dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank the participants and the Myaware patient association in addition to the investigators and their teams who contributed to these interviews and trial;  Nadine McGale  for her support in the qualitative analyses and  Flora Mazerolle  for her support with quantitative CTT analyses. Veronica Porkess, PhD , CMPP, of  UCB Pharma  provided editorial review and assistance. Paolo Eusebi  provided data visualisation support. Rebekka Harding-Smith  of  Ogilvy Health  provided writing and editorial support.
Manipulation of soil diversity. To obtain two soils with contrasting levels of microbial diversity, gamma-irradiated soil was inoculated with undiluted and diluted soil suspensions (see Materials and Methods). After 39 days of incubation, a plateau of 10 9 bacterial CFU and 10 5 fungal CFU per g of soil was reached for each soil (see Fig. S2 ). The final bacterial and fungal recolonization levels and the total amounts of extracted DNA were similar in soils S1 and S2 (data not shown). These two endpoints are considered proxies for soil microbial biomass. Therefore, we estimate that the microbial biomasses were similar for both soils. The impact of soil diversity manipulation was assessed on a range of physicochemical parameters. The acidity level of soil S2 significantly (P = 0. 003) increased by 0.
6. 0 (64) using the following parameters: truncLen, c(190, 200); maxN, 0; maxEE, c(1); truncQ, 5 for gyrB reads. ITS1 reads were processed with the same parameters, except that reads were not trimmed to a fixed length and trunQ was set to 2. Chimeric sequences were identified and removed with the removeBimeraDenovo function of DADA2. Taxonomic affiliations of amplicon sequence variants (ASV) were performed with a naive Bayesian classifier (65) implemented in DADA2. ASVs derived from gyrB reads were classified with an in-house gyrB database (train_set_gyrB_v4. fa. gz), available upon request. ASVs derived from ITS1 reads were classified with the UNITE v7. 1 fungal database  (66). Microbial community analyses. Analyses of diversity were conducted with the R package phyloseq version 1. 26. 0 (67).
Since the primer set gyrB_aF64/gyrB_aR553 can sometimes coamplify parE, a paralog of gyrB, the gyrB taxonomic training data also contained parE sequences. Hence, ASVs affiliated with parE or unclassified at the phylum level were removed. Sequences were aligned with DECIPHER 2. 14. 0 (68), and neighbor-joining phylogenetic trees were constructed with phangorn 2. 5. 5  (69). Identification of sequence contaminants was assessed with decontam version 1. 4 (70) using the "prevalence" method at a threshold of 0. 1. Fungal ASVs unassigned at the phylum level were removed. For the ITS1 data set, we created a hybrid-gene phylogenetic tree (i. e. , ghost tree) (71) based on the 18S rRNA gene sequence database (Silva 132) and ITS database (UNITE v8) in order to perform phylogenetic analyses. Data were normalized based on sequencing depth.
For soil analyses, data were rarefied at 30,000 and 25,000 reads per sample for gyrB and ITS1, respectively. For plant (root and stem) analyses, data were rarefied at 1,000 reads per sample for gyrB and ITS1 (see Fig. S5  in the supplemental material), a validated choice to keep an optimal number of reliable samples. We did not apply a rarefaction filter for the analysis of presence/absence of taxa to investigate taxa detection and their transmission. Faith's phylogenetic diversity was calculated on gyrB and ITS1 data sets with the R package picante version 1. 8  (72). Differences in alpha-diversity estimators were assessed with analysis of variance. Differences were considered significant at a P value of ,0. 05. Changes in microbial community composition were assessed on log 10 1 1-transformed values with the Bray-Curtis (BC) index.
Principal-coordinate analysis (PCoA) was used for ordination of the BC index. The homogeneity of group dispersions was assessed with the betadisper function of vegan 2. 5. 3  (73). Differences in dispersion between groups were assessed with a Wilcoxon nonparametric test. To quantify the relative contributions of seed microbiota, soil diversity, development stage, habitat, and initial soil physicochemical parameters in microbial community profiles, permutational multivariate analysis of variance (PERMANOVA) (74) was performed with the function adonis2 of the R package vegan 2. 5. 3  (73). The relative abundances of ASVs belonging to ammonia-oxidizing bacteria (AOB) and nitric-oxidizing bacteria (NOB) according to their taxonomic classification were investigated in soils S1 (high diversity) and S2 (low diversity).
These include Nitrosospira sp. and Nitrosovibrio sp. for AOB and Nitrobacter sp. and Nitrospira sp. for NOB  (75, 76). Differences in relative abundances of AOB and NOB were considered significant with t test at a P value of ,0. 08 and a P value of ,0. 05, respectively. Transmission analyses. A prevalence matrix of common ASVs between seeds, soils, roots, and stems was constructed on nonrarefied data to have the most exhaustive presence-absence analysis of all taxa. Owing to the microbial variability between individual seeds, an ASV was recorded as present if detected in at least one of the 3 seed sample repetitions. Visualization of bacterial and fungal ASVs shared between biological habitats or specific to one was assessed with the R package UpSetR 1. 4. 0  (77). Data availability. The data sets supporting the conclusions of this article are available in the European Nucleotide Archive under the accession number PRJEB41004.
ACKNOWLEDGMENTS We thank  Muriel Bahut  from the  ANAN platform (SFR QuaSav ) for amplicon sequencing and  Laurent Philippot ,  Christine Baysse ,  Stéphane Uroz , and  Alexandre Jousset  for helpful discussion. We apologize to authors whose relevant work was not included in this article due to space constraints. This project named RhizoSeed was funded by  INRAE  ( Metaomics and Ecosystems Metaprogram, Plant Health and Environment division ) and conseil régional de Bretagne (ARED). We declare no conflicts of interests in relation to the work described.
7. Among the new species records for the Chukchi are several appear that to be new records for the Arctic Ocean: Eutintinnus pectinus, E. turris, Stenosomella pacifica, Tintinnopsis cylindrica, T. levigata, and T. strigosa. Thus, there are now 95 species known to have been found in the Arctic Ocean and of those 95 species, 60 have been found in Chukchi Sea. The list of the 60 species now known for Chukchi Sea, with sampling year first found for each species, and the reference, is given in the supplementary file. Data are from  Bursa 1963 , Matsuno et al. 2014 , Li et al. 2016 , Yokoi et al. 2016 , Dolan et al. 2017 , Xu et al. 2018 , Wang et al. 2019 , and this study. One might postulate that many apparently new species records are 'mistaken'. This is because many of the species recorded are likely to be morphological variants of another species or are synonyms.
ABSTRACT Inbreeding depression is associated with a decrease in performance and fitness of the animals. The goal of this study was to evaluate pedigree-based and genomic methods to estimate the level of inbreeding and inbreeding depression for 3 semen traits (volume, concentration, and motility score) in the Basco-Béarnaise sheep breed. Data comprised 16, 196 (or 15, 071)  phenotypic records from 620 rams (of which 533 rams had genotypes of 36,464 SNPs). The pedigree included 8,266 animals, composed of the 620 rams and their ancestors. The number of equivalent complete generations for the 620 rams was 7. 04. Inbreeding coefficients were estimated using genomic and pedigree-based information. Genomic inbreeding coefficients were estimated from individual SNP and using segments of homozygous SNP (runs of homozygosity, ROH).
Phenotypic and Genomic Data Due to the use of fresh semen, a large population of rams is held at the AI center (Centre Départemental de l'Élevage Ovin), in Ordiarp, Pyrénées Atlantiques, France). Semen production and quality data is routinely collected; we use data for rams born between 1997 and 2015. Sperm volume (mL) was evaluated directly by reading the collection tube. Sperm concentration (×10 6 spermatozoa/mL) was determined by spectrophotometer. Motility score was evaluated by a technician on a scale from 0 (no motion) to 5 (numerous rapid and vigorous waves) under the microscope. Sperm quality data were assessed immediately after collection. Diluted semen was packaged in straws and stored at 15°C until cervical insemination (within 6 h following collection). Data comprised 16,196 phenotypic records from 620 rams born between 1997 and 2015.
Summaries of the whole data for different semen traits are presented in Table  1. Of the 620 rams with records, genotyped animals were a subset of 533 rams. These rams were genotyped using the OvineSNP50 BeadChip (Illumina Inc. , San Diego, CA). The distribution of rams and status (genotyped or not) across year of birth is shown in Figure  1. After quality control using preGSf90  (Aguilar et al. , 2014) , 36,464 autosomal SNP were retained for analyses and used to build genomic relationship matrices. In quality control, SNPs with a call rate <0. 98, a minor allele frequency <0. 05, and high deviation from Hardy-Weinberg equilibrium (absolute difference between expected and observed heterozygotes >0. 15) were removed. Animals with a call rate <0. 98 were discarded. Pedigrees were extracted from the national database.
ACKNOWLEDGMENTS The authors thank  Valérie Loywyck  ( Institut de l'Élevage)  who provided the data, and  Francis Fidelle , head of  AI center (Centre Départemental de l'Élevage Ovin) , who took the measures. This study received funding from the  European Union   Horizon 2020 Research and Innovation programme  under grant agreement No. 772787 -SMARTER, projects  ARDI  (grant agreement  EFA 208/16 ) from  POCTEFA funds, and GDivSelGen (INRA SelGen metaprogram ). Project was partly supported by  Toulouse Midi-Pyrénées bioinformatics platform. The authors have not stated any conflicts of interest.
HIV-1 and HTLV-1 RNA production correlated with viral protein expression. Nanoparticle tracking analysis (NTA) showed increase of EV concentration over time in both uninfected and infected samples. Finally, the HIV-1 supernatant from the 6-h samples was found not to be infectious; however, the virus from the 24-h samples was successfully rescued and infectious. Overall, our data indicate that EV release may occur prior to viral release from infected cells, thereby implicating a potentially significant effect of EVs on uninfected recipient cells prior to subsequent viral infection and spread.
Nanoparticle Tracking Analysis Quantification of EVs was performed using the ZetaView Z-NTA (Particle Metrix, Inning am Ammersee, Germany) and its corresponding software package. The machine was calibrated with the use of 100 nm polystyrene nanoparticles (Applied Microspheres, Leusden, Netherlands) prior to sample readings at a sensitivity of 65 and a minimum brightness of 20. For each measurement, the instrument pre-acquisition parameters were set to a temperature of 23 • C, a sensitivity of 85, 30 frames per second, and a shutter speed of 250. For each measurement, 1 mL of the sample diluted in deionized (DI) water was loaded into the cell, and the instrument measured each sample at 11 different positions throughout the cell, with three readings at each position. After automated analysis and removal of any outliers from the 11 positions was completed, the concentration, mean, median, and mode sizes of the samples were calculated using the ZetaView 8.
All three cell types exhibited p24 positivity, although it was higher in T cells than in myeloid U937 cells. Collectively, these data indicate that although 6-h samples may be positive for viral proteins or RNA, they are not generally infectious, whereas 24-h samples contain both functional viral particles and EVs. were cultured in 1% fetal bovine serum (FBS) media and treated with cART for 72 h, washed and placed in EV-depleted, 20% FBS complete media containing PHA/IL-2 to induce HIV-1 gene expression. The 6-and 24-h samples were harvested to perform an infectivity assay. (A) The culture supernatants were harvested at 6 and 24 h. The J1. 1 (HIV-1-infected T cells) samples from 6 and 24 h were used to treat naïve CEM, Jurkat, and U937 cells. A total of 10 6 naïve cells were resuspended in 400 µL supernatant and incubated for two days.
The data in Figure  6B  quantify the p19 protein levels from recipient cells. We finally asked whether the particle (EV and virions alike) concentration changes overtime. Nanoparticle tracking analysis (NTA; ZetaView) was utilized to quantify EVs released from the uninfected (CEM), HIV-1-infected (J1. 1) and HTLV-1-infected (HUT102) cells (Figure  S1 ). All three cell lines exhibited increased concentration of particles over time that peaked at 24 h. Infectivity assay of condition medium samples from HTLV-1-infected cells. HUT102 cells (5 × 10 7 cells/mL) were cultured in 1% fetal bovine serum (FBS) media for 72 h, washed and placed in EV-depleted, 20% FBS complete media containing PHA/IL-2 to induce HTLV-1 gene expression. The 6-and 24-h samples were harvested to perform an infectivity assay.
The absence of active caspase-3 in the cells tested in Figure  7A  suggests lack of cell death. Bcl-2 exhibited no differential expression over time in either HIV-1-or HTLV-1-infected cells and was abundant at 0, 6, and 24 h. Cytochrome C was mostly absent in all the samples tested. Actin exhibited an unusual expression pattern in the uninfected cells, perhaps due to actin solubility differences. As such, we stained the gel to assess protein quantity. Images of the Coomassie staining in Figure  7A  reveal that the amounts of protein loaded were consistent between each sample, especially notably post-release. Collectively, these data suggest that cells post-release are alive and show reduced markers of apoptosis.
The data in Figure  8B  indicate that TAR was detected at 6 and 24 h, peaking at 24 h in all four PBMCs. Interestingly, TAR-gag expression was detected both at 6 and 24 h; however, its expression did not differ significantly in the 6-and 24-h samples. Genomic RNA was found in the 6-and 24-h samples and showed increase over time. Overall, RT-qPCR data from condition medium samples from the PBMCs showed gradual increase in concentration of HIV-1 RNAs over time peaking at 24 h, except for TAR-gag that remained stable at 6 and 24 h. The experimental design used for primary cell infection and EV collection. PBMCs from four independent donors were treated with PHA and IL-2 and allowed to grow for seven days in culture. The PBMCs were then infected with HIV-1 89. 6 (MOI: 10) and treated with cART and IL-7 to promote latency.
Interventions We defined 4 groups depending on the treatment option chosen after the consecutive trials of CROS hearing aids and BAHSs: abstention, CROS, BAHS, and randomized for CI (RCI). The subjects successively tried CROS hearing aids and BAHSs before choosing which treatment they opted for. This choice was mainly based on the subjective outcomes reported after the 2 trials but also guided by the auditory outcomes obtained with each device, the recommendations of the corresponding manufacturer, and the counselling of the physician. In the group CROS, a Phonak CROS system was fitted by an experienced audiologist, using corresponding fitting software (Phonak ® Target 3. 0). The subjects with AHL were fitted with Bi-CROS systems to correct hearing loss in the better ear. In the group BAHS, Baha 110 ® (Cochlear Ltd) and Ponto ® (Oticon medical) were adapted on the abutment 3-5 weeks after the surgical procedure and fitted by an audiologist according to the company's guidelines.
Acknowledgements We sincerely thank  Ms. Soumia Taoui  for her precious help in coordinating all the centers and the numerous institutions involved. We are grateful to all the participants who took part in this study, and we wish to extend our sincere thanks to the collaborating clinical teams, research associates, and audioprosthesists at the  university hospitals of Toulouse, Paris, Lille, Montpellier, Tours, Rennes , and Grenoble.
In addition, unlike the traditional recursive neural network where content is re-written at any time, the LSTM network can decide on preserving the current memory through the proposed gateways. In that case, it can easily transfer this information over long paths to receive and maintain long-term dependencies. Using real databases, the experiment results illustrate the better performance of RoLSTM applied to SOC estimation of Li-Ion batteries in comparison with a neural network modeling and unscented Kalman filter method that have been used thus far.
Robust and Adaptive Online LSTM for SOC Estimation In this inquiry, a RoLSTM model is used for the SOC estimation of a Li-ion battery with voltage (V), current (I), and temperature (T) as input variables and the SOC of the battery as output. The structure of this method is depicted in Figure  3. Three LSTM networks are used for the inputs. In fact, each input variable contains two hidden layers with 128 and 8 LSTM units, respectively, which they are working in parallel. The concatenation of these layers is realized to calculate the output with a dense layer, which is a linear operation that relates every input to every output with the following equation: SOC t = Wh t + b (8) where W and b are defined, respectively, as the weight matrices and biases of full connected layers. The input datasets for the LSTM layer should be three-dimensional (3D), which are defined as "(Samples, Time steps, Features)".
Samples indicate the number of sequences, Time steps symbolize the number of observations in each sample, and Features represent the number of features for each element in the samples. The voltage, current, and temperature are the input vectors. In the preprocessing step, the input datasets must be converted to a 3D array for use as an input of the RoLSTM and normalized in the range [-1, 1] as: x = [((x -x min )/(x max -x min )) * (max -min)] + min (9) In (3), the minimum and maximum values of input vector x are shown by x min and x max , respectively. In addition, max equals 1 and min is -1. To scale the testing dataset, the maximum and minimum values of training inputs must be used. To train the LSTM and update the weights and biases in the network for SOC estimation, the Robust and Adaptive online optimization algorithm (RoAdam) is used  [33].
Battery Specification and Experimental Conditions This method was applied on two different battery databases. The first database is related to the Panasonic 18650PF Li-ion battery which is used in some Tesla Electrical Vehicles. This database is collected by McMaster University in Ontario (Canada)  [34]. The second one concerns the A123 LifePO4 Li-ion battery collecting by the Center for Advanced Life Cycle Engineering (CALCE) at University of Maryland  [35]. The battery parameters are listed in Table  1. The discharge datasets are realized from 10 different standard drive cycles e. g. Los Angeles 92 (LA92), Supplemental Federal Test Procedure Driving Schedule (US06), the Highway Fuel Economy Test (HWFET), and the Urban Dynamometer Driving Schedule (UDDS), with a variety of current steps and temperatures.
The optimal number of epochs is determined as 500. The servers of the Strasbourg University Computing Center (CCUS) were used for the training process. The RoLSTM network was deployed on three NVIDIA Tesla V100 Graphical Processing Units (GPUs). In this study, the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Maximum Error (MAX) were used to evaluate the performance of the suggested network for SOC estimation. Equations (  13 )-(  15 ), respectively, were used to calculate the MAE, RMSE, and MAX. MAE = 1 n n ∑ t=0 |SOC t -ŜOC t | (13) RMSE = n ∑ t=0 1 n (SOC t -ŜOC t ) 2 (14) MAX = max |SOC t -ŜOC t | ( 15 ) where SOC t and ŜOC t are the measured value and the estimated value at time step t, respectively, while n is the number of data points in the sequence.
Results for the Panasonic 18650PF The existing data of all ten cycles at different ambient are more than 8 million data. They are categorized into two groups, training and validation data. To train the system, a random dataset containing the mixed eight drive cycles in different ambient temperature was used. The validation was applied to the remaining two cycles. In most cases, the length of the dataset for one drive cycle is more than 100,000 time steps. In reality, to implement a RoLSTM network with an n-step size equal to this length sequence, more than three GPUs are needed. Therefore, in this paper, the optimal values of n-step is chosen 500. The time required for training system with three Tesla V100 GPU is about 8 h. For testing the system, two categories with fixed and varying ambient temperatures were used.
The current and voltage of the testing dataset are shown in Figure  7. The results of SOC estimation and SOC error for a dataset of mixed drive cycles with fixed ambient temperature at 0 and 25 • C and varying ambient temperature are described in Figures  8  and 9 , respectively. Table  3  details the MAE, RMSE and MAX error metrics, at 0, 10, and 25 • C for RNN  [25] , GRU-RNN  [25] , LSRM-RNN  [26] , and RoLSTM. Looking at these values for each temperature, one can see that all are decreased when RoLSTM method is applied. When varying temperature is considered, the RMSE of RoLSTM is equal to 1. 8%, while the same error using LSTM-RNN  [26]  method is calculated as 2. 08%.
MAE (%) MAX (%) RMSE (%) RNN  [25]  GRU-RNN  [25]  LSTM-RNN  [26]  RoLSTM RNN  [25]  GRU-RNN  [25]  LSTM-RNN  [26]  RoLSTM LSTM-RNN  [26  The SOC estimation obtained based on DST and FUDS tests in the variant temperature are illustrated in Figure  10. Figure  11  presents the SOC error, which is defined as the difference between the SOC measurement and the SOC estimation with this model. As can be seen in these figures, the SOC estimate achieved by the RoLSTM is quite smooth. The SOC estimation and the SOC error for datasets of discharging at -10 and 50 • C based on the low current voltage test with 500 epochs are shown in Figures  12  and 13 , respectively. For these datasets, SOC estimation error does not have an observable change while changing the temperature. The results show that, with 500 epochs in the RoLSTM method, the loss function converges to zero in all ambient temperatures.
In summary, these results show that the maximum error of SOC estimation for Li f ePO 4 battery by the RoLSTM method for all datasets with fixed ambient is less than 2% and for different ambient is less than 2. 5%. The values of RMSE and MAX error derived from the RoLSTM were compared with previous models used for SOC estimation of A123 Li f ePO 4 Li-ion Battery. In  [22] , the authors introduced a method based on simple neural network and combined it with an unscented Kalman filter. Their neural network is static and does not have a memory, so UKF is used to filter out the outliers. Meanwhile, in the RoLSTM model, multiple LSTM layers can be learned by a bigger dataset, and it uses a self-learning algorithm to tune the parameters against uncertain outliers. In addition, the RoLSTM has a memory that is fitted for time series sequences.
Generally, our proposed model has better performance in comparison with NN and UKF  [22]. The MAX error at 0 • C is 1. 9 by the RoLSTM while this values is 3. 1 by NN and UKF  [22]. The 25 and 50 • C the MAX error is equal in both methods. From the MAX error values in Table  4 , it is apparent that the performance of RoLSTM is better than the simple NN  [22]. Table  4  reveals that the RMSE has dropped using RoLSTM method on the same database. In addition, in another study, Yang et al. [37]  introduced a multi-hidden LSTM network for SOC estimation. The minimum RMSE reported by them was 1. 07, and an overall comparison shows RoLSTM gives a better performance. NN  [22]  NN and UKF  [22]  Adaptive-GRU  [29]  RoLSTM NN  [22]  NN and UKF  [22].