dataesr/scientific-paragraphs-categorization huggingface dataset:
The pathogenic process of neurodegenerative disorders such as Parkinson's disease, aging, and Alzheimer's disease may be influenced by a lack of iron-sulfur cluster formation and an accumulation of iron metabolism  (Zecca et al. , 2004; Isaya, 2014). Inhibition of NFS1 expression was found to cause the loss of iron-responsive protein and ICSs, upregulation of the iron hunger response (increased transferrin receptor and decreased ferritin), and an increase in intracellular free iron, all of which led to ferroptosis in a prior study  (Alvarez et al. , 2017). As a result, our research suggests that ferroptosis could be a critical factor in CCI-induced memory loss. The role of NFS1 in the hippocampus in response to pain stress should be the focus of future research. Cluster analysis revealed that the CCI-induced learning and memory impairment model was highly consistent with the sham group's dataset and that the two groups were significantly isolated.
RNA-seq is a method for predicting preliminary genes or pathways. We also used qPCR and Western blot to validate our RNA-Seq dataset at random, and we were able to identify specific genes and targets consistent with the pattern of our RNA-Seq profiles. When we analyzed our data to other well-known published datasets, we discovered certain DEGs that may be implicated in neuropathic and cognitive common pathogenic processes (neuropathic pain models and cognitive disease models). Future RNA-Seq analyses are likely to include further RNA-Seq analyses of more samples, which will be combined with our current datasets. Increased endoplasmic reticulum stress is linked to ferroptosis, which upregulates the production of activated transcription factors (ATFs). Recent research has discovered that ATF3 can bind to the promoter of SLC7A11, suppressing its expression and system Xc-transport function (which prevents lipid peroxidation and protects cells from nonapoptotic, iron-dependent death), resulting in reduced GSH production, increased free Fe 2+ , and lipid peroxidation accumulation, and ultimately causing DNA, protein, and lipid membrane damage  (Wang et al.
e. decompensated) phase. Earlier stages of liver fibrosis are traditionally assessed by liver biopsy, which over the past decade has been largely replaced by transient elastography (TE). In resource-limited settings, however, these fibrosis assessment tools are rarely available, and management of CHB patients relies on clinical assessment alone. The World Health Organization's (WHO) first guidelines for CHB published in 2015 recommended the use of non-invasive fibrosis markers based on low-cost routinely available laboratory tests in resource-limited settings, and this was adapted into national guidelines in many low-and middle-income countries. Specifically, the aspartate aminotransferase-to-platelet ratio index (APRI) at a threshold of 2. 0 was recommended to identify patients with cirrhosis in the WHO 2015 guidelines, although the lack of data from sub-Saharan Africa (sSA) was acknowledged as an important knowledge gap.
Individual patient data We contacted authors of all publications which met our study inclusion criteria, as well as researchers active in this area, to share individual patient data. For the current analysis, we excluded patients who had received antiviral treatment for HBV within the preceding six months, or who had started therapy for at least 7 days prior to the time of evaluation, patients with co-infection with hepatitis C (anti-HCV) or hepatitis D (anti-HDV) or human immunodeficiency virus (HIV), pregnant women, patients with ALT or AST exceeding 5 times the upper limit of normal (in accordance with European Association for the Study of the Liver reliability recommendations),  19  and those with suspected or confirmed hepatocellular carcinoma. Centre-specific definitions were used to categorise patients with hazardous alcohol consumption as described in appendix 3; the majority used the WHO AUDIT tool.
Validation We validated the model using bootstrap resampling. We obtained 500 bootstrap samples from the original dataset, then fitted the model and estimated rule-in, rule-out and Youden show a bimodal distribution. However, this is an artefact of the grid search we performed for optimal thresholds: 0. 65 and 0. 74 are consecutive values, so the validation shows that the bootstrapped estimates only switch between 2 consecutive values of the grid search.
We explored variables that were anticipated a priori to be clinically important comprising age, sex, BMI category and reason for hepatitis B testing (asymptomatic vs suspected liver disease). To examine an association between APRI test sensitivity and liver stiffness measurement among patients with cirrhosis, we used the Wilcoxon rank sum test and plotted the distribution of liver stiffness and APRI classification using a kernel density plot and a restricted cubic spline plot for liver stiffness against test sensitivity. Analyses were conducted in R version 4. 1. 0 (R Foundation for Statistical Computing, Austria), JAGS 4. 3. 0, using the rjags package (v4. 10)  22  and Stata v17 (Statacorp, USA). All R and JAGS code is available from GitHub (https://github. com/gitMarcH/HEPSANET).
RESULTS Database searches identified 1,334 articles following removal of duplicates. After screening of title and abstract, the full text of 72 articles were obtained for detailed review (Appendix 7). In total 24 articles met our inclusion criteria. Cohorts represented by HEPSANET group members were described in 21 articles. 11, 13, [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42]  We contacted corresponding authors from the remaining 3 articles,  [43] [44] [45]  and all authors agreed to contribute individual patient data. We additionally included unpublished data from three sites (Cape Town, South Africa; Thiès, Senegal; and Dakar, Senegal) and data from one additional study published since the search was conducted from Malawi. 46  Overall, contributing centres reported on 3960 HBsAg positive patients from 12 cohorts in eight countries comprising Burkina Faso, Ethiopia, The Gambia, Malawi, Nigeria, Senegal, South Africa, and Zambia (Figure  1 ).
, 2016). The Bayesian approach resolves a correlation between slope and intercept for numerous different regressions within a predefined set of errors in x-y data (i. e. , 10 Be-26 Al concentration space). For burial dating isochrons, the maximum slope of the regression is set at 6. 75, to be consistent with the standard surface production ratio of 26 Al/ 10 Be of 6. 75  (Balco and Rovey, 2008). The minimum slope is zero. The likelihood of each regression of slope and intercept is recorded in normalized probability histograms, which also illustrate whether the set of regressions represents a Gaussian distribution. The calculations were performed with a Matlab script that runs Monte Carlo simulations to conduct 1,000,000 trial runs to calculate slope and intercept values from 10 Be and 26 Al concentrations and associated 1 measurement errors  (Bender et al.
To calculate a reference concavity, we used standard algorithms contained in TopoToolbox  (Schwanghart and Scherler, 2014). We initially extracted a stream network with a high drainage density using a low-threshold stream area of 50,000 m 2. We then identified the transition from colluvial to fluvial domains based on the change in gradient on a plot of log-drainage area versus log-gradient to be around 10 6 m 2 (Figure  S2 ). The slope of a linear regression through the data from drainage areas >10 6 m 2 has a slope of 0. 45. However, using a minimum threshold area of 10 6 m 2 results in many streams which underestimate the true stream length as visible in the USGS 10-m digital elevation models (DEM). A minimum threshold area of 0. 5 × 10 6 m 2 returns stream lengths that show a better agreement with what we interpret to be the channel heads in the DEM.
h: Age calculated using version 3. 0 of the CRONUS-Earth online exposure age calculator (available online at: https://hess. ess. washington. edu/). i: Constant (time-invariant) scaling scheme of  Lal (1991)  and  Stone (2000)  with a reference production rate of 4. 24 +/-0. 16 atoms g -1 yr -1 based on Promontory Point (PPT) calibration data from  Lifton et al. , (2015). All ages assume an erosion rate of 0 mm yr -1. (Nishiizumi et al. , 2007). All uncertainties are 1σ confidence level. e: 10 Be concentrations and associated measurement uncertainties are blank corrected relative to the average of the two blanks processed alongside the samples in the laboratory. Total atoms 10 Be in process blanks: Catchments 1-13 = 31126 +/-4227, catchment 14-25 = 30933 +/-4391, average value = 31029 +/-136 atoms.
This dataset includes functional magnetic resonance imaging (fMRI) data collected while five subjects extensively listened to 540 music pieces from 10 music genres over the course of 3 days. Behavioral data are also available. Data are separated into training and test samples to facilitate the application of machine learning algorithms. Test stimuli were repeated four times and can be used to evaluate the signal to noise ratio of brain activity. Using this dataset, both neuroimaging and machine learning researchers can test multiple algorithms regarding the prediction performance of brain activity induced by various music stimuli. Although two previous studies have used this dataset, there remains much room to apply different acoustic models. This dataset can contribute to integration of the fields of auditory neuroscience and machine learning.
Value of the Data • Brain activity data with music genre labels to allow for supervised machine learning of music information. • Neuroimaging and machine learning researchers can test multiple algorithms regarding the prediction and decoding performances of brain activity induced by various music stimuli. • The large amount of data for each participant allows researchers to construct participantwise modeling of brain activity. • The test dataset with multiple repetitions of identical stimuli can be used to evaluate the signal to noise ratio of brain activity. • Behavioral assessment of a genre recognition task allows for a comparison of categorical judgments based on behavior and brain activity. • Formatting in the Brain Imaging Data Structure (BIDS) allows researchers to efficiently process the dataset.
Data Description All raw brain data are available in OpenNeuro ( https://openneuro. org/datasets/ds003720 ). The brain data are organized based on the BIDS  [1]  , which makes it easier for researchers to preprocess and analyze brain data. The dataset includes anatomical magnetic resonance imaging (MRI) and functional MRI (fMRI) data. The behavioral data, presentation software script, and stimuli preprocessing scripts are available in the Open Science Framework (OSF) ( https://osf. io/2zwkt/ ). The original music stimuli are available at ( http://marsyas. info/downloads/datasets. html ). These original stimuli are preprocessed for the experiments, as described in the Materials and Methods section. Fig. 1  illustrates the experimental design and stimuli. Table  1  reports the age, sex, and number of fMRI runs and samples for each participant.
Our experiment is based on a small-N  design (five subjects). We obtained 3 hours of neuroimaging data for each participant, allowing researchers to construct participant-wise modeling of brain activity. Although this dataset has been used in two previous studies  [2 , 3]  , there remains much room to apply different acoustic models. The original music stimuli have been widely used in previous music information retrieval studies (see  [4]  for a review). Therefore, various hand-crafted models and neural network models can be tested using this dataset.
Stimuli and task We obtained the original 1,0 0 0 music pieces from the GTZAN music genre dataset ( http:// marsyas. info/downloads/datasets. html )  [5]. This dataset consists of 10 music genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, and rock). From the original music pieces, we randomly selected 54 music pieces (30 s, 22,050 Hz) from each music genre. A total of 540 music pieces were thus used in the current study. A 15 s music clip was randomly extracted from each music piece. We applied 2 s of fade-in and fade-out effects for each music clip. All music clips were normalized for their root mean square. The fMRI experiment consisted of 12 training runs and 6 test runs (18 runs in total). Each run lasted 10 min and consisted of 40 music clips. At the beginning of each run, 15 s of dummy images were acquired to allow for the equilibration of the MRI signal; this should be omitted from further analysis.
MRI data acquisition We used a 3. 0 T MRI scanner (TIM Trio; Siemens, Erlangen, Germany). A 32-channel head coil was used. All experiments were performed at the Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka, Japan. For functional scanning, we scanned 68 interleaved axial slices with a thickness of 2. 0 mm without a gap using a T2 * -weighted gradient echo multi-band echo-planar imaging (MB-EPI) sequence  [6]  (repetition time (TR) = 1500 ms, echo time (TE) = 30 ms, flip angle (FA) = 62 °, field of view (FOV) = 192 × 192 mm 2 , voxel size = 2 × 2 × 2 mm 3 , MB factor = 4). We obtained 410 volumes (615 s) for each run. We also used a magnetization prepared rapid acquisition gradient echo sequence (MPRAGE) to acquire high-resolution T1-weighted images of the whole brain (TR = 2530 ms, TE = 3.
Behavioral experiment To compare brain activation patterns and behavioral performance, we asked the subjects to participate in additional behavioral experiments. The behavioral experiments were conducted in a soundproof room. Subjects were first instructed to listen to three original music clips (30 s) for each music genre as a reference. These music clips were selected randomly from the 460 music pieces which were not used in the MRI experiment. The subjects were provided the information on the correct music genres in this training session. The subjects then listened to the 60 music clips which had been used as the test dataset in the MRI experiment. The subjects selected the music genre of the target music clip by filling in 1 of 10 cells on the answer sheet displayed on a laptop computer screen.
Acknowledgments This work was partially supported by  MEXT/JSPS KAKENHI  (grant number  JP20K07718 ,  JP20H05023  in #4903 (Evolinguistics),  JP15H05311 ) as well as  JST CREST JPMJCR18A5  and  ER-ATO JPMJER1801. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
18112/openneuro. ds003720. v1. 0. 0 , Direct URL to data: https://openneuro. org/datasets/ds003720/versions/1. 0. 0 (2) Repository name: OSF, Data identification number: 10. 17605/OSF. IO/2ZWKT , Direct URL to data: https://osf. io/2zwkt/ Related research article (1) Nakai, T. , Koide-Majima, N. , & Nishimoto, S. (2021). Correspondence of categorical and feature-based representations of music in the human brain. Brain and behavior , 11 (1), e01936. (2) Nakai T, Koide-Majima N, Nishimoto S (2018) Encoding and decoding of music genre representations in the human brain. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics.
2018). We used the same data set to analyse niche use patterns along the urbanization gradients, to 1) assess differential niche use among cladoceran species, 2) assess differential niche use for contrasting functional groups linked to body size) and 3) evaluate niche shifts within species associated with urbanization pressure. Additionally, 4) we examined the relative contribution of niche differentiation compared to other (environmental and spatial) processes in explaining community assembly patterns and species coexistence. We hypothesized that larger species would occupy niche space in favourable environments (high productivity) because they are stronger competitors  (Brooks and Dodson 1965 , Burns 1969 , Gliwicz 1990 , Shurin et al. 2009 ). We also hypothesized that mainly generalist species would persist in urban environments due to the high disturbance level, based on the specialization-disturbance/urbanization-tolerance hypothesis  (Preston 1962 , Futuyma and Moreno 1988 , Vázquez and Simberloff 2002).
Occurrence and environmental data The dataset used in this study is previously described in  Gianuca et al. (2018). It contains information for 81 zooplankton pond communities from a field survey in the northern part of Belgium (Supporting information). Briefly, sites were selected using an a priori GIS analysis of percentage built-up area (%BUA, percentage of area covered by buildings) from polygons around the cities of Ghent, Brussels, Antwerp and Leuven. Sites were chosen according to a stratified hierarchical design, with 27 regional scale (3 × 3 km) plots chosen at each of three classes of built-up area (high: > 15% BUA, medium: 5-10% BUA and low: < 3% BUA), and a total of three ponds from each of these categories sampled within each regional plot. The scores for percentage built-up area used for each site were calculated a posteriori by combining ground building value and biologically valuable area, for both a 50 and 3200 m radius around each pond  (Brans et al.
(2017)  and  Gianuca et al. (2018). The occurrence dataset consisted of 28 species in total, but some species were more commonly observed than others. The rarest species do not provide enough information to separate niche use from occurrence, so we restricted our niche analysis to the 16 species in the dataset with at least 5 occurrences. We measured available environmental (niche) space using the 21 abiotic, biotic and morphometric pond measures (Supporting information). These variables represent water quality requirements and other important aspects of pond environment that have been shown to structure zooplankton communities  (Davies et al. 2010 , Casas et al. 2011 , Pinel-Alloul and Mimouni 2013 , Hassall 2014).
Niche volume A principal component analysis was performed on the entire environmental dataset (21 variables) after standardization of the variables to zero-mean and unit variance. For each species, niche volume was quantified as the convex hull volume, or the volume of the smallest convex set in the first two PC axes (which explain 34. 2% of total variance) that contained all the sites where the species was present (using the Quickhull algorithm in 'convhulln' implemented in the R package 'geometry',  Habel et al. 2019 Habel et al. , in R ver. 3. 5. 0, 2018)). As such, niche volume represents the environmental conditions that a species occupies, i. e. the realized niche. Niche volume ranged from 7. 06 to 48. 80 (Table  1 ), out of a maximum of 58. 36 which represents the niche volume of the entire available environmental niche space (in the first two PC axes).
Niche overlap We quantified niche overlap between species pairs (aim 1) and between urban and rural populations of the same species (aim 3) using the framework of  Broennimann et al. (2012). We first reduced the available environmental space to the first two axes of a principal component analysis that includes the environmental properties of all sites within the study area, and those sites associated with species occurrences (PCA-env;  Broennimann et al. 2012 ). These axes explained 34. 2% of the variation in PC-env space (Supporting information). We then divided this environmental space into a 100 × 100 grid of cells. For each cell, we calculated the density of occurrences of each species (o ij , the kernel density estimation of the number of occurrences of the species at sites with the environment given in cell ij, where i and j refer to the cell corresponding to the ith and jth bins of the environmental space), the density of available environments (e ij , the kernel density estimation of the number of sites with the environment given in cell ij), and the occupancy of the environment by each species (z ij , which is o ij /e ij , scaled by the maximum o/e value in the community occurrence dataset).
D ranges from 0 (no niche overlap) to 1 (complete overlap). To determine the change in niche use of species in response to urbanization (aim 3), we compared niche occupancy for each species in a subset of the total dataset with sites in an urban setting or in a rural setting. Different spatial scales of urbanization may influence species niche shifts, so we calculated urbanization using percentage of built-up area for an area within 50 m radius around the pond (local scale, n = 45), and for an area of 3200 m radius around the pond (regional scale, n = 47) and calculated niche overlap between rural and urban populations at these two scales separately (urban setting: n = 21 using 50 m radius, 23 using 3200 m radius; rural setting: n = 24 sites). Metrics were calculated using the 'ecospat' package  (Broennimann et al.
Co-occurrence patterns Based on the occurrence dataset, pairwise species co-occurrences were calculated using normalized checkerboard scores (Cscore,  Stone and Roberts 1990). Non-random cooccurrences (whether or not species co-occurred more or less than expected by chance) were assessed via a standard null model approach  (Gotelli 2000, Gotelli and Ulrich 2010). This 'fixed-equiprobable' null model uses an algorithm that reshuffles species occurrences across sites while keeping the number of occurrences for each species over all sites constant. This analysis was repeated for 10 000 iterations (using ecospat. Cscore in 'ecospat' package), producing the mean of predicted null Cscore distribution, the observed Cscore for Table  1. Niche volume, number of site occurrences (for all sites, and for urban and rural sites -values are given at the 50 and 3200 m spatial scale for urban sites), and functional properties (habitat specialization and body size) of species that occur in 5 or more sites.
2019 , Hijmans 2020). For each species, we split the record of presence-absence across the 81 sites into a training and testing dataset depending on the number of occurrences of that species (80:20 training:testing of the 81 sites for species with N > 25 occurrences, 60:40 for 25 ≥ N ≥ 13 and no split for N < 13). The occurrence probability for species with N < 13 occurrences was taken as the average across all 81 sites, and for species with N ≥ 13 occurrences, the value for each site was taken as the average across 100 repetitions of the training and testing procedure. The mean probability of occurrence of each pair of species (Geomean) thus measures the probability of species co-occurrences predicted solely on geographic proximity (i. e. independent of environmental suitability), where low values correspond to species that are likely to be found in different geographic regions.
Occurrence and environmental data The occurrence dataset consisted of 28 species in total, with species richness varying from 0 to 8 species (Supporting information; measures for alpha, gamma and beta diversity, estimated using Shannon entropy, were α = 1 D α = 3. 98, γ = 1 D γ = 15. 87 and β = 1 D β = 3. 98; Jost 2007). Species occur on average in 11. 39 ± 2. 62 (average ± SE) sites, and the number of occurrences ranges from 1 to 64 sites (Supporting information, Table  1 ).
Acknowledgements -We thank all partners in the  Belspo IAP consortium SPEEDY  for their support and help with logistics. We thank  Kristien I. Brans  for helping with the sampling. Funding -Data collection was financially supported by  Belspo IAP  project  SPEEDY P7/04. JE was financially supported by an  IWT  PhD fellowship. LDM acknowledges financial support from  KU Leuven Research Fund  project  C16/2017/002  and  FWO  project  FWO G0B9818. JHP acknowledges support from the  The Richard Lounsbery Foundation , for the project  Urbanization  and land use change effects on aquatic biodiversity. Conflicts of interest -The authors declare no conflicts of interest.