dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing and SNP calling From the African Genome Variation Project 2,26 we extracted 50 Egyptian genome sequences and 24 from each of the following Ethiopian populations: Amhara, Ethiopian Somali, Oromo, Wolayta, and Gumuz. The African Genome Variation datasets were obtained from European Genome-Phenome Archive,  27  EGA: EGAD00001000598, EGA: EGAD00001003296, EGA: EGAD00010001221, under the terms of the Wellcome Sanger Institute (WSI) data access agreement.
Data Quality Control and Filtering The data were filtered to minimize batch effects potentially introduced by the presence of samples sequenced at different depths by different labs. For descriptive statistics of the TrypanoGEN dataset all loci were retained. For all other analyses, sites that met any of the following criteria were removed; missing data > 10%, loci with < 3 SNP calls, minor allele frequency (MAF) < 0. 01, Hardy-Weinberg equilibrium p < 0. 001. For population analyses, the remaining SNP loci were thinned in order to retain only loci with r 2 < 0. 1. Individuals with >10% missing data were also removed. Data were phased with Shapeit2 v2. r837,  28  which also imputed missing data, prior to combining our data with genomes from the 1000 genomes and African Genome Variation projects using BCFtools (v.
Signatures of Selection The sequence data were scanned for regions that might be under selection using the Extended Haplotype Homozygosity (EHH) test within and between populations. 39 The SNP were phased using SHAPEIT v. 2. 2,  28  and the R software package rehh3 40 was used to calculate two EHH derived statistics: the intra-population integrated Haplotype Score (iHS)  41  and inter-population xpEHH score,  42  that identify SNPs that are under selection in one population but not in another. Only SNPs with a MAF > 0. 05 were included in the analysis. We used the method of Voight et al. to identify the regions of the genome under the strongest selection pressure;  41  the genome was divided into 100 kb bins and the fraction of SNP with iHS > 2 in each bin was obtained. Bins with <20 SNP were disregarded.
Results We sequenced the genomes of 50 individuals from the Nilo-Saharan Lugbara population and 250 from 17 linguistic groups from Guinea, Cote d'Ivoire, Cameroon, Democratic Republic of Congo, Uganda, and Zambia (Tables  S1  and S2 ). The samples from Zambia and Cameroon were sequenced to 303 coverage while other populations were sequenced to 103 coverage. The call rate was 97. 4% in the 103 samples and 99. 4% in the 303 samples. The 303-sequenced samples had higher proportions of heterozygotes (9. 3%) compared with the 103 sequenced samples (7. 5%) and there was a concomitant higher frequency of low Hardy-Weinberg p values in the 103 data (Figure  S1 ). There were 38,963,563 raw variants, filtering removed fourteen individuals and 23,017,723 loci leaving 286 samples and 15,945,844 variant loci that were available for population and signatures of selection analyses.
We assessed the impacts of the variants on function using snpEff; 99% of SNP were classified as ''modifier,'' and these were mainly intergenic; the remaining 1% of SNPs had more informative classifications: low, moderate, or high impact (Table  S4 , Figures  S3B  and S3C ). Of the 1% of SNP with informative classifications (low, moderate, or high impact), nearly 90% were predicted to have moderate impact in both known and novel variants. The frequency of high-impact variants was twice as high in the novel variants as it was among the known variants (6. 3% cf. 3. 0%). There was a larger proportion of rare alleles (MAF < 5%) in the set of novel SNPs than in the known SNPs (Figure  S4 ), as expected for SNPs that are unique to a specific population or geographic region. The Nilo-Saharan Lugbara Population Is Distinct from Other African Populations Bi-allelic loci from the 286 TrypanoGEN samples were merged with 1,000 Genomes and African Genome Variation Project data to obtain 10,857,449 loci that were present in all three datasets for population analysis.
These were filtered to remove linked loci (r 2 > 0. 1) yielding a final dataset of 1,465,578 SNP and 731 samples that were used for MDS, Admixture, and F3 analysis. Multidimensional scaling analysis (Figure  2 ) showed that samples formed tight geographic groups irrespective of data source or sequence coverage. The exception was the Nilo-Saharan Lugbara population from North West Uganda, which was distinct from both the Nilo-Saharan Gumuz of Ethiopia and the Basoga from southeast Uganda. The two Nilo-Saharan populations were well separated from each other and from the East African Niger-Congo B and the Ethiopian Afro-Asiatic populations. Even when combined with a West Eurasian dataset (Figure  S5B ), the two putative Nilo-Saharan populations (Lugbara and Gumuz) appeared as divergent from each other as Niger-Congo-A and Niger-Congo-B populations from East and West Africa.
Signatures of Selection in the Lugbara and Basoga Populations We compared the regions under selection within the Lugbara and Basoga populations. The Basoga population was selected due to their geographic proximity to the Lugbara (500 km) (Figure  1 ), the minimally shared genetic ancestry between these two Ugandan populations (Figure  3 ), and because the Ugandan Basoga can act as representatives of Niger-Congo B populations. Using the phased haplotype dataset of the Lugbara and Basoga populations, the EHH derived integrated haplotype score (iHS) values were calculated using the rehh3 software for which we observed a normal distribution of the absolute iHS values (Figure  S9 ). The Manhattan plot (Figure  5 ) shows 12 regions with extreme iHS (jiHSj > 6). However, there were protein-coding genes within 100 kb of only two of these peaks (ROCK1, DCUN1D4).
SNP Discovery Africa has the most genetically diverse populations on earth but while there are projects to sequence in excess of 100,000 genomes from populations in Europe,  74  Asia,  75  and the Americas 76 the 1000 Genomes Project is still the single largest dataset for Africa with 661 genome sequences. Not only do African genomes have a greater density of polymorphisms than genomes elsewhere, they also frequently have shorter haplotypes, which require a greater density of markers to phase accurately. 77 To date, most African genome-wide association studies (GWASs) have been undertaken using chips designed for West Eurasian populations. This can severely limit researchers' power to discover loci controlling disease. For example, a GWAS to identify loci regulating severe malaria failed to recapture the sickle cell locus because of limited linkage  The American Journal of Human Genetics 107, 473-486, September 3, 2020 479 between markers and the functional SNP.
Demographic Inference In this study, we carried out whole-genome sequencing on populations from six different sub-Saharan African countries, and combined our data with genome sequences from the 1000 Genomes and African Genome Variation projects to better understand the relationship of the Lugbara to neighboring populations. The great diversity of Nilo-Saharan languages meant that they were recognized as belonging to a single family only in 1966 and there is still a debate about whether all these languages share a common root. 80 The Lugbara belong to the large Central Sudanic group of languages, while the Gumuz language has been hard to classify within the Nilo-Saharan family; the language may be an early branch from the family or it may be a language isolate and not related to Nilo-Saharan languages at all.
The patterns of genetic contribution from the Lugbara and Gumuz to the Luhya and Basoga in the F3 data are most consistent with the Admixture data at K ¼ 6 where Gumuz but not Lugbara share a small ancestry component with the Afro-Asiatics. This component (pink) is also present in the Luhya but is marginal in the Basoga (Figure  3 ; K ¼ 6). This component shared between the Gumuz, Basoga, and Luhya may represent an ancient East African population that was present before the Bantu Expansion. The data are consistent with the Gumuz being genetically members of the Nilo-Saharan family and not an isolate, as some linguists have suggested. 10, 12 The large genetic distance between the Lugbara and Gumuz may be indicative of the deep splits within the Nilo-Saharan fam-ily, which merit much greater efforts to capture.
Acknowledgments The authors would like to acknowledge the study participants who donated their specimens, the personnel involved in the community engagement and coordinating sample collection and processing, and the national sleeping sickness control programs of the participating countries. We thank  Dr. Neil Hall  and  Dr. Andy Tait  for their expert advice on the study strategy. We thank  Dr. Zane Lombard  ( University of Witwatersrand ) and  Dr. Adebowale Adeyemo  ( NHGRI ) for facilitating sequencing of samples from  Zambia and Cameroon at Baylor College of Medicine  as well as the H3ABionet for training and support on data analysis. Fiona Marshall  and  Rebecca Grollemund The  American Journal of Human Genetics 107 ,  473-486 , September 3, 2020 483.
, 1999)  was found in 18 species including representatives of Archaea, Bacteria and Protists. The presence of the TRAM domain (PF01938) responsible for nucleic acid binding, has been identified in 54 of the 59 aligned sequences 2 ; however, the remaining five accessions revealed similar sequence structure to those of TRAM domains from the other analyzed taxons (Figure  3 ). The A. thaliana homolog of bacterial miaB gene is known as AT4G36390. Structurally, AT4G36390 resembles miaB as it contains a C terminal RNA-binding TRAM domain and functions similarly to bacterial tRNA methylthiotransferases  (Dabravolski, 2020). AT4G36390 is predicted to localize mainly to the mitochondria and carry an [Fe-S]; however, much of its role remains unknown  (Dabravolski, 2020). In mammals, cyclin dependent kinase 5 regulatory subunitassociated protein 1 (CDK5RAP1) is a type of radical SAM enzyme that reductively cleaves S-adenosyl-L-methionine  (Atta et al.
Genetic analysis We performed WES of the DNA from the proband at a depth of 80× and analyzed the data following GATK's best practices (v3. 4). We kept variants which were: (1) exonic or at splice sites (±10 bp), (2) with a frequency below 0. 5% in gnomAD and our internal database of 1000 exomes, and (3) covered by at least 10 reads and with an allele frequency above 0. 2. We identified 245 variants matching those criteria, including 62 variants in known disease-causing genes in OMIM (Supplementary Table  S1 ). For the analysis, in a diagnostic setting, we focused on those disease-causing genes as we suspected it could be a phenotypic expansion of a known disorder. From the 62 variants, none was identified on X-chromosome. One gene, FDXR, carried two rare variants and could fit with an autosomal recessive inheritance.
FDXR is known to cause a syndrome associating an auditory neuropathy and an optic atrophy (MIM: 617717), but the phenotype did not match with the patient's clinical presentation. Then, we analyzed the 19 rare variants associated with an autosomal dominant disorder. No variant has been identified in a gene associated with a syndromic phenotype with aging-related symptoms. Then, given the severity of the phenotype, we kept only variants absent from 1000 genome and gnomAD, and with either a predicted loss-of-function effect or a CADD phred score (v1. 3) above 20. We ended up with two missense variants: c. 293 C > G p. (Pro98Arg) (GenBank: NM_001130698. 1) in TRPC3 and c. 1160 T > C p. (Leu387Pro) (GenBank: NM_ 003380. 4) in VIM. TRPC3 was associated with a spinocerebellar ataxia (MIM: 616410) and VIM to cataract (MIM: 116300).
Acknowledgements We thank  Dr Hans Heid  and  Professor Harald Herrmann  ( German Cancer Research Center, Heidelberg, Germany ) for providing anti-perilipin antibody and human vimentin cDNA, and  Strelkov SV  ( KU Leuven, Leuven, Belgium ) for crosschecking the mutant structure model. We are grateful to the staff of the zebrafish (in particular  Sandrine Geschier ) and imaging facilities of the  Institut de Geńetique et de Biologie Molećulaire et Cellulaire. TMM dedicates this work to  Prof. Werner W. Franke  on the occasion of his 80th birthday. Funding This work was partially supported by the  DFG (Deutsche Forschungsgemeinschaft)  ( MA1316-15 ,  MA1316-17 ,  MA1316-19 ,  MA1316-21 ,  INST 268/230-1 ) to TMM and  J-EB  ( MA 1316/15-2 ), the  French Programme Investissements d'Avenir  ( ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT ) to CG, and  ROTARY Club des Sables d'Olonne  to BI.
Statistical analyses We used the statistical software XLSTAT 2018. 3 to run the various statistical tests and correlation coefficients. For more information about the data (e. g. , descriptive statistics) please refer to the supplementary material. The condylobasal length estimates were made based on reconstruction images of Endothiodon mahalanobisi mandible and skull  (Ray, 2000) , which was digitally overlaid with photos of our specimens. The South African Endothiodon specimens that we compared to the Mozambican specimens are figured in the Supplementary Data (Fig. S1  to S20).
Acknowledgments We would like to thank  Roger Smith  and  Zaituna Skosan  for help accessing the South African Iziko Museum specimens,  Johann Neveling  and  Nonhlanhla Mchunu  for accessing the Council for Geosciences specimens, and  Bernhard Zipfel  and  Sifelani Jirah  for accessing the  ESI (Witwatersrand University ) collections. Financial support was provided by the  Museu Nacional de Geologia  and by the  Fundação para a Ciência e a Tecnologia (R. A. postdoctoral fellowship   SFRH/BPD/96205/2013 ),  FCT -AGA KHAN Development Network  grant number  333206718 ,  National Geographic Society  grant number  CP-109R-17. We also thank  Dino Milisse ,  Issaia Macaneta ,  Estela Cuambe ,  Marcelino Moiana  and  Lino José  from  Museu Nacional de Geologia (Mozambique). Finally, we thank two anonymous referres for perusing this manuscript.
Data Analysis Residency The total residence time (TRT) provides an indication of the residency of tagged fish at FADs. TRT is defined as the amount of time between the first detection and last detection before the fish permanently left the FAD. The time when an individual first associated with the floating object, prior to the experiment, is unknown. Hence, the real residency of individuals remains unknown. Additionally, some experiments were prematurely interrupted by either commercial fishing operations, resulting in the capture of the entire FAD aggregation, or by equipment failure. TRTs that were not interrupted artificially indicate that the individuals left the FAD of their own accord and were recorded as "natural departures". In order to standardise the varying experimental durations to allow comparisons between the different FADs where tagging was conducted, a residency index was calculated for each individual.
This index was calculated by dividing the TRT by the experimental duration for each FAD (Table  1 ). This standardised index ranges from 0 to 1 and is commonly used in acoustic telemetry to provide estimates of the relative residency of tagged individuals within an acoustic telemetry array [Afonso2012, Ledee2015]. In order to estimate the durations of residency of the two species at FADs, a survival analysis of the TRT was conducted. Survival analysis was used to analyse data where the outcome variable is the time until the occurrence of a particular event (here departure from FAD) with the added ability to handle censored 40° E 45° E 50° E 55° E 60° E 65° E 30° S 25° S 20° S 15° S 10° S 5° S 0° N Longitude Latitude MOZ32 MOZ31 MAY42 SEY43 SEY37 SEY41 SEY59 MOZ34 MAY41 observations (i. e. truncations).
Survival analysis has previously been used in acoustic telemetry experiments with truncated data [Ohta2005, Robert2012a, Stehfest2013]. Kaplan-Meier estimates of the survivor function of TRTs was calculated with 95% confidence intervals. For comparative purposes, two Kaplan-Meier survival functions were generated using i) TRTs considering censored data (i. e. stratified into natural departures and censored data) and, ii) all TRTs with no censoring considered (i. e. all TRTs) to provide residence time estimations and assess the effect of censored data on estimates. A log-rank test was used to test the differences in survival functions between the two species. An exponential survival regression model was then fitted to each of the censored and uncensored curves to provide an estimation of residency for the two species at FADs.
The spectral analysis was performed on individuals that had ≥ 5 days of data to ensure sufficient sample size for the analysis. Additionally, a continuous wavelet transformation (CWT) was computed to examine the stability of the cyclic rhythms, such as a diel pattern, over time. The CWT decomposes a time series into time-frequency space [Percival2000] and has previously been used to examine cyclic rhythms in fish behaviour [Alos2012a, March2010]. Morlet wavelets, using the "dplR" package in R, was used to construct a 2-dimensional wavelet spectrum and calculate a point wise test with a 95% significance level. Spectrograms were then inspected to determine how persistent the cyclic rhythms were over time according to the significance of the signal. Gaps in the detection time series are indicative of periods when individuals ventured outside the range of the receiver.
ANN with two hidden layers was trained by using the back-propagation algorithm to predict different process responses, including effluent COD, biogas production, VFA, alkalinity and effluent pH. The OLR and Influent pH were considered as model inputs. Briefly, most of the studies in the past were focused on predicting VFA for a laboratory-scale anaerobic digester by using available input parameters without considering the difficulty of measuring them. Furthermore, most of the developed models were trained based on the very limited operational conditions, thus the generalization ability and performance of the models in different situations is ambiguous  [14, 15]. In this paper, we study the ability of different data-driven modelling techniques, such as artificial neural networks (ANNs), extreme learning machine (ELM), support vector machine (SVM), random forest (RF) and genetic programming (GP), to develop robust VFA monitoring software sensors from exclusively online easy-to-measure variables.
The wrapper feature ranking method combining different models was used to select the most influential process variables for developing a data-driven soft sensor to increase the accuracy and reduce computation time. The procedure was applied to the wastewater treatment Benchmark Simulation Model No. 2 (BSM2) running in the Matlab Simulink environment. The developed software sensors were compared in terms of accuracy, robustness and transparency. Transparency is important because transparent models are very beneficial for controlling and gaining insight into the modelling procedures of AD processes. Although the Genetic Algorithm, which is very similar to the GP, has been applied for modelling AD processes, there is no soft sensor designed with the GP technique in this context  [16, 17]. Therefore, in this study, we discuss using the GP model for designing more transparent and interpretable soft sensors.
The plant capacity has been designed for 20648 m 3 d -1 of average influent with a dry weather flow rate and 592 mg L -1 of average biodegradable COD in the influent. The Activated Sludge Model No. 1 (ASM1) and the Anaerobic Digestion Model (ADM1) were used to describe the biological phenomena that take place in the activated sludge and AD reactor respectively. The influent characteristics consist of a 609 days dynamic influent data file (sampling frequency equal to a data point every 15 min) that includes rainfall and seasonal temperature variations over the year  [18, 19]. The first 245 days of influent data are used for plant stabilization under dynamic conditions and the last 364 days are used for the plant performance assessment.
Data Collection The first step in designing a data-driven soft sensor is obtaining the process data. Therefore, we used synthetic data produced by BSM2. It should be noted that this paper describes a preliminary study for designing soft sensors for AD processes by applying different techniques to synthetic data; therefore, for applying the approaches discussed in this paper to real systems, real process data is necessary. Thirteen process variables obtained from the simulation are listed in Table  1. These variables are measured from the influent, effluent and gas line of AD every 15 min. COD: chemical oxygen demand; TSS: total soluble solid; BOD: biological oxygen demand.
There are many parameters that need to be tuned for ANN, the most important ones include the number of layers, number of neurons in each layer, type of transfer function and regularization terms (L1 and L2), which prevent overfitting and improve generalization, were considered for a grid search. The number of epochs in this work was considered to be high (2000) because the early stopping method was used. The ANN models were trained in R  [24]  using the H2O package  [25]. To determine the best ANN structure, the number of neurons in the hidden layer was varied from 5 to 160.
Feature Ranking Using many features for model building can cause many problems and directly affect the model accuracy. Hence, the redundant and unnecessary features should be eliminated as they may add noise and have no impact on the dependent variable. Using feature ranking helps to determine the importance of features and reduce the dimension of the data set  [37]. The other benefits of the feature ranking method are shorter training time, ease of interpreting models, overfitting reduction and lower cost in data collection. The wrapper method obtains different variable importance results depending on the feature ranking model applied. We used the fscaret package of the R environment to avoid this problem  [24, 38]. Briefly, variable ranking is performed in three steps: model training, variable ranking extraction, and variable ranking scaling according to the generalization error.
Due to the simplicity of the LR model, it is not necessary to perform feature ranking prior to modelling, thus the rest of the parameters according to Table  1  were used for the LR model. Next, to determine the effect of other hard to measure parameters, such as TSS and Ammonia, on the outcome, they were removed one by one from the input data of the LR model and the error was calculated. The results of the LR models with different input vectors are shown in Table  2. The results clearly show that the relationship between parameters is highly linear. The LR model can still predict VFA with high accuracy even when TSS and Ammonia are eliminated from the input vector. This is contrary to the real behavior of the AD process, which is a very non-linear system. The most logical reasons for this linearity are the default influent data file and the design of the AD reactor itself.
Soft Sensor Design Before the soft sensors were designed, the data set was split into training and validation partitions. The uniformly sampled data from day 245 to 450 were used for training and the rest were used for validation of the final models. All calculations were performed using Amazon Elastic Compute Cloud with 36 cores and 60 GB of random-access memory (RAM) running on the openSUSE operating system (SUSE, Nürnberg, Germany). All modelling procedures were performed in the R environment, except GP, which was developed using the Eureqa Formulize software package  [40, 41]. This package has been optimized to find simple models, which are expected to have good generalization ability. The tuning parameters of each model were estimated using an extensive grid search. In this method for searching algorithm parameters, a tuning grid must be specified manually.