, France Identification of nonhuman sequences uses different databases, including cleaned National Center for Biotechnology Information nucleotide and nonredundant databases (GenBank release 229, December 2018) that contain all known microorganisms. For new species, we conducted verification using an algorithm combining de novo assembly with Meta-SPAdes version 3. 12. 0 (http://cab. spbu. ru/software/metaspades) and contig alignment with the ViPR database (https://www. viprbrc. org). After manual curation of full-length viral sequences, we performed alignment and phylogenetic analyses with the closest known viral species using Muscle version 3. 8. 31 (http://www. drive5. com/muscle) and IQ-Tree version 1. 3. 11. 1 (http://www.
All conserved cysteine positions  (9)  were also conserved. We identified 2 overlapping ORFs in the S segment, one corresponding to the nucleocapsid (N) protein (714 bp, 238 aa), the other to nonstructural protein s (NSs, 240 bp, 80 aa). We found few differences with Umbre virus for these proteins (Table ). The amounts of viral RNAs we semiquantified by shotgun metagenomics were 5. 4 log viral genomes/ mg of biopsy for segment L, log viral genomes/mg of biopsy 5. 0 for segment M, and 6. 2 log viral genomes/ mg of biopsy for segment S. For confirmation, we designed specific reverse transcription PCRs for Cristoli virus segments L, M, and S. Brain extracts were positive for all three, with amplicon sequences identical to those in the full-length genome (Appendix 1 Figure  2 ). We deposited the annotated genome sequence into Genbank (accession nos.
Conclusions The etiologic diagnosis of encephalitis is difficult because >25% of cases remain of unknown cause  (5). Diagnostic methods without prior knowledge of the microorganisms sought, such as metagenomics, detect infectious agents when other techniques have failed  (10). We used an original shotgun metagenomics approach and in-house software MetaMIC to discover a new virus belonging to the Orthobunyavirus genus of the Peribunyaviridae family in an immunodepressed patient with slowly progressive fatal encephalitis. Cristoli virus is close to Umbre virus, a member of the Turlock serogroup not previously associated with human disease. Because the patient had not traveled during the year preceding the onset of her symptoms, our findings suggest that Cristoli virus is endemic to France. Like other members of its family, it could have been transmitted through a mosquito bite.
The other conformal methods using unstructured meshes such as FEM and BEM present an advantage of avoiding stair casing error but are less used in numerical dosimetry  [15]. In this paper, the authors study the effects of a 160 W wireless charging system designed for the charging of an electric kick scooter on a persons' head and hands by modelling using COMSOL software in order to determine the magnetic flux density, Specific Absorption Rate (SAR), and temperature elevation due to the magnetic field exposure. This paper is organised as follows. Section 2 introduces the principles of the electromagnetic field, the heating mechanism, and a brief explanation about their effects on the human body. In Section 3, the COMSOL model is built to study the magnetic flux density, the induced electrical field, specific absorption rate (SAR), and the heat transfer in the head and hands.
COMSOL Model In order to study the effect of our wireless charging system built for a kick scooter, a model is built under the finite element method (FEM) and multi-physics software package COMSOL Multi-Physics. Many models have studied the effect of a high power wireless charging system going from 3. 2 kW to 20 kW for electric vehicles, where the systems are shielded to limit the leakage magnetic field. Besides, humans are not directly exposed to the source of the magnetic field due to the presence of the vehicle chassis  [24] [25] [26]. However, in this paper, the authors have studied the effect of a 160 W wireless power transfer (WPT), where the human body can be directly exposed to the magnetic field between the two coils, and therefore, the magnetic field density at this position is the most important.
The carrying data file is created from a magnetic-resonance image (MRI), and its resolution is modified to correspond to that of our multi-physics model mesh, as shown in Figure  3a. According to the adult body size, a CAD model of a human hand is built base on the 3D morphology of referents  [33]. The human hand of a 70 kg male is modelled with just two biologics tissues (skin and bone) as shown in Figure  3b  with specific parameters shown in Table  2. The hand is positioned between the two coils in order to study the effect of a magnetic field if someone would try to put his hand between the two coils of the charging system. The skin is composed of multilayer tissues, which are the epidermis, dermis, and subcutaneous tissue. It is considered by the ICNIRP 2010 as one of the target tissues for the peripheral nervous system, and the ICES considers it as a topic to be solved.
The skin is composed of multilayer tissues, which are the epidermis, dermis, and subcutaneous tissue. It is considered by the ICNIRP 2010 as one of the target tissues for the peripheral nervous system, and the ICES considers it as a topic to be solved. Previous literature has provided data on dielectric parameters of the skin  [9, 34]. In reference  [35] , the authors have provided new data on the conductivity of the tissue of the skin at frequencies. They found that the conductivity of the epidermis determines the conductivity of the skin. In our model, the conductivity of the skin is based on the findings in reference  [35]. The thickness of the hand skin is taken from reference  [36]. (a) (b) The reason we chose these two human limbs (head and hand) is to take two possible extreme cases of a human being in front of or near the induction charging system.
Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial.
It can reduce the number of missing samples, since the sharing of material is simplified; it enables rapid turnaround times; reduces manual errors; and can streamline local versus central assessment of biomarker. For clinicians and researchers to use digital pathology, applications and websites should be user-friendly and intuitive. As an example, a web-based tool called Slide Score (www. slidescore. com) was developed as a cross-platform web application to facilitate the scoring of whole slide images and tissue microarray (TMA) cores. Application programming interface (API) was implemented that allowed programmatic administration of studies, uploading slides, fetching results, and retrieving pixel data for regions of images. This API enabled automating creation of new studies from internal database system for managing biobanking workflows.
Additionally, a plugin was developed for QuPath  14  -open-source image analysis software-which uses this API to run image analysis algorithms on slides stored on the Slide Score platform avoiding the need to download the slides. This web-based platform was used in high-impact projects  6, 15  , for example, for the digital scoring of biomarkers in the first stage of the TONIC trial  6  , and the estimation of the immune infiltrate of tumors of melanoma patients used for single-cell sequencing  15. Furthermore, the web-based platform is currently used for several other types of research, such as interrater variability studies, retrospective TMA, and whole slide scoring and prospective biomarker scoring.
IMPLEMENTATION OF WORKFLOW IN THE TONIC TRIAL The TONIC trial (NCT02499367)  6  is a phase II, non-comparative randomized multi-cohort single-center trial (full title: Adaptive phase II randomized non-comparative Trial Of Nivolumab after Induction treatment in TNBC patients), designed to assess the efficacy of induction of an anti-cancer immune response by lowdose chemotherapy or irradiation to increase response to anti-PD-1 in patients with metastatic TNBC. In the first part of the trial  6  , patients with metastatic TNBC were randomized to nivolumab (1) without induction or two-week low-dose induction, with (2) irradiation (3 × 8 Gy), (3) cyclophosphamide, (4) cisplatin, or (5) doxorubicin, all followed by nivolumab (anti-PD-1; 3 mg/kg). Based on a Simon's two-stage design  17  and prespecified pick-the-winner criteria, only the doxorubicin cohort was allowed to continue in the second part of the trial  6.
ADVANTAGES AND LIMITATIONS OF A WEB-BASED RISK-MITIGATION WORKFLOW Our proposed solutions involved standardization of our workflow, obtaining digital images and the use of a web-based tool such as Slide Score for the managing and scoring of digital images. Anticipating the incorporation of digital images in routine diagnostics, our workflow shows that it is feasible for a pathologist to score digital images with high reliability. Moreover, a webbased tool can facilitate the process of coordinated uploading of digital images, pseudonymizing slides, and regulate access to studies and proper data management. Web-based platforms are therefore of high interest in biomarker research and can help with automation that can be transferred to clinical practice in the future. In this study, we obtained sTIL scores within 72 h after a biopsy was taken, which is a reasonable timeframe for clinicians to start randomization of patients to treatment arms in a clinical trial.
FUTURE APPLICATIONS OF THE WORKFLOW Our strategy can serve as a template for risk management and mitigation of all identified risks in future clinical trials incorporating biomarkers for inclusion, enrichment, or stratification. By no means will risks identified in this study be similar for all clinical trials. Each trial will have its own risks that need to be mitigated, although there will be similarities between the risks across clinical trials. Defining the risks that come with biomarker development will help tested biomarkers eventually make their way to the clinic. However, one may even argue that a similar risk-management strategy can be applied in daily practice. In the BELLINI trial (NCT03815890), two cycles of neo-adjuvant anti-PD-1 are administered in patients with early-stage TNBC or luminal B breast cancer.
ACKNOWLEDGEMENTS The  Department of Pathology of the Netherlands Cancer Institute  is thanked for the support of this study and ensuring the rapid turnaround times. The  Breast Cancer Research Foundation and Bristol-Myers-Squibb (BMS)  are thanked for financial support. We also thank the  BMS-International Immuno-Oncology Network (BMS/II-ON ) and the  Dutch Cancer Society  ( NKI2015-7710 ) for funding the clinical trial costs and this feasibility study ( NKI2016-10510 ). S. L. , R. S. , and M. K. are supported by a grant from the  Breast Cancer Research Foundation  ( BCRF, NY, US ). The following is a list of current members of the  International Immuno-Oncology Working Group (TILs Working Group). A member is defined as a person willing to be involved, informed and be part of the activities of the TILs Working Group.
71 years, 0. 28 SD) with that in similarly spaced scans (mean 0. 98 years, 0. 76 SD) in 20 control macaques that lacked experience of DisRev (S1 Table ). Half the control animals (n = 10) had no experience of formal training (no-discrimination control [NoDis Control]), whereas the other half (n = 10) had experience of discriminating visual stimuli but in the absence of any reversal requirement (discrimination control [Dis Control]). There was no significant difference in the number of days between scan 1 and scan 2 when DisRev learners and all controls were compared (t 27 = 1. 03, p = 0. 312). Structural MRI data were submitted to a DBM analysis using the Oxford Centre for Functional Magnetic Resonance Imaging (FMRIB) Software Library (FSL) tools FNIRT and Randomise  [51]  (see Methods). The logic of the approach is that if a group of brain images can be warped to an identical image, then volumetric changes involved in that warping process give measures of the local differences in brain structure between individuals.
During schedule 2, monkeys performed at approximately 55% accuracy, but by schedule 5 reversal, they performed at 80% accuracy. Data are available on https://www. jeromesallet. org/data-ofc-reversal-learning and https://doi. org/10. 5281/zenodo. 3776631. Dis Cont, discrimination control; DisRev, discrimination reversal; ITI, intertrial interval; Rev, reversal; Sch, schedule; Sched, schedule. https://doi. org/10. 1371/journal. pbio. 3000605. g001 (two levels: left and right) × structural scan (two levels) × group (two levels: Dis Controls, NoDis Controls). This analysis failed to identify an interaction between session and group (F 1,18 = 0. 169, p = 0. 686) or any other subsidiary interactions including session and group (all p's > 0. 292). It is, of course, possible that gray matter changes may occur in relation to simple discrimination task performance in other brain areas outside or partially overlapping with the four frontal ROIs that were identified by the comparison of DisRev learners with controls.
Changes were also noticeable in some other areas (S5 Table ). For illustrative purposes, we present the average Jacobian values, after age and sex had been accounted for, in the lesion group and controls. Jacobian values were extracted from bilateral ROIs (3. 375 mm 3 in diameter) at coordinates reflecting the center of gravity of the bilateral cluster. In some cases, the effects were in relatively adjacent and large frontal clusters (lOFC/ 12o; lPFC). Therefore, to ensure that Jacobian values extracted from each ROI were independent of one another, they were taken from ROIs centered on coordinates just off the center of gravity of lOFC/12o and lPFC. Thus, each illustration reflects separate data as much as possible while still representing the anatomical regions. The precise coordinates of the ROI centers are all reported (S5 Table ).
Experiment 4 Finally, we examined functional coupling of plOFC/AI in the lesion animals relative to controls (we considered here fMRI data collected at scan 1). Again, a seed-based correlation analysis focused on the same plOFC/AI ROI. The GLM used was equivalent to the one used to examine structural changes in experiment 2; it sought regions in which functional connectivity was reduced in lesion animals compared with controls. We report reduced coupling between plOFC/AI and lOFC/12o and bilateral ACC/MCC in the lesion group  (Fig 5D and S6 Table). For illustration, we present the averaged residual time series, which was extracted from bilateral ROIs placed at the center of gravity of the significant clusters in the two lesion animals and the 22 control animals used in this analysis (although structural data were available for 28 control animals, fMRI data were only available for 22 animals).
Experiment 1: DisRev learning Four animals (OB1-4) learned an object-based DisRev task (object DisRev), five animals (SB1-5) learned a spatial-based DisRev task (spatial DisRev), and a total of 20 (control) animals acted as controls (C1-20). See S1 Table for details. Half of the 20 control animals (C1-10) had no experience of formal training (NoDis Control), whereas the other half had experience of performing two-option reward-guided visual discrimination tasks (Dis Control). Crucially, however, no controls had experience of DisRev. Two MRI scans were acquired in all animals, one prior to DisRev training and one after the learning criterion had been met (see Methods). Data from all of these animals were analyzed in a DBM analysis of brain structural changes associated with DisRev learning.
MRI data collection Protocols for animal care, MRI, and anesthesia were similar to those that we have previously described  [48, 49]. During scanning, under veterinary advice, animals were kept under minimum anesthetic levels using isoflurane. A four-channel phased-array coil was used (Windmiller Kolster Scientific, Fresno, CA). Structural scans were acquired using a T1-weighted MPRAGE sequence (no slice gap, 0. 5 × 0. 5 × 0. 5 mm, TR = 2,500 milliseconds, TE = 4. 01 milliseconds, 128 slices). Whole-brain BOLD fMRI data were collected for 53 minutes, 26 seconds from each animal, using the following parameters: 36 axial slices, in-plane resolution 262 mm, slice thickness 2 mm, no slice gap, TR = 2,000 milliseconds, TE = 19 milliseconds, 1,600 volumes.
DBM analysis of structural MRI data The custom-made scripts used for our DBM analysis uses the FSL linear and nonlinear transformation tools FLIRT and FNIRT. The logic of the approach is that if a group of brain images can be warped to a near-identical image, then differences in that warping process give a measure of the local differences in brain structure between individuals. DBM has been previously used in several studies on brain plasticity  [38, 39, 42, [48] [49] [50]. All the brains were first aligned to the MNI rhesus macaque atlas template  [92, 93]  using the affine registration tool FLIRT  [94, 95]  followed by nonlinear registration using FNIRT  [96] , which uses a b-spline representation of the registration warp field  [97]. The resulting images were averaged to create a study-specific template.
fMRI analysis of activity coupling Prior to fMRI analysis, the following preprocessing was applied: removal of nonbrain voxels, discarding of the first six volumes of each fMRI dataset, 0. 1-Hz low-pass filtering to remove respiratory artifacts, motion correction, spatial smoothing (Gaussian 3-mm FWHM kernel), grand-mean intensity normalization of the entire 4D dataset by a single multiplicative factor, high-pass temporal filtering (Gaussian-weighted least-squares straight line fitting, with sigma = 50. 0 seconds). Registration of functional images to the skull-stripped structural MRI scan and to the MNI macaque template  [92, 93]  was achieved with linear and nonlinear registration using FLIRT and FNIRT, respectively  [94]. To establish changes in functional connectivity within the network as a function of DisRev learning (experiment 1) or lesions (experiment 2), we used a voxel-wise whole-brain approach to map resting-state functional connectivity of the plOFC/AI.
JS is funded by a  Wellcome Trust   Henry Dale Fellowship  ( 105651/Z/14/Z ). MPN is supported by an  Academy of Medical Sciences  and  Wellcome Trust  Springboard fund SBF003\1143. KK, AHB, MEW, and MFSR are funded by the learning, affected structure and activity in the same frontal cortical circuit. The results identify a distributed frontal cortical circuit associated with behavioral flexibility. Wellcome Trust   Strategic Award   101092/Z/13/Z. KK is also funded by  BBSRC  grant  BB/H016902 /. The work of RBM is supported by the  Biotechnology and Biological Sciences Research Council (BBSRC)   UK [BB/N019814/1 ] and the  Netherlands Organization for Scientific Research NWO  [ 452-13-015 ]. The  Wellcome Centre for Integrative Neuroimaging  is supported by core funding from the  Wellcome Trust  [ 203139/Z/16/Z ].
Introduction Since its birth in the 1960s, biocrystallography has been a primary source of structural information, contributing more than 90% of 3D structures accessible in the Protein Data Bank  [1]  and remains a central player in structural biology, alongside NMR and CryoEM. Over the past decade, new experimental setups have been introduced that widen its applicability and transform the daily practice of crystal growers and crystallographers. The recent advent of X-ray free electron lasers (X-FEL) has enabled the serial femtosecond diffraction (SFX) analysis of micro-and nano-crystals, and offers unprecedented possibilities for time-resolved experiments  [2] [3] [4]. At the same time, electron microscopes have been hijacked to perform micro-electron diffraction (µED), opening the way to the characterization of nanocrystals using laboratory-based instruments  [5] [6] [7] [8].
Crystal Analysis Crystals of PhaCCA were analyzed in cryogenic conditions at FIP/BM30A beamline (ESRF, Grenoble, France) using an ADSC Quantum 315r detector. A crystal was soaked for a few seconds in the mother liquor supplemented with 20% (w/v) glycerol, mounted in a cryoloop and flash-frozen in liquid nitrogen. 240 images were collected with a rotation of 0. 5 • and an exposure time of 1 s per frame, yielding a dataset at a resolution of 2. 28 Å. Crystals of HEWL were analyzed at ambient temperature using a Rigaku FR-X diffractometer at the FRISBI platform (IGBMC, Illkirch, France) with an EIGER R 4M detector (DECTRIS) with a 2θ offset of 10 •. Several crystals grown with Roche, Sigma, and Seikagaku lysozymes were tested and diffracted to up to 1. 5 Å resolution but showed rapid decay because of radiation damage at ambient temperature.