dataesr/scientific-paragraphs-categorization huggingface dataset:
Results To train and test the CNN architectures (ResneXt-50, DenseNet-161 and Inception-v3), we used Pytorch 20 library and SGD optimizer with momentum equals 0. 9 is used during the training phase. All experiments were carried out on PC with 64 GB Ram and NVIDIA GPU Device Geforce TITAN RTX 24 GB. Each CNN architecture was trained for 30 epochs with initial of 10 -4 with decays by 0. 1 every 10 epochs and batch size equals 20. In addition, we used active data augmentation by rotating the input image by random angle between (-10 to 10 degrees). In summary, our experiments are divided into two scenarios. In the first scenario, we used retrained models of ImageNet. While, in the second scenario, we used pretrained models that were trained on medical imaging task.
Conclusion In this paper, we introduced Per-Covid-19 database which presents Covid-19 infection percentage estimation. Moreover, we evaluated the performance of three CNN architectures which are ResneXt-50, Densenet-161 and Inception-v3. For the three CNN architectures, we use two loss functions which are MSE and Dynamic Huber loss. In addition, we evaluate two pretrained models scenarios. In the first scenario, we used ImageNet pretrained models. In the second scenario, we used pretrained models that where trained on X-ray scans to investigate the influence of using pretrained models that were trained on medical imaging task. The experimental results show that using the X-ray pretrained models improve the results. Moreover, the experiments using Dynamic Huber loss function achieved better performance than the ones used standard MSE loss function.
Per-Covid-19 database consists of 183 CT-scans that were confirmed to have Covid-19 infection. The patients were from both genders and aged between 27 to 70 years old. Each volumetric CT-scan of Per-Covid-19 database were taken from different patient, so the number of CT-scans equals to the number of patients. The diagnosis of Covid-19 infection is based on positive Reverse transcription polymerase chain reaction (RT-PCR) and CT-scan manifestations identified by two experienced thoracic radiologists. The CT-scans were collected from two hospitals; Hakim Saidane Biskra and Ziouch Mohamed Tolga (Algeria) from June to December 2020. Each CT-scan consists of 40-70 slices. The two Radiologists estimated Covid-19 infection percentage based on the area of infected lungs over the overall size of the lungs.
57 This claim is supported by results from a single-center study in Italy: lower circulating IL-6 at baseline and lower NLR, features of earlier disease, were associated with a more favorable response to sarilumab therapy for COVID-19 and clinical improvement. 58 he CORIMUNO-VIRO study of sarilumab for COVID-19 was discontinued due to futility (ClinicalTrials. gov identifier: NCT04341870); however, this lack of observed clinical benefit may have resulted from administration of sarilumab at an inopportune time in the disease course. Evolving clinical experience suggests the need for more precise stratification of severity in hospitalized COVID-19 AP-1, activator protein 1; ATII, alveolar epithelial type II; BTK, Bruton's tyrosine kinase; CTLA4, cytotoxic T-lymphocyte-associated protein 4; ERK 1/2, extracellular signalregulated protein kinase 1/2; HCK, hematopoietic cell kinase; ICI, immune checkpoint inhibitor; IL1-R, interleukin 1 receptor; IL-6, interleukin 6; IL-6R, interleukin 6 receptor; JAK, Janus kinase; MHC, major histocompatibility complex; NF-KB, nuclear factor kappa B; NLRP3, neutrophil-lymphocyte ratio family pyrin domain containing 3; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; TCR, T cell receptor; TLR, toll-like receptor; TNF, tumor necrosis factor; TNFR, tumor necrosis factor receptor.
72, 80 As CRS is a shared feature of cancer and severe COVID-19, patients with cancer may preferentially benefit from JAK inhibitor therapy for severe COVID-19. 48, 49 The University of Southern California (USC) is recruiting patients to a study investigating baricitinib alone and in combination with other therapeutics as a treatment of moderate and severe COVID-19 in patients with cancer (ClinicalTrials. gov identifier: NCT04373044). Another phase III clinical study is recruiting hospitalized patients with severe COVID-19 with or without cancer to receive pacritinib, an oral JAK2 and fms-like tyrosine kinase 3 (FLT3) inhibitor, as a treatment of COVID-19 (ClinicalTrials. gov Identifier: NCT04404361). 81.
105 In contrast, OnCovid demonstrated reduced mortality rates among patients with concomitant cancer and COVID-19 exposed to antimalarials compared with patients receiving no anti-COVID-19 therapy; although, estimation of a precise effect of antimalarials over other coadministered therapies cannot be fully appreciated due to the retrospective design. 4 These results differ from those of the CCC19 study. 4, 98 In the CCC19 cohort, patients with cancer and COVID-19 receiving hydroxychloroquine were characterized by higher mortality compared with untreated controls and patients receiving any other anti-COVID-19 therapeutics. 98 A clinical trial hosted by Memorial Sloan Kettering Cancer Center is recruiting patients with cancer receiving radiotherapy to study the potential of hydroxychloroquine to prevent SARS-CoV-2 infection (ClinicalTrials.
gov identifier: NCT04381988). USC is recruiting patients to study baricitinib alone or in combination with anti-COVID-19 therapeutics, including hydroxychloroquine, to treat moderate and severe COVID-19 in patients with cancer (ClinicalTrials. gov identifier: NCT04373044). 6] [107] [108]  The practice of routinely prescribing antimalarials for COVID-19 has largely fallen out of favor following publication of the results of RECOVERY, which showed no benefit from the utilization of these therapies in the context of SARS-CoV-2 infection. 105.
115 The GRECCO-19 randomized trial of colchicine in patients hospitalized with COVID-19 revealed reduced clinical deterioration in the group receiving colchicine compared with standard of care and increased time to clinical deterioration in the colchicine arm. 116 8] [119] [120] [121]  Colchicine may prevent NET-and NLRP3-mediated synergistic inflammation in this patient population. A search on ClinicalTrials. gov revealed no clinical trials currently investigating colchicine treatment specifically for patients with cancer and COVID-19.
While ongoing ICI therapy for cancer does not appear to increase susceptibility to fatal COVID-19, there are no studies of ICIs administered as anti-COVID-19 therapeutics. Study of ICI treatment of SARS-CoV-2 infection in patients with cancer and COVID-19 not previously receiving this antineoplastic therapy will clarify the safety and utility of ICIs for reversal of T-cell exhaustion in infectious disease. Prospective trials of ICIs for COVID-19 for the general patient population will elucidate differences in immune responses and mechanisms of T-cell exhaustion between patients with or without concomitant oncologic disease. No clinical trials testing ICIs as anti-COVID-19 therapeutics for patients with concomitant cancer and SARS-CoV-2 infection are registered, but nivolumab, a PD-1 inhibitor, is being explored as a therapeutic for obese individuals with COVID-19 (ClinicalTrials.
Sequence and phylogenetic analysis The core gene-based phylogenetic tree was reconstructed as described previously  [25]. Briefly, core genes were determined using Roary  [27]  and SNP sites were extracted from the core gene alignment using SNP-sites  [28]. The maximumlikelihood (ML) tree was reconstructed using RAxML  [29]  with the GTR-GAMMA model and displayed using iTOL  [30]. For the phylogenetic analysis of the entire pks island, the genome sequences of pks-positive strains were aligned with the entire pks island sequence of strain IHE3034 using MUMmer  [31]  and the SNP sites located therein were identified. After removing SNP sites on the VNTR region, a neighbour-joining (NJ) tree was reconstructed by mega7  [32]  using the Tamura-Nei evolutionary model. Co-phylogenetic analysis of the core-gene-based ML tree and the pks-based NJ tree was performed using the 'cophylo' function of the R package Phytools  [33].
Participants Once a hospital was included in the study, a survey coordinator was chosen among the medical team of each emergency department. There was no condition to become survey coordinator. The survey link was sent to all survey coordinators, who were asked to relay it to all the practitioners who worked in their department. Practitioners included emergency physicians, other specialists that worked during daytime or nightshift in the emergency room, and current residents. Along with the link, a note detailed that the data would be used anonymously for a multi-centre survey and that practitioners were free not to participate. The note also emphasised the importance of answering honestly. The entire questionnaire was completed online. The survey URL remained active for a period of 10 weeks, between September 14th and December 1st.
Statistical methods Bivariate analysis: we compared every answer to our initial data: • Age (< 30 yo / 30-45 yo / > 45 yo). • Trained or not in hospital hygiene (Q. 1). • Ultrasound diploma or not (Q. 3). To identify a potential link between behaviour and validated qualitative characteristics, we used Fisher's exact test (when the sample size permitted it). The significance threshold was p < 0. 05. Missing data: Surveys whose compliance questions parts had five or more not answered questions were excluded from analysis. Software used: 'SAS' , version 9. 4.
Data sources, search strategy A search was performed using PubMed, Web of Science, Embase and Cochrane Review up to 25 July 2020 with the following string search: (COVID-19 OR SARS-CoV-2) AND (MORTALITY OR DEATH) AND (HYDROXYCHLOROQUINE OR hydroxychloroquine) (see Supplementary material, Text S1). Given that the number of articles about hydroxychloroquine and COVID-19 is rapidly growing, we also manually searched for additional references on the MedRxiv preprint server and on Google Scholar with the same terms. An additional search on PubMed, Web of Science and Cochrane Review was conducted for CQ with the search terms described in the Supplementary materials (Text S1): (COVID-19 OR SARS-CoV-2) AND (MORTALITY OR DEATH) AND (CHLOROQUINE OR chloroquine). This meta-analysis was conducted following the PRISMA statements in the Supplementary material (Text S2).
Data extraction Two investigators (TF and YM) extracted the following data for each study: study design, publication date, journal, location, number of participants and deaths (in treatment and control groups), hydroxychloroquine or chloroquine doses when available, effect size (HR, OR or RR) and 95% CI for reported risk estimates. The estimates from the model, adjusted for the maximum number of covariates, were used to control potential confounders, according to Cochrane Methodology  [22]. For each study, risk factors associated with higher mortality were taken into account through the reported adjusted effect sizes. When studies did not report an effect size for mortality risk  [17, 23, 24] , we used the number of deaths per group to calculate an unadjusted relative risk using metabin function in meta package in R Software  [25].
Another work found that in 84 patients treated with hydroxychloroquine þ azithromycin, nine had a severe prolongation of QTc  [71]. The combination of hydroxychloroquine þ azithromycin was associated with a greater variation in the QTc interval compared with hydroxychloroquine alone in a study with 90 patients  [72]. In a study conducted in New York on 1438 patients, cardiac arrest was significantly more likely in patients receiving hydroxychloroquine with azithromycin compared to patients receiving neither of the two drugs (adjusted OR 2. 13, 95% CI 1. 12e4. 05) [18]. Finally, a study conducted on the WHO database bringing together more than 167 000 patients found an increased risk of potentially fatal acute cardiac events in patients treated with azithromycin alone or with hydroxychloroquine alone  [73].
, 2018b; Ziltinkevich et al. , 2019; Petäjä et al. , 2020 submitted; ; Bondur et al, 2019 a,b,c,d,e,f; Yuanhuizi He et al, 2020) , while in China the primary focus is on the development of atmospheric in-situ stations and advanced air quality monitoring in megacity environments (e. g. Ding et al. , 2016 b; Yao et al. , 2018; Ying et al. , 2020; Wang et al. , 2020). Furthermore, PEEX is closely collaborating with the Digital Belt and Road (DBAR) Program coordinated by the Institute for Digital Earth and Remote Sensing (RADI). PEEX collaboration with DBAR is driven by a need for a novel in situ station network and ground based data as a complementary information for the remote sensing in the Silk Road economic region. Long-term development on a "Station Measuring the Earth Surface and Atmosphere Relations" (SMEAR) concept could provide baselines for this  (Hari et al.
Observations on the elemental composition of surface aerosols on the coastal Kandalaksha Bay of the White Sea were indicative of the dominance of biogenic aerosol particles during summer time, with heavy metal concentrations in aerosols being at Arctic background levels  (Starodymova et al. , 2016). Increases in Ni and Cu concentrations were observed in air masses arriving from the western part of the Kola Peninsula indicative of emissions from the smelters in that region. Black carbon (BC) is a potentially large contributor to climate forcing in the Arctic region, however, the assessment of its pollution is hampered by the lack of aerosol studies in Northern Siberia  (Popovicheva et al. , 2019). Spatial variability of Arctic BC was studied using a harmonized dataset from 6 circumpolar Arctic observatories  (Backman et al.
, 2019; Varentsov et al. , 2018a)  and in 57 Scandinavian cities  (Miles and Esau, 2020). The mean wintertime temperature anomalies, the UHI intensity, varied from 0. 8 K to1. 4 K and had extreme intensities of up to 7 K during cold anticyclone weather conditions. The complete dataset of surface UHI intensity derived from MODIS LST data products is freely available and published in Miles (2020). Such a UHI induced strong mediation of cold temperature spells might cause significant socioeconomic and environmental impacts in the cities  (Konstantinov et al. 2018, Fig. 4 ). A survey of other UHI studies in 11 Arctic cities and towns confirmed that even relatively small cities at high latitudes may exhibit intensive UHIs. A recent analysis confirms the important role of the surrounding temperature in explaining spatial-temporal variation of UHI intensity  (Miles and Esau, 2017).
The major contribution to the UHI was revealed for water, sparse vegetation, grassland and scrubland. The mechanisms and pathways of the UHI maintenance requires involvement of numerical experiments with turbulence-resolving models to advance the understanding of the local climate features (Urban Heat Islands -UHIARC dataset see http://urbanreanalysis. ru/uhi arc. html). We would need a denser meteorological network, especially high quality temperature data, to better understand the urban climatology and the thawing processes in urban soils and to better assess climatic trends relevant to Arctic societies and welfare  (Konstantinov et al. 2018 ). Urban climate anomalies may cause more extreme weather and climate phenomena in densely populated megacities. The Moscow agglomerationthe largest megacity in the boreal continental climate within the PEEX domaindemonstrates profound effect of interactions between the UHI and urban winds, known as a cross-over effect  (Varentsov et al.
, 2017). The SAR-based discrimination between sea ice and open-water works well, and areas of thinner and thicker ice can be distinguished. However, a larger comprehensive training dataset is needed to set up an operational algorithm for the estimation of sea ice concentration and for the weighting scheme for sea ice thickness  (Karvonen et al. , 2017). Multi-decadal Arctic sea-ice state estimates are important for the strategic planning of Arctic navigation. These estimates are usually based on climate models with a thermodynamic-dynamic sea-ice models. An up-to-date assessment of large-scale sea-ice models was with the aid of sea-ice models as a climate model component, a comprehensive review was carried out by  Leppäranta et al. (2020). Specifically,  Uotila et al. (2015)  found that a model with the subgrid-scale sea-ice thickness distribution reproduces more realistic sea ice and upper ocean, due to better captured spring evolution, than a model with just single sea-ice thickness category.
huxleyi in the world's oceans. In combination with statistic-based-mathematical models of E. huxley blooms  (Pozdnyakov et al. , 2019) , the available IPCC climate models permit mid-term projections of the forthcoming changes  (Gnatiuk et al. , 2020). However, our knowledge on the reciprocal influence of climate change and both the structure and functioning of marine ecosystems (even at the level of primary producers!) is still insufficient to confidently prognoses the future dynamics of the E. huxleyi phenomenon. More studies are required even to fully understand the mechanism of intracellular light-dependent reaction of calcification, its dependency on both seawater carbonate chemistry and environmental FFs  (Vihma et al. , 2019). Creation of respective multidecadal databases (as in  Kazakov et al.
ACKNOWLEDGEMENTS We thank the following funding agencies and projects:  Academy of Finland  contracts: No  280700 ,  294600 ,  296302  ( Novel Assessment of Black Carbon in the Eurasian Arctic: From Historical Concentrations and Sources to Future Climate Impacts (NABCEA ),  307331  ( FCoE Atmospheric Sciences ),  311932 ,  314798/99 , https://doi. org/10. 5194/acp-2021-341Preprint. Discussion started: 19 May 2021 c Author(s)  2021. CC BY 4. 0 License.
Variants were identified using trio exome sequencing (ES), trio genome sequencing (GS), or gene panel sequencing. According to data from gnomAD, SNAP25 (NM_130811. 3) shows a reduced number of LoF and missense variants in controls: (1) a probability of loss-of-function intolerance (pLI) score = 0. 99 and a lossof-function observed/expected upper bound fraction (LOEUF) = 0. 23 for LoF variants with one nonsense variant deposited at amino acid residue 204, two codons before the canonical stop; and (2) a z-score = 2. 96 and LOEUF = 0. 36 for missense variants. This indicates a selective constraint on these variant types in a control population not affected by early-onset neurodevelopmental disorders (NDD). 19 Therefore, causality of both LoF and missense variants was assessed according to the guidelines of the American College of Medical Genetics and Genomics (ACMG),  20  focusing on the following criteria: PS2 (de novo origin), PM2 (absent in population databases), and PP3 (multiple lines of computational evidence support a deleterious effect on the gene/ gene product).
Variant analysis Of the 23 enrolled individuals with de novo variants, 15 different missense variants were identified, in addition to 4 LoF variants (2 nonsense and 2 splice donor variants). All 19 variants were absent from the gnomAD database (last accessed July 2020). All pathogenic or likely pathogenic missense variants affected highly conserved amino acid residues (mean GERP++: 5. 9) of the t-SNARE coiled-coil homology domain 1 (amino acid position 14-81) and t-SNARE coiled-coil homology domain 2 (amino acid position 135-202; Fig. 1b, c ). 32 ll de novo missense variants were predicted to be deleterious by multiple in silico prediction programs (mean CADD: 29. 6; for a complete overview of in silico analysis see Supplemental Tables S5. 1 and S5. 2). Recurrent variants comprise the missense variant p.
All pathogenic or likely pathogenic missense variants are located in the coiled-coil homology domains 1 and 2 of SNAP25, representing potential hotspots, also considering that little variation is observed in population databases in these domains (see Fig. 1 ). The variants of four individuals aged 2 to 23 years with notable brain volume loss or signs of a leukoencephalopathy were located in the t-SNARE coiled-coil homology domain 2 (amino acid position 140-202), indicating a possible location-specific clinical observation (4/7; 57% of individuals with variants in this domain). Future studies with additional individuals with SNAP25-DEE will shed more light onto the underlying clinical course. Recent modeling suggests an incidence of de novo variants in SNAP25 in live births of approximately 1:100.
ACKNOWLEDGEMENTS We thank the patients and their families for their participation and support of this study. We especially thank  Elizabeth Dellureficio  for her continuous efforts to bring affected families together. Work on individual 5 was supported in part by grants from  SFARI  and the  JPB Foundation. We thank the clinicians involved with the care of these patients including  Stephen Nirmal ,  Alasdair Parker , and  Manali Chitre, UK. A. M. , K. B. , and M. A. K. are funded by the  National Institute for Health Research (NIHR) GOSH BRC. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health. Individual 7 was enrolled in the  NIHR BioResource research study , supported by the  Cambridge Biomedical Research Centre  and the  NIHR for the NIHR BioResource  (grant number  RG65966 ).
Individual 17 was ascertained in the  Duke Genome Sequencing Clinic. Funding for the  Duke Genome Sequencing Clinic  which is supported by the  Duke University Health System. Individual V6 was enrolled in Care4Rare  Canada  Consortium, funded by  Genome Canada ,  Ontario Genomics Institute  ( OGI-147 ),  Canadian Institutes of Health Research, Ontario Research Fund, Genome Alberta ,  Genome British Columbia, BC Children's Hospital Foundation, BC Children's Hospital Research Institute, BC Provincial Health Services Authority ,  Genome Quebec , and  Children's Hospital of Eastern Ontario Foundation. This study makes use of data generated by the  DECIPHER Consortium. A full list of centers who contributed to the generation of the data is available from https://decipher. sanger. ac. uk/ and via email from decipher@sanger.
ac. uk. Funding for the project was provided by the  Wellcome Trust. R. S. M. was supported by a grant from the  Lundbeck Foundation  ( R277-2018-802 ). Funding for the Duke Genome Sequencing Clinic was provided by  Duke University Health System. Research reported in this publication was supported by the  National Human Genome Research Institute of the National Institutes of Health  under award number  U01HG009599. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank  Katherine R. Chao  for her help with the exome data analysis. The work in C. G. Bönnemann's laboratory is supported by intramural funds from the  NIH National Institute of Neurological Disorders and Stroke. Sequencing and analysis were provided by the  Broad Institute of MIT and Harvard Center for Mendelian Genomics (Broad CMG ) and was funded by the  National Human Genome Research Institute , the  National Eye Institute , and the  National Heart, Lung and Blood Institute  grant  UM1 HG008900  to  Daniel MacArthur  and  Heidi Rehm.
593G>C p. (Arg198Pro) F 0 y 3 Severe ES MH, Normal Bilateral clubfeet months spasticity, CVI c. 596C>G p. (Ala199Gly) F 16 years Severe - Stereotypic hand CVI Leukoencephalopathy Plagiocephaly, hip movements dysplasia, joint hypermobility, short stature c. 596C>T p. (Ala199Val) M 3 years Moderate GS, FS Stereotypic hand MH Normal ASD 10 movements, ataxia months c. 72 p. (?) F 13 years Mild - Normal Joint hypermobility +1G>A 9 months c. 114 p. (?) M 2 years Mild GS Ataxia MH, dystonia Normal +2T>G 6 months c. 520C>T p. (Gln174*) F 2 years Profound ES Severe MH, Leukoencephalopathy, High-arched palate, spasticity, CVI brain volume loss abnormal dentition 12 F 0 years + GS Normal 11 months c. 589C>T p. (Gln197*) M 0 years Profound - CVI Normal Plagiocephaly, 11 bilateral clubfeet months Variant nomenclature corresponds to GenBank: NM_130811.