dataesr/scientific-paragraphs-categorization huggingface dataset:
As expected, the RMSE is still important (about 300) but at least we have a baseline model that can be quickly deployed. As soon as more measurements are collected, individual models can be created by incrementally training the model. Also, specific parameter optimizations can be performed to improve the accuracy. The scalability and portability of the developed solution can be ensured by relying on basic ETL (Extraction, Transformation, Load) using MapReduce, while more advanced operations can be conducted with high-level tools (e. g. , Pig or Apache Spark and Tensorflow/Keras). Storage and access to the data can be made using NoSQL databases, which are specially adapted to store   [6, 32]  data series from sensors and other data sources (e. g. , power generation, historical consumption charts, weather forecast)  [31].
Further developments can include energy disaggregation, also referred to as non-intrusive load monitoring (NILM). NILM is a technique to make inferences about the different individual loads of the system based on an aggregate energy signal, such as that coming from a whole-home power monitor. NILM is a full research domain with several challenges, such for example the identification of devices signatures even when they do not work always in a constant regime (Figure  10 ). Indeed, most devices present acyclic consumption behaviors, like for example the different phases of a washing machine program. An extended library of NILM datasets and papers can be found at http://wiki. nilm. eu/datasets. html. In the case of our project, we can focus on the identification of power-hungry devices, like heaters and air conditioning systems.
The Weather Research and Forecasting (WRF) Model is a mesoscale NWP system designed for both atmospheric research and operational forecasting applications [33, 34]. pvlib-python is a community-supported tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems  [35, 36]. pvlib-python was originally ported from the homonymous MATLAB toolbox developed at Sandia National Laboratories and implements many of the models and methods developed at the Labs. The pvlib-python library comes with some functions to retrieve weather forecast data from some particular web services, thus through minimal modifications, it was adapted to read data produced by our installation of the WRF model. In this work, we present a comparison of the outcomings of two strategies using off-the-shelf configurations of the NWP model to provide radiation input to the PV modeling library.
WRF-coverage: In this second case, we post-process WRF output through the NCEP Unified Post Processing System (UPP)  [38]  obtaining low, mid, and high-level cloud covers. Those cloud coverages are then composed to get a total cloud cover value, and processed through pvlib-python routines to get the surface global radiation  [39]  which is then decomposed into direct and diffuse components using the DISC model  [40]. Figure  11  shows a comparison of the surface global irradiance (GHI) forecasted by each of the two strategies along nine days during December 2017, for a location in Buenos Aires, Argentina. GHI results are compared with measurements provided by the SAVER-Net Project. Minutely measurements were averaged over 12-minute periods to make them coincide with WRF output frequency. This period was selected as it contains days with a clear sky, partial cloudiness and full overcast conditions.
After global radiation is decomposed in direct and diffuse components, the pipeline to get generation data from weather prediction models for a particular PV system is as follow (see Figure  14 ): 1. Any of the two strategies provides a forecast for irradiances: global horizontal (GHI), diffuse horizontal (DHI), and direct normal (DNI); and other variables which affect the PV system working condition like temperature and wind speed as already exposed. 2. Irradiance data is processed through pvlib-python  4. Regarding the information about the system inverter, the AC power is forecasted. To evaluate the performance of the above-mentioned pipeline, power generation measurements from a This plant can be regarded as a typical community distributed generation installation. The actual plant is composed of two 5 kWp (the 'p' letter denotes peak power) inverters connected to 250 Wp PV modules and one 8 kWp inverter connected to 215 Wp PV modules.
The simulated PV system was defined inside pvlib-python as composed by one inverter (SMA America STP5000TL) and modules (Amerisolar AS-6P30-250W) arranged in two strings of 11 modules each, equivalent to the circuit of one of the 5 kWp inverters of the actual plant. To generalize the results, power generation was normalized concerning peak plant power. The installation 15-minutes normalized power is shown in Figure  15. The outcomes of the forecasting pipeline with inputs provided by the two strategies are Figure  14  PV generation forecast pipeline including the two meteorological variables forecasting strategies also shown therein. In this case, an excellent agreement is given for clear sky days with the WRF-direct pipeline, stressing the importance of the AOD correction. In cloudy days, the results resemble the differences observed in GHI.
For modeling multiple systems assigned to the same WRF grid point, for which no difference will be detected in the radiation forecast, equivalent systems could be defined. Results suggest that: • Other WRF and pvlib-python configurations need to be explored, assessing their forecasting performance, • Some type of cloudiness (intermittent on Dec 22 and 26, constant in the morning Dec 23) is not detected by WRF-direct in the adopted configuration, more insight needs to be gained to clarify this behavior, • The effect of cloud coverage on radiation is overestimated by WRF-coverage, • DISC model misbehaves near noon on clear sky days, other models could be evaluated. • Performance of PV generation for days with great diffuse components need to be verified against radiation components for days with high cloudiness is verified.
At 120 dpi 1,356 genes were identified as DE and at 180 dpi 655; 58 genes displayed deregulation toward the same direction at both time points (38 up-regulated and 20 down-regulated) (SI Appendix, Fig. S3 ). Preclinical-stage profiles involved neuronal and synaptic pathways, as well as signaling cascades associated with oxidative or endoplasmic reticulum (ER) stress. Clinical disease-stage profiles were relevant to cell survival, proliferation, differentiation, lysosome function, and immune system (SI Appendix, Fig. S4  and Table  S1 ). DE profiles of prion-infected mice (RML, Me7, 22A, 79A, 139A, 22L scrapie strains)  (9) (10) (11) (12) (13) (14) (15)  or sCJD patients' postmortem brain tissue  (16, 17)  from gene-expression datasets, revealed common patterns of deregulation between our model and at least 1 of the analyzed murine prion models at preclinical and clinical disease stages (Fig.
1 , SI Appendix, Tables  S2  and S3 , and Dataset S1). We detected similar profile alterations between our study and human sCJD cases. Interestingly, some genesincluding Dbi, Rgs4, Abca1, and Gfap-were deregulated similarly in our study, in human sCJD cases and at least 2 scrapie-murine models (Dataset S1).
S5 ; editome profile lists are provided in Dataset S2; diagrammatic representation of RNA editomes per animal group and time-point is shown in SI Appendix, Fig. S6 ). DNA-RNA differences and the number of events for the 12 types of differences between RNA reads and genomic DNA sequences showed that putative editing is highly concordant between biological replicates. Strikingly, over 90% of these sites correspond to A-to-G and C-to-T differences, consistent with A-to-I and C-to-U editing. This observation supports the existing knowledge that A-to-I editing is the primary type of RNA editing. Other types of differences are much less abundant. ADAR-mediated editing is predominant in the mouse cortex compared to APOBEC-mediated editing (SI Appendix, Fig. S6, ring A ). Similar genomic distributions of editing events were observed for all tested groups and time-points.
Most editing events were detected in intergenic regions, 3′UTRs, exons, or introns, while fewer were in 5′UTRs and other noncoding regions (SI Appendix, Fig. S6 , rings B and C). Profiling of control and sCJD animals' editomes for each time point and clinical stage revealed high similarities between the 2 animal groups and only a few transcripts were uniquely edited either in control or in sCJD. In contrast to global editomes, differentially edited transcript analysis showed APOBEC-mediated editing is overrepresented (∼49 to 60%) (Dataset S3) in almost all phenotypic groups and time points (Fig. 6 , ring A). Dissimilar genomic distributions highlight an increase of 3′UTR editing events in sCJD animals compared to controls at clinical disease (Fig. 6 , rings B and C). The observed increase in 3′UTR editing is mainly attributed to APOBEC, even though A-to-I editing is also increased at this stage.
Majer et al. (10)  reported the induction of cell survival and neurite remodeling pathways in hippocampal neurons of RML-infected mice at preclinical disease and suggested asymptomatic disease as a critical time point for intervention. In contrast, clinical disease-related molecular mechanisms, when extensive degeneration has been established, are  Significant overlaps are observed between sCJD-mice and the tested scrapie models. For a complete list of overlapping genes, please refer to SI Appendix, Tables  S2  and S3  and Dataset S1. more likely to reflect infection. We provide proof-of-principle experimental validations of prion disease-specific transcriptomic profiles at the mRNA and protein level using murine and human sCJD autopsy material. Twenty of 34 DE-validated targets were previously associated with prion diseases, and 14 novel ones are being disease-associated herein (SI Appendix, Table  S5 ).
For RNA-seq and qPCR analysis, 4 animals per group and time point were used; for immunoblotting, samples from 3 to 4 animals per group and time point were analyzed. RNA Purification. RNA was purified using the miRVANA RNA isolation kit, following the manufacturer's protocol. RNAs were treated with DNase Set (Qiagen) for 15 min to eliminate genomic DNA contamination. RNA integrity was assessed by its corresponding RNA Integrity Number (RIN value), determined with the Agilent 2100 Bioanalyzer (Agilent). RNA-Seq and Differential Gene-Expression Analysis. Libraries were prepared using the TruSeq RNA Sample Preparation v2 kit (Illumina). Library quality was assessed with an Agilent 2100 Bioanalyzer and a Qubit dsDNA HS Assay Kit. RNA sequencing was performed as described in ref. 61. All original RNA-seq data were deposited in the National Center for Biotechnology Information's Gene Expression Omnibus, GEO accession no.
GSE90977. Briefly, RNA-seq data were subjected to an in-house quality control workflow. Read quality was assessed using FastQC (v0. 10. 1), to identify sequencing cycles with low average quality, adapter contamination, or repetitive sequences from PCR amplification. Alignment quality was analyzed using SAMtools flagstat (v0. 1. 18), with default parameters. Sequences were aligned to the genome using gapped alignment, as RNA transcripts are subject to splicing and reads might therefore span 2 distant exons. Reads were aligned to the whole Mus musculus mm10 genome using STAR aligner (62) (2. 3. 0e_r291) with default options, generating mapping files (BAM format). Read counts for all genes and all exons (Ensembl annotation v72) were obtained using FeaturesCount (http://bioinf. wehi. edu. au/featureCounts/).
For qPCR and Western blot experiments, normality distribution was analyzed following the D'Agostino and Pearson test. Mann-Whitney U tests were used to compare 2 groups of samples. GraphPad Prism 6. 01 was used for statistical calculations. Differences between groups were considered statistically significant at *P < 0. 05, **P < 0. 01, and ***P < 0. 001. Bioinformatics Pipelines for Identification of RNA Editing Events. Quality control and processing of RNA-seq raw data. High-depth RNA-seq data (26 to 58 million reads per sample, average 33 million reads per sample, GEO accession no. GSE90977) were subjected to quality control analyses using the FastQC software. Adapter sequences, as well as the first 6 bases of the 5′ end and the first 3 bases of the 3′ end of each read were trimmed utilizing the Trim Galore software (http://www.
bioinformatics. babraham. ac. uk/projects/trim_galore/). Adapter sequence contamination was removed and false-positive RNA editing calls originating from random hexamer primer bias were minimized  (64, 65) , trimming excluded reads displaying lengths lower than 20 bases. Processed reads were aligned against the mouse reference genome (mm10), using TopHat2 software (66) (https://ccb. jhu. edu/software/tophat/index. shtml). Higher mapping rates were allowed by providing a set of known gene annotations for the reference mm10 genome. Multimapping reads were excluded and up to 3 mismatches per read were allowed. A high proportion of acquired reads (98. 7 to 99%, average 98. 8%) were mapped to the murine reference sequence. Pertinent data for each of the analyzed samples is provided in SI Appendix, Table  S8.
Calling of single-nucleotide variants for the identification of possible RNA-editing events was performed utilizing 2 approaches based on the REDItools  (67)  and VarScan (68) suites, respectively. For the REDItools suite, an initial Blat Correction analysis (using the script REDItoolBlatCorrection. py) was performed to identify ambiguous aligning reads and the REDItoolDenovo. py script was subsequently used to call possible RNA editing events. For the VarScan suite, the function mpileup2snp was used to process pile-up files generated by the SAMtools software (http://samtools. sourceforge. net/pileup. shtml). True RNA-editing events and exclusion of false positives was achieved by: 1) Mapping and base quality thresholds of 20 and 25, respectively; 2) a minimum coverage of 10 reads, of which at least 3 contain the variation; 3) a P value lower than 0.
05 for FDR for REDItoolDenovo. py (Benjamini) and Fisher's exact test for VarScan; and 4) minimum editing frequency of 0. 01. Annotation was performed using the ANNOVAR software (http://annovar. openbioinformatics. org/ en/latest/) (69) providing dbSNP v142. Strand information was extracted from ENSEMBL. gtf files (mouse gene set) and annotated using custom R scripts and the refGenome R package. SNP exclusion, strand selection, and further statistical analysis for extracting and processing possible C-to-U and A-to-I RNA-editing events was performed using custom R scripts (procedure summarized in SI Appendix, Fig. S5 ). A github repository containing the in-house scripts used can be accessed through the link https:// github. com/athanadd/CJD-mice. Global RNA-editing profiles establishment and identification of differentially edited transcripts between control and sCJD animal groups.
05 were considered significant. As differentially edited were considered transcripts that were called by at least 1 of our bioinformatics suites, were detected in more than 50% of the animals of at least 1 of the studied phenotype groups and displayed editing frequencies ≥0. 01 as well as P ≤ 0. 05 when dual phenotype comparisons were performed. GO and pathway analysis. GO analysis utilizing the differential editome sets representative of each phenotype group and disease stage were performed using the DAVID 6. 8 (https://david. ncifcrf. gov/) (70) software for identification of cellular processes and pathways affected by editing events. RNA-Editing Validations. Reverse-transcription reactions were performed using 500 ng of total RNA and both the oligodT and random hexamer primers included in the PrimeScript kit (TAKARA), following the manufacturer's instructions.
Plasmids corresponding to 30 clones of transformed TOP10 Escherichia coli cells in each case were prepared using the Nucleospin plasmid kit (Macherey-Nagel) and sequenced at both directions. All sequencing reactions were performed by Cemia (https://cemia. eu/) using the v3. 1 BigDye Terminator chemistry (ABI) and were analyzed on a 3730 genetic analyzer (ABI). Raw sequencing data were processed with the Bioedit software v7. 09. 0. RDDs corresponding to RNA-editing events were identified through alignment of cDNA sequencing data with the corresponding reference sequence. Murine and human genomic DNA regions corresponding to the candidate targets were also amplified, sequenced and used as reference sequences for RDDs identification.
ACKNOWLEDGMENTS. Special thanks to Mr. Christos Nikolaou for his longterm technical and administrative contribution to the Neurodegenerative Disease Group at Aristotle University; also to Dr. Alexandros Beis for the creative conversations and common experiences. We also thank the Scientific Computing Center of the Aristotle University of Thessaloniki and the national HellasGrid Certification Authority, through which the RNA-editing identification pipeline was run, for their support; Dr. Alexandra Charalampidou for providing technical support in computational analysis; Ms. Aikaterini Kerezoglou for providing administrative support; and Ms. Georgia Christoforidou (School of Biology, Aristotle University of Thessaloniki) for providing technical support in experimental validations of RNA-editing events.
This study was supported by the Research Funding program ARISTEIA II (Grant RNA edit 3739), cofinanced by the European Union (European Social Fund-ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (to T. S. ); the Alliance BioSecure Foundation project "RNA editing in CJD" (T. S. and I. Z. ); Federal Ministry of Education and Research grants within the German Network for Degenerative Dementia (to I. Z. ); the Red Nacional de Priones AGL2015-71764-REDT-MINECO (to F. L. , I. Z. , J. M. T. , and I. F. ); the Spanish Ministry of Health-Instituto Carlos III/Fondo Social Europeo CP16/00041 (to F. L. ); by the IKYDA Greek-German collaboration Project 57260006 (to F. L. , I. Z. , D. D. , and T. S. ); and in part by Grant AGL2016-78054-R (AEI/FEDER, UE).
Acknowledgments J. B. is funded by a PhD fellowship from the  French Ministry of Education and Research. This study was supported by the  European Union 's  Horizon 2020 research and innovation program  under the grant agreement  CODOVIREVOL  ( ERC-2014-CoG-647916 ) to I. G. B. The authors acknowledge the CNRS and the  IRD  for additional, intramural support. The computational results presented have been achieved in part using the  IRD Bioinformatic Cluster itrop , which also hosts the COUSIN online server (http://cousin. ird. fr). We thank  Fr ed eric Delsuc  for driving our attention onto the composition particularities of the G. gallus genome.
These post-transcriptional editing mechanisms have been shown to affect miRNAs' function and stability  (Kawahara et al. 2007; Burroughs et al. 2010; Chiang et al. 2010; Katoh et al. 2015). Lastly, genetic variations have also been shown to contribute to the sequence diversity of miRNAs, and to affect their function and expression  (Mencía et al. 2009; Gong et al. 2012; Han and Zheng 2013; Cammaerts et al. 2015). MiRNAs subject to post-transcriptional events and/or genetic variations are generally referred to as isomiRs. In the following, we will use the expression "polymiR" to refer to the subclass of isomiRs harboring genetic polymorphisms in their miRNA sequence. The first step in the bioinformatics analysis of miRNA sequencing (miRSeq) data consists in aligning sequenced reads to a reference library of mature miRNAs.
This is a situation we anticipate to become rather common with the rise of increasingly affordable genome-wide association stud-ies (GWAS) and the decreasing cost of next-generation exome/genome sequencing techniques. In that context, we developed an original bioinformatics workflow called OPTIMIR, for pOlymorPhism inTegratIon for MIRna data alignment, that integrates genetic information from genotyping arrays or DNA sequencing into the miRSeq data alignment process with the aim of improving the accuracy of polymiRs alignment, while accommodating for other isomiRs detection and ambiguously aligned reads. In addition, OPTIMIR allows to assess the association of genotypes on polymiRs with corresponding polymiRs' expression. OPTIMIR was evaluated in the plasma samples of 391 individuals, part of the MARTHA study  (Oudot-Mellakh et al.
RESULTS OPTIMIR is composed of three main steps (Materials and Methods). First, miRSeq data are aligned to a reference library upgraded with sequences integrating alternative alleles of genetic variations. A correction is then applied for ambiguous and unreliable alignments via a scoring approach. Finally, polymiR alignments are evaluated to only retain those that are consistent with input genotypes in case these have been provided by the user. The general workflow was summarized in Figure  1.
MiRNA alignment OPTIMIR was evaluated on 391 miRNA sequencing data files totaling 7,390,947,662 sequencing reads. After preprocessing the sequenced reads, that included adapters' removal and selecting only reads with size ranging between 15 and 27 nt, 2,922,446,965 reads (39. 54% of total reads) remained for alignment. 562,040,494 of these reads (19. 23%) were then mapped to mature miRNA reference sequences, of which 10,937,479 (1. 95% of mapped reads) aligned ambiguously to two sequences or more. The application of the OPTIMIR scoring algorithm for alignment disambiguation resulted in a unique solution for 91. 6% of these cross-mapping reads. OPTIMIR computes a score based on the number of editing events that a sequenced read could be compatible with and keeps alignments with the lowest scores (i.
Alignment on polymiRs Over all samples, 220,156 reads mapped to 46 polymiRs for a total of 1786 distinct alignments. As detailed in Table  1 , 19 polymiRs have reads that aligned to an FIGURE 1. OPTIMIR workflow. From an optional VCF file and a FASTQ file, OPTIMIR performs the alignment of miRSeq data and results in the generation of abundances files containing the expressions of sequenced miRNAs. (a) The score S al is based on the number of editing events observed on the 5 ′ end and 3 ′ end of a read (N 5 and N 3 , respectively), with a weight W 5 applied to the 5 ′ end events (set to four in this study). (b) The genotype consistency is checked only if the user provided genotypes for the variants in the VCF file. alternative sequence that was introduced in the upgraded reference library. Two polymiRs (hsa-miR-6796-3p and hsa-miR-1269b) harbor two SNPs, and for both of them only the reference sequence with both common alleles were found to be expressed.
The polymiR with the expression closest to allelic balance for many samples is hsa-miR-4433b-3p but, even then, the average rate of alternative reads across all 147 samples is of 0. 8 (see Supplemental Table  4  for details on polymiRs involved in heterozygous situations). As the last case, 65 MARTHA individuals were heterozygous for the rs2925980 variant associated with polymiR hsa-miR-7854-3p. These genotypes could be considered as reliable as this SNP was directly typed on the array. Among these   Finally, we used the RNAfold program  (Lorenz et al. 2011)  to predict the secondary structure of the 29 pri-miRNA (i. e. , hairpins) induced by the presence of a SNP in the polymiR sequence (Supplemental Fig. S1 ). Most genetic variations create either a new bulge, or a wobble pairing, or have no impact on the secondary structure.