dataesr/scientific-paragraphs-categorization huggingface dataset:
M odels in systems biology typically integrate knowledge and hypotheses on molecular interactions, manually or semi-automatically, gathered from experimental data found in databases and the literature. These models are often qualified as mechanistic, in opposition to those solely based on biophysical laws. Since their introduction in the late 1960s  1, 2  , logical models, such as Boolean Networks (BNs), have been widely adopted for reasoning about signaling and gene networks  [3] [4] [5] [6] [7] [8] [9] [10] [11]  as they require few parameters and can easily integrate information from omics datasets and genetic screens. These models represent processes with a high degree of generalization and can offer coarse-grained but robust predictions. That makes them particularly suitable for large biological networks, for which ample global knowledge exists about potential interactions with little precise data on actual molecules abundances and reaction kinetics.
Thus, CG33970 belongs to the group of half-type ATP-binding cassette (ABC) transporters  [25]. Using specific primers, we confirmed the expression of the predicted long and the short transcripts by qPCR. To learn about the potential molecular function of CG33970, using the full-length CG33970 protein, we searched the NCBI database for human homologous sequences that have been functionally characterised. The most homologous sequences were ABCA-type transporters including ABCA3 and ABCA12 (Fig  1 ). ABCA-type transporters are full transporters that are mainly involved in lipid transport across membranes  [26]. Hence, it is possible that CG33970 is implicated in lipid transport in D. melanogaster.
Three lethal excision alleles were sequenced, ΔMiM18, ΔMiM8 and ΔMiM6. These three alleles have insertions of 4 (ΔMiM18), 8 (ΔMiM8) or 6 bp (ΔMiM6) at the position of transposon insertion (in codon number 273). The ΔMiM18 and ΔMiM8 insertions cause frame-shifts resulting in stop codons soon downstream of the insertion site in turn yielding proteins that are devoid of the ABC2 domain. Through the ΔMiM6 insertion, the protein gains two amino acids (TR) in the region linking the ABCH and the ABC2 domains. Alignment of the full-length Osy protein with human ABCA3 and ABCA12 (C). The sequence of the full length Osy protein was blasted against human proteins in the NCBI database using the BlastP software (https://blast. ncbi. nlm. nih. gov/). The Osy ABC cassette domain displays significant homology to both of the respective domains (domain 1 and 2) in all human ABCA transporters.
Sequence analyses DNA and protein sequences were analysed using online software provided by NCBI conserved domain database and by the Expasy bioinformatics resource portal (expasy. org). Osy domain composition was predicted by the PSORT (http://www. psort. org), SignalP (http://www. cbs. dtu. dk/services/SignalP/) and by TMPred (https://embnet. vital-it. ch/software/TMPRED_ form. html) software.
Microscopy For transmission electron microscopy (TEM), embryos and larvae were treated and analysed on a Philips CM-10 electron microscope as described in detail previously  [37]. Bright field, differential interference contrast (DIC) and fluorescence microscopy were performed with a Nikon eclipse E1000 or a Zeiss Axioplan 2 microscope. Confocal imaging was performed with Zeiss LSM 710 Axio Observer or LSM 800, Bio-Rad Radiance 2000 or Leica TCS SP2. Figures were prepared using the Adobe Photoshop CS6 and Adobe Illustrator CS6 software.
The ΔMiM18 and ΔMiM8 insertions cause frame-shifts resulting in stop codons soon downstream of the insertion site in turn yielding proteins that are devoid of the ABC2 domain. Through the ΔMiM6 insertion, the protein gains two amino acids (TR) in the region linking the ABCH and the ABC2 domains. Alignment of the full-length Osy protein with human ABCA3 and ABCA12 (C). The sequence of the full length Osy protein was blasted against human proteins in the NCBI database using the BlastP software (https://blast. ncbi. nlm. nih. gov/). The Osy ABC cassette domain displays significant homology to both of the respective domains (domain 1 and 2) in all human ABCA transporters. Here, we show the alignment of these domains in ABCA3 and ABCA12 (isoform a). Those amino acids mutated on ABCA12 variants are highlighted in red(Akiyama, 2010).
Data Acquisition The extension of the fluctuating probe is acquired in real-time using the high-resolution magnetic tweezers setup described in  [3]  at an acquisition frequency of 1300Hz with a CMOS camera (UI-3030CP-M, IDS Ueye). The home-made C/C++ acquisition program Xvin can be found at https://tig. phys. ens. fr/ABCDLab/xvin/.
Locomotor activity was measured using optical beam motion detection (Philips Respironics), and data were collected using the Mini-Mitter VitalView 5. 0 data acquisition software. Actograms and activity profiles were calculated using ClockLab software (Actimetrics). Period and amplitude were calculated by obtaining the slope of onset of the free running activity and computed by  2 periodogram analysis. The Institutional Animal Care and Use Committee of the University of California, Irvine, and the Animal Experimental Committee of Kyoto University approved all experiments.
The six most intense peptide ions with charge states between 2 and 4 were sequentially isolated to a target value of 10,000 and fragmented in the linear ion trap by collision-induced dissociation (CID). All fragment ion spectra were recorded in the LTQ part of the instrument. For all measurements with the Orbitrap detector, three lock-mass ions from ambient air were used for internal calibration as described before  (75). Typical MS conditions were as follows: spray voltage, 1. 5 kV; no sheath and auxiliary gas flow; heated capillary temperature, 200°C; normalized CID energy, 35%; activation q = 0. 25; activation time, 30 ms. MaxQuant 1. 5. 2. 8 was used to identify proteins and quantify by iBAQ with the following parameters: database, UP000000589_10090_ Mmusculus_151030; MS tol, 10 ppm; MS/MS tol, 0.
The samples were excited with the 900-nm laser line using a 63× water immersion objective (1. 2 numerical aperture, Olympus Plan Apo) applying a two-photon excitation scheme. The fluorescence was split using a dichroic mirror (FF495-Di03-25x36, Semrock) and then passed through two separate filters (520/35 nm to 641/75 nm, Semrock), collected using two separate photomultiplier tube (Hamamatsu), and recorded using FLIMBox. The pixel dwell time for the acquisitions was 32 s, and the images were taken with sizes of 256 × 256 pixels. To have high signal-to-noise ratio, 30 to 50 frames were collected. The data from each pixel were recorded and analyzed using the SimFCS software (developed by E. Gratton in Laboratory for Fluorescence Dynamics, University of California, Irvine, CA). The intensity decays collected at each pixel of the image were transformed to the Fourier space, and the phasor coordinates were calculated using the following relations g i,j ( ) = ∫ 0 T I(t ) ⋅ Cos(nt ) dt / ∫ 0 T I(t ) dt s i,j ( ) = ∫ 0 T I(t ) ⋅ Sin(nt ) dt / ∫ 0 T I(t ) dt where g i,j () and s i,j () are the x and y coordinates of the phasor plot, respectively, and n and  are the harmonic number and the angular frequency of excitation, respectively.
The transformed data were then plotted in the phasor plot in a way that the data from each individual pixel are transformed to a point in the phasor plot. The phasor histogram was analyzed using the fractional intensity distribution between two phasor components: phasor positions of non-FRET donor-only component and that of the decreased lifetime of the donor in donor-acceptor sample. According to the linear combination of the phasor, the shift in the phasor at image pixels, where both donor only and donor-acceptor samples are present, will be along the linear combination of the individual phasor positions of the donor lifetime in donor only and donor-acceptor samples. The relative fractions of the two components-donor only and donoracceptor-will determine the phasor position of that image pixel according to their relative fractional intensity contribution.
RNA sequencing To generate libraries, total RNA was isolated from Ctrl MEF, AHCY KO MEF, DMSO, and 10 M DZnep-treated MEF using TRIzol (three biological replicates). Libraries were prepared from three biological replicates according to TruSeq RNA Library Prep Kit v2. Libraries were assessed for quality and quantity using BioAnalyzer (Agilent) and qPCR (Kapa Biosystems). The multiplexed libraries were sequenced on eight lanes using single-end 100-cycle chemistry for HiSeq 4000 [100 base pairs (bp), single-end reads]. The version of HiSeq control software was HCS 3. 3. 76 with real-time analysis software, RTA 2. 7. 6. Demultiplexed FASTQ files were aligned to the reference genome assembly mm10 using TopHat-2. 1. 1 with default parameters. Aligned reads were mapped to genomic features and quantified using Cufflinks 2.
2. 1. Reads per kilobase of transcript per million mapped reads (RPKM) were used to quantify the relative abundance of each transcript in a sample and to perform gene expression analyses. Genes systematically observed with no expression in all replicates were not considered in the study. To generate libraries from SCN samples, RNA was extracted from SCN punches using the RNeasy Micro Kit (QIAGEN) according to the manufacturer's instructions (four biological replicates for all CT points with the exception of CT21 saline that has three biological replicates). Libraries were prepared using SMARTer Stranded Total RNA-Seq Kit v2, Pico Input Mammalian (Clontech) according to the manufacturer's instructions. Libraries were assessed for quality and quantity using BioAnalyzer (Agilent) and qPCR (Kapa Biosystems).
The multiplexed libraries were sequenced on eight lanes using singleend 100-cycle chemistry for HiSeq 4000 (100 bp, paired-end reads). After trimming the first three nucleotides from the 5′ end of the paired end read, FASTQ files were aligned to the reference genome assembly mm10 as described above. For time series datasets, rhythmic transcripts were detected with the BIO_CYCLE algorithm  (78)  and results were confirmed with JTK algorithm  (79). Output measures including amplitude and phase of rhythms were also determined. Genes were considered rhythmic over the circadian cycle if their permutation-based, adjusted P value was <0. 01 for MEF samples and <0. 05 for SCN samples. Differential analysis of gene expression at individual CTs was defined using Cyber-T, a differential analysis program using a Bayesian-regularized t test  (80).
GO biological process enrichment analysis was conducted using The Database for Annotation, Visualization and Integrated Discovery (DAVID) version 6. 8  (81). TFBS enrichment was performed using MotifMap  (82) , and ChIP-seq experiments were obtained from ENCODE. RNA-seq data are deposited in, and are available through, the CircadiOmics web portal at http://circadiomics. igb. uci. edu/ (83). ChIP sequencing Samples were prepared as described above in the ChIP section. Library construction was performed from two biological replicates using a PrepX Illumina Chip-seq kit and the PrepXComplete ILMN DNA barcodes on the Apollo324 System. DNA quantity was measured by Qubit DNA High Sensitivity, and the fragment size was measured by Agilent Bioanalyzer 2100 High Sensitivity DNA chip assay. The input amount of DNA was 4 ng for H3K4me3 and 0.
The HiSeq real-time analysis software (RTA 2. 7. 7) converted the images into intensities and base calls. Postprocessing of the run to generate the FASTQ files was performed at the Institute for Genomics and Bioinformatics (UCI IGB). The raw reads were mapped to mouse reference genome NCBI build 37 (mm9) by bowtie v1. 1. 0, allowing up to one mismatch. To adjust the difference of library size, uniquely mapped reads of each sample were down-sampled to the lowest read count among all samples including input. Then, the ChIP-seq peaks were called by MACS v1. 4. 2  (84)  with a cutoff of P ≤ 1 × 10 -8 , and clonal reads were automatically removed by MACS. The peaks that overlapped with any "negative peaks" (peaks are called by swapping the ChIP and input) were discarded. As the ChIP-seq profile of two replicates is highly correlated (H3K4me3 Pearson's R ≥ 0.
96, BMAL1 Pearson's R ≥ 0. 76), data from replicate 1 were applied in downstream analysis. For peak overlap analysis, peaks from all four samples were merged together as consensus regions. Then, peaks of each sample, which have at least 500 bp or 50% overlap with the consensus regions, were defined as overlapped peaks. To reduce false-positive calls, BMAL1 peaks with lower than twofold enrichment over input in all four samples were also discarded. The ChIP-seq read density was determined by deepTools v2. 3. 4 (85), and then the average binding profile was visualized using R v3. 2. 3. Gene promoter regions were defined as 1 kb upstream to 1 kb downstream of gene TSS. H3K4me3 occupancy at gene promoters was quantified as RPKM, which was normalized by both promoter length and total read number in each sample.
Acknowledgments: In memory of  Paolo Sassone-Corsi , an exceptional mentor and scientist. We thank  C. Partch  and all members of the  P. S. -C. laboratory for discussion  and support. We thank  A. Ballabio  ( TIGEM ) for the pCMV TFEB plasmid. We also thank  M. Oakes ,  S. -A. Chung , and  V. Ciobanu  of the  Genomics High Throughput Facility. Funding: C. M. G. was supported by the  National Cancer Institute of the NIH  under award number  T32CA009054  and by the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Sklodowska-Curie  grant agreement  749869. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. M. C. was supported by the  NIH   F31 Kirschstein-NRSA Predoctoral Fellowship  ( F31GM117942 ) and the  American Heart   Association Predoctoral Fellowship  ( 17PRE33410952 ).
The work of N. C. , M. S. , and P. B. was, in part, supported by  NIH  grant  GM123558  to  P. B. Work  in the H. O. laboratory was supported by the  CREST Japan Science and Technology Agency  ( JPMJCR14W3 ). Work in the E. G. laboratory was supported by  NIH  grants  P50 GM076516  and  NIH P41GM103540. Work in the P. S. -C. laboratory is supported by  NIH  grants  R21DK114652  and  R21AG053592  and a Challenge Grant from the  Novo Nordisk Foundation  ( NNF-202585 ). K. K. was supported by a fellowship from the  Japan Society for the Promotion of Science (JSPS). Work in the A. I. laboratory is partly funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )-Project  ID   325871075-SFB 1309. Author contributions: C. M. G. and P. S. -C. conceived and designed the study and wrote the manuscript.
M. C. performed the biochemistry experiment to identify BMAL1 nuclear interactors. K. B. K. performed molecular biology analyses J. -M. F. , K. I. , and R. K. performed U2OS cell studies and in vivo studies. N. C. , M. S. , J. S. , and P. B. performed bioinformatics analyses. I. F. and A. I. performed MS analyses. S. R. performed FRET analyses. J. G. and K. K. provided technical support for project design and data analysis. E. G. , P. B. , A. I. , W. L. , and H. O. provided technical support. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The data that support the findings of this study are available upon request from the corresponding author. The RNA-seq ChIP-seq data have been deposited in the  Gene Expression Omnibus (GEO)  with accession numbers GSE126851 (RNA-seq) and GSE125720 (ChIP-seq).
Data and methods We used functional trait information from over 39,260 species of plants, 4,953 mammals, 9,802 birds, 6,567 reptiles, 6,776 amphibians and 10,705 freshwater fishes from different published databases  (19, (23) (24) (25). For each of these groups, we chose a set of fundamental functional traits associated with different key aspects of their ecology (see Methods and table S1 for further details). We first characterised the global functional space occupied by each group by means of principal component analyses (PCA) based on the compiled traits  (16, 17). To account for functional redundancy between organisms, we used a trait probability density approach (TPD), which allows mapping the functional spectra of organisms as probabilistic surfaces  (20). Instead of simply characterizing the boundaries of the spectra, the TPD approach reflects the abundance of species with similar suites of traits  (16, 21).
Discussion Using the most species diverse trait databases collected to date, we show that, in most groups, realised functional strategies are constrained to a single plane in which species are clumped around a few strategies that are rather prevalent  (16, 17, 31). Contemporary functional spectra are probably rather different from those in the relatively recent past due to extinctions in the last millennia  (32). For example, since the Pleistocene, larger animal species have gone extinct at much higher rates than smaller species  (33). This trend continues nowadays, triggered by the rise of human population and technologies that disfavours large and slow pace of life species  (9, 33, 34). In addition, the smallest animal species are also among the most threatened  (35) , as already reported for fish and reptiles, because small animals often have low dispersal abilities and remain endemic from restricted areas, making them vulnerable to extinction  (7, 10).
Functional traits and phylogenies We collected published information on functional traits for all the studied groups of organisms from different sources (see table  S1  for detailed descriptions of each trait): • Vascular plants: We used six traits previously shown to capture global spectrum of plant form and function  (16) : plant height (ph, m), specific stem density (ssd, g/m 3 ), leaf area (la, mm 2 ), specific leaf area (sla, mm 2 /mg), N content per unit leaf mass (ln, mg/g) and seed mass (sm, mg). We used publicly available data for these traits from the latest version of the TRY Plant Trait Database (version 5. 0, https://www. try-db. org/TryWeb/Home. php, accessed April 2019; 23). Altogether our dataset included over 955,000 trait measurements for 44,431 vascular plant taxa. In the analysis, each taxon was represented by an average trait value (excluding outliers with >3 SD).
To account for within-species variation, the averages for each species-trait combination were calculated first within individuals (if multiple measurements were taken from a single individual), then within datasets (if multiple individuals were measured in the same location) and finally within species (if multiple individuals were measured in various locations). Plant height data included 179,263 measurements of adult plant vegetative height for 15,008 taxa. In most datasets this was represented as observed height or average of measurements. In some cases, plant height was represented as the maximum observation (12,942 records). Specific stem density (SSD) data included 28,723 measurements for 8,840 taxa. As this trait is usually measured for woody species, we estimated SSD for herbaceous plants using leaf dry mass content information (127,067 measurements for 5,764 taxa), following the procedures described in  (16).
Leaf area data included 119,172 measurements for 13,928 taxa. Different datasets in TRY reported various measurements of leaf area (e. g. leaflet or leaf, petiole included or excluded). To maximize our data coverage, we included both leaflet and leaf measurements, and preferred measurements including petiole (if both data types, petiole included or excluded, were reported for the same individual). Specific leaf area data included 223,126 measurements for 10,674 taxa. Similarly to leaf area data, we preferred measurements that included petiole. Data for nitrogen (N) content per unit leaf mass included 92,850 measurements for 10,530 taxa. Data for seed mass included 185,182 measurements for 25,394 taxa.
• Mammals, Birds and Reptiles We used the Amniote database  (25)  including data for 4,953 species of mammals, 9,802 species of birds and 6,567 species of reptiles. The database includes data for 29 traits, but information is very incomplete for many of them (see table  S3 ). Hence, for each group, we selected subsets of traits with sufficient information. In the case of mammals, we selected eight traits: litter size (ls, number of offspring), number of litters per year (ly), adult body mass (bm, g), longevity (long, years), gestation length (gest, days), weaning length (wea, days), time to reach female maturity (fmat, days), and snout-vent length (svl, cm). We also selected a total of eight traits for birds: clutch size (ls, number of eggs), number of clutches per year (ly), adult body mass (bm, g), incubation time (inc, days), longevity (long, years), fledging age (fa, days), egg mass (em, g) and snout-vent length (svl, cm).
• Amphibians We used the AmphiBIO database  (24)  to get data for 6,776 species of amphibians (see table  S3 ). Within this dataset, we selected a total of four traits with sufficient information: age at maturity (am, years), body size, measured in Anura as snout-vent length, and in Gymnophiona and Caudata as total length (bs, mm), maximum litter size (ls, number of individuals), and offspring size (os, mm).
• Freshwater fishes We used the last updated version of the most comprehensive database to get morphological traits, available for 10,705 species of strictly freshwater fishes  (19, 42). Morphological measures and conventions are detailed in previous studies  (19, 42, 43). All morphological traits have been measured on side view picture, using one specimen per species and applying conventional rules for unusual morphologies (e. g. species without tail, flatfishes) as defined in previous studies (see details in  19, 43). Briefly, this database encompasses 11 traits describing size and shape body parts involved in food acquisition and locomotion. The fish body shape and weight was described through the size using the standard length (svl) and body mass (bm) taken directly from FishBase  (45) , body elongation (elo, ratio between body length and body depth) and body lateral shape (bls, ratio between head depth and body depth).
• Phylogenies We obtained published phylogenies for each of the considered groups  (46) (47) (48) (49) (50) (51) (52). Species that were not present in the phylogeny were added to the root of their genus, using the 'add. species. to. genus' from the R package Phytools  (53). Species for which we did not have evolutionary information were removed from the trait databases before the missing trait imputation procedure (see below).
Imputation of missing traits None of the functional trait databases assembled were complete (see table  S3 ). We completed this information by performing a trait-imputation procedure for each group using the missForest R package  (18, 60). Before the imputation process, all traits were log-10 transformed, centred and scaled. The missForest package uses random forest techniques to impute trait data, which allows to include phylogenetic information in the trait imputation process, which is known to improve the estimations of missing values  (61). We included the evolutionary relationships between species in the imputation process by including the first ten phylogenetic eigenvectors in the matrix to be imputed, as recommended in  (61). The final numbers of species with functional trait information used in each group are shown in table  S3.