dataesr/scientific-paragraphs-categorization huggingface dataset:
com/rjcommand/PAN-Environment. The independent data availability statement for each study is reported in Table  A5  (Appendix 5). Different datasets were analyzed using statistical models with parameters dependent on the type, duration and complexity of each response and study design. Table  S5  (Appendix 5) provides a summary of the information that was collected from the authors who contributed each study, a description of the methods and relevant references, analysis type, spatial scale, details on the temporal or spatial baselines and how they were accounted for or interpreted, reports of any confounding factors (included as covariates), model results summary table links to GitHub, interpretation, and confidence score that the observed effect was indeed due to the lockdown (with a rationale for this selection).
Effects on wildlife around the world As humans retreated, animals quickly moved to fill vacated spaces (Fig. 3 )  (Derryberry et al. , 2020; Zellmer et al. , 2020). In our dataset, approximately half of the qualitative observations and more than one third of all measured quantitative species responses that were linked with some confidence to the lockdown related to unusual animal sightings in urban areas (both land and waterways), and to species occurring in different abundances compared to pre-perturbation baseline estimates (Figs. 4  and 5 ). Many initial observations painted a rosy picture of wildlife "rebounding"; indeed, our qualitative observations of wildlife responses are predominantly positive, likely reflecting reporting biases (Fig. 4 ). Reports include changes in behavior, reproductive success, health, and reductions in mortality, apparently in response to altered levels of human activity (Fig.
4 ), our quantitative assessments were balanced between negative and positive responses (Fig. 5 ). Even if our dataset does not represent a random sampling design, the reports collated are a comprehensive inventory of information across the globe. Emerging from this initial dataset is support for both negative and positive responses of wildlife to human activity and the systems in place to monitor and protect nature. Thus, the lockdown provides a striking illustration of the positive role humans can play as custodians of biodiversity. While negative impacts were expected, the potential for humans to positively influence biological conservation through scientific research,   A3 , albeit with biases in effort such as greater coverage in the Northern Hemisphere and South Africa), and based on empirical data of time series surveys and bio-logging data using statistical modeling to quantify change.
Only changes that were attributed to the lockdown with high confidence are included here (Appendix 4, Table  A4 ). Bubble size represents data density (the largest bubble represents 41-60 observations and the smallest is 1-20). environmental monitoring, opportunistic citizen reporting, conservation management, restoration, and enforcement activities was strong in our datasets. Combined, these activities jointly deliver conservation benefits. Another major take-home from this synthesis effort is that humans and their activities have measurable impacts on food availability for animals from both land and marine habitats, including that of top predators and scavengers. The role of human-sourced food is an important driver of wildlife occurrence and condition. For instance, in Singapore, feral pigeons shifted their diets from human foods to more natural food sources and their numbers declined (Appendix 4, Table  A4 , StudyID 75,  Soh et al.
Acknowledgments We especially thank volunteers and community scientists who reported sightings, photos, conducted beach clean-ups and participated as divers. Data, field and logistics support was also provided by  G. Mowat ,  B. McLellan ,  L. Smit ,  L. Bird ,  E. Oldford ,  A. N. Guzman ,  J. A. Mortimor ,  J. -O. Laloë ,  M. Bigg ,  H. Valverde ,  M. Knight ,  L. Burke ,  J. Campbell ,  L. Curtis ,  S. Davies ,  O. Fontaine ,  C. Hansen ,  V. Hodes ,  S. Jeffery ,  J. Nephin ,  C. St Germain ,  C. Sanderson ,  S. Taylor ,  L. Gittens ,  S. Cove ,  T. Jones ,  C. James ,  S. K. Kinard ,  A. Solis ,  C. Holbert ,  A. Johnson ,  J. P. Richardson ,  J. Lefcheck ,  S. Marion ,  B. W. Lusk ,  B. Gonzales ,  K. Ariotti ,  T. Clasen ,  A. Field ,  K. Fraser ,  J. Grosso ,  G. LeFevre ,  H. Seaman ,  L.
rTMS A Magstim Super Rapid stimulator (Magstim, Wales) was used with a 70 mm double air film coil. For the control group, a sham coil providing the same acoustic sensation, visual impact and shape as the active coil was used. This sham coil was equipped with a magnetic shield that attenuated the magnetic field to a biologically inactive level  [40]  but stimulated the skin and muscle overlying the scalp, giving the subjects the sensation of magnetic stimulation. The coil was located tangentially to the scalp over the left DLPFC using MRIguided neuronavigation with Brainsight software (Rogue Research Inc. , Canada). In order to optimize the coils' placement and decrease inter-subject and inter-session variability, the left DLPFC was targeted using MNI coordinates (x, y, z = -50, 30, 36) that corresponded to the juncture of Brodmann areas (BA) 9 and 46  [41].
This brain area was labeled on a 3D rendering of each subject's T1 MRI sequence using Statistical Parametric Mapping software (SPM12, Wellcome Department of Cognitive Neurology, London, UK). iTBS consisted of a burst of 3 50-Hz pulses, which was repeated at intervals of 200 ms  [4]. A 2-s train of TBS was repeated every 10 s for a total of 600 pulses in 190 s (Figure  1 ). In order to define TMS intensity parameters, each subject's individual motor threshold, considered to be an indicator of cortical excitability, was determined. The motor threshold can be estimated when the target muscle is at rest (rMT) or during active contraction of the target muscle (aMT). In the present study, 80% of the resting motor threshold (rMT) was applied instead of 80% of the active motor threshold  [4] , allowing higher stimulation power, while being well tolerated  [42].
The coil was located tangentially to the scalp over the left DLPFC using MRI-guided neuronavigation with Brainsight software (Rogue Research Inc. , Canada). In order to optimize the coils' placement and decrease inter-subject and inter-session variability, the left DLPFC was targeted using MNI coordinates (x,y,z = -50, 30, 36) that corresponded to the juncture of Brodmann areas (BA) 9 and 46  [41]. This brain area was labeled on a 3D rendering of each subject's T1 MRI sequence using Statistical Parametric Mapping software (SPM12, Wellcome Department of Cognitive Neurology, London, UK). iTBS consisted of a burst of 3 50-Hz pulses, which was repeated at intervals of 200 ms  [4]. A 2-second train of TBS was repeated every 10 seconds for a total of 600 pulses in 190 seconds (Figure  1 ). In order to define TMS intensity parameters, each subject's individual motor threshold, considered to be an indicator of cortical excitability, was determined.
MRI Data Acquisition Acquisitions were performed using a 3T Verio MRI scanner (Siemens, Erlangen, Germany). First, a 3D sequence was conducted for TMS neuronavigation, Voxel-Based Morphometry (VBM) and cortical thickness analyses, and localization of functional MRI (fMRI) activation (176 contiguous slices, slice thickness = 1. 0 mm, TR = 2300 ms, TE = 2. 98 ms, field of view = 256 × 256). Next, a functional run was acquired during an experimental task using a gradient echo-planar imaging (EPI) sequence (40 slices separated by a 1 mm inter-slice gap, slice thickness = 2 mm, TR = 2500 ms, TE = 25 ms, flip angle = 90 • , field of view = 208 mm, number of volumes = 195, duration = 8 minutes 7. 5 seconds). To allow for T1 equilibrium, each run started with 4 dummy scans, which were later discarded before analysis.
Experimental Task The experimental task was programmed using Cogent 2000 software (Matlab). Three types of photographs were presented: (i) positive, being happy images; (ii) negative, being sad images, and (iii) neutral images. The photographs came from the International Affective Picture System (IAPS)  [49]  and from royalty-free photo libraries. They had previously been selected by an independent sample of 20 healthy participants. They rated 257 pictures in terms of valence (from 1 = highly negative to 9 = highly positive) and intensity (from 1 = very low to 9 = very high) with a 9-point scale. After having transformed the valence scores into z scores, the 40 images with the highest z scores were selected for the positive category, the 40 images with the lowest z scores for the negative category, and the 40 images with z scores closest to 0 for the neutral category.
Cortical Thickness Three-dimensional structural images were processed using FreeSurfer software (version 5. 3. 0; [50] ; https://surfer. nmr. mgh. harvard. edu/). FreeSurfer automatically computed subject-specific measurements (such as cortical thickness) of the regions of interest (ROIs), which were labeled using the Desikan atlas  [51]. These ROIs were located either in the region targeted by iTBS (the left middle frontal gyrus) or in regions that showed a change in their activity during fMRI.
Cortical Thickness Three-dimensional structural images were processed using FreeSurfer software (version 5. 3. 0; [50] ; https://surfer. nmr. mgh. harvard. edu/, accessed on 23 April 2021). FreeSurfer automatically computed subject-specific measurements (such as cortical thickness) of the regions of interest (ROIs), which were labeled using the Desikan atlas  [51]. These ROIs were located either in the region targeted by iTBS (the left middle frontal gyrus) or in regions that showed a change in their activity during fMRI.
Finally, an ROI analysis was conducted to observe if brain activity changed after iTBS over the target area (x, y, z = -50, 30, 36) during the presentation of positive and negative stimuli. Using Marsbar software (version 0. 42, http://marsbar. sourceforge. net/, accessed on 23 April 2021)  [53] , the ROIs were defined as spheres with a diameter of 8 mm. For each subject and for each ROI, the estimated regression coefficients (beta values) were extracted before and after iTBS.
Statistical Analyses Statistical analyses were performed with SPSS software (version 26, Chicago, IL), excluding functional imaging (whole-brain) data and VBM. A repeated measures analysis of variance (ANOVA, with the time condition rTMS) as a within-subject factor and group (active/sham) as a between-subject factor) was performed to analyze variables after the equality of variances (using Levene's test) and the normal distribution of the data (using the Kolmogorov-Smirnov test) were ascertained. When one of these tests showed significant results, non-parametric tests were undertaken (using the Mann-Whitney test). Size effects were estimated with partial eta squared (partial η 2 ) when ANOVA was performed or with eta squared (η 2 ) when the Mann-Whitney test was performed  [54].
Islam et al. (2020)  conducted a survey to find out how many data items people represent on their watch face, which type of data, as well as which type of representations they use. They found that people have between 3 and 5 data items shown on their watch face together with time. The most common type of data represented were health and fitness data and people mostly used icon and text together. Representations using charts were less common, however, there is still a lot of potential for representing data using charts on watch faces. Islam et al. (2020)  focus was on understanding the current usage of watch face space, but they neither compared display sizes nor performance.
Thresholds Overall, we collected 324 staircases. We calculated a time threshold for each staircase, which should represent ~91% correct responses for the particular combination of chart type × data size  (García-Pérez, 1998). Following the same procedure as  Blascheck et al. (2019) , for each participant and each staircase, we computed the threshold as the mean time of all reversal points after the second. We first present the thresholds for the small stimuli (320 px×320 px), then for the large stimuli (1280 px×1280 px), and then the comparison between both. Last, we compare the trends from both stimuli sizes to the trends reported in the second study (called "random differences") by Blascheck et al. (  2019 ) using a smartwatch. Some of the detailed results can be found in the supplemental material uploaded to an Osf repository (https://osf.
The urinary proteome is a promising pool of biomarkers of kidney disease. However, the protein changes observed in urine only partially reflect the deregulated mechanisms within kidney tissue. In order to improve on the mechanistic insight based on the urinary protein changes, we developed a new prioritization strategy called PRYNT (PRioritization bY protein NeTwork) that employs a combination of two closeness-based algorithms, shortest-path and random walk, and a contextualized protein-protein interaction (PPI) network, mainly based on clique consolidation of STRING network. To assess the performance of our approach, we evaluated both precision and specificity of PRYNT in prioritizing kidney disease candidates. Using four urinary proteome datasets, PRYNT prioritization performed better than other prioritization methods and tools available in the literature.
Moreover, PRYNT performed to a similar, but complementary, extent compared to the upstream regulator analysis from the commercial Ingenuity Pathway Analysis software. In conclusion, PRYNT appears to be a valuable freely accessible tool to predict key proteins indirectly from urinary proteome data. In the future, PRYNT approach could be applied to other biofluids, molecular traits and diseases. The source code is freely available on GitHub at: https ://githu b. com/Boiza rd/PRYNT and has been integrated as an interactive web apps to improved accessibility (https ://githu b. com/Boiza rd/ PRYNT /tree/maste r/AppPR YNT). Kidney diseases can be defined as any chronic or acute disorder that affects renal structure and function 1. In their most severe form, they are associated with a variety of complications, such as anemia, mineral and bone disorder or cardiovascular disease, leading to overall increased mortality 2.
from a list of hundreds of potential candidates. Most of these studies considered urinary proteins showing most prominent changes, either based on fold change or p-value, as new promising disease-related candidates. However, not all renal proteins can be found in urine and not all urinary proteins originate from the kidney. Hence, ranking disease proteins solely based on observed urinary changes might limit the complex view of the disease and insight in its pathophysiology. To help decipher the picture of the deregulated molecular networks and prioritize disease candidates, computational methods and tools have been proposed  13. Some approaches prioritize candidates based on their similarity to the list of disease-modified genes  14. These methods use databases (e. g. OMIM), ontologies (e.
g. Gene Ontology) or text-mining from literature to assess similarity of sequence (e. g. POCUS  15  ), functional annotation (e. g. PANDA  16  , Endeavour  17  , ToppGene  18  ) or locus proximity (e. g. OPEN  19  , PhenoRank  20  ). Other approaches use biological networks in order to prioritize candidates (e. g. MaxLink  21  , ToppNet  18  ). One of the network-based software most commonly used by biologists in order to interpret high-throughput expression data is Ingenuity Pathway Analysis (IPA)  22. This suite is based on a PPI network containing millions of structured, manually curated experimental observations. In IPA, the "Upstream Regulator Analysis" (URA) algorithm prioritizes disease candidates using in-house causal network approach to elucidate upstream biological causes that can explain the observed molecular changes  23, 24.
One of the main limitations hampering the use of IPA is that the software is proprietary and therefore its use cannot be broadly generalized to the biology community. Many other computational prioritization methods already exist  13. Some are looking for candidates that directly interact with known disease genes, following the principle of "guilt-by-association"  14, 25. Other, such as shortest-path  26  or random walk  27  algorithms, further consider the closeness between candidates and known disease genes in a network considering both direct and indirect relationships. Previous studies have shown that closeness-based approaches outperformed direct neighbour-based methods and that combining closeness-based approaches further improved disease candidate prioritization  14, 28. However, most of these strategies have been used to identify disease candidates at the transcriptome level and not at the proteome level.
Contextualization of PRYNT PPI network. In order to test PRYNT approach, four urinary proteome datasets were used: two associated with ADPKD (ADPKD1 and ADPKD2) and two associated with UPJ (UPJ1 and UPJ2) (Table  1  and Supplementary Tables  S1-S4 ). We constructed a PPI network based on STRING database. Approximately 50-60% of the deregulated urinary proteins from ADPKD and UPJ proteomic datasets were present in the raw PPI network (Fig. 1 ). This rather low percentage could be explained either because part of the deregulated proteins were absent from STRING v10. 5 database altogether, or because they did not match the STRING settings that were selected i. e. sharing a protein. actions interactions with other proteins in the network, directional interaction and interaction reaching the highest confidence level (Fig.
1 ). Moreover, 56% (3569 proteins) of the 6391 proteins present in the network were grouped in 265 cliques, which are sets of proteins that all interact with each other and often share similar biological functions. In order to assess the impact of the missing biological input and of the presence of clique sub-graphs in the network, we modified the raw PPI network into three additional contextualized PPI networks (Fig. 2 ). The first contextualization consisted in generating a PPI network where the deregulated urinary proteins were added regardless of their confidence level (Fig. 2 , +DP). The second contextualization consisted in generating a PPI network where cliques were taken into account (Fig. 2 , +C). The last network combined both contextualization strategies (Fig. 2 , +DP +C). We applied the prioritization strategy combining shortest path and random walk on the four different PPI networks on the  Part of the deregulated proteins (DP) present in the proteomics datasets could not be included as they were absent in String v10.
5 database (Homo sapiens). Moreover, a number of DP was excluded as they did not share any interaction with other proteins (absent from protein. actions PPI) or did not have a directional interaction with highest confidence (> = 0. 9). PPI: protein-protein interaction network; DP: deregulated protein. , and only directional interaction with confidence > = 0. 9 were selected. The raw PPI network (Raw) was further contextualized by adding the deregulated proteins (+DP) regardless of their confidence level and by grouping the proteins within cliques (+C). PRYNT prioritization approach was based on the combination of shortest-path (SP) and random walk (RW) algorithms and was achieved by multiplying the rank of the protein with the shortest-path ranking strategy (ranksp), and the rank of the protein with the random walk strategy (rankrw).
PPI: protein-protein interaction network; DP: deregulated proteins; C: clique. four proteomics datasets (Fig. 2 ). We compared the ranked lists to a list of 500 reference disease candidates of ADPKD for ADPKD1 and ADPKD2, and of UPJ for UPJ1 and UPJ2. The precision was plotted (Fig. 3a ) and the areas under the precision curves (AUC) were compared (Fig. 3b ). Compared to the raw PPI, the use of the contextualized PPI + DP and PPI + C networks slightly increased the AUC of the precision. However, in the four datasets, the combined PPI + DP + C showed much better performance in terms of prioritizing disease candidates. Based on these results, we generated a contextualized PRYNT PPI network combining both the addition of the deregulated proteins and the management of the cliques (Fig. 2 ). Precision of PRYNT compared to other approaches.
4b  and Supplementary Figure  S2 ): direct ranking (Direct), interconnectedness combined with random walk (ICN + RW), Phenolyzer  30  , Endeavour  17  , MaxLink  21  , ToppGene 18 and ToppNet  18. Direct ranking and interconnectedness combined with random walk were applied to String raw PPI network. Direct ranking was performed by applying out-degree centrality as described in the study of Oti et al. 25. Disease candidates were prioritized based on the number of directly interacting differentially abundant urinary proteins. The interconnectedness-based approach combined with random walk was implemented following the study of Hsu et al. 28. Phenolyzer, Endeavour and ToppGene are similarity-based prioritization approaches, extracting knowledge from diverse databases such as OMIM, Disease Ontology, or Gene Ontology.
In Exp however, the deregulated proteins are the disease candidates and their prioritization is based on a p-value ranking, the most significant proteins being the highest ranked candidates. In the four datasets, PRYNT showed higher performance to prioritize reference disease candidates compared to URA and Exp, with better precision and superior AUC (Fig. 4c  and Supplementary Figure  S3 ). We next analyzed the overlap of reference disease candidates ranked in the top 100 by PRYNT, URA and Exp in the four datasets (Fig. 5 ). We observed that only a minority of reference disease candidates prioritized by PRYNT and URA were commonly prioritized by both approaches (59-70% uniquely prioritized by PRYNT and 48-64% uniquely prioritized by URA). For  Exp, not only the number of prioritized reference disease candidates was very low, but it also showed very poor overlap with URA and no overlap with PRYNT.
Specificity of PRYNT compared to reference approaches. We next assessed how the different prioritization strategies ranked candidates that were specific to the disease under study. First, we studied crossspecificity by analyzing whether prioritization in ADPKD datasets was better for specific ADPKD reference disease candidates compared to non-specific UPJ reference disease candidates, and conversely for UPJ datasets (Fig. 6a ). For ADPKD1 and ADPKD2, all prioritization strategies showed similar cross-specificity, with the AUC for specific ADPKD candidates (AUC ADPKD ) being superior to the AUC for non-specific UPJ candidates (AUC UPJ ). However, for UPJ1 and UPJ2, only PRYNT displayed adequate cross-specificity in both datasets. We next compared overall specificity of the approaches by comparing the AUC of the specific disease to the AUC of 80 non-specific diseases (list in Supplementary Table  S5 ) (Fig.
6b ). For APDKD datasets, overall specificity was similar for all strategies in ADPKD1 with the AUC of the specific disease (AUC ADPKD ) being in the top 15 out of 80 non-specific diseases. In ADPKD2, PRYNT showed better performance compared to URA and Exp (rank of specific AUC ADPKD of 14/81, 34/81 and 21/81 for PRYNT, URA and Exp respectively). For UPJ datasets, overall specificity was lower compared to ADPKD datasets and in both datasets, PRYNT prioritization showed best specificity, with a rank of specific AUC UPJ of 21/81 and 27/81 for UPJ1 and UPJ2 respectively. In UPJ2, Exp showed the lowest specificity with the specific AUC UPJ being ranked 65/81.