dataesr/scientific-paragraphs-categorization huggingface dataset:
The membranes were incubated with primary antibodies and peroxidase-conjugated secondary antibodies at 4°C, and visualized by chemiluminescent HRP substrate (WBKLS0500, EMD Millipore). The blots were developed with autoradiography films (HyBlot CL, Denville Scientific). The films were scanned and densitometry was analyzed through ImageJ software. Chromatin Immunoprecipitation-Approximately 250 mg of frozen liver tissues were homogenized in 4 mL PBS containing Protease Inhibitor Cocktail (Roche), 20 mM NaF, 0. 5 mM PMSF, 10 mM Nicotinamide, and 330 nM Trichostatin A (T8552, Sigma). Liver homogenates were first cross-linked with 2 mM disuccinimidyl glutarate and 1 mM MgCl 2 for 30 min, and further cross-linked with 1% formaldehyde for 5 min at room temperature. Cross-linking reaction was stopped by incubation with 125 mM glycine for 10 min.
The resulting libraries were validated by qPCR and sized by Agilent Bioanalyzer DNA high sensitivity chip. The concentrations for the libraries were normalized and then multiplexed together. The concentration for clustering on the flowcell was 12. 75 pM. The 72 samples were multiplexed into three libraries of 24 samples each, and sequenced on an Illumina HiSeq 2500 instrument during two single-end 100 cycles sequencing runs by the Genomics High-Throughput Facility at the University of California, Irvine. The version of HiSeq control software was HCS 2. 2. 58 with real time analysis software, RTA 1. 18. 64. Each library of 24 samples was sequenced between four and five times on distinct flowcell lanes for a total of 13 flowcell lanes. The resulting sequencing data for each library were post-processed and demultiplexed to produce FastQ files using Illumina software CASAVA 1.
8. 2. Reads failing Illumina's standard quality tests were not included in the FastQ files. The quality of the remaining sequences was further assessed using the PHRED quality scores produced in real time during the base-calling step of the sequencing runs. Short reads alignment to reference genome and transcriptome-The sequencing reads from each replicate in the experiment were separately aligned to the reference genome mm10 and corresponding known splice junctions extracted from the RefSeq database using the short-read aligner ELAND v2e (Illumina). Reads with a non-unique best match or with more than two mismatches with the reference sequences were discarded from the analyses. The remaining uniquely aligned reads were used to estimate both mature RNA and precursor mRNA relative abundance in the next steps of the analyses.
Read counting methods-Gene expression results produced by standard RNA-seq analysis tools are obtained by normalizing the number of reads located inside exonic regions of known genes. To compare the rhythmicity of the pre-mRNAs and mRNAs in our datasets, we implemented the Exon Intron Split Analysis (EISA) method using a protocol similar to the one proposed by the authors of the EISA method  (Gaidatzis et al. , 2015). All the components of this analysis were performed using programs specifically developed inhouse. This section describes the method used in these programs to decide if a read is exonic or intronic, based on the RefSeq gene annotations available at the time of the analysis as well as the alignment results. Exonic reads are detected following a common protocol. In brief, all isoforms of a given gene are merged together to create a single meta-transcript for the gene, where each exonic position of the meta-transcript belongs to an exon of at least one gene isoform.
This first set of gene expression results is equivalent to the gene expression results of a standard RNA-seq analysis. Since only 19,433 genes were found suitable for the EISA analysis, we then normalized both exonic and intronic read counts considering only these 19,433 genes in the normalization procedure in order to remove the bias that would be introduced by normalizing using a different set of genes. The first set of exonic expression results was used to further study the mRNA abundance in each sample, as described in the next sections. In addition, comparisons between mRNA and pre-mRNA abundance in each sample were performed using the second set of exonic expression results and the set of intronic expression results. The three sets of gene expression results for each sample are available for download from the GEO database.
QUANTIFICATION AND STATISTICAL ANALYSIS Bioinformatic Analysis-Time series of expression levels were then used to determine circadian behaviors of transcripts using JTK-CYCLE  (Hughes et al. , 2010). Transcripts found with no expression in greater than six replicates were filtered before analysis of circadian behavior. A gene with p < 0. 01 was considered significantly rhythmic over the circadian cycle. Heatmaps of the rhythmic transcripts were generated by R package 'gplots'. Differential analysis of transcript expression levels between FED and FAST at a specific ZT was done using Cyber-T  (Kayala and Baldi, 2012) , a differential analysis program using a Bayesian-regularized t test. Fold change (FC) in the fasting study was calculated with RPKM values. Differential expression were determined with p < 0.
05 in Bayes-regularized t test and FC ≥ 1. 3. Gene Set Enrichment Analysis was performed to determine whether or not a specific subset of genes are enriched either in FED or FAST mice  (Subramanian et al. , 2005). Gene sets with false discovery rate of less than 25% were considered significantly enriched. The Database for Annotation, Visualization and Integrated Discovery (DAVID) pathway analysis tool was used to identify gene ontology terms related to biological process (https://david-d. ncifcrf. gov/). Analysis was done using pipelines implemented for the Circadiomics  (Patel et al. , 2012)  database and web portal (circadiomics. ics. uci. edu) where all the transcriptomic data associated with this work is publicly available. Analysis of Published Dataset-We obtained time-restricted fed, hepatic transcriptome by microarray  (Vollmers et al.
, 2009)  from GEO (GSE13060), hepatic BMAL1 ChIP-seq  (Koike et al. , 2012)  from ''Master peak lists for ChIP-seq experiments for BMAL1,'' inducible Bmal1 -/-liver transcriptome by RNA-seq  (Yang et al. , 2016)  from GEO (GSE70497), Rev-ERBα -/-liver transcriptome by microarray and hepatic REV-ERBα GRO-seq  (Fang et al. , 2014)  from ''List of Differentially Expressed Genes Identified in Rev-ERBα -/-Livers,'' prednisolone-induced liver transcriptome by microarray  (Frijters et al. , 2010)  from GEO (GSE21048), cAMP  (Zhang et al. , 2005)  and glucagon induced liver transcriptome by microarray  (Ravnskjaer et al. , 2013)  from ''The lists of cAMP responsive genes from hepatocytes'' (http://natural. salk. edu/CREB/) and GEO (GSE47179), respectively, liver specific Foxo1,3,4 -/-liver transcriptome by microarray  (Haeusler et al.
, 2014)  from GEO (GSE60527), liver specific PPARα -/-liver transcriptome by microarray  (Montagner et al. , 2016)  from GEO (GSE73299), TFEB-overexpressed liver transcriptome by microarray  (Settembre et al. , 2013)  from GEO (GSE35015), pre-and post-exercise gastrocnemius muscle transcriptome by microarray  (Perry et al. , 2014)  from GEO (GSE61712), muscle specific Bmal1 -/-tibialis anterior muscle transcriptome by microarray  (Dyar et al. , 2013)  from GEO (GSE43071), muscle specific Hdac3 -/-muscle transcriptome by RNA-seq from ''Differentially expressed genes from RNA-seq''  (Hong et al. , 2017) , dexamethasone-treated C2C12 myotubes transcriptome by microarray  (Kuo et al. , 2012)  from GEO (GSE28840), cAMP induced muscle transcriptome by microarray  (Pearen et al. , 2009)  from GEO (GSE15793), muscle specific Foxo1,3,4 -/-muscle transcriptome by microarray  (Milan et al.
, 2015)  from GEO (GSE52667), PPARδ overexpressing muscle transcriptome by microarray  (Gan et al. , 2011)  from GEO (GSE29055), and TFEB overexpressed muscle transcriptome by microarray  (Mansueto et al. , 2017)  from GEO (GSE62975). Calculation of FC in microarray was carried out by logarithmic normalization unless already logarithmic-transformed. Differential expression was determined by p < 0. 05 in Bayes-regularized t test along with FC ≥ 1. 3 in RNA-seq and LogFC ≥ 0. 37 in microarray, except for Foxo1,3,4 -/-muscle transcriptome where LogFC ≥ 1. 5 was applied because of a single replicate. Specifically, among the genes in master peak lists of BMAL1 ChIP-seq  (Koike et al. , 2012) , differentially expressed genes with FC (WT/iKO) ≥ 1. 3 at CT8 and CT12 were selected as hepatic BMAL1 target genes at ZT8 and ZT12, respectively  (Yang et al.
, 2016). ''Derepressed genes in Rev-ERBα -/-'' among ''List of Differentially Expressed Genes Identified in Rev-ERBα -/-Livers'' were used as hepatic REV-ERBα target genes  (Fang et al. , 2014). Differentially expressed genes with LogFC (shNS, Glucagon/shNS, Control) ≥ 0. 37 were selected as glucagon induced genes in the liver (GSE47179). ''The lists of cAMP responsive genes from hepatocytes'' were selected as cAMP induced genes in the liver  (Zhang et al. , 2005). Genes included either in glucagon or cAMP induced genes in the liver were finally extracted as hepatic CREB target genes. Likewise, we selected differentially expressed genes with LogFC (Prednisolone-treated WT/ Vehicle-treated WT) ≥ 0. 37 as hepatic GR target genes (GSE21048), with LogFC (WTfasted/L-FoxO1,3,4 -/--fasted) ≥ 0. 37 as hepatic FOXO target genes (GSE60527), with LogFC (LWT_Fasted/PPARα-LKO_Fasted) ≥ 0.
37 as hepatic PPARα target genes (GSE73299), with LogFC (Injected/CTL) ≥ 0. 37 as hepatic TFEB target genes (GSE35015), with LogFC (WT post-exercise/WT pre-exercise) ≥ 0. 37 as exercise-induced genes (GSE61712), with LogFC (WT pre-exercise/WT post-exercise) ≥ 0. 37 as exercise-repressed genes (GSE 61712), with LogFC (Tibialis Anterior Muscle_Ctrl/Tibialis Anterior Muscle_mKO) ≥ 0. 37 at ZT8 and ZT12 as muscle BMAL1 target genes at ZT8 and ZT12, respectively (GSE43071), with LogFC (myotube_Dex/myotube_ETOH) ≥ 0. 37 either at 6 hr or at 24 hr after treatment as muscle GR target genes (GSE28840), with LogFC (Formoterol/ Saline) ≥ 0. 37 either at 1 hr or at 4 hr after treatment as muscle CREB target genes (GSE15793), with LogFC (wild-type, starved/Foxo 1,3,4 muscle specific knock-out, starved) ≥ 1.
5 as muscle FOXO target genes (GSE52667), with LogFC (MCK-PPARb/Nontransgenic mouse) ≥ 0. 37 as muscle PPARδ target genes (GSE29055), and with LogFC (Injected/Control) ≥ 0. 37 as muscle TFEB target genes (GSE62975). Genes with FC (KO/WT) R1. 3 among the ''Differentially expressed genes from RNA-seq'' was used as muscle REV-ERBα-HDAC3 target genes  (Hong et al. , 2017). Genes with JTK-CYCLE p < 0. 01 were extracted as rhythmic genes under time-restricted feeding (GSE13060). Latest version of annotation files was downloaded from GEO and applied to microarray dataset. Updated version of gene symbol was obtained from BioGPS to overlay fasting transcriptome data (https://biogps. org). Other Statistical Analysis-Experimental data were presented as means + SEM. For some points, where the error bars are shorter than the height of the symbol, the error bars were not drawn.
(J-L) Chromatin immunoprecipitation in liver for GR, CREB, PPARα, or IgG followed by RT-qPCR specific for class III genes (J) Got1, (K) G6pc, and (L) Acot2. Data are presented as means + SEMs (n = 3-4 replicates per time point per group). Diagram above shows regions of amplification by primers designed for analysis (arrows) and TSS (transcription start site). Gene expression profiles are shown next to ChIP-qPCR data for visualization. Transcripts were normalized to 18S rRNA and presented as means + SEMs (n = 5 replicates per time point per group). ZT0 is double plotted for visualization. *p < 0. 05, ** p < 0. 01, *** p < 0. 001, and **** p < 0. 0001 by two-way ANOVA (interaction/time/group) with Bonferroni post hoc tests. (H and I) Gene expression profiles of (H) hepatic and (I) muscle genes displaying rhythmic response to 24-hr fasting (24h-FS) and 48h-FS.
ACKNOWLEDGMENTS We thank members of the  Sassone-Corsi laboratory , as well as  Melanie Oakes ,  Seung-Ah Chung , and  Yuzo Kanomata  at the  Genomics High-Throughput Facility at the University of California, Irvine. K. K. was supported by a  Japan Society for the Promotion of Science (JSPS)  fellowship. C. M. , N. C. , Y. L. , and P. B. were supported by grants from the  Defense Advanced Research Projects Agency  (DARPA;  D17AP00002 ) and  NIH  ( GM123558 ) to P. B. This study was supported by the  NIH , a  Novo Nordisk Foundation Challenge Grant , and  INSERM (France).
We offer an R package 14 , which we also use in the software application CINeMA (Confidence In Network Meta-Analysis)  11  , that aims to simplify the evaluation of confidence in the findings from NMA. While CINeMA largely follows the framework previously developed by Salanti et al. 3  , the refinement of several methodological aspects is currently under development. Core aspects of the approach include the consideration of the relative contributions of each direct comparison to each NMA treatment effect estimate. To this end, CINeMA uses the proportion contribution matrix as described in this paper. The command netweight in Stata has also been updated to use the described approach. We believe that the approach described in this paper is a useful and novel addition to network meta-analysis methodology, which allows the consistent derivation of the proportion contributions of direct evidence from individual studies to network treatment effects.
Data availability Dataset 1: Outcome data from the example network of topical antibiotics for the treatment of chronic otitis media with ear discharge in patients with eardrum perforations  7. Data labels: study, name of individual studies; id, id of the individual studies; t; treatment; r, number of events; n, sample size; rob, risk of bias per study. DOI: 10. 5256/f1000research. 14770. d203174  15.
Again, the reason I wanted this, is that I am an applied user and such discussions are very helpful. In particular, I would like to have seen some discussion of the 1st comment. I would also like to see if the comment about Figure  3  could be incorporated -perhaps not feasible. Thank you very much for your positive feedback. Our answers to the first reviewer, Dr. Jochem König, can be found in. In summary, regarding his first https://f1000research. com/articles/7-610/v2 comment, we distinguish between representation of contribution of pieces of evidence according to a study characteristic (addressed in this paper) and quantification of the impact of within-study bias in NMA treatment effects (subject of future research). A new paragraph in the Discussion highlighting these concerns reads: "Study limitations may lead to biased NMA treatment effect; however, the amount and direction of bias in the NMA treatment effect as a result of the within-study bias is not straightforward to define and is not currently accommodated within the proportion contribution matrix.
would be translated into NMA treatment effects. " No competing interests were disclosed. Competing Interests: 14 August 2018 Reviewer Report https://doi. org/10. 5256/f1000research. 16071. r36470 © 2018 O'Connor A. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited.
June 2018 Reviewer Report https://doi. org/10. 5256/f1000research. 16071. r34177 © 2018 König J. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited. Jochem König Division of Paediatric Epidemiology, Institute of Medical Biostatistics, Epidemiology, and Informatics (IMBEI), Johannes Gutenberg University Medical Center, Mainz, Germany.
[ 3 ] 3 would be translated into NMA treatment effects. " No competing interests were disclosed. Competing Interests: 14 August 2018 Reviewer Report https://doi. org/10. 5256/f1000research. 16071. r36470 © 2018 O'Connor A. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited.
Unfortunately, both the white cane and guide dog are only meant for obstacle detection and still need to be complemented with another device that addresses the orientation process  [20]. Decades of research and a wide assortment of academic prototypes and commercial products have shown that any attempt to replace the primary aids in the obstacle detection task has failed. One should wonder if it would be more pertinent to design complementary systems to the primary aids rather than to keep trying to replace them. This paper introduces a novel outdoor navigation assistive system that is a complement to the primary aids by trusting them with the immediate environment sensing. Localization is conducted by GPS data provided by a smartphone, which are processed in the context of spatial databases for the computation of optimal routes of travel.
Localization and Guidance The operation principle and main components of the proposed system are shown in Figure  2a. Modern smartphones containing GPS, a digital compass, accelerometers, inertial sensors, and connectivity capabilities have become ideal candidates for portable computing applications  [40]. Nowadays, they are the simplest and most common solutions for GPS receiver modules. In this current prototype, a smartphone (Samsung Galaxy S5 running Android 4. 4. 2) was used for user GPS coordinate and orientation acquisition. Using the smartphone's internet connectivity, the user's geospatial information is transmitted to a cloud server as a data sentence. Upon reception, the server constructs a text file containing longitude, latitude, and orientation and discards other information, such as time, altitude, number of satellites, etc.
The smartphone updates the server at 5 Hz, which extensively covers any walking speed. A remote computer accesses the server's text file via internet and locates the user in a locally running spatial database. The GIS used is OpenStreetMap  [41]. A dedicated script links to the YOURS application programming interface (API)  [42] , an open source route planner, to compute the shortest pedestrian route to a previously chosen destination. The route waypoints are returned from YOURS, and they are placed and stored in the GIS. Directions to the destination are processed locally. The GIS no longer needs to communicate with the route planner API unless a route recalculation is necessary. Directions are translated to actuator commands, which are transmitted via radiofrequency (RF) to an electronic module that the user carries with him/her.
The average walking speed of a normally sighted person is 1. 4 m/s  [47] , which suggests that the navigation system does not reduce walking pace. The subjects' navigation was performed on sidewalks except when crossing streets. Typical urban obstacles, such as street poles, trees, trash bins, and other people, were encountered and overcome using the white cane. A natural fearless walking could be observed during navigation together with firm doubtless post-instruction movements. An approximate 2-4 m resolution of the GPS longitude/latitude data provided by the smartphone was observed during the test. Its impact on navigation was quantified by calculating the path efficiency (defined as the ratio between the length of the path actually traversed versus the length of the optimal path) for each path section bounded by two waypoints where the user received directional instructions.
Guidance to a destination is achieved by dedicated navigation software that processes the GPS data and computes the optimal route of travel. Directions to a destination are conveyed to the user via an on-shoe tactile display. The aim of this system is to enhance outdoor navigation of visually impaired and blind people with a truly wearable, inconspicuous, reliable, and low-cost device. The prototype was evaluated in two stages. The first one verified the interface's ability to transmit tactile information to the user, and the user's comprehension level to this feedback. The results showed very high recognition rates, which suggests that the information displayed is intuitive and fast to understand. The second stage evaluated both the system and subjects' performances in real outdoor urban environments.
The results showed that the system is capable of guiding users from a starting point to a destination by providing the pertinent directional instructions. The prototype reported in this paper was recently featured by the BBC Horizons Series. Similar navigation experiments and blind users' testimonials can be seen in this documentary program  [48]. Future work will focus on the implementation of an Android application that fully runs in the smartphone, integrating the GIS and the route planner, and that generates the instructions for the tactile display. Figure  8  shows the proposed architecture. Note that a high-sensitivity GPS external chipset receiver will be used to increase the smartphone's position accuracy to the one-meter or sub-meter level (enhancement technologies to the GPS, such as differential GPS and satellite-based augmentation system (SBAS) will be evaluated as well).
As was mentioned before, it is common that VMI images are recorded as a function of an experimental variable of interest. Examples of this are pump-probe time delay, laser wavelength, polarization angle, temperature or sample density. This implies that a standard experiment performed in this way can produce tens or hundreds of images (for instance, 640 × 480 pixels, with 12-bit dynamic range). With the conditions above, a single experiment with 100 images produces 50 MB in a stack of 3D data. Since the larger the size of the data stack, the slower the convergence of the minimization algorithm, it is important to find either symmetries or constraints that can reduce the number of data points. For example, in the case of symmetric Abel-inverted images, it is possible to work only with a square quarter of the image (corresponding to, say, 240 × 240 pixels).
As an indication of the fit described in this section, considering around 30 parameters, it takes only 1-2 min with an un-paralleled CPU and a computer with an Intel core i7-2700K CPU (Santa clara, CA, USA) at 3. 5 GHz with 16 Go RAM Matlab (R2013a) (Mathworks, Natick, MA, USA) based program running on Debian operating system.
2  Beyond their importance for surveillance and decision-making, disease maps can also be interesting communication tools towards clinicians, partners, healthcare managers and the general public. Because of their visual and intuitive appeal, maps released in official surveillance reports are often published by mainstream media during outbreaks. 3, 4 nerating reliable disease maps requires two important features: the availability of high-quality data and the use of sound statistical methods. Epidemiological surveillance is often based on sentinel systems, in which a network of selected general practitioners (GPs) or healthcare facilities report cases. Sentinel surveillance systems have made it possible to considerably improve our understanding of epidemic dynamics. For instance, surveillance data from the French Sentinelles network was used to quantify the impact of school closure on influenza epidemics.