dataesr/scientific-paragraphs-categorization huggingface dataset:
They affect all the images acquired by passive satellites, including Landsat 4-5-7 and 8. The downloaded Landsat images are ortho-rectified, the preprocessing concerns the atmospheric correction of these images and their re-projection in the local system (WGS_84_UTM_Zone_32N). For this, neo-channels are created, to increase the readability of the data by enhancing certain less obvious properties in the original image, thus showing more clearly the elements of the scene. Three indices are therefore created, namely the Normalized Difference Vegetation Index (NDVI), the Brightness Index (IB) and the Normalized Difference Water Index (NDWI). These indices highlight respectively the vegetated surfaces, the sterile (not-chlorophyllin) elements such as the urban and the water bodies. Their formulas are as follows NDVI = (NIR -R)/(NIR + R); IB = (R 2 + NIR 2 ) 0.
Acknowledgments: The authors wish to thank the Cameroonians authorities and national services of  Meteorology and Hydrology , for providing data to the  Joint International Laboratory (LMI) DYCOFAC. The authors also thank the  National Observing System M-TROPICS (Multiscale TROPIcal CatchmentS) from France , which subsidizes observations in experimental tropical watersheds. The authors warmly thanks the agents of the  IRD (Research Institute for Developping countries) Représentation in Cameroon  and to the direction of the  LMI DYCOFAC, at Yaounde , for their administrative and financial support. The authors also wish to address a memoriam to  Ms. Claudine Dieulin , from the  IRD , who passed away late January, and who spent years in developing the open-source database HSM-SIEREM, dedicated to the African hydrology and climatology, and which data have been used partly in this study.
Chromosome Analysis Karyotypes were prepared from bone marrow cells, following standard protocols used in earlier studies  [4] , namely the 'air-drying' procedure  [29]  and G-banding of chromosomes  [30]. A minimum of three metaphases per individual were analysed, making sure that all homologous pairs of chromosomes were identified in each. Metaphases were observed under an Olympus microscope and karyotyped using Leica Chantal software. Chromosomes were identified following Cowell  [31]  at the Faculty of Sciences in Lisbon and at the Institute of Evolutionary Sciences, Montpellier. Chromosomal analyses were performed using an Olympus BX41 microscope with an attached Leica DC 250 camera equipped with a Leica CW4000 Karyo image analysis system. The distribution of metacentric races was recorded following the methodology described in Nunes et al.
Temporal Analysis For the temporal analysis, we compared our new dataset with the one obtained between 1998 and 2002 (Table  S2 ). Previously sampled sites were re-projected orthogonally along the main road to avoid errors when comparing distances among sites between the two periods. The 1998-2002 dataset includes the results reported by Nunes et al. [17]  and unpublished data (Table  S2 ). Temporal comparisons included: i) the chromosomal characteristics of sites and groups of sites for the whole transect, ii) the percentage of acrocentrics and metacentrics in sites and groups of sites also for the whole extension of the transect, and iii) the clinal pattern for diagnostic fusions of PEDC (Rb 6. 7) and PADC (Rb 7. 15) along the previously sampled transect analysed by Nunes et al. [17] , i.
e. , sites 6 to 50 (km 10. 1 to 58. 17). The software package C-Fit8 was used to compute eight regression cline models (Table  S3 ). Model selection was then performed based on the Akaike's information criterion  [32]  (Table  S3 ). Logistic or scaled logistic functions were selected to best fit the metacentric clines: f(x) = (e s(x-c) /1 + e s(x-c) )*h, (1) where x is the geographic distance, s and c are the maximum slope and the centre of the cline, respectively, and h is the height. The width (expressed as an inverse of the slope) and cline centres were compared between the pair of fusions Rb (6. 7) and Rb (7. 15) for both time periods. To check for differences between clines, we assumed that twice the difference in log e likelihood values between the constrained and unconstrained model (on cline centre for coincidence and on cline width for concordance) followed a χ 2 distribution with a d.
Acknowledgments: We here pay tribute to our colleague Janice Britton-Davidian -for her dynamism and dedicated scientific contribution that was absolutely central to our studies of the Madeira Rb system -and for her unforgettable friendship. We would like to thank  Manuel Biscoito , Director of  Estação de Biologia Marinha do Funchal  for all the support in Madeira, in particular for the mice maintenance and laboratory facilities,  Tomé Neves  for his precious help with the Madeira map and statistical analysis, and  Sophie von Merten  for additional statistical advice. Finally, we would like to thank the three anonymous reviewers whose comments greatly improved the final version of the manuscript.
Funding: This study was supported by  Fundação para a Ciência e a Tecnologia (FCT)  project  PTDC/BIA-EVF/116884/2010  'Speciation or despeciation?Zooming in on a chromosomal hybrid zone in the Madeiran house mouse', involving European FEDER funds and FCT post-doc fellowships to SIG ( SFRH/BPD/88854/2012 ) and  AMC  ( SFRH/BPD/47070/2008 ). SIG and AMC were also funded by national funds (OE), through FCT in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law  57/2016 , of August 29, changed by Law  57/2017 , of July 19. Thanks are also due to  FCT/MCTES  for the financial support to ( UIDP/50017/2020+UIDB/50017/2020 ) through national funds.
Results Overview of Moonlight. We here present Moonlight: a tool designed to identify cancer driver genes that moonlight as opposite roles when observed in the context of transcriptomic networks. The name refers to (i) the concept of protein moonlighting (or gene sharing) is a phenomenon by which a protein can perform more than one function  13  , and (ii) casting genes in a new light can lead to improved treatment regimens and prognostic indicators. Moonlight can detect cancer driver-gene events specific to the tumor and tissue of origin, including potential dual-role genes, as well as elucidate their downstream impact. To accomplish this, Moonlight integrates information from literature, pathways databases, and multiple -omics data into a comprehensive assessment of a gene's role and function (Fig.
1a ). Moonlight is freely available as an open-source R package within the a b Bioconductor project at http://bioconductor. org/packages/ MoonlightR/. The main concept behind Moonlight relies on the observation that the classical approach to experimentally validated cancer driver genes consists in the modulation of their expression in cellular assays, together with the quantification of process markers, such as cellular proliferation, apoptosis, and invasion. We thus selected apoptosis and cell proliferation as main gene programs to detect cancer driver genes. To accomplish this task, we manually curated over 100 biological processes linked to cancer, including proliferation and apoptosis. During this manual curation, we gave Moonlight information on whether the activation of each process leads to promotion or reduction of cancer (Methods; Supplementary Data 1 and 2).
We compared tumor and normal samples using sample profiles from multiple -omics data retrieved from the Genomic Data Commons using the TCGAbiolinks 14 package and a workflow that we developed to process cancer data  15, 16  (Methods; Supplementary Data 3). Specifically, we selected breast-invasive carcinoma from TCGA for illustrative purposes. In this step of the analysis, we found 3390 genes that were differentially expressed (Methods, Supplementary Data 3) when comparing normal and tumor breast-cancer tissue samples. Functional Enrichment Analysis (Methods) revealed that these genes were significantly enriched in 32 biological processes (Fig. 2a ; Supplementary Data 4). Several biological processes promoting cancer progression (cell proliferation, invasion of cells, inflammatory response) were significantly increased.
Concurrently, processes counteracting cancer progression (branching of cells, apoptosis of tumor cell lines) were significantly decreased. One example of a biological process associated with cancer progression is increased cell proliferation. The cell proliferation biological process, as defined by Gene Ontology and KEGG database, has 3938 annotated genes, of which 1172 were identified by Moonlight to be differentially expressed genes (Student's t test FDR-adjusted p = 4. 38E-113) (Fig. 2a ; Supplementary Data 4, Methods). Another example is apoptosis, which is generally downregulated in association with cancer progression. This process had 1284 annotated genes, of which 390 were found to be differentially expressed (Student's t test FDRadjusted p = 3. 15E-34) (Fig. 2a ; Supplementary Data 4).
These genes are either linked to gastrointestinal cancer in the COSMIC database (BCL2, KIT and PDGFRA) or through literature findings (MET  68  and KLF4  69  ). Taking a closer look at the tumor suppressors, we found that at least two of these genes, CDKN2A  70  and SOCS1  64  , have been linked to colorectal cancer. For the cancer types that performed the worst, liver hepatocellular carcinoma included none of the used oncogenes (AR, KLF4, PDGFRA, and RET) or tumor suppressors (BRCA2, CDKN2A, and TSC1) that were linked to it. This suggests that when a well-curated, cancer type specific list of oncogenes and tumor suppressors is present, Moonlight is successful in using gene expression data to detect the role of cancer driver genes. For rectum adenocarcinoma (one of the cancer types with the best performance), the top biological processes are able to cluster the two classes accurately (Fig.
Recently, multiple drugs were shown to act on the same cell lines in a first-of-its-kind study  72. To aid in effective cancer treatments which concurrently activate tumor suppressors and inactivate oncogenes, novel drug-combination therapies are required. For this reason, we validated the predicted cancer driver genes in silico and further analyzed gene expression data from 1001 cancer cell lines retrieved from the Genomics of Drug Sensitivity in Cancer (GDSC) database  72. We created a pipeline to automatically retrieve these data along with the gene expression matrix for 18 cancer types from GDSC data set (Methods). Within these GDSC cell lines, we observed that 41% of the oncogenes upregulated in TCGA's breast-invasive carcinoma  tumors had high expression. Simultaneously, 31% of the tumor suppressors downregulated had low expression (Methods; Fig.
Discussion In summary, Moonlight provides a platform for multi-omics integration and utilizes a wealth of prior knowledge (Fig. 1a ). Such knowledge includes gene networks and ontologies unharnessed by many current bioinformatics tools for oncological discovery. Moonlight combines multiple functionalities to reproducibly integrate regulatory networks by means of gene expression, literature information, and evidence from multiple  bulk-tumor -omics data (mutation, DNA methylation, chromatin accessibility, cell lines, and clinical data) (Methods; Fig. 1b ). Because of Moonlight's ability to combine information from multiple sources, this software has the capability to define critical events when two or more key alterations appear. Moonlight highlights cancer driver genes currently undetected by other tools and detects dual-role genes (oncogene in one cancer type and tumor suppressor in another).
MutSig2CV and DriverNet detect driver genes integrating genomic and transcriptome data. Compared with existing tools, Moonlight is able to extract, for each driver gene, the multilayer profile elucidating the BPs underlying their specific roles and interactions. Furthermore, the majority of the current methods use only mutation data to detect cancer drivers, limiting the knowledge of the related molecular mechanisms. Indeed, mutations can cause different effects such as a loss or reduction of mRNA transcripts impacting on the protein function. In line with this scenario to increase functional information and generate new hypotheses of gene function, transcriptome data have been used. Data sets and preprocessing. The legacy level-3 data of the Pan-Cancer studies (18 cancer types), for which there were at least five samples of primary solid tumor (TP) or solid tissue normal (NT) available, were used in this study and downloaded in May 2018 from The Cancer Genome Atlas (TCGA) cohort deposited in the Genomic Data Commons (GDC) Data Portal (Supplementary Data 4).
RNA-seq raw counts of 7962 cases (7240 TP and 722 NT samples) aligned to the hg19 reference genome were downloaded from GDC's legacy archive, normalized, and filtered using the R/Bioconductor package TCGAbiolinks 14 version 2. 9. 5 using GDCquery(), GDCdownload(), and GDCprepare() functions for tumor types (level 3, and platform "IlluminaHiSeq_RNASeqV2"), as well as using data. type as "Gene expression quantification" and file. type as "results". This allowed for the extraction of the raw expression signal for expression of a gene for each case following the TCGA pipeline used to create level-3 expression data from RNA Sequence data. This pipeline used MapSplice  86  to do the alignment and RSEM to perform the quantification  87. DNA methylation beta values of primary solid tumors (TP) and solid tissue normal (NT) from Pan-Cancer studies (18 cancer types) aligned to the hg19 reference genome were downloaded from GDC's legacy archive using the R/Bioconductor package TCGAbiolinks 14 version 2.
Gene programs. To further investigate gene programs enriched by genes differentially expressed between two conditions, we employed Gene Set Enrichment Analysis (GSEA) for ten collections from the Molecular Signatures Database  93  as follows: H: hallmark gene sets, C2: BIOCARTA pathway database, C2: KEGG pathway database, C2: REACTOME pathway database, C3 TFT: transcription factor targets, C5 BP: GO BP, C5 CC: GO cellular component, C5 MF: GO molecular function, C6: oncogenic signatures, C7: immunologic signatures. Gold-standard gene set of driver genes. A recent review 66 has argued that a comparative assessment of role prediction methods is not straightforward due to the lack of a clear gold standard of known OCGs and TSGs. To create the best currently available training set of known OCGs and TSGs, we used those genes in our training set that have been verified by at least two sources.
LPA interrogates PubMed to obtain a table with information for each gene and a particular BP such as apoptosis or proliferation to understand the number of publications reporting the relationship of a gene-BP (increasing, decreasing, or affected). To filter out false positives obtained from text co-occurrence, it is possible to integrate Beegle's  92  results applied on individual BP, considering the overlapping results. Here with the LPA function, it is possible to extract a BP-genes database from the literature with a twofold aim: (i) producing updated literature information, and (ii) flexibility for BPs of relevant interest. Pattern recognition analysis (PRA). PRA allows for the identification of a list of TSGs and OCGs when BPs are provided such as apoptosis and proliferation, otherwise a random Forest-based classifier can be used on new data.
We define a pattern when a group of genes classified as OCGs share similar BP as apoptosis (DOWN) and proliferation (UP) while genes classified as TSGs share apoptosis (UP) and proliferation (DOWN). Dynamic recognition analysis (DRA). This analysis detects multiple patterns of BPs when different conditions are selected. For the breast-cancer molecular subtypes application, we used fgsea package with the ten collections from the Molecular Signatures Database 93 using the following parameters: minSize = 15, maxSize = 500, and nperm=1000. Categories were considered significantly enriched with permutation P < 0. 05. ROMA score for pathway activity. For the pathway activity evaluation, Representation and quantification Of Module Activity (ROMA) (https://github. com/ sysbio-curie/Roma)  97  was also employed as an alternative to the Moonlight Process Z-score.
For each module under analysis, the algorithm applies principal component analysis to the sub-matrix composed of the expression values of the signature genes across samples. ROMA then evaluates the module overdispersion by verifying if the amount of variance explained by the first principal component of the expression sub-matrix (L1 value in ROMA) is significantly larger than that of a random set of genes of the same size. This represents an unsupervised approach that can be used in combination with the supervised Moonlight Process Z-score to detect concordant signals. An example of application of ROMA to TCGA breastinvasive carcinoma is shown in Supplementary Data 14, where the ROMA activity score of biologically processes potentially modulated by cancer driver genes is reported. Machine-learning approach.
We used the Moonlight Process Z-score matrix as input to the random forest procedure, such that the BPs are the features that the learning method can include in the model. The obtained model can then be used to predict the role of genes that were not included in model building and obtain a relevance score for each of the BPs. This model is trained on a gold-standard gene set of known OCGs and TSGs based on the intersection of two sources: (i) the list provided by the COSMIC database  98, 99  , and (ii) the cancer genes identified by Vogelstein et al. 2. Evaluation criteria. We used two different quality measures in our evaluation. The first one is the multi-class log-loss measure. The lower the log-loss value, the better the model's performance. The log loss is defined as: À 1 m X m i¼1 X 3 j¼1 y ij logðp ij Þ; ð3Þ where y ij is a binary variable, that is equal to one when i = j and zero otherwise.
Acknowledgements We are grateful to  Matthieu Defrance ,  Kridsadakorn Chaichoompu ,  Kristel Van Steen ,  Benjamin Haibe-Kains  ans  Thuc Duy Le  for suggestions and scientific advice in the Moonlight project. We would also like to thank  Lisa Cantwell  for her scientific proofreading of the paper. The project was supported by the  BridgeIRIS  project [http://mlg. ulb. ac. be/BridgeIRIS], funded by  INNOVIRIS, Region de Bruxelles Capitale, Brussels, Belgium , and by  GENGISCAN: GENomic profiling of Gastrointestinal Inflammatory-Sensitive CANcers , [http://mlg. ulb. ac. be/GENGISCAN]Belgian FNRS PDR ( T100914F  to A. C. , C. O. , and  Gi. B. ). Gi. B. was also supported by the project  WALINNOV 2017 -N°1 710030 -CAUSEL I. C , C. C. and Gl. B. were supported by  INTEROMICS  flagship project (http://www.
Data availability The -omics data sets (gene expression, methylation, copy number, chromatin accessibility, clinical, and mutation) analyzed during this study are publicly available in the repository https://portal. gdc. cancer. gov/ and can be downloaded directly by using the TCGAbiolinks R package as described in the Methods section. The cell lines data set analyzed during this study are publicly available in the repository https://www. cancerrxgene. org/downloads. All data generated or analyzed during this study are included in this published article, its supplementary information files, and in the publication folder https://github. com/ibsquare/.
Code availability Updated links to the packages and tutorials related to Moonlight are available within the Bioconductor project at http://bioconductor. org/packages/MoonlightR/ and in GitHub https://github. com/ibsquare/MoonlightR. The package vignette with R scripts to reproduce the results and figures at the time of publication are provided as Supplementary. Data with intermediate results and code to generate specific analysis are available from the corresponding author, Dr. Antonio Colaprico, and will be uploaded to GitHub [https://github. com/torongs82/] upon request.
This article presents a comparison of different ML algorithms for estimating maximum releasable capacity of Li-Ion batteries, with a special focus on the implementation of both Forward and Recurrent ANNs (FNNs and RNNs, respectively), using prognostic Li-Ion battery data sets provided by the National Aeronautics and Space Administration (NASA). After an evaluation of models performances in terms of RMSE and MAE, STM32Cube. AI tool was used to convert pre-trained ANNs to optimized ANSI C code for STM32 microcontrollers (MCUs), and to profile their complexity automatically. Finally, in order to decrease models size with minimal accuracy loss, the implemented ANNs were quantized via STM32Cube. AI, converting weights and activations from 32-bit floating-point to 8-bit integer precision. TensorFlow Lite for Microcontrollers (TFLM) was used as benchmark in the analysis and validation of both non-quantized and quantized models, and the performances obtained via STM32Cube.
Weng et al. [13]  developed a model based on Support Vector Regression (SVR), using partially charging data of a single Li-Ion cell to estimate capacity fading of other 7 cells. However, due to the same charge profile used for all data generation, the complexity of the estimation was reduced. Wei et al. [14]  proposed a SVR-based model for batteries capacity fading prediction, based on one of the Li-Ion battery datasets made available by National Aeronautics and Space Administration (NASA)  [15]. Also in this work, the data used for the forecast were only those generated by the same load profile. Additionally, although in real-world conditions the charge/discharge may not be complete, the entire charge/discharge cycles were used as model input. In the study of Li et al. [16]  random forest regression was used to estimate online the capacity of Li-Ion batteries, again using complete charge/discharge cycles as model input.
A random decision forest was proposed by Xu et al. [17]  for battery health estimation, using one of the Li-Ion battery prognostic datasets made available by NASA  [15]. The same batteries used for both training and test sets considerably reduced the complexity of capacity estimation. Despite the high performances obtained through traditional algorithms, Deep Learning (DL) methods have proven to be more flexible and effective, due to the high non-linearity and non-direct observability of SoH. For example, a Convolutional Neural Network (CNN) was used to estimate SoH from raw battery measurements, obtaining excellent results. Reference  [18]  However, the same load profile used for data generation and the entire cycles of charge given as input limited the algorithm practicality. Then partial charge profiles were used for State of Charge (SoC) generation, which was given as input to the model together with voltage, current and temperature.
However, since SoC is not directly observable and estimable, further errors are introduced a priori in the model  [19]. Choi et al. [7]  developed a Feedforward Neural Network (FNN), a CNN and a Long Short-Term Memory (LSTM) network to estimate the capacity of Li-Ion batteries from their voltage, current and temperature charge profiles. The dataset used is one of the Li-Ion battery datasets made available by NASA  [15] , and as in other studies the complexity of the forecast decreased due to the same load profile used for data generation. Another research work  [20]  proposes an Independently Recurrent Neural Network (IndRNN) for SOH estimation which uses randomized battery usage data collected from Li-Ion batteries adopting dynamic load profiles  [15] , obtaining a Root Mean Squared Error (RMSE) of 0.