dataesr/scientific-paragraphs-categorization huggingface dataset:
The Kherson, Mykolaiv, Odesa, and Zaporizhzhia oblasts, being adjusted to the coasts of the Black and Azov Seas, are located in the steppe zone and constitute the southern region of Ukraine. The environmental parameters and health indicators of the population of the region are sensitive to the impact of natural (e. g. , climate change) and anthropogenic processes. An analysis of satellite remote sensing data (NOAA NDVI time series) for the assessment of vegetation condition demonstrates an increase in frequency and duration of drought events in the region during the last few decades. It may have a relation to climate change processes. Data analysis of local meteorological observations over the past 100 years proved alterations of some bioclimatic indexes. The Equivalent Effective Temperature (I EET ) increases in winter and summer (due to the increasing repeatability of high anomalous temperatures) and remains stable in spring and autumn seasons.
Materials and Method In this study, empirical data from the meteostations Kherson, Mikolaiv, Odesa, and Zaporizhzhia (namely, averaged annual and monthly surface air temperature, precipitations, extreme weather events, and some other parameters) for the period 1900-2020 were used  [22, 23]. Moreover, the values of the meteorological norm for the above-mentioned parameters for the period 1991-2020 were calculated. For the period 1961-1990, the respective climatic norms were obtained from  [24]. Location data and key climate indicators for regional meteostations are presented in the Table  1. NOAA AVHRR Surface Reflectance satellite imagery was used to calculate the Normalized Difference Vegetation Index (NDVI) to obtain the long-term datasets of vegetation condition and productivity over the period of 1982-2021  [25].
Satellite imagery was processed in Google Earth Engine, a large-scale web platform for cloud remote sensing data processing (https://earthengine. google. com, accessed on 6 May 2022). In order to assess the climatic comfort for humans in a given region, the bioclimatic index Equivalent-Effective Temperature (I EET ) was used. It was calculated in accordance with the Missenard formula  [26] , using the daily empirical data (surface temperature, air humidity, and wind velocity) from regional meteostations for the period of 1991-2020  [17]. Data for the analysis of human health patterns and tendencies were obtained from the open sources of the State Statistics Service of Ukraine and the Ministry of Health Care of Ukraine  [27, 28]. The daily data on the confirmed cases of infection with the SARS-CoV-2 virus and the number of people who were suspected of being infected with the same virus in the Kherson, Mykolaiv, Odesa, and Zaporizhzhia oblasts in 2020-2021 were obtained from  [29].
In temperate latitudes, the spread of respiratory viruses is mainly seasonal, as their onset occurs in the first half of October and the end of March, but an atypical spread of diseases caused by the SARS-CoV-2 virus was also recorded in the warm periods of 2020 and 2021. The main factors that can affect the spread and development of the influenza virus, in addition to strain-specific characteristics, social, and behavioral aspects of the population, the immune status of an individuals, and the level of economy and medical care in the country, are also related to the environmental conditions  [27, 28]. According to the data  [29] , by April 2022, over 4975. 8•10 3 confirmed cases of SARS-CoV-2 infection and more than 108. 0 × 10 3 fatal cases were recorded in the south of Ukraine. The percentage of confirmed cases of infection for the number of people living in the region is 4-6%, and for deaths is 0.
For example, although the first confirmed case has been hypothesized as the index of the outbreak (due to travel from Nigeria to the United Kingdom on 3-4 May 2022 (refs. 1, 3 )), this scenario can be discarded as the earliest symptom onset dates for confirmed cases in Portugal and in the United Kingdom were in late April  15, 16. Notably, the 2022 MPXV diverges from the related 2018-2019 viruses by a mean of 50 single-nucleotide polymorphisms (SNPs) (Figs. 1  and 2 ), which is far more (roughly 6-12-fold more) than one would expect considering previous estimates of the substitution rate for Orthopoxviruses (1-2 substitutions per genome per year)  17. Such a divergent branch might represent accelerated evolution. Of note, among the 46 SNPs (24 non-synonymous, 18 synonymous and four intergenic) (Supplementary Table  3 ) separating the 2022 MPXV outbreak virus from the reference sequence (MPXV-UK_ P2, 2018; GenBank accession no.
MT903344. 1), three amino acid changes (D209N, P722S and M1741I) occurred in the immunogenic surface glycoprotein B21 (MPXV-UK_P2-182)  18. Serological studies have previously indicated that the monkeypox B21 protein might be an important antibody target with several key immunodominant epitopes  18. As discussed previously  19  , fine inspection of the mutation profile of those 46 SNPs further revealed a strong mutational bias, with 26 (14 non-synonymous, ten synonymous and two intergenic) and 15 (nine non-synonymous and 16 synonymous) being GA > AA and TC > TT nucleotide replacements, respectively (Fig. 2  and Supplementary Table  3 ). A tool (https:// github. com/insapathogenomics/mutation_profile) was built to rapidly screen these and other mutation profiles. The observed (hyper)mutation signature might suggest the potential action of apolipoprotein B mRNA-editing catalytic polypeptide-like 3    2 , and the alignments are provided as Supplementary Data.
Acknowledgements We thank  A. Rambaut  from the  University of Edinburgh  for launching the discussion on MPXV genomics at virological. org,with emphasis on his first insights on potential APOBEC3-mediated genome editing. We also gratefully acknowledge the authors and the originating and submitting laboratories of the genetic sequences released in GenBank. The acquisition of WGS-associated equipment used in this study (including the Illumina NextSeq 2000) was funded by the  HERA  project (Grant /2021/PHF/23776 ) supported by the  European Commission  through the  European Centre for Disease Control and Prevention  and partially funded by the  GenomePT  project ( POCI-01-0145-FEDER-022184 ), supported by  COMPETE 2020-Operational Programme for Competitiveness and Internationalisation (POCI) ,  Lisboa Portugal Regional Operational Programme  ( Lisboa2020 ) and  Algarve Portugal Regional Operational Programme  ( CRESC Algarve2020 ), under the  PORTUGAL 2020 Partnership Agreement , through the  European Regional Development Fund (ERDF) , and by the  Portuguese Science and Technology Foundation (FCT).
This study was also supported by the  ERINHA-Advance  project (funding from the  European Union 's  Horizon 2020 Research and Innovation program , grant agreement no. 824061 ) and also benefited from co-funding from the  European Union 's  Horizon 2020 Research and Innovation program , grant agreement no. 773830:  One Health European Joint Programme (EJP) , in particular by co-funding postdoctoral fellowships for J. S. D. and V. M. and  INSaFLU development. We also thank  M. Pinheiro  ( iBiMED/Universidade de Aveiro ) for his continuous support on updating the INSaFLU platform and the  Infraestrutura Nacional de Computação Distribuída (INCD)  for providing computational resources for testing it. INCD was funded by  FCT  and  FEDER  under project  22153-01/SAICT/2016. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Data availability Monkeypox reads mapping to the reference sequence MPXV-UK_P2 (GenBank accession no. MT903344. 1) were deposited in the European Nucleotide Archive (BioProject accession no. PRJEB53055). Assembled consensus sequences were deposited in the National Center for Biotechnology Information under accession nos. ON585029-ON585038. All accession numbers are included in Supplementary Table  1.
Period of study From the climatological study presented in  Ricaud et al. (2020) , the SLWCs are mainly observed above Dome C in summer, with a higher occurrence in December than in January: 26% in December against 19% in January representing the percentage of days per month that SLW clouds were detected during the YOPP campaign (summer 2018-2019) within the LIDAR data for more than 12 hours per day. We have thus concentrated our analysis on December and the 4 years: 2018-2021. Since we have to use the three data sets (LIDAR, HAMSTRAD and BSRN) in time coincidence, the actual number of days per year selected in our analysis is presented in Table  1. Based on these 5 , we performed a systematic study over the 4-summer period by calculating the surface radiation anomaly ∆ defined as: ∆ = - (2) for Net, LWD, LWU, SWD and SWU.
Statistical Method The datasets are binned into 1°C-wide bins for , 0. 2 g m -2 -wide bins for LWP, and 5 W m -2 -wide bins for ∆. The number of points per bin is calculated for all the paired datasets, namely -LWP, and ∆ -LWP (Net ∆ -LWP, LWD ∆ -LWP, LWU ∆ -LWP, SWD ∆ -LWP and SWU ∆ -LWP). The 2D distribution of the Probability Density Function (PDF) is calculated for the paired datasets and defined as = 100 , where and are the count number in the bin ij and the total count number ( = ∑ ∑ ), respectively, with M and N being the total number of bins in LWP on one side, and in temperature or ∆ on the other side, respectively. For each value of within a 1°C-wide bin j or ∆ within a 5 W m -2 -wide bin j, a weighted average of LWP ( ) is calculated together with its associated weighted standard deviation ( !"# ) considering all the values within 0.
2 g m -2 -wide bins from i=1 to M, with M the total number of LWP bins and $ the weight, namely the number of points ($ = ), associated to the bin ij: = ∑ % !"# & '( ∑ % & '( (3) and !"# = ) ∑ % (!"# +!"# , ). & '( ∑ % & '( (4) For each and ∆ dataset, the distribution of the total count numbers per 1°C or 5 W m - 2 lists all the fitted parameters (0 1 , C 1 , 1 and c with k = 1 to 2 or 3). In the relationship between x ( or ∆ ) and LWP, we have considered / ( or ∆ ) to be significant when: / ≤ C 1 ± 1 for k = 1 or 2 (for ∆ ) or 3 (for ) (6) and highlighted this significant point by showing the associated and !"# with j = 1, …,.
02°C). The LWP random and systematic errors are difficult to evaluate since there is no coincident external data to compare with. Nevertheless, the 1-RMS error over a 7-min integration time can be estimated to be 0. 25 g m -2 giving a random error on the weighted average LWP less than 0. 08 g m -2. Based on clear-sky observations, the positive bias can be estimated to be less than 0. 4 g m -2. Theoretically, SLW should not exist at temperatures less than -39°C although it has been observed in recent laboratory measurements down to -42. 55°C  (Goy et al. , 2018). Using equation (  8 ) with an LWP bias of 0. 4 g m -2 gives a temperature of -39. 8°C (~0. 8°C lower than the theoretical limit of -39°C), so the biases estimated for temperature and LWP are very consistent with theory. The estimation of systematic and random errors on LIDAR backscattering and depolarization signals and their impact on the attribution/selection of SLWC is not trivial.
Figure2highlights the time evolution of the SLWC obtained on 27 December 2021 together with some snapshots from the HALO-CAM video camera taken with or without SLWC on: 01:00 (no SLWC), 07:19 (SLWC), 09:00 (no SLWC), 10:14 (SLWC), 13:00 (no SLWC), 16:03 (SLWC), 18:01 (no SLWC) and 20:53 UTC (SLWC). SLWCs (high backscatter and low depolarization signals) are clearly detected at 07:00-08:00, 10:00-11:00, 16:00-17:00, 21:00-22:00 and 23:00-24:00 UTC over an altitude range 500-1000 m above ground level (agl). In general, SLWCs observed over the station did not correspond to overcast conditions. Over the 4 summers (December 2018-2021), we have selected 3 datasets in time coincidence with SLWC: LWP, and SR. In order to estimate the impact of the SLWC onto.
Acknowledgments The present research project Water Budget over Dome C ( H2O-DC ) has been approved by the  Year of Polar Prediction (YOPP) international committee. The HAMSTRAD programme (910) was supported by the  French Polar Institute, Institut polaire français Paul-Emile Victor (IPEV) , the  Institut National des Sciences de l'Univers (INSU) / Centre National de la Recherche Scientifique (CNRS) ,  Météo-France  and the  Centre National d'Etudes Spatiales (CNES). The permanently manned Concordia station is jointly operated by  IPEV  and the  Italian Programma Nazionale Ricerche in Antartide (PNRA). The tropospheric LIDAR operates at Dome C from 2008 within the framework of several Italian national (PNRA) projects. We would like to thank all the winterover personnel who worked at Dome C on the different projects: HAMSTRAD, aerosol LIDAR and BSRN.
Data availability HAMSTRAD data are available at http://www. cnrm. meteo. fr/spip. php?article961&lang=en (last access: 3 May 2022). The tropospheric depolarization LIDAR data are reachable at http://lidarmax. altervista. org/lidar/home. php (last access: 3 May 2022). Radiosondes are available at http://www. climantartide. it (last access: 3 May 2022). BSRN data can be obtained from the ftp server (https://bsrn. awi. de/data/data-retrieval-via-ftp/) (last access: 3 May 2022).
3b ), and copy number gains of small fragments (<1 kb) were observed in two cases (Supplementary Fig. 3b ). We compared the curated SVs to the database gnomAD-SV  20  and did not find SVs with matching coordinates (tolerance of the breakpoints ±1 kb and allele frequency >0. 01). To identify SV-breakpoints that are potentially disease-causing, we searched for genes associated with intellectual disability (ID), the most prevalent phenotype in our cohort, that was hit by a curated small-or large-scale SV (Supplementary Table  2 ). Six out of eleven cases harbor breakpoints within genes associated with ID (Supplementary Table  2 ). For instance, we detected a breakpoint in GRIN2B (CT3), which was previously identified in this case and described as causative  21. Moreover, further breakpoints affecting SOX5 were identified, which might contribute to the phenotypic spectrum in this case.
As a next evaluation approach, we mapped the PacBio long-read data to custom genomes generated based on our reconstructions. Next, we checked, if PacBio long-reads span breakpoint junctions predicted by our curated novel-adjacency set. For 365/376 junctions, one or several supporting PacBio reads could be found (Supplementary Fig. 19b ). For 9/11 cases, all tested junctions are covered by junction-spanning reads (Supplementary Fig. 19b ). For case CT2, a single junction has no supporting PacBio read, but the fraction of junction-spanning reads is, for unknown reasons, very low for this sample in general, which also leads to large deviations in the SV calling when comparing Illumina-based and PacBio-based SV calling (Supplementary Fig. 1b ). For the most complex case CT5, 10 junctions had no support by spanning PacBio reads.
A useful extension of our approach could be the testing for polymorphisms. During the recomposition, the different pieces of the Hi-C map are assembled, but no additional normalization for the individual pieces, such as proposed in ref. 35 was implemented. The recomposition of the Hi-C map, as well as the permutation of possible reconstructions, was limited to fragments that cover a complete Hi-C bin (25 and 100 kb, respectively); smaller fragments were removed for these steps. An alternative approach without the issues of small fragments is the mapping of Hi-C reads to a custom rearranged genome as performed for the evaluation of our reconstructions (Supplementary Data 2). Our curated set of novel adjacencies provided the basis for an indepth analysis of the rearranged chromosomes, the distribution of the breakpoints, and their effect on gene expression.
Subjects From our in-house cohort of individuals with chromosomal rearrangements detected by karyotyping, we selected seven cases with >3 chromosomal rearrangements to be enrolled in this study. Through collaborative efforts, we obtained an additional four cases. In total, 11 constitutional CGRs were selected for this study. Informed consent to publish genomic and clinical data were obtained from all patients (or their legal guardian). The study adhered to the Declaration of Helsinki standards, and was approved by the internal Ethics Committee of the Institute for Human Genetics of Charité-Universitätsmedizin Berlin, Berlin, Germany. Peripheral blood lymphocytes were used for establishing lymphoblastoid cell lines by EBV transformation. Molecular cytogenetics investigations were performed before for CP2  36  and CT3  21.
Range selection mode "BPstart" 30,000 bp, "BPEnd" 80,000 bp with library input of 3-5 µg. Library sequencing was performed on Sequel II system (Pacific Biosciences). The eleven PacBio CLR datasets were generated on a PacBio Sequel II machine. One sequencing run with a single SMRT cell was performed per patient with the exception of CT3 (2 SMRT cells). We aligned all datasets to the GRCh37 human reference genome using pbmm2 (version 1. 3. 0, parameters: -preset "SUBREAD", -median-filter), yielding alignment coverages between ~20× and 40× (Supplementary Fig. 15 ). Median read lengths varied between 8 kb for patient CT3 and 29 kb for patient CP4.
Windows with an average coverage higher than three times the genome-wide average coverage were annotated as high-coverage regions, and novel adjacencies found in such regions were filtered out. Gap-based filtering. To remove spurious novel adjacencies caused by the presence of gaps in the reference sequence, novel adjacencies with a distance of less than 10 kb to a reference gap were filtered out. Gap locations were detected using seqtk (version 1. 3, https://github. com/ lh3/seqtk, parameters: cutN -n 1000). Duplication-based filtering. Due to their length and similarity, segmental duplication regions can confuse the read alignment algorithm leading to erroneous alignments. To remove unreliable novel-adjacency calls between related segmental duplication regions, novel adjacencies overlapping annotated segmental duplication regions (source: http:// humanparalogy.
Hi-C bioinformatics analysis Paired-end sequencing data were processed using the Juicer pipeline v1. 5. 6, CPU version  42  with BWA-MEM (version 0. 7. 17)  38  for aligning short reads to reference genome hg19. Alternative haplotypes were removed from hg19, and the sequence of the Epstein-Barr virus (NC_007605. 1) was added. Replicates were merged by combining filtered and deduplicated read-pairs output from the Juicer pipeline. Juicer tools (version 1. 7. 5)  42 was used to create hic-files for visualization and downstream analysis. Juicebox (Desktop version 1. 8. 8)  43  was used for the inspection of raw count maps, as well as maps normalized with Knight and Ruiz (KR) normalization  25, 42, 44  at different bin sizes. For the generation of Hi-C maps, we used read-pairs with mapping quality (MAPQ) ≥ 30.
Haplotyping and phasing Preparation of Hi-C reads for haplotyping. In order to use Hi-C reads for haplotyping, we separately mapped the Hi-C reads of different replicates to the human reference genome hg19. For this, we followed the practice suggested by HapCUT2  47. We used BWA-MEM (version 0. 7. 17)  38  and Samtools (version 1. 10))  40  for mapping single reads of Hi-C pairs and for sorting the aligned files, respectively. We used the Hi-C_repair tool developed in HapCUT2  47  and Samtools to combine single-read alignment files. We finally used the Picard tools (version 2. 20. 8-0) (https://broadinstitute. github. io/picard/) for marking duplicates. Finally, we merged the alignment files of different technical replicates into one Hi-C BAM file for each sample. Haplotyping with small variants.
PacBio, Illumina, and Hi-C data were integrated to compute the haplotypes. PacBio and Illumina reads were mapped to the GRCh37 reference genome (See sections:'PacBio genome sequencing' and 'Illumina genome sequencing', respectively). We used Illumina reads for variant calling. We parallelized the jobs over the chromosomes to speed up the processes. The following steps were performed on individual chromosomes. First, we called small variants (variants < 50 bp) using freebayes (version 1. 2. 0)  48. At this step, we filtered out homozygous variants and those heterozygous variants with a calling quality of <30. We then extracted haplotype informative reads, the reads with at least two variants, from PacBio and Hi-C data using extractHAIRS module of HapCUT2. Afterward, Hi-C and PacBio informative reads were merged and fed to HapCUT2 for haplotyping.
Analysis of breakpoint signature For each breakpoint, Illumina reads with a minimum of 10 softclipped bases and a mapping quality ≥20 that are located ≤10 bp away from the called location were fetched using the python package pysam (version 0. 15. 2). The reads were separated into left (L) and right (R) depending on the distance of their alignment to the breakpoint location. If no supporting clipped reads were found in one or both directions, the window was iteratively extended up to 10 kb. The genomic distance between the median clipped-positions of the L-and R-read-groups was computed, representing the amount of gained or lost material i. e. , the InDel size at hg19 aligned breakpoints. Only breakpoints supported by 3 or more reads clipped at the same reference position for each side were included in the InDel analysis.
All others are specified as NA. To compute the junction homology, the 50 bp consensus sequence of L/R-reads around the respective median clipped position was aligned to the 25 bp aligned consensus sequences of the L/R-reads at the target breakpoint. The alignment was performed using the pairwise2. align. localms function of the python package biopython (version 1. 73), assigning a +2 for a match, -1 for mismatch or opening a new gap, and -0. 1 for extending an existing gap. These sequences were chosen with respect to the individual breakpoint's strand annotation. For example: Considering a breakpoint with a "++"-annotation, the 50 bp consensus sequence of all L-reads around the median clipped position (i. e. , 25 bp aligned sequence and 25 bp clipped) was aligned to the 25 bp aligned consensus sequence of all R-reads at the target breakpoint.
Evaluation of the reconstructions of derivative chromosomes We performed several evaluations of our reconstruction. We generated custom genomes as Fasta files for each case according to the respective reconstruction of derivative chromosomes. In the next step, we aligned PacBio long-reads to the corresponding custom genome and checked, how many reads spanned the junctions in a window of 100 bp around the junction Supplementary Fig. 19b. We also mapped the Hi-C data to custom genomes and inspected the Hi-C maps. The Hi-C data is more difficult to evaluate because the map is an overlay of the rearranged allele, which is now ideally in order, but the WT allele produces patterns of rearrangements. We tried to mitigate the effect of the WT allele, by subtracting a control sample which was scaled to 50% of the overall intensity on autosomes (neglecting the diagonal and first subdiagonal).
Acknowledgements We would like to thank the individuals and families for their collaboration and contribution to this project. We thank  Volkmar Beensen  and  Gotthold Barbi  for the collaboration on two published cases. We thank  Michael Robson ,  Lila Allou , and  Alexandra Despang  for their comments on the manuscript. This work was supported by grants  MU 880/16-1  from the  Deutsche Forschungsgemeinschaft (DFG)  to S. M and the  Bundesministerium für Bildung und Forschung (BMBF)   FKZ 031L0169B  to S. M. and  FKZ 031L0169A  to M. V. M. S. is a DZHK principal investigator and supported by grants from the  DFG  ( SP1532/3-1 ,  SP1532/4-1 , and  SP1532/5-1 ) and the  Deutsches Zentrum für Luft-und Raumfahrt  ( DLR 01GM1925 ).