dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this study, we tested the sub-daily module of the SWAT model when applied to a catchment of ~1000 km 2. To our knowledge, this is the first time that SWAT has been run at a sub-daily time step over such a catchment and calibrated and validated with sub-daily-measured discharge data. We compared simulated discharges with observed discharges at two gauging stations (Marquixanes and Catllar). We compared the time-continuous hourly discharges simulated with SWAT (with two sub-basin delineation schemes) to the event-scale, sub-daily discharges simulated with the MARINE model. We also compared the soil water contents before the flood and after the flood. To our knowledge, this study is also the first comparing the performance of the conceptual SWAT model at the sub-daily time-step with a process-oriented model, namely the MARINE model, over a large river catchment.
Acknowledgments: This research was supported by the  CRUE-SIM  project, funded by the  Fondation RTRA-STAE  and by the  National EC2CO Programme Biohefect/Ecodyn/Dril/Microbien (Multi-scale modelling  to quantify the role of wetlands on biogeochemical transfers across watersheds). The authors sincerely thank the  Service de Prévision des Crues Méditerranée Ouest in Carcassonne  for rain gauge measures records and  INRA Infosol , including  Christine LEBAS , for soil properties data. The authors are also grateful to  Yves AUDA ,  Grégory ESPITALIER-NOEL , and  Youen GRUSSON  for their support in data processing, and to the SWAT team, namely  Raghavan SRINIVASAN ,  Nancy SAMMONS  and  Jaehak JEONG , for their kind help with the upgraded SWAT executable 635 version.
C. PRESENT-80 Implementation There are many implementations of PRESENT-80 algorithm. For example, Axel Poschmann et al. proposed a serial implementation of the PRESENT algorithm  [20]. This unprotected implementation (4-bit data path) requires 1100 gate equivalents (GEs) and 547 clock cycles to process one block of data. Generally, the more parallel level of the data path, the harder it is to attack (Side Channel Attack) the design because parallelism is one way of hiding countermeasures. For this reason, we proposed a parallel architecture of PRESENT in the aim to evaluate its vulnerability against EMA and compared its attack results with serial PRESENT architecture. Our proposed PRESENT-80 implementation given in Fig. 3  is based on a parallel hardware processing rather than a sequential processing.
This parallel architecture is based on 64bit data bath. It means the 16 S-box blocks operates at the same time which makes saving electromagnetic traces corresponding to one S-box operation is very difficult. The attack setup will be presented in details in the next section. Our PRESENT version has two inputs (data-in, key) and one output (data-out). The data-in and data-out are both on 64-bit and the key is on 80-bit. The architecture consists of two MUXs, one XOR, two 64-bit registers Reg1 and Reg2, 16 4-bit S-boxes, 64-bit shiftregister (permutation layer operator), 80-bit key update and 5bit counter. Due to the parallelism of our implementation, one round requires only one clock cycle to substitute the data (S-box), to perform the data permutation and the key updater. Including the initialization phase, 33 clock cycles are required to process one block of data.
5 ms). When the attack is performed at the first or the second round of PRESENT, for each trigger signal event the oscilloscope saves one EM radiation trace. A matrix T of 100 000 plaintexts is used as PRESENT encryption inputs and thus a matrix M comprising 100 000 Electromagnetic traces is obtained. The same 100 000 plaintexts and their relative 100 000 EM traces are used to recover the 16 MSB 4-bits sub-keys. Each of these traces consists of 4002 oscilloscope samples. In this work, we use the previous EM attack model: the Hamming Weight (HW) at the S-box  (10). This attack model was developed with Matlab. The first step performs the attack at the first round of the encryption unit where we are able to recover 16 MSB 4-bits sub-keys. After recovering these 16 sub-keys, the second step performs the attack at the second round of PRESENT to get the last 4 sub-keys.
Together, all these eQTL data have provided new types of resources and associated databases for the study of relationships between gene expression and phenotype of interest  [12]. Few eGWASs have been reported in pigs. Liaubet et al. [13]  analyzed gene expression in pig skeletal muscle sampled shortly after slaughtering, and showed an overrepresentation of genes that encoded proteins involved in processes induced during muscle development and metabolism, cell morphology, stress response, and apoptosis. More recently, eGWASs have been integrated with phenotyping studies in order to identify candidate genes and causative mutations associated with phenotypic variations in several tissues: for example, liver gene expression linked to blood and lipid traits  [41] , gene expression in longissimus dorsi or gluteus medius muscles associated with growth, fatness, yield, and meat quality  [14] [15] [16] [17] [18] [19] , and hypothalamus gene expression connected to coping behavior  [20].
Since genome re-sequencing of the 38 pigs was not available, SNP identification was carried out using the RNA-Seq data. Thus, this analysis only detected SNPs with different expressed alleles in the samples included in the ASE study, and was not able to detect silencing events. After SNP calling on RNA-Seq reads, 82,419 SNPs met the quality control criteria, including 71,365 (86. 2%) SNPs already known in the dbSNP database. Among these SNPs, 80,939 were heterozygous in at least one of the 38 samples, and 86. 3% of these were already known in dbSNP. Overall, 6824 genes were expressed and overlapped with at least one heterozygous SNP in one individual, and were thus detectable as affected by ASE. From our analysis, 2286 SNPs showed allelic imbalance (Additional file 4: Table  S3 ). These SNPs were further referred to as ASE-SNPs.
Overlap of eQTL-SNPs and ASE-SNPs with blood biomarkers previously identified in pigs We compared the lists of genes found using the ASE or eGWAS datasets to lists of genes that we previously reported to be differentially expressed (DE) in the blood of pigs; this included results from in vitro production of IL-2 and IL-10 after stimulation of total blood, in vitro phagocytosis tests, and counts of T lymphocytes CD4-CD8+  [4]. For each immunity-related trait, numerous DE genes were associated with eQTLs or ASE (Table  5 ), indicating that expression levels of blood biomarkers for these traits are genetically controlled. For example, local eQTL-SNPs were associated with expression changes in the ALOX12 gene, which has been identified as a blood biomarker for phagocytosis, as well as the genes GZMB or KLRG1, which are correlated CD4-CD8+ cell count.
However, we found only limited overlap between the local associations that were identified by eGWAS and the cis-associations that were identified by the ASE analyses, which is consistent with results from other studies  [10, 11]. Indeed, this was expected due to inherent differences between the approaches and their respective limits. For eGWAS, only eQTLs that corresponded to SNPs represented on the 60K SNP chip could be detected and in this design, intergenic SNPs are overrepresented  [35]. Consequently, the functional annotation of eQTL-SNPs remains limited, and they mainly reflect cases of linkage disequilibrium with nearby causal variants and not direct causal links. The ASE analysis was limited by the lack of whole-genome sequence data for the animals included in this analysis, which meant that SNPs were called using RNA-Seq data.
Therefore, in order to limit errors in SNP discovery, only ASE-SNP positions for which both alleles were expressed (Minor Allele Count >3) were considered in the final analysis, and transcripts with low read counts were discarded. In addition, due to the lack of parental information, we could not differentiate between allelic imbalance and parental imprinting. However, among the 763 ASE genes found in our study, only three are described as imprinted genes in the Geneimprint database (http://www. geneimprint. com/ ): SNRPN, which is imprinted by the paternal allele in Homo sapiens, Macaca mulatta, and Bos taurus; and SNORD64 and DMTN, which are reported as imprinted only in Homo sapiens. Overall, all these limitations suggest that these eQTL-SNPs and ASE-SNPs are not causal variants but SNPs in linkage disequilibrium with the causal variants that could be used as genetic markers for gene expression in the pig population included in our study.
They thus confirmed that whole-blood eQTL studies are a good resource for the discovery of biomarkers, especially in the context of disease in humans. With the goal of improving selection for health traits in pigs, blood is also a relevant surrogate tissue for phenotyping immune capacity  [39]. As shown in human studies, whole blood can be used for robust eQTL analysis despite its heterogeneous cell composition. Indeed, Mehta et al. [33]  demonstrated that variations in cell count would exert only a minor effect on expression levels, as eQTLs are consistently replicated between whole blood and independent human cohorts. We showed a notable degree of overlap between the genes found in this study to be genetically controlled and the QTLs available in the AnimalQTL database  [40]  for pigs. More precisely, we found overlap between the lists of eQTL-and ASE-SNPs generated here and QTLs of the AnimalQTL database that had been associated with immune-related phenotypes, including interferon-gamma level, IgG level, lymphocyte count and other traits related to blood function (Additional files 14 and 15: Table  S8  and Table  S9 ).
Refinement of probe sequence annotation of the Agilent-037880/INRA Susscrofa60K v1 platform Probe mapping and annotation were updated to refine all genomic locations. The 60-mer sequence probes included in the Agilent-037880/INRA Susscrofa60K v1 platform were mapped onto the pig reference genome (SusScrofa v10. 2) with the TopHat aligner (v2. 0. 14)  [48]. Allowing two mismatches and two gaps per probe sequence, we found 44,326 probes mapping to unique genomic positions. Among these, 41,839 probes were expressed in our samples. The annotation of these probes was performed by combining annotation information provided by the Ensembl (release 79) and NCBI (GCF_000003025. 5) databases.
STAR includes a soft-clipping step, which takes care of adapter contamination. After the two-pass procedure, we used a GATK (v3. 4, [52] ) tool called SplitNCigarReads, which split reads into exon segments and hard-clips any sequences overhanging into the intronic regions to reduce the number of called false variants. Then, reads were realigned around indels to limit artifacts due to the alignment algorithm. Finally, the standard GATK base recalibration procedure re-adjusted the quality scores assigned to the individual base calls in each sequence read, thus reducing the systematic technical error linked to the sequencing machines. In order to prevent false positive ASE signals due to allelic mapping biases that could influence allele counts, variants were called in the dataset, filtered using vcftools (v.
Acknowledgments We are grateful to the  Genotoul bioinformatics platform at INRA de Toulouse Midi-Pyrenees (BioinfoGenotoul)  for providing computing and storage resources. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for help and support. We are grateful to  Laurence Liaubet  ( INRA, France ) for sharing information on the expression microarrays and to  Maria Ballester  ( IRTA, Spain ) for useful comments and suggestions during the data analysis. We are grateful to the  INRA @bridge platform  for the secure storage of samples and the personnel at  GENESI 's farm at  Le Magneraud  for their implication on the generation of animals and samples.
Funding TM was financially supported by a PhD fellowship co-funded by the  INRA Animal Genetics Division  and  BIOPORC  (association composed of AXIOM, CHOICE GENETICS FRANCE, NUCLEUS, and IFIP). Experiments were funded by the  Sus_Flora  ( ANR-10-GENM-016 ) and  IMMOPIG  ( ANR-06-GANI-08 ) projects of the  French National Agency and INRA's Animal Genetics Division.
Availability of data and materials Expression microarrays: All microarray data are MIAME compliant and have been submitted to NCBI's Gene Expression Omnibus with accession number: GSE97374. RNA-seq data: The raw reads are available at NCBI's SRA repository (Bioproject PRJNA360366; accessions SRR5155448 to SRR5155485). Track hub: All significant eGWAS and ASE results are available in a public Track Hub entry (https://www. trackhubregistry. org/search/view_trackhub/ AVszgl3NpL2YcDCmVC8X) that can be explored using Ensembl's and UCSC's genome browsers.
We have completed the collection of one of the richest accurately annotated mobile dataset of modes of transportation and locomotion. To do this, we developed a highly reliable Android application called DataLogger capable of recording multisensor data from multiple synchronized smartphones simultaneously. The application allows real-time data annotation. We explain how we designed the app to achieve high reliability and ease of use. We also present an evaluation of the application in a big-data collection (750 hours, 950 GB of data, 17 different sensor modalities), analysing the data loss (less than 0. 4% ) and battery consumption (≈ 6% on average per hour). The application is available as open source.
The slaves' interface only presents information about the logging status, the connection status to the master and a button to force the reconnection. The data synchronization during the logging is fundamental to obtain a valuable dataset. Therefore, the master sends a keep-alive packet every 30 seconds to the slaves. This packet includes the status of the logging, the current label and the nanosecond time of the master. The nanosecond time is used by the slave to compute the difference between the received time and their own, in order to save this offset in the log files. These keep-alive packets assure also that the system does not close the Bluetooth connection. In case the Bluetooth connection is dropped during the data logging despite all the precautions, the master device vibrates to notify the user that something may happened to the slaves which are not usually under constant supervision.
RELIABILITY AND BATTERY LIFE We used DataLogger in a data collection that involved 3 participants, wearing 4 smartphones on 4 location (hand, torso, bag and hips) for 7 months. We collected more than 750 hours of data from 17 different sensors for a total of 950GB of raw data  [5]. The participants were asked to perform 8 activities related to different transportation modes: staying still, walking, running, cycling, driving (or being in a car as passenger), travelling on a train, a bus or in the subway. Thanks to this data collection, we were able to study the reliability and the battery consumption of the application. In our data collection campaign, we used Huawei Mate 9 smartphones that run Android 7. This model is equipped with a 4000 mAh battery. Despite all our precautions, we experienced few reboots of the phones during the data collection.
Within 750 hours of collected data, the amount of lost data is lower than the 0. 15% for each device (see Table  1 ). The device on the hips appears as the most subject to reboot, likely because it is more subject to accidental long-pressing of the power off button. We analysed the discharging rate (in percentage) per hour. The network interfaces, especially the mobile network module, and the display are the most power consuming  [6]. In this context we  studied the discharging rate per location and per activity in order to evaluate the impact of these factors during the data collection. Figure  1a  displays the discharging rate per hour per location. During the data collection campaign, the smartphones of the User 1 updated their firmwares from the build B156 to B182, without changing the Android version.
CONCLUSION We presented DataLogger as the first highly reliable tool for multimodal multi-device data collection and real-time annotation. The evaluation results show that even in the worst scenario, it can be used for up to 10 hours with a single power charge. Moreover, with only 0. 4% of total data loss, it is a very dependable solution for big data collection. The code of the application is released under MIT License and it is available at  [7]. The dataset will be released soon to the community 1.
Data collection The final questionnaire was composed of three sections: 1. demographic data: gender, race, birth date, marital status, and family income; 2. SPAI-BR; and 3. Goodman's Criteria. For data collection, we used an electronic data collection system based on tablet computers. In sum, we transferred the whole questionnaire to the web-based platform iSurvey1 (HarvestYourData, CA) and installed the software on tablet computers. Respondents completed the demographic questionnaire and the SPAI-BR (self-applied) and handed the tablet to a psychiatrist who performed a structured interview assessing Goodman's Criteria. Data was uploaded through Wi-Fi, transmitted to a database and exported to SPSS format (IBM Corporation, CA). Data bank in SPSS format can be seen in S1 File.
Statistical analysis In the descriptive analysis, we calculated mean, standard deviation, median, quartiles and minimum /maximum for continuous variables. For categorical variables, we calculated frequency and proportion. Data normality was assessed using Kolmogorov-Smirnov test. Factorial structure of the SPAI-BR was evaluated using Confirmatory Factor Analysis (CFA). We used the Weighted Least Squares Mean and Variance estimator (WLSMV) as a method for parameter estimation. Model fit was assessed considering the following fit indexes: Chi-square (χ 2 ), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error (RMSE) and Weighted Root Mean Square Residual (WRMR). The overall model fit was judged using the following cutoff values: for the CFI and TLI, values larger than 0.
Subsequently, costs and probabilities are analyzed for true negatives (TN), false positives (FP) and FN (false negatives). We calculated relative costs with the proportions of expenses for true positives (0. 89), true negatives (0. 001), false positives (0. 03) and false negatives (1. 0) as described by Smits and colleagues  [66, 67]. As proposed by decision theory, the optimal cut-off point was chosen according to lowest expected costs. To determine the convergent validity between SPAI-BR and Goodman's Criteria we calculated the Spearman Correlation Coefficient for the total scale and the factors. In this study, each subject responded a questionnaire on an electronic platform, and the non-response of an item precluded the progression to the next items. For this reason, our database did not include missing values.
Goodman Factor We performed the Confirmatory Factor Analysis (CFA) in our dataset to assess if our instrument presented a factorial structure similar to the original SPAI  [72, 73]. CFA showed good model fit indexes for both the one-factor model and the Four-factor model. However, the 'Tolerance' factor presented a lower index of internal consistency (less than 0. 7). The adequacy of the presence of tolerance for the diagnosis and for the screening of smartphone addiction has been questioned. It is controversial whether tolerance is a core symptom of smartphone addiction. "Tolerance" is the factor with the most unstable structure, the smallest eigenvalue, and the fewest number of items in most smartphone addiction questionnaires  [11, 39, 74]. Additionally, tolerance in smartphone addiction is difficult to measure, since smartphone use is carried out for short periods of time, with great frequencies, and the total time of use is very inaccurately self-reported.
For this optimal cutoff point, sensitivity was 90. 54%, much greater than specificity (59. 93%), and, although specificity can be considered low, this is adequate for a screening instrument  [43]. Additionally, the cut-off point of seven has a high accuracy (70. 85%) in our population. Investigators and clinicians may consider a distinct cut-off point according to the expected prevalence and estimated costs in their target population, as displayed in S4 Table. One possible limitation of this study was the fact that it was carried out with a convenience sample composed of UFMG students, and therefore it could be expected that the application of this same instrument in a community sample would present different results. Therefore, data from this study cannot be generalized to other populations.
Namely, it is sufficiently strong (to be consistent with the cluster detection) and sufficiently weak to be consistent with the non-detection from the outskirts of the Milky way  [191]. However, in the Galactic Center the flux of the line is also consistent with the expectations for plasma emission lines with no need for a dark matter component  [608, 610]. We note that the line is also recently detected in the NuStar blank-sky  [620]  and Galactic center  [621]  datasets. While it may have an instrumental origin, related to Solar activity  [620] , it was shown in  [620]  that in the 'no sun' dataset the line is present with the same intensity, and it appears several times stronger near the Galactic center, as Fig. 4  of  [621]  demonstrates. A difficulty in interpreting the origin of a weak emission line is inherent uncertainty in the astrophysical backgrounds, in particular in the flux of plasma emission lines.
The Potassium interpretation also cannot explain the origin of the line in the Andromeda galaxy reported in  [191] , the significance of which is, however, disputed  [610, 624]. Systematic errors in instrumental calibration and/or systematics induced by the analysis procedure may impact the significance of weak lines. The calibration systematics was explored in  [191]  who demonstrated that no line is detected in an extremely long exposure combination of the off-center observations of the Milky way ("blank sky" dataset). A 3. 5 keV line is not detected in the stacked dwarf galaxies  [596]  arguing against a global calibration problem. It has also not been seen in a long exposure towards M31 reported in Ref. [313]. In order to assess possible systematic effects, the Ref. [610]  considered the XMM-Newton spectra of the Tycho supernova remnant, assuming that this system displays similar plasma emission lines to those detected in clusters and the GC but would have no expected DM signal.
The signal has radial surface brightness profiles in the Perseus cluster, Andromeda galaxy  [191]  and in the Milky Way  [608]  that are consistent with our expectations for decaying Dark Matter and the mass distribution in these objects. Instrumental origins of this signal have been shown to be highly unlikely for a variety of reasons: the signal's presence in all of XMM-Newton's detectors and in Chandra's; the correct behavior with redshift (especially in the stacking procedure employed by ref. [190] ); its absence in a very-long exposure blank-sky dataset. It has also not proved possible to attribute the signal plausibly and consistently between datasets to any known atomic emission line or lines  [190, 191, 624, 625]. The center of our Galaxy is a classical target for DM searches. Its proximity allows the observations to concentrate on the very central part and therefore, one can expect a significant gain in the signal if the DM distribution in the Milky Way happens to be steeper than a cored profile.