dataesr/scientific-paragraphs-categorization huggingface dataset:
Data for this particular study were collected between 2011 and 2015 from 40 agrarian, trading and fishing communities. Ethics. The study was independently reviewed and approved by the Ugandan Virus Research Institute, Scientific Research and Ethics Committee, Protocol GC/127/13/ 01/16; the Ugandan National Council of Science and Technology; and the Western Institutional Review Board, Protocol 200313317. All study participants provided written informed consent at baseline and follow-up visits using institutional review board-approved forms. Sampling fraction. To estimate the number of infected participants with unsuppressed virus, we first calculated the expected number of infected participants who did not use antiretrovirals at time of survey, and had thus unsuppressed virus. Participant reported ART use was previously validated as a proxy of actual ART use with a specificity of 99%  26  , giving 3878/0.
Deep-sequencing was performed on Illumina MiSeq and HiSeq instruments in the DNA pipelines core facility at the Wellcome Trust Sanger Institute, Hinxton, United Kingdom. a Partner 1 tested HIV-negative, while partner 2 tested HIV-positive at or before the same time, and partner 1 was subsequently found HIV-positive b Partner 1 had first CD4 measurement >800 cells per mm 3 , while partner 2 had a CD4 measurement <400 cells per mm 3 within 2 years of the first CD4 measurement of partner 1 Assembly of HIV-1 reads. Deep-sequencing reads were assembled with the shiver sequence assembly software  51. Where no contigs could be generated with IVA  52  , contigs were generated with SPAdes and metaSPAdes v3. 10 53,54 , after excluding reads classified as Homo sapiens by Kraken v0. 10. 5-beta  55. Contigs with at least 300 bp matching known HIV-1 diversity were used for shiver analysis.
The phyloscanner command line specification for this first analysis stage is given in Supplementary Tables  1  and 2. Shell scripts were used to handle calculations in parallel, and are available upon request. From stage 1 output, we identified potentially phylogenetically close pairs and, from those, networks of pairs that were connected through at least one common, phylogenetically close individual. Networks were extended to include spouses of partners in networks, couples in no network, and the ten most closely related individuals from stage 1 as controls. For computational considerations, reads of individuals that differed at one nucleotide position were merged. In a second analysis stage, phyloscanner was used to confirm potential transmission pairs by considering also the topological configuration of subgraphs in deep-sequence phylogenies, and to resolve the ordering of transmission events within transmission networks.
Acknowledgements We thank the participants of the RHSP RCCS; as well as the PANGEA-HIV steering committee for their input and their comments on a previous version of this article. Computations were performed at the  Imperial College Research Computing Service , https://doi. org/10. 14469/hpc/2232. This study was supported by the  National Institute of Mental Health  ( K23MH086338 ,  R01MH107275 ); the  National Institute of Allergy and Infectious Diseases  ( R01AI110324 ,  U01AI100031 ,  R01AI110324 ,  R01AI102939 ); the  National Institute of Child Health and Development  ( RO1HD070769 ,  R01HD050180 ); the  Division of Intramural Research, National Institute for Allergy and Infectious Diseases, National Institutes of Health ; the  Bill & Melinda Gates Foundation  ( 22006. 02 ,.
Data availability The deep-sequence phylogenies and basic individual-level data analysed during the current study are available in the Dryad repository, https://doi. org/10. 5061/ dryad. 7h46hg2. HIV-1 reads are available on reasonable request through the PANGEA consortium (www. pangea-hiv. org) or the corresponding author. Please contact project manager Lucie Abeler-Dörner (lucie. abeler-dorner@bdi. ox. ac. uk) for further details. Additional individual-level data are available on reasonable request to RHSP or the corresponding author.
Adult skeletal muscle is capable of complete regeneration after an acute injury. The main parameter studied to assess muscle regeneration efficacy is the cross-sectional area (CSA) of the myofibers as myofiber size correlates with muscle force. CSA analysis can be time-consuming and may trigger variability in the results when performed manually. This is why programs were developed to completely automate the analysis of the CSA, such as SMASH, MyoVision, or MuscleJ softwares. Although these softwares are efficient to measure CSA on normal or hypertrophic/ atrophic muscle, they fail to efficiently measure CSA on regenerating muscles. We developed Open-CSAM, an ImageJ macro, to perform a high throughput semi-automated analysis of CSA on skeletal muscle from various experimental conditions. The macro allows the experimenter to adjust the analysis and correct the mistakes done by the automation, which is not possible with fully automated programs.
We showed that Open-CSAM was more accurate to measure CSA in regenerating and dystrophic muscles as compared with SMASH, MyoVision, and MuscleJ softwares and that the inter-experimenter variability was negligible. We also showed that, to obtain a representative CSA measurement, it was necessary to analyze the whole muscle section and not randomly selected pictures, a process that was easily and accurately be performed using Open-CSAM. To conclude, we show here an easy and experimenter-controlled tool to measure CSA in muscles from any experimental condition, including regenerating muscle.
Manual quantification is potentially the most accurate method as the experimenter keeps total control over the myofibers being analyzed. However, the manual procedure is highly time-consuming and may present variability between experimenters. This is why automated programs were developed to limit the experimenter input and save time  [2] [3] [4] [5] [6] [7]. For example, SMASH  [7]  is a semi-automated open source MATLAB script allowing the assessment of several parameters, including CSA. MyoVision, a Windows program  [2] , was developed to fully automate the quantification process. This program combines several algorithms that were previously used separately in other programs, thus reducing the errors in the fiber identification and leading to a more accurate CSA measurement compared to SMASH on uninjured muscle  [2].
Image acquisition and quantification As much as possible, the various conditions to be compared (e. g. , WT vs. KO, normal vs. dystrophic muscle) should be recorded in similar conditions (microscope, magnification, exposure time, binning). For Open-CSAM validation and comparison with other softwares, at least 10 images were acquired manually at × 20 of magnification on an Axio Imager. Z1 microscope (Zeiss) connected to a CoolSNAP MYO CCD camera (photometrics) using MetaMorph Software (molecular devices). For whole cryosection analysis, slides were automatically scanned at × 10 of magnification using an Axio Observer. Z1 (Zeiss) connected to a CoolSNAP HQ2 CCD Camera (photometrics). The image of the whole cryosection was automatically reconstituted in MetaMorph Software. Open-CSAM was able to analyze images up to 110 Mo in 8 bit or 220 Mo in 16 bit.
In case the final picture was too big to be analyzed at once because of limitation of ImageJ capacities, the picture was split into several parts (two to four) for the automated analysis. Between 2000 and 4000 fibers were analyzed in a few seconds. Open-CSAM workflow is presented in Fig. 1. The detailed macro code and explanations of the various functions are given in Additional file 1: Figure  S1. If necessary, at the end of the automated measurement, manual correction is performed using ImageJ. The selection tools in the ROI Manager were used to remove "false" myofibers created by the automation, and the "Freehand selections" tool was used for hand-drawing "lacking" myofibers missed by the automation. These tools were used for full manual quantification. A tutorial is provided in Additional file 2: Figure  S2.
Results Open-CSAM is more accurate than previously described softwares In order to test the accuracy of Open-CSAM, its performance was compared with a fully manual quantification, a semi-automated software (SMASH), as well as two fully automated softwares: MyoVision and MuscleJ (Fig. 2 ). We analyzed TA muscle from various conditions, including uninjured muscle, regenerating muscle at several time points after acute injury (D8 and D14) in young and old mice, and a model of fibrotic dystrophy (Fib-mdx). Quantification was performed on cryosections immunofluorescently labeled with anti-laminin antibody, which labels the myofiber basal lamina. Immunolabeling against dystrophin or sarcolemal proteins was not used as this precludes the analysis in dystrophies where these proteins are lacking or altered.
During the revision process of this manuscript, MuscleAnalyzer, a customized pipeline within the CellProfiler program allowing fully automated CSA measurement, was released  [11]. We tested this pipeline on the same samples as above (Additional file 3: Figure  S3 ). As it strongly overestimated mean CSA as compared with manual quantification (between 38. 4% and 254. 8% increase depending on the condition), this software was not considered for further analysis. Despite an increased CSA value obtained using MyoVision, the correlation between MyoVision and manual quantification for each picture was strong in uninjured muscles (young and old mice), as well as 14 and 28 days post-injury regenerating muscles (Fig. 2b  and Additional file 4: Figure  S4A , R 2 > 0. 95), suggesting that in these conditions, CSA overestimation by MyoVision was similar on all the pictures and did not introduce a specific bias.
MyoVision missed 33 myofibers (red asterisks) and identified 1 false fiber, which could be excluded by size filter in MyoVision software (red line), inducing a 29% artificial increase in CSA, as compared with manual quantification. Similarly, MuscleJ identified 7 false myofibers (red lines) and missed 14 fibers (red asterisks), leading to a 12. 3% increase as compared with manual quantification. Finally, SMASH missed 14 myofibers and misidentified 9 fibers, leading to an 8. 2% decrease of mean CSA compared to manual quantification. The distribution of the myofiber CSA in Fib-mdx muscles (same samples as in Fig. 2a ) clearly showed that MyoVision and MuscleJ, and to a lower extent, Open-CSAM, preferentially missed the small myofibers (< 500 μm 2 ) (Fig. 2d ), thus artificially overestimating the mean CSA.
On the contrary, SMASH overestimated the proportion of small myofibers. Taken together, these results show that Open-CSAM accuracy is more consistent as compared with other softwares through various experimental conditions. However, despite its performance, manual correction is necessary for best accuracy. Open-CSAM was designed to enable easy manual correction, allowing the user to directly draw missing fibers and delete the false ones at the same time in ImageJ (Additional file 2: Figure  S2 ). As shown in Additional file 4: Figure  S4B , manual correction applied to Open-CSAM improved the measurement accuracy in all conditions (R 2 > 0. 97) except in old mice 28 days post-injury (R 2 = 0. 9665 before and 0. 962 after correction). In Fib-mdx muscle, this was associated with a convergence of the overall myofiber size distribution towards the distribution obtained with manual quantification (Fig.
This is mainly due to the non-detection of the small myofibers (Fig. 5d ) and, in the case of MuscleJ, to the merging of several myofibers into one (Additional file 5: Figure  S5B , Fig. 5b ). Again, correction in Open-CSAM induced a negligible change in CSA measurement on day 8 post-injury muscle (Fig. 5a , + 0. 3% before correction). Finally, manual correction of Open-CSAM in day 28 post-injury muscle corrected a slight underestimation of small fibers (Fig. 5d ), leading to an artificial increase in CSA (Fig. 5a , + 7. 8% before correction). The main defect of Open-CSAM is the time of experimenter that is required for the complete implementation of CSA measurement. Although running Open-CSAM macro in ImageJ took less than 1 min (even for the whole muscle pictures), which was faster compared to other softwares (4 to 10 min for MuscleJ, 5 to 17 min for SMASH, and 166 to 340 min for MyoVision), the post-automation manual corrections lasted between 10 and 60 min in the examples provided (Fig.
Among the recent described softwares are SMASH, MyoVision, and MuscleJ. SMASH is a MATLAB application which is also available as a free Windows program (SMASH Stand Alone). MyoVision is a program operating in Windows environment whereas MuscleJ is a macro working in ImageJ program (Table  2 ). They were developed to automate, with various levels of flexibility, the quantification of several muscle parameters including fiber number, CSA, myonuclei number and fiber type distribution. As previously described  [2] , MyoVision showed a significant CSA overestimation as compared with manual quantification. This could be explained by the stronger overlapping of MyoVision outlines with the laminin staining (Fig. 2c ), thus integrating a part of the basal lamina into the myofiber area. Despite this overestimation of the mean CSA as compared with manual quantification, MyoVision performed accurately on isolated images from uninjured muscles.
However, regenerating, old and dystrophic muscles are characterized by high myofiber size heterogeneity. In this context, MyoVision measurement was still good at day 14 and 28 after injury, i. e. at time points when regenerating myofibers are already formed. On the contrary, correlation with manual estimation was much lower in muscles at 8 days post-injury in young mice, at 28 days post-injury in old mice and in muscles of dystrophic fibrotic mice. These three conditions are characterized by a lower mean CSA and the presence of high numbers of very small myofibers, which are not considered by MyoVision, thus overestimating the mean CSA. MuscleJ software was efficient in measuring mean CSA in individual × 20 pictures from uninjured young as well as 14 and 28 days post-injury muscles. However, it measured higher mean CSA values in 8 days post-injury regenerating muscles, old muscles (uninjured and 28 days post-injury) and dystrophic muscles, even though the values were closer to manual quantification than that calculated by MyoVision.
Then, Open-CSAM is capable of measuring the myofiber CSA on the whole section in a few seconds. Depending on the size of the image, it may be necessary to split it in two or four parts. This may be also useful when the laminin labeling intensity is not homogenous on the entire section since Open-CSAM may omit faint labeled areas. When measuring CSA on whole muscle section pictures, using uninjured or 8-day and 28-day regenerating muscles, we found that Open-CSAM (including the manual post-automation corrections) was the most accurate way to measure myofiber CSA. MuscleJ program was efficient at implementing CSA from uninjured muscle (still with overestimated values) but failed to accurately measure myofiber CSA of regenerating muscle, due to failure to detect small fibers and to merging of several myofibers into one.
and the atmosphere is a fundamental component of our climate and the biogeochemical development of the oceans. Therefore, the accurate calculation of these air-sea gas fluxes is critical if we are to monitor the health of our oceans and changes to our climate. FluxEngine is an open source software toolbox that allows users to easily perform calculations of air-sea gas fluxes from model, in-situ and Earth observation data. The original development and verification of the toolbox was described in a previous 30 publication and the toolbox is already being used by scientists across multiple disciplines. The toolbox has now been considerably updated to allow its use as a Python library, to enable simplified installation, verification of its installation, to enable the handling of multiple sparingly soluble gases and greatly expanded functionality for supporting in situ dataset analyses.
35 analyse in situ CO 2 data to a common temperature dataset and the ability to easily calculate gas fluxes using in situ data from drifting buoys, fixed moorings and research cruises. Here we describe these new capabilities and then demonstrate their application through illustrative case studies. The first case study demonstrates the workflow for accurately calculating CO 2 fluxes using in situ data from four research cruises from the Surface Ocean CO 2 Atlas (SOCAT) database. The second case study shows that 40 reanalysing an eight month time series of pCO 2 data collected from a fixed station in the Baltic Sea can remove errors equal to 35% of the net air-sea gas flux. The third case study demonstrates that.
Introduction The exchange of climate relevant gases between the oceans and atmosphere including that of carbon dioxide (CO 2 ), nitrous oxide (N 2 O) and methane (CH 4 ) is a major component of the climate system, 50 and the ability of the oceans to absorb and desorb these gases varies both temporally and spatially. The need to monitor this exchange has been the driver for international data collation initiatives such as the Surface Ocean CO 2 ATlas (SOCAT,  (Bakker et al. , 2016) ) and the MarinE MethanE and NiTrous Oxide database (MEMENTO,  Kock and Bange, 2015). These collaborative efforts are now routinely collecting, quality controlling and collating over a million new in situ data points each year.
g. Ashton et al. , 2016; Pereira et al. , 2018) , evaluate the European shelf sea CO 2 gas-fluxes and sink  (Shutler et al. , 2016)  and investigate biological and physical controls of air-sea exchange  (Henson et al. , 2018). FluxEngine has also been   (Ardhuin et al. , 2018) ) and to verify our understanding of vertical temperature profiles and concentration gradients (as described by  Woolf et al. , 2016)  120 through the analysis of a novel fiducial reference dataset. The results from these studies will be reported elsewhere, but their needs have driven some of the advancements presented here. F = k(G W -G S ) (2.
155 (for year 2000) and the  Woolf et al. , (in-review)  Surface Ocean CO 2 Atlas (SOCAT,  Bakker et al. , 2016)  derived sea-to-air CO 2 flux reference dataset (for year 2010). The results are then evaluated against the published reference data provided by  Holding et al. , (2018)  and the installation is deemed successful if all results are identical to the reference dataset within a precision of 5 decimal places. An additional utility (run_full_verification. py) enables the user to perform a more detailed verification 160 against both of these climatologies by executing a suite of 12 different configurations and scenarios, the justification for which are described within  Woolf et al. , (in-review). Owing to the large volume of data required to execute and verify all of these scenarios, the verification data are not packaged with the standard FluxEngine download, but are all freely available and contained within  Holding et al.
165 FluxEngine is now implemented as a Python module available on a creative commons license via http://github. com/oceanflux-ghg/FluxEngine. This means that FluxEngine and its accompanying utilities can be used as command line tools (stand-alone tools or called from another piece of software) or imported as a Python module and easily integrated with other software. This approach offers a larger degree of flexibility than offered by version 1 of the toolbox and supports advanced exploitation. For approximately double the execution time, whilst doubling the resolution of both spatial dimensions will lead to a factor of four increase in execution time.
Flexible input data specification Previous versions of FluxEngine required the user to make changes to the underlying software in order 185 to use new or differently formatted sources of input data. This required additional (and time consuming) testing and verification after modifications were made, making FluxEngine less accessible to those unfamiliar to Python programming. Two features have been added in version 3. 0 to address this issue: i) file pattern matching (through standard Unix glob patterns and custom date/time tokens, described fully in FluxEngineV3_instructions. pdf) allows input file name format and directory structure 190 to be customised using the plain text configuration file, ii) optional pre-processing functions can be used to manipulate input data after the data have been read into memory.
235 In situ fCO 2 measurements are often made using water sampled from differing depths and/or a range of different instrument setups. A second new utility, reanalyse_socat_driver. py enables fCO 2 measurements to be re-analysed to a consistent temperature field at a consistent depth. This reanalysis tool is CO 2 specific and is required for an accurate gas flux calculation as it allows the in situ gas concentration to then be calculated at the bottom or top of the mass boundary layer, rather than 240 assuming that the gas concentration at some depth is representative of that at the sea surface  (Woolf et al. , 2016). This reanalysis is especially important if the in situ data consist of a collated dataset originating from multiple instruments, sampling strategies or sources. In this situation the in situ measurements are more likely to be collected from a range of different depths.
265 which makes it simpler for the user to extend the functionality and incorporate new gas transfer parameterisations. Custom parameterisations can be implemented as separate Python classes without modifying the core FluxEngine software. This is achieved by copying and modifying the template class provided in rate_parameterisation. py. Storing the new class within rate_parameterisation. py means that the new parameterisation will be automatically incorporated into the toolkit ready to be selected in 270 the configuration file for all users of the particular FluxEngine installation. These custom parameterisations can define new input variables and therefore make use of additional input data files that can be included in the configuration file and will be automatically loaded into memory without requiring any additional setup.
New features utilised Case study 1: Calculating sea to air CO 2 gas fluxes from research cruise data Flexible input data specification to select in situ data files and unit conversion using preprocessing functions (Sect. 2. 2). Utilises new support for in situ data analysis, including the use of the text2ncdf. py and append2insitu. py tools, custom temporal resolution, reanalysis of fCO 2 to a consistent temperature field. Case study 2: calculating sea to air CO 2 gas fluxes from Östergarnsholm fixed station data. Flexible input data specification and unit conversion using pre-processing functions (Sect 2. 2). Utilises new support for in situ data analysis, including use of the text2netcdf. py and append2insit. py tools, custom temporal resolution and cruise-specific time interval (Sect.
325 The majority of the measurements needed for the sea-to-air CO 2 gas flux calculation were measured in situ and exist within the downloaded datasets. However, wind speed (for the gas transfer parameterisation) was missing in all cases. Therefore to complement these in situ data, multi-sensor merged wind speed data at 10 m were downloaded (Cross-Calibrated Multi-Platform, CCMPv2, 6 hour temporal resolution, 0. 25 o × 0. 25 o spatial grid  (Atlas, et al. , 2011) ). These wind speed data were 330 appended to the in situ data by matching each in situ measurement to the closest temporal and spatial grid point. This same process was used to add columns for the second and third moments of wind speed, which were estimated by taking the second and third power of wind speed, respectively. Two datasets  (Schuster, 2016; Steinhoff, et al.