dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the unique structure of the Red Sea basin and surrounding deserts, and the ocean and atmospheric connections to the global circulation, the need for integrated state-of-the-art modeling and supporting datasets is acute. The newly established King Abdullah University of Science and Technology (KAUST) acts as a hub of frontline research in the biological, physical, geological aspects of the Red Sea region. In this context, KAUST dedicated important resources for studying the Red Sea and understanding its resources, with the goal of preserving its fragile ecosystem while at the same time supporting the local authorities to exploit its unique resources. In parallel to the on-going hydrographic and ecological observational efforts, and through sustained and strong collaborations with many international organizations, the Red Sea Modelling and Prediction group at KAUST has led the development of a comprehensive and integrated modeling and prediction system to study, monitor and predict the atmospheric and oceanic phenomena of the region.
The Red Sea MITgcm outputs were validated against different datasets and in various settings and applications. These include studying the general and overturning circulation  (Yao et al. 2014a,b) , the mixed layer variations  (Krokos et al. 2019b ), the deep water formation events  (Yao and Hoteit 2018) , the eddies characteristics  (Zhan et al. 2014; 2016; 2019) , the Red Sea internal/baroclinic tides  (Guo et al. 2018) , phytoplankton phenology (bloom timing) in the southern  (Dreano et al. 2016 ) and northern Red Sea  (Gittings et al. 2018 ). The adjoint model of MITgcm is being further used to conduct sensitivity studies of quantities-of-interest with respect to the model states and atmospheric forcings across the Red Sea  (Zhan et al. 2018). WWIII  (Tolman 2008) , the third-generation wave model developed at the NOAA/NCEP, is used to simulate the waves in the Red Sea at 1-km resolution.
SKRIPS was implemented to investigate the heatwaves in the Red Sea region on seasonal scales, demonstrating that the evolving ocean temperatures considerably improves the prediction of the amplitude of these heatwaves, particularly in the coastal region  (Sun et al. 2019). Data assimilation: Assimilation is a process by which a model is constrained towards available observations to obtain improved forecasts/hindcasts. Our atmospheric and ocean general circulation models are equipped with state-of-the-art assimilation packages; the Data Assimilation Research Testbed -DART  (Hoteit et al. , 2014) , which comprises advanced ensemble schemes  (Anderson et al. 2009 ) and the adjoint-based ECCO inverse system  (Stammer et al. 2002)  for MITgcm, and the extensive assimilation package of WRF  (Barker, 2012) , which also includes DART.
Visualization and data analytics: Advanced visualization is an integral component of our system to support data analysis tasks for scientists and non-expert users. We have developed a visual analytics application, called RedSeaAtlas  (Afzal et al. 2019a ), considering the tasks, computational and operational requirements of the variety of simulation models and observational datasets of the Red Sea region. The application provides a suite of interactive visualization tools that can facilitate diverse research tasks through interactive visual exploration and analysis of spatio-temporal multivariate data (Fig. 5 ). These include interactive feature selection, spatial and temporal filtering, overview and details-on-demand, and capability to analyze data from disparate data sources  (Afzal et al.
Study of the Red Sea Region Circulation, Climate and Ecosystem Atmospheric variability: Motivated by the background knowledge from the analysis of global fields, and to meet the need for high-resolution datasets to resolve mesoscale characteristics, we developed a regional reanalysis at 5 km resolution for a 39-year period. The reanalysis fields accurately reproduced the known Red Sea climatic features such as Red Sea Trough, Red Sea Convergence Zone, Arabian anti-cyclone, and seasonal circulation patterns  (Viswanadhapalli et al. 2017). It has been further extensively used to drive iReds ocean models and for subsequent regional applications, and to support the mega-projects being developed along the Red Sea coast. Several studies were also conducted using convective-permitting WRF configurations to investigate the predictability of extreme rainfall and winds, presented in more detail below.
2013; Dreano et al. 2016; Xie et al. 2019). The ocean and ecosystem model simulations, combined with various remote sensing datasets, have provided new insights on how ocean circulations and biogeochemical processes drive the ecosystem in the Red Sea. This unified framework led to new understandings of the interplay between physical drivers such as buoyancy fluxes and ocean mixing, and important tracers for the ecosystem functioning. Despite a limited supply of nutrients, subsurface intrusion from the Gulf of Aden and the winter deep convection drive elevated values of primary productivity in the south during summer  (Raitsos et al. 2013; Dreano et al. 2016 ) and in the north during winter  (Gittings et al. 2018). Moreover, marine larvae/planulae disperse nearly passively with the circulation, forming genetic connectivity across geographically separated reef sites within the basin  (Raitsos et al.
Ecological indicators through remote sensing: The recent development of multi-sensor based satellite missions, such as the Ocean Colour Climate Change Initiative (OC-CCI), has increased the coverage of surface Chl-a measurements in the Red Sea, by enhancing the retrieval of satellite data under adverse atmospheric conditions (Fig. 6 ). Using recent available in-water measurements of chlorophyll,  Brewin et al. (2013 Brewin et al. ( , 2015 Brewin et al. ( , 2019) )  and  Racault et al. (2015)  have shown that both standard ocean-colour algorithms and the OC-CCI algorithm perform well in the Red Sea, supporting the use of satellite Chl-a datasets and derived ecological indicators. The open waters of the Red Sea Chl-a depict a distinct seasonality with maximum concentrations seen during wintertime, which we attributed to vertical mixing in the north and wind-induced horizontal intrusion of nutrient-rich water in the south, and minimum concentrations during summer  (Raitsos et al.
Acknowledgments The development of the Red Sea modeling system is being supported by the  Virtual Red Sea Initiative  and the  Competitive Research Grants (CRG) program  from the  Office of Sponsored Research at KAUST ,  Saudi Aramco Company  through the  Saudi ARAMCO Marine Environmental Center at KAUST , and by funds from  KAEC, NEOM  and  RSP through Beacon Development Company at KAUST. iReds is implemented on the Shaheen-II XC40 -supercomputer at  KAUST , a 36-cabinet Cray composed of 6,174 dual socket compute nodes based on 16 core Intel Xeon E5-2698v3 processors running at 2. 3GHz. Shaheen has a total of 197,568 processor cores and 790TB of aggregate memory, and is capable of delivering a theoretical peak of 7. 2 PFLOP/s.
The CMS Phase-2 tracker will consist of the Outer Tracker, made of silicon modules with strip and macro-pixel sensors, and the Inner Tracker (IT), based on silicon pixel modules  [6]. The high granularity of the IT offers excellent spatial resolution, which is important for a precise three-dimensional reconstruction of particle trajectories, as well as the identification of primary interaction vertices and secondary decay vertices. One quarter of the latest layout of the Phase-2 IT in the - view is shown in figure  1. The IT will consist of a barrel component with four CMS adopts a right-handed coordinate system with the origin centred at the nominal collision point inside the experiment. The  axis points towards the centre of the LHC, the  axis points vertically upwards and the  axis points along the beam direction.
A : The CMS Inner Tracker, made of silicon pixel modules, will be entirely replaced prior to the start of the High Luminosity LHC period. One of the crucial components of the new Inner Tracker system is the readout chip, being developed by the RD53 Collaboration, and in particular its analogue front-end, which receives the signal from the sensor and digitizes it. Three different analogue front-ends (Synchronous, Linear, and Differential) were designed and implemented in the RD53A demonstrator chip. A dedicated evaluation program was carried out to select the most suitable design to build a radiation tolerant pixel detector able to sustain high particle rates with high efficiency and a small fraction of spurious pixel hits. The test results showed that all three analogue front-ends presented strong points, but also limitations.
The Differential front-end demonstrated very low noise, but the threshold tuning became problematic after irradiation. Moreover, a saturation in the preamplifier feedback loop affected the return of the signal to baseline and thus increased the dead time. The Synchronous front-end showed very good timing performance, but also higher noise. For the Linear front-end all of the parameters were within specification, although this design had the largest time walk. This limitation was addressed and mitigated in an improved design. The analysis of the advantages and disadvantages of the three front-ends in the context of the CMS Inner Tracker operation requirements led to the selection of the improved design Linear front-end for integration in the final CMS readout chip. Green lines correspond to pixel modules with two readout chips and orange lines represent modules with four chips.
RD53A analogue front-ends A high-performance radiation tolerant pixel readout chip is essential for good tracking performance of the IT operating during the HL-LHC era. Such a readout chip is being designed in TSMC  [11]  65 nm CMOS technology by the RD53 Collaboration  [12] , a joint effort between the ATLAS and CMS experiments. A large-scale demonstrator chip called RD53A  [13]  containing design variations was produced. Its purpose is to demonstrate the suitability of the chosen technology for low threshold, low noise, and low power operation at high hit rates, to verify sufficient radiation tolerance  [14] , and to select the most suitable design for the final readout chip. It is a mixed signal chip, having both analogue and digital circuits. It features custom-designed intellectual property blocks, such as clock data recovery and phase locked loop blocks  [15]  for the clock recovery from the command stream running at 160 Mb/s; a high speed output transmitter with a current mode logic cable driver  [16]  sending data at 1.
28 Gb/s on up to four output lanes; and a shunt low-dropout regulator  [17]  for serial powering of the pixel modules. The chip size is 20. 0 × 11. 8 mm 2 , which is about half the size of the final chip, as it shares the chip reticle with CMS Outer Tracker chips. The pixel matrix is composed of 400 × 192 square pixels with 50 µm pitch. All the common analogue and digital circuitry needed to bias, configure, monitor, and read out the chip is placed at the bottom chip periphery  [13]. The analogue-to-digital conversion is performed by the analogue front-end (AFE), whose basic structure (shown in figure  3 ) includes a charge sensitive amplifier (CSA), usually referred to as preamplifier (PA), a feedback circuit taking care of the signal return to baseline and leakage current compensation, a threshold discriminator, a threshold trimming circuit to address pixel-to-pixel variation of the threshold voltage, and a time-over-threshold (TOT) counting of the input signal amplitude.
A dedicated evaluation program was established and the most relevant detector performance parameters were studied. The key measurements that enabled CMS to identify the most suitable option for integration into the CMS pixel detector are presented in this paper. All presented test results were obtained with the BDAQ53 test system  [27] , using the calibration injection circuit, with RD53A chips bump-bonded to sensors with rectangular pixels, i. e. 100 × 25 µm 2 , if not -5 - -6 -otherwise stated, and operated at cold temperature (T ≈ -10 °C), which is the lowest temperature that could be achieved with the cooling systems available for the lab setups.
The detection threshold was set to 1000 e -and the full range of available charges up to 35 ke -was scanned, using a finer charge step for low charges where timing is critical. For every pixel the charge was injected 50 times for each time delay. Combination with time of arrival simulation. A Monte Carlo simulation was performed within the standard CMS simulation and reconstruction software framework called CMSSW  [30]  to evaluate the influence of the time response of each AFE on the detector performance and to estimate the resulting fraction of spurious hits. The time of arrival of particles was simulated for different locations in the IT detector, given that it depends on the position of the pixel module with respect to the interaction point. Sixteen different locations were studied: the centre ( = 0) and edge module of each barrel layer, the innermost and outermost module of the first and last small disc, as well as of the first and last large disc (figure  1 ).
Acknowledgments The tracker groups gratefully acknowledge financial support from the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CERN ;  MSE  and  CSF (Croatia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NKFIA K124850 , and  Bolyai Fellowship of the Hungarian Academy of Sciences (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  INFN (Italy) ;  PAEC (Pakistan) ;  SEIDI, CPAN, PCTI  and  FEDER (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  STFC  ( United Kingdom );  DOE  and  NSF  (U. S. A. ). Individuals have received support from  HFRI (Greece).
We sequenced an average of 73 million paired reads per individual, 12% of which were unique (Supplementary Table  S3 ). On average, 0. 38% of the data mapped to the target space, representing an on-target effective coverage of 53. 89-fold (range: 2. 52-fold to 230. 70-fold; Fig. 1B , Supplementary Table  S3 ), with 72% of the target space covered by at least 4 reads per individual. Samples G282, G1392 and G638 performed poorly, with <50% of the target space covered at a minimum depth of 4 reads (Supplementary Fig. S1 ). Overall performance can be assessed by calculating how well the capture resequencing experiment went relative to expectations had we performed random shotgun sequencing. In that regard, we observe an average enrichment of 125-fold (88-346fold) (Supplementary Table  S3 ). Individuals with extremely low proportions of target space covered by at least 4 reads (<30%) (G638 and G282) and high heterozygosity and high levels of human contamination were removed from further analysis (individuals G374 and G1392, Fig.
1C  and Supplementary Fig. S2 , Table  S3 ). With the final dataset of 13 survivors and 14 nonsurvivors, we validated that the genotype information obtained was in concordance with previously published gorilla whole-genomes, as determined by a principal component analysis (PCA) (Supplementary Fig. S3 ). Since the sampling of the gorilla population was done to avoid related individuals by sampling from different groups (a total of 45 groups and 31 solitary males were present before the outbreak), we identified a single case of close relatedness (1st degree) among them (non-survivor G374 and survivor G739; kinship coefficient = 0. 4) (Supplementary Fig. S4 ). In addition, we observed no stratification correlating with survivor/non-survivor classification. Individuals appear to be dispersed randomly across a dendrogram derived from shared genotype likelihood dosage states (Fig.
2A ), and a univariate linear regression of survivorship on the top 5 PCs identified no significant structure associated to survivorship (P-value >0. 1; Supplementary Fig. S5 ). Hence, we determined no genome-wide group structure differences between survivors and non-survivors. The overall level of genetic diversity within the target space of the studied gorillas was, on average, lower than that of western lowland gorillas obtained from whole-genome sequencing (Supplementary Fig. S6 )  [30, 31] , an expected outcome following the target capture procedure. Moreover, there are no statistically significant differences in heterozygosity between survivors and non-survivors (Student's ttest, p-value = 0. 34; Fig. 2B ). In order to determine genetic differences between the groups, we calculated three summary statistics on a dataset of 6852 high-quality variants: (1) the difference in allele frequency (ΔFrequency), (2) the fixation index (F ST ), and (3) the significance level (α) of each variant for its association with the binary trait survivor/non-survivor (Fig.
3 ). We found 118 SNPs within the target space that surpassed our α threshold in the association test, and we also reported their ΔFrequency and F ST values. However, after controlling for type I error none of these remained significant. Out of these, seven genes have multiple nominally significant SNPs (CD1B, IGKV4-1, HLA-A, ACTB, LYN, CD68 and MX1), while 10 neutral regions (~10 kb each) have at least 1 nominally significant variant (Supplementary Table  S4 ). For comparative validation, we repeated the association analysis using ANGSD  [32] , a software explicitly built to work with low coverage data that relies on genotype likelihoods. With this method, we were able to recover the majority of the genes found above (30 out of 36). However, ANGSD returned more hits and thus more genes (Supplementary Figs.
Mapping and Variant Discovery Prior to mapping, paired-end reads belonging to the same library but sequenced in different lanes were merged into a single FASTQ file. PCR duplicates were directly removed from FASTQ files using FASTuniq (v1. 1)  [57]. Overlapping reads were merged (minimum overlap of 10 bp, minimum length of final read to 50 bp) using PEAR (v0. 9. 6)  [58]. Reads were mapped using BWA mem (v0. 7. 12)  [59]  to the human reference genome Hg19 (GRCh37 from the UCSC database). Assembled reads were mapped considering single-end specifications and unassembled reads considering paired-end specifications. Any remaining PCR duplicates were removed using PicardTools MarkDuplicates (v1. 95) (http://broadinstitute. github. io/picard/). Non-primary alignments and reads with quality below 30 were filtered from the dataset with samtools (v1.
5)  [60]. Finally, single-end and paired-end reads were merged into a single BAM file using Picard-Tools MergeSamFiles (http://broadinstitute. github. io/ picard/). The percentage of aligned reads for each DNA extraction and sample was calculated by dividing the number of uniquely and high-quality mapped reads (without duplicates) by the total number of sequenced reads. The percentage of on-target aligned reads was calculated for each sample by dividing the number of on-target filtered reads by the number of sequenced reads. The average target effective coverage was calculated dividing the number of aligned bases by the total length of the targeted genomic space. Finally, the enrichment factor (ER) of the capture performance was calculated using the ratio between the on-target reads by the total mapped reads over the targeted size by genomic size (ER = (On-Target Reads/Mapped Reads)/(Target Size/Genome Size)).
The coverage for each target region was retrieved using SAMtools bedcov  [60]. For variant calling, all BAM files belonging to the same sample were merged into a single BAM file using Picard-Tools MergeSamFiles (v1. 95) (http://broadinstitute. github. io/picard/). Variant discovery was performed using GATK 'Unified Genotyper'  [61]  for each sample independently with the following parameters -out_mode EMIT_ALL_SITES -stand_call_conf 5. 0 -stand_emit_ conf 5. 0 -A BaseCounts -A GCContent -A RMSMap-pingQuality -A BaseQualityRankSumTest. Afterwards, we merged each sample gvcf to a single one using GATK `CombineVariants'  [61]  with the following parameters -genotypeMergeOptions UNIQUIFY -exclude-NonVariant. We also included in the gvcf the genotype information of available whole genome data of six Gorilla beringei beringei, eight Gorilla beringei graueri, one Gorilla gorilla dielhi, and twenty-three Gorilla gorilla gorilla samples  [30, 31].
Genetic distance, relatedness and heterozygosity We used the genotype likelihood information for the studied individuals to obtain the genetic distance by running ngsDist in ANGSD  [66]  with the following parameters: --n_sites 5477 --probs TRUE --pairwise_del. Then, we constructed an Euclidean distance matrix based on the genotypes and performed a hierarchical clustering using the R package ape  [67]. We also run PCAngsd  [64]  considering only the study gorillas to discard any possible intra-group structure. The theta coefficients of kinship (probability of a pair of randomly sampled homologous alleles are identical by descent) were calculated using the NgsrelateV2  [68, 69]  on the genotype likelihood obtained with ANGSD  [32]. Note that all possible genotype likelihoods, even outside the target space (N = 226,094), were used since the coverage of the kinship and neutral markers was insufficient.
Association analysis The genotype calls obtained with GATK were further filtered with Plink  [65]  to exclude variants considering their missing rate (--geno 0. 05), minor allele frequency (--maf 0. 01) and Hardy-Weinberg equilibrium (--hwe 0. 00001). The final dataset consists of 27 samples (13 survivors and 14 non-survivors) and 6852 high-quality variants. Nominal significance was set at an alpha of 0. 05 and a Bonferroni corrected alpha threshold of 7. 3x10-6 (0. 05/6852) was defined to account for familywise error. Associations were tested for by a chi-square allelic test with one degree of freedom and p-values were estimated by permutation in plink (plink -assocmperm 10,000)  [65]. The p-values were plotted in a Manhattan plot in R (v3. 4. 1). The allele frequency for each group (survivors and non-survivors) was obtained using the -freq2 option in VCFtools  [62].
Then, we calculated the allele frequency difference per SNP by subtracting the allele frequency in non-survivors from the allele frequency in survivors (ΔFrequency). We chose a threshold of ±0. 2, and plotted the allele frequencies using R. We also reported the allele frequency differences per SNP considering all samples (pre-Ebola outbreak: survivors and non-survivors) and the survivors (post-Ebola outbreak), which results in more modest allele frequency differences. The fixation index (F ST ) between both groups was calculated using VCFtools -weir-fst-pop option (Weir and Cockerham)  [62]  with a threshold at 0. 15, and results were plotted in R. We retrieved markers with α ≤ 0. 05 in the association test and a p-value <0. 05 in the permutation test. The ANGSD software  [32]  was used to perform a replication of the association analysis using the following parameters -minQ 20 -minMapQ 30 -doAsso 1 -GL 1 -out assocGQ_filter -doMajorMinor 1 -doMaf 1 -SNP_pval 1e-6 -minInd 22 -minMaf 0.
02. The output of the association analysis are LRT values (Likelihood Ratio Test), which are chi square distributed with one degree of freedom. Since we set a threshold of significance at 95% confidence, the minimum score to be significant is LRT = 3. 84. In both association analyses, we linked the nominally significant SNPs with their genes (Supplementary Table  S4  and S5 ). Genes with multiple nominally significant SNPs were considered to be potentially more relevant. Subsequently, we compared the overlap of discovered genes between the datasets (Unified Genotyper and ANGSD) in a Venn diagram (Supplementary Fig. S8 ) using the R package VennDiagram  [71].