dataesr/scientific-paragraphs-categorization huggingface dataset:
After incubation, samples were rinsed three times and washed at least four times for 15 min each, mounted in 80% glycerol and imaged using an SP5 Leica confocal microscope. Occupancy of 50 or greater randomly selected NMJs was scored blinded to genotype on a Nikon E600 fluorescence microscope. Full occupancy of NMJ was defined as when presynaptic nerve staining fully overlaid with AChR, partial occupancy as when positive for AChR but only partially stained for presynaptic nerve, and denervation as when positive for AChR but negative for presynaptic nerve staining. It was determined that there were no sex differences in phenotype and therefore the data was presented as mixed sex, with similar numbers of males and females. Images were obtained on a Leica SP5 or SP8 laserscanning confocal microscope with a 40× or 63× objective lens.
Z stacks were collapsed into projected images and merged using FIJI (v1. 52p)  30  (NIH, http://rsb. info. nih. goc/ij/). Oxygen saturation measurements. The MouseOx TM Pulse-oximeter (Starr Life Sciences, Oakmont PA) was used to measure SpO2 on 10-and 11-day-old D106* mice and unaffected littermates. It was determined that there were no sex differences in phenotype and therefore the data was presented as mixed sex, with similar numbers of males and females. Mice were measured in a darkened room and held until calm, before placing the extra-small collar probe on the neck with the reading window near the carotid artery. Readings were taken for approximately 2-10 min from each mouse. Readings were sorted by Excel spreadsheet and R, for readings that had no SpO2 error codes in a block. S. cerevisiae strains and constructs.
Immunoblots were developed using ECL reagent (Thermo Fisher, GE healthcare) and imaged in LAS 4000 Imager or on X-ray film. Uncropped blots from Fig. 3a  and Supplementary Fig. 7  are available in Supplementary Figs. 11  and 12. Sequence alignment. Amino acid sequences were aligned using CLUSTAL W (V2. 1)  32  and files were generated by Strap software (September 2012 version)  33. Sequence similarity was calculated using the Sequence Manipulation Suite(V2. 0)  34. Alignment plot was generated by PLALIGIN(V2. 0)  35.
Acknowledgements We thank  Pete Finger ,  David Schroeder ,  Christina Chiang ,  Erin Torti , and  Anastasia Singalevich  for technical assistance, ReproGenomics group ( John Eppig ,  Mary Ann Handel , and  Janice Pendola ) for the R86S mouse,  Patsy Nishina 's Eye Mutant Resource for the R487G mouse,  Charles  ( Sam) Umbaugh  for comments on the paper,  Jonathan Weissmann  for the GRR construct and  Elizabeth Craig  for anti-Sis1 antibody. Supported by  NIH -grants  R01 NS102414  (G. A. C. and C. A. P. J. ) and  R01 NS075719  (C. A. P. J. ) and  R35 NS105078  (J. R. L. ) from the  National Institute of Neurologic Disorder and Stroke (NINDS) , the  Baylor Hopkins Center for Mendelian Genomics  ( UM1 HG006542 ), and  ALS Association  grant  16-IIP-287  (G. A. C. ). NHGRI K08 HG008986  (J. E.
Data availability The data that support the findings of this study are available as source data in the data source file. Variant information will be available upon reasonable request and have been submitted to ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/?term=NEMF%5Bgene% 5D)  37  , AnVil (https://anvilproject. org/) and the European Genome-phenome Archive (https://www. ebi. ac. uk/ega/home) for public access. Data for genetic matches with NEMF variants can be found on GeneMatcher  19  available to clinicians and researchers describing patients with variants of interest. The variant summary statistics work makes use of the following publicly available databases: ClinVar (https://www. ncbi. nlm. nih. gov/ clinvar/?term=NEMF%5Bgene%5D)  37  , Genome Aggregation database (Gnomad. V2. 1.
1, GRCh37/hg19; https://gnomad. broadinstitute. org/)  41  , Greater Middle East (GME) Variome (V1) (http://igm. ucsd. edu/gme/)  42  , Iranome (http://www. iranome. ir/)  43  , and 1000 Genomes Project (https://www. internationalgenome. org/1000-genomes-browsers)  44. Source data are provided with this paper.
Background: The rates of non-communicable diseases (NCD's) appear to be increasing in human immunodeficiency virus (HIV) infected people as compared to non-HIV infected people and this will have major implications for clinical care. The aim of the current study was to profile selected cardiovascular disease risk factors among HIV patients on anti-retroviral therapy (ART) in Bushbuckridge sub-district. Methods: The current study followed a quantitative cross-sectional study design using a questionnaire which was adapted from World Health Organization STEPwise approach to Surveillance (WHO STEPS). Participants were HIV infected people on ART and data was entered into a computer software Microsoft excel, then imported to Stata 12 for analysis. Discussion: The overall prevalence of overweight at the initiation of ART amongst the participants was 18.
Statistical methods Data analysis was conducted through the use of STATA statistical software (STATA Corporation, College Station, Texas) version 12. Categorical variables were presented as percentages whilst continuous variable were expressed as mean ± SD. The coding of data was done in line with WHO guidelines  [20]. Comparison of categorical variables was performed using Chi-Square and a level of 0. 05 was considered significant. We report 95% confidence intervals (95% CIs) on all proportions. Univariate logistic regression was employed to determine predictors of selected cardiovascular disease risk factors  [20].
Acknowledgements We acknowledge all technical assistance provided to the authors by the  TREC  and anyone who had helpful discussions or contributed less tangible concepts. The  Department of Public Health  within the  University of Limpopo  is acknowledged for hosting the study and the  Department of Health in Mpumalanga Province of South Africa  for granting permission to conduct the study. Lastly we would like to acknowledge the study participants for their time and efforts to make this study a success.
Crop traits One intention of this study was to observe the variations in biophysical plant growth indicators, for instance the differences in plant yield, induced by biochar presence in the soil. Non-invasive measurements of crop traits were performed on the 15th of June 2018. This dataset assessed the impact of biochar on the leaf length (using a measuring tape) and chlorophyll content (using a hand-held Opti-Sciences CCM-200 chlorophyll content meter) of the chicory crop. The CCM-200 reading represents the chlorophyll content index (CCI) based on the leaf transmittance values of the spectral bands centred at 653 and 931 nm. A regular sampling method of 25 measurements in each of the 22 experimental plots, totalling 550 measurements of leaf length and chlorophyll content were carried out.
In this case, each measurement was made on the most developed leaf of the plant selected for data acquisition, and the leaves were also visually checked to avoid any potential disease. For each plot, the 25 measurements were spatially distributed over the plot to better represent intra-plot variability. The median of 25 measurements made within each plot (i. e. median used instead of mean in order to avoid the possible bias introduced by outliers) were used to describe the canopy in each experimental plot. A set of destructive crop trait measurements were performed on plant roots and leaves. This sampling took place on the 6th and 7th of September 2018, a few days before the harvest date. Destructive field measurements are usually time-consuming and expensive. As a result, in the destructive sampling campaign three randomly selected rows of 0.
5 m length consisting of approximately 10 chicory plants were collected in each of the 22 test plots, totalling 66 samples (for nearly 660 plants) for the entire field. The median of the 10 plants in each plots was used. Fresh weight and yield of leaves were measured in the field immediately after sampling, leaf dry weight was measured in the laboratory after oven-drying the leaves at 80 °C until a constant weight was reached in order to compute leaf dry matter content. Similarly, root yield, length, and perimeter at the soil surface level were measured to obtain data as regards root characteristics. A previous study by  Abawi (1998)  reported that specific soil conditions, such as excess soil moisture or high contents of organic acids (i. e. C/N greater than 30), could induce forking in carrot plants.
Airborne campaigns and image processing A total of 13 flights were executed over the 2018 growing season (Table  1 ) using RGB, multispectral and thermal cameras on board of two UAVs. A DJI Phantom 4 Pro (DJI, Nanshan, Shenzhen, China) was flown over the chicories at 65 m altitude above ground level (AGL) at nadir, field of view (FOV) of 84°, an airspeed of 5 ms -1 , with 85 % front and 75 % side image overlaps obtaining 5472 × 3648 pixel RGB images. With these specifications, the resulting ground sampling distance (GSD) was 1. 8 cm. We planned DJI Phantom 4 Pro UAV flights using Pix4D capture software (Pix4D S. A. , Lausanne, Switzerland). The second set of UAV images were acquired from a DJI Matrice 100 (DJI, Nanshan, Shenzhen, China), a quadcopter with a flight endurance of 30 min using two TB48D 5700 mA h batteries simultaneously, which was developed to carry the multispectral and thermal sensors concurrently.
The multispectral sensor used was a MicaSense RedEdge-M integrated with MicaSense downwelling light sensor (MicaSense, Seattle, WA, USA) with five discrete narrow bands comprising blue (475 nm centre wavelength, 20 nm bandwidth), green (560 nm, 20 nm), red (668 nm, 10 nm), red-edge (717 nm, 10 nm), and near-infrared (840 nm, 40 nm). The FLIR Vue Pro Radiometric thermal imager (640 × 412 pixels, 13 mm lens, and 9 Hz frequency) with spectral response in the range of 7. 5-13 μm was used together with the FLIR GPS geo-tagger (FLIR, Oregon, USA) to conduct the thermal imagery. The UAV flight path of DJI Matrice 100 was planned using Atlas Flight software (MicaSense, Seattle, WA, USA). The flight altitude was set at 60 m AGL, an airspeed of 7 ms -1 , and with 75 % forward and sideways image overlap resulting in a spatial resolution of 3.
7 cm and 7 cm for multispectral and thermal images, respectively. Neither multispectral nor thermal data were acquired before July 2018 (Table  1 ) since the Matrice 100 platform was developed at the end of June 2018. Acquired RGB images were processed using Pix4Dmapper photogrammetric software (Pix4D S. A. , Lausanne, Switzerland). The processing chain includes: (i) aerial triangulation and geometric correction using the ground control point coordinates obtained from the DGPS to assign the high accuracy geo-coordinate system to the photogrammetric block, (ii) construction of the dense point cloud and mesh, (iii) orthorectification of the images, and (iv) derivation of the digital surface model (DSM) along with the RGB orthomosaic (Fig. 2 ). Likewise, the multispectral imagery was processed using Pix4Dmapper software following the same photogrammetric processing chain.
Crop water stress and Topographic wetness index modelling Meteorological data was retrieved from a local automated weather station situated in the middle of the study field. Meteorological data was recorded as 30 min averages, including air temperature, precipitation, wind speed and direction, relative humidity, and solar radiation. First, the computed RGB-based vegetation mask (Section 2. 4. 2) was resampled to the spatial resolution of the thermal images using the nearest neighbour algorithm in order to retrieve the true canopy pixels from the thermal imagery. Then, the air temperature recorded by the local weather station at the UAV overpass time (Table  1 ) was used to determine the implemented T c -T a index as the crop water stress indicator. It is important to highlight that the developed Matrice 100 platform at 60 m flight altitude enabled the thermal image acquisition to cover the entire field in approximately 30 min, and hence, a constant value of air temperature was used to model T c -T a for each recording date.
RGB canopy cover First, the canopy cover of the eleven experimental pairs (Fig. 1 ) was evaluated. Fig. 5a  shows the temporal profiles of the canopy cover percentage for the biochar-enriched plots in comparison to the reference plots. The canopy cover displayed greater values in biocharenriched plots over the green-up phase, except for plots 8 and 10. The area under the curve (AUC) of the canopy cover yielded a significant statistical difference (p-value = 0. 00032) between the biochar-enriched and reference plots (Fig. 5b ). In addition, the global T-test of the AUCcanopy cover confirmed a significant positive impact of biochar presence on the crop growth (p-value = 0. 00038) counting the entire dataset regardless of the other potential effects. This finding is in accordance with the positive growth of maize observed by  Kerré et al.
(2017)  who also studied relict charcoal hearths in Belgium. As stated before, the destructive in situ dataset was collected on the 6th and 7th of September 2018 at the end of the growing season and a few days prior to the harvesting period. The analysis of the UAV-based canopy cover indicated that it is worthwhile to pay more attention to the plant biophysical indicators during the green-up phase in order to have a better assessment of the impact of biochar on the crop growth. However, a more detailed evaluation is needed to completely evaluate the biochar amendment feedback in this regard. We recommend developing further sampling schemes that account for the temporal effects associated with phenology. High frequency sampling schemes would also help to provide a better interpretation of the results.
Delta area under the curve (AUC) of canopy cover exhibited no linear relationship with delta SOC (Fig. 6a ). Similar to the delta SOC results, the UAV-based canopy cover showed no relationship with delta total nitrogen, nor with C/N atomic ratio (Fig. 6b-c ). To assess the cause of these weak relationships, the key assumptions related to the design of the experimental plots were examined. Looking at the scatterplots (Fig. 6a-c ), one plot (plot 3) displayed the highest value of delta AUC of canopy cover with a low delta SOC that did not align with the other pairs. We split the data into delta AUC of canopy cover and delta SOC. Plot 3 was detected as an outlier based on the median absolute deviation test of delta AUC of canopy cover. In addition, one plot (i. e. plot 1) showed the highest value in delta SOC while having a low delta AUC of canopy cover (Fig.
CNV Calling and Somy Estimation Reads were associated to each sequenced cell based on their 10X-barcode sequence and mapped to a customized version of the reference L. donovani genome LdBPKv2 CNV values represent the integer NRD of each bin after scaling of NRD values by the baseline ploidy factor (S factor) of each cell determined by the scaling algorithm of the pipeline  20. We used the arguments min-soft-avg-ploidy and max-soft-avg-ploidy set to 1. 9 and 2. 1, respectively, to encourage the scaling algorithm to establish cells baseline ploidy to 2. This was done to avoid overscaling artifacts generated by the scaling algorithm that were observed in some cells when these options were not used. CNV values were visualized in the Loupe scDNA Browser (10X Genomics)  21  , with cells arranged in 512 clusters, the maximum number of clusters allowed by the software.
Karyotypes identification and network analysis In order to identify different karyotypes present in the sequenced population, somy values of each cell were rounded to their closest integer. Cells displaying the same rounded somy values for all chromosomes were considered as having the same karyotype. Each unique karyotype identified in the population received an identifier composed by the concatenated rounded somy values of all chromosomes, from chromosome 1 to chromosome 36 (e. g. , "222232223222222322222232232222423232"). Unique karyotypes were numerically named according to their frequency in the sequenced population. To perform a network analysis, a pairwise distance matrix was built based on the number of different chromosomes between all karyotype and used to generate a randomized minimum spanning tree with 100 randomizations, using the Pegas R package  24, 25.
Moreover, our SCGS data revealed that high cell-to-cell somy variation was chromosome-specic. For instance, chromosomes 21 and 18 were disomic in 99,23% and 99,42% of the cells respectively, while chromosomes 13, and 35 were the most variable ones, with 33,54% and 25,24% of the cells displaying a copy number divergent from the most frequent somy. In the filtered data, we identified a cell where 4 chromosomes were estimated as nullisomic (Supplementary table 1 -kar89). The bam file of this cell shows that these chromosomes had no sequenced reads mapping to them (Figure  2A , first plot), indicating that they were absent in this cell. An investigation of the bam files of all cells, including the ones eliminated after data filtering, revealed a total of 12 cells displaying at least one chromosome with no reads mapping to it, of which 11 displayed a high intrachromosomal bin-to-bin variability of the mapped chromosomes and were excluded during data filtering.
The HTC Vive was also commercially released in 2016. Furthermore, 2016 has been defined as "a golden year for virtual reality"  (Gaggioli, 2017)  due to the growth in popularity that the technology reached in that year due to its commercialization to retail customers. However, it is worth noting that both of the major development companies (Oculus and HTC) had released previous development kit versions of their HMD  (Oculus since mid-2013, and HTC in August 2015). However, the development kit versions of the HMDs have also been widely used in the analyzed literature body from 2016 and onward. We searched Scopus, Web of Science, and IEEExplore. We chose the first two databases for their multidisciplinary focus, which fits the topic of the present investigation. We selected IEEExplore for the focus on technology developments of the IEEE conferences.
The keywords were searched using title+abstract+keywords option on Scopus, topic option (title+abstract+keywords) on Web of Science, and all metadata in IEEExplore. No other limiters were inserted in the database searches. We imported the entries (294 articles) into EndNote and removed duplicates. A few more articles (6) were added from previously known articles on the topic and from the relevant references from those. One of these articles was added under suggestion during the peer-review phase of the present manuscript. After duplicates were removed, there were 198 articles. We first screened titles and keywords from the remaining articles, and we eliminated those that we considered outside the topic of interest (e. g. , did not have anything to do with HMDs or VR). The articles that remained after this phase were 95.
We included these articles that we found to fit the topic of interest for full-text evaluation. In this phase, we excluded the following types of articles from further analysis: articles that did not report first-hand experimental data and articles that were not relevant for the topic of interest (e. g. , from a completely different field, from full-text evaluation, n = 17); articles that fit the topic but did not include an explicit (and statistically verified) comparison between genders (n = 50); and articles that analyzed gender difference variables outside the interest of the present study (e. g. , gender tastes, n = 5). One article was excluded as it reported the same data of an article already included in qualitative synthesis. We included articles that reported more than one experimental finding, using different samples of participants, for each sample separately in the final analysis table.
It is also worth mentioning that most of the studies that have used Oculus equipment used development kits (DK1 and DK2), and not the final consumer-oriented version. However, too few studies have utilized the Oculus CV1 or a later Oculus version; hence, it is not possible to draw a conclusion. The difference reported in Figure  2A  regarding SS susceptibility and gender for different HMDs may be due to pure chance: the study sample is limited, and many other variables may be at play at the same time (e. g. , study design and VR content). Nevertheless, the problem of an insufficient IPD range in modern HMD technologies is worth considering. Based on the data of ANSUR II, large public datasets that report IPD measurements (see  Paquette, 2009) , the remarkable article of  Heaney (2019)  showed that the modern Oculus Quest's mechanical IPD adjustment makes it most suitable for around 99% of men and 93% of women.
Furthermore, the HMD Rift S, which employs non-adjustable fixed lenses, could optimally suit only 46% of men and 43% of women. Heaney (2019) indicated a small (but still notable) difference in suitability of the Oculus HMDs between males and females. However, please note that Heaney (2019) article was published on the VRrelated news website "uploadvr. com" and not in a peer-reviewed scientific journal. Saredakis et al. (2020)  performed a comprehensive systematic review and quantitative meta-analysis focused on factors that correlate with SS. They attempted to quantitatively estimate the female susceptibility to SS, correlating the percentage of females in studies and total Simulation Sickness Questionnaire (SSQ) scores reported in the analyzed studies. However, the authors explained that it was not possible for them to perform a more accurate quantitative analysis because the vast majority of the published studies have not supplied data breakdowns by sex.
Limitations and Future Studies The fast development of new technologies complicates the generalization of the present results for past and future types of HMD equipment. For example, even though the present review analyzed studies from 2016, it includes quite a bit of data from experiments that utilized beta versions of HMD products (Oculus DK1 and 2). The results obtained from those obsolete and nonconsumer-ready technologies may be different from those that would be obtained from more updated tools. Furthermore, the relatively specific focus of the present study (i. e. , modern HMD technologies), as well as the relatively recent introduction of these technologies to the market, makes the study of this topic challenging. The published scientific literature is limited, and the article sample size analyzed in the present review is not large enough for us to be able to generalize the findings of the present review.
The system temperature and other calibration parameters were measured every 10 to 15 min. Jupiter and standard mm calibrators were observed regularly to calibrate the pointing and focus. During the observation, the weather condition in general was good and the radiometer opacity τ at 225 was measured around 0. 5, except for two OTF maps observed on 9th September which have a precipitable water vapor (PWV) larger than 40 mm, the data was therefore dropped and the area was reobserved later. The spectra were calibrated with CLASS, which is part of the GILDAS software package 6. We converted the data from antenna temperature (T A ) to main beam brightness temperature (T mb ) using a forward efficiency (F eff = 95% at 3 mm) and main beam efficiency (B eff = 81% at 3 mm) following equation T mb = T A × F eff /B eff.