dataesr/scientific-paragraphs-categorization huggingface dataset:
These include PALM, a Bayesian network that is designed to work with multi-omic longitudinal time scale data  (Ruiz-Perez et al. , 2021) , and TIGRESS, a regression approach that uses the behavior of some features to predict the behavior of others  (Yan et al. , 2017). Further analysis of both of these methods and others is warranted due to the rather specific input data on which GENIE3 ranked so highly. Most of these data were from tightly controlled in vivo studies, and it is likely that in a natural microbiome setting (the gut, soil, marine, or other sites), natural variability may lead to other network inference methods being able to draw more edges between features, lowering the impact of GENIE3. While networks can provide a great deal of data, the possibility exists for spurious links or edges to be inferred in a network, reducing the accuracy and use of the network.
One such approach is the Metabolite-Expression-Metabolic Network Integration for Pathway Identification and Selection (MEMPIS) program. This program collects transcriptomic and metabolic data from a system and integrates it in combination with annotated metabolic pathways to identify which processes are being expressed by a biological system such as a microbiome. Interestingly, the current applications of MEMPIS are not on wellannotated model systems but on complex soil microbiome samples showing that this approach can be applied to sites where little specific knowledge of genome annotation has yet been collected  (McClure et al. , 2020). Another tool, XCMS Online, also includes metabolomics data but is able to integrate proteomic data or genomic data as well  (Forsberg et al. , 2018). However, unlike MEMPIS, this tool has not yet been tested extensively in complex microbiome systems.
e. , contain many zeros), and high-dimensional  (Tsagiopoulou et al. , 2022). Apart from normalization, transformation, or scaling steps used before integration, another challenge is the use of reproducible pipelines and a better community effort for data analysis strategies based on the ecological niche. The common approach now used is based on network-based methods, multi-variate random forests, and Bayesian approaches used to find probabilistic causal relationships between variables, and to identify the most probabilistic network that is predictive of the observed data  (Chong & Xia, 2017 ). An extensive, but not exhaustive, list of integration tools is accessible via GitHub (https://github. com/mikelove/awesome-multi-omics) along with commercial platforms that may be more user friendly.
Most methods utilize randomizations to generate data without regard to biochemical structure; thus, biological significance is sometimes overestimated. Univariate correlations are relatively straightforward but lack context for interpretation in terms of biological plausibility and mechanistic insight  (Chong et al. , 2020). Various tools have been developed to integrate -omics datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. To give an example, the COSMOS platform  (Dugourd et al. , 2021)  offers the opportunity to find potential mechanisms by linking deregulated protein activities and metabolite concentrations with a known network by coupling signaling, transcription, and metabolites. The advantage of this tool is of interest, since apart from proposed mechanisms between pairs of molecules (metabolites and proteins) it can also take into account the interaction with other inputs (molecules or genes) that can be included in the model.
Throughout the workshop discussion, several propositions emerged including: (i) the need for classical microbiology to produce knockouts to extract mechanistic hypotheses; (ii) advertising how important the mechanistic hypothesis is, since at present it is still often simply ignored; (iii) obtain more knowledge about the biological system as well as updating and integrating this information into popular databases; (iv) drive scientists to apply mechanistic, rather than descriptive studies; and (v) focusing on the combination of extensive prior knowledge and (new) computational methods to obtain new mechanistic insight. Only in this way will microbiome science efficiently contribute to the food system, allowing for solutions to be designed which can help to address the challenges our planet currently faces.
Apparatus Stimuli were projected onto a standard 6: ( x cm) video-projection screen (Celexon HomeCinema, Tharston, Norwich, UK), mounted on a wall, cm (Ex-6 periments , , and ) and 8 cm (Experiments and ) in front of the participant, who rested their head on a 8 chin rest. The high-speed PROPixx DLP projector (Vpixx Technologies, Saint-Bruno, QC, Canada) updated the visual display at Hz, with a spatial resolution of 6 x pixels. The experimental code was implemented in MATLAB (Mathworks, Natick, MA, USA), using the Psychophysics and Eyelink toolboxes 6,66 running on a Dell Precision T8 Workstation with a Debian 8 operating system. Eye movements were recorded via an EyeLink 6 head-mounted system (SR Research, Osgoode, ON, Canada) at a sampling rate of Hz, except in Exper-8 iments and , in which we used an EyeLink + system at a sampling rate of Hz.
Analysis of psychophysical data We assessed performance (visibility during high-speed motion) by computing observers' percentage of correct identication of the stimulus' curvature (up or down) in 6 each stimulus condition (e. g. , a combination of movement amplitude and movement speed). Using hierar-8 chical Bayesian modeling with JAGS 68 Markov-Chain-Monte-Carlo sampling ( chains) in the Palamedes toolbox (Version. ), we then tted sigmoidal psychometric functions to the performance values of a given stimulus parameter (i. e. , a set of absolute movement speeds in Figure  a ). We used Gumbel functions dened as (x; ✓, , , ) = + (1 )(1 e 10 (x ✓) ), where (x) is the proportion correct at a stimulus value 6 x, ✓ is the visibility threshold, is the slope of the function, is the guess rate, and is the lapse rate.
For Gumbel functions, thresholds evaluate to -e -= 6. % of the function's range. Thus, ✓ is the value at which an observer's proportion correct equals (✓) = +(-e -)( -), which corresponds to 8. 6% correct for a two-alternative-forcedchoice task and lapse rate of zero. For inferential statistics, we reparameterized threshold parameters in each model as orthogonal contrasts 6,   6   that evaluated the impact of a stimulus variable on We provide code for these models at https://github. com/richardschweitzer/PostsaccadicOscillations. Specically, we rst extracted raw gaze trajectories for both left and right eye for each saccade detected in Experiments and. To prepare the data for tting, we normalized these trajectories such that they 6 could be interpreted as distance traveled over time relative to the time and position of saccade onset.
Peak velocities larger than 8 deg/s (. % of all tra-8 jectories) and durations longer than ms (. % of all trajectories) were excluded from further analysis. Third, to estimate main-sequence relationships, we tted two functions , one predicting peak velocity and another to predict saccade duration based on saccade amplitude, to the combined data of left and right eye, separately for all four cardinal saccade directions. To predict peak velocity v p , we tted the function 6 v p = v 0 (1 exp( A a 0 )) and, to predict saccade duration, we tted the linear relationship 8 D = d 0 + c • A where A denotes saccade amplitude in dva. Fits were performed in a mixed-effects framework using the R package nlme (starting parameters: v 0 = 37. 5, a 0 = 0. 66, d 0 = 1. 5, c = 0. 18). Models included observers as random effects, thus allowing individual parameter estimates for each observer and saccade direction condition.
Early-vision model To simulate visual processing of the stimulus used in our 8 experiments, we implemented a model that convolved retinotopic stimulus trajectories over time (and a size corresponding to the aperture width of stim = 1 /3 dva we used in our experiments) with spatial and temporal response functions characteristic of the early visual system. All code relevant to this model is publicly available at https://github. com/richardschweitzer/ ModelingVisibilityOfSaccadelikeMotion. Spatial 6 processing (see Figure  6b ) was modeled using twodimensional Gaussian kernels with a standard deviation 8.
Funding 6 This research was supported by the  European Research 68 Council (ERC)  under the  European Union 's  Horizon 6 research and innovation programme  (grant No  86 ) 6 as well as the  Emmy Noether  and  Heisenberg Pro-6  grammes of the  Deutsche Forschungsgemeinschaft , 6  DFG  (grants  RO /- ,  RO /8- ,  RO /- and  6 RO /- ). M. R. and R. S. were supported by the  DFG 6 under Germany 's  Excellence Strategy -EXC /  " Sci-6 ence of Intelligence " -project no. R. S. was 66 funded by  Studienstiftung des deutschen Volkes  during 6 the early stages of the project. M. R. wrote the rst draft of 68 the manuscript during a sabbatical at  Dartmouth College , 6 funded by the  Harris German/Dartmouth   Distinguished 66 Visiting Professorship  program. The collaboration be-66 tween M. R. and T.
6 6 / 6 odes horizontally, each with a distance of dva from the screen center(Figure Sc), resulting in a distance of dva 6 between the two sensors. We collected data from trials, in which a white Gaussian blob on black background 6 traveled across the two sensors with varying movement amplitudes and relative movement speeds (v rel = {/, /, , /, /, , /}) as used in our experiments. As speed was xed during each trial, the time passed between the 6 two photodiode responses directly indicated the speed at which the stimulus traveled across the screen. Physical 66 stimulus speed was indeed exactly as specied for both leftward and rightward motion directions, and across all 6 movement amplitudes (FigureSd). Minimal code to drive the LM light meter, as well as data and analyses of the 68 above described measurements are provided at https://github.
Materials and methods Diploid S. cerevisiae strains and genetic constructions. The genotype of the diploid S. cerevisiae strains (W303 RAD5 + background) used in this study are listed in Supplemental Table  S1. Diploidy buffered for rearrangements that may cause loss of essential genes and allowed for capture of more chromosomal rearrangements. Most genetic constructs have already been described in  Piazza et al. (2017)  and are available as GenBank files in the Supplemental Data Set S1. Briefly, strains contained a heterozygous copy of the HO endonuclease gene under the control of the GAL1/10 promoter at the TRP1 locus on chromosme IV. The DSB-inducible construct contained the 117-bp HOcs (Fishman-Lobell and Haber 1992), various possible fragments of the LYS2 gene lacking a start and a stop codon, and a unique 453-bp-long sequence derived from the PhiX genome.
Acknowledgments We are grateful to  Lorraine Symington  and  Neil Hunter  for providing Pol3 and Rfc1 depletion systems. We thank  Zhou Xu  for providing the Cas9 and guide-expressing pZX006 plasmid and instructions to knock in the cdc5-ad mutation. D. R. was supported by  T32CA108459  and a fellowship from the  A. P. Giannini Foundation. Research in the Heyer laboratory is supported by grants  GM58015  and  GM137751. Research in the Piazza laboratory is supported by the  European Research Council  ( ERC-StG 3Dloop , grant agreement  851006 ). Genome stability research in the Argueso laboratory is supported by  National Institutes of Health/National Institute of General Medical Sciences  award  R35GM11978801. Author contributions: W. -D. H. and A. P. conceived the study. D. R. , Y. D. , R.
Data availability Raw sequencing data have been deposited at SRA BioProject PRJNA967211. Processed data (coverage of Illumina reads) aligned on the reference R64-2-1 S. cerevisiae genome, raw aCGH data, and SV-supporting nanopore reads have been deposited at GEO projects GSE232732 and GSE233144. A summary of data availability for MIR samples is provided in Supplemental Table  S4.
a b s t r a c t Food safety is a common concern at the household level, with important variations across different countries and cultures. Nevertheless, identifying the factors that best explain similarities and differences in consumer awareness pertaining to this topic is not straightforward. Starting from a questionnaire administered in seven countries from four continents (Argentina, Brazil, Colombia, Ghana, India, Peru, and the United Kingdom), we present an analysis of the answers related to food safety concerns, aimed at identifying possible explanatory factors. As classical statistical approaches can be limited when dealing with complex datasets, we propose an analysis with machine learning techniques, that can take into account both categorical and numerical values. With the questionnaire as a base, we task a machine learning algorithm, Random Forest, with predicting consumers' answers to the target questions using information from all other answers.
Introduction To approach households' habits and beliefs at the domestic level, questionnaires and surveys have been an effective means of study. Among the fields of study, practices and perceptions about food, and more specifically food safety, have been well represented both recently  ( Sollid et al. , 2022 ; Wallace et al. , 2022 )  and in earlier decades  ( Cuperus et al. , 1996 ; Medeiros et al. , 2004 ) , in various countries  ( Wilcock and Ball, 2014 ; Parikh et al. , 2022 ; Ma et al. , 2019 ; EFSA, 2019 ). The collection of large quantities of data and their analysis for scientific research, social assessment, or business purposes, naturally moved to the digital world (see e. g. ,  Jin et al. , 2020  for a recent review). On the one hand, the ease of participation offered by internet and the IoT (Internet of Things) boosted this type of studies, so that datasets are increasingly being made available  ( Kurtz and Thomopoulos, 2021 ; Salliou et al.
Data pre-processing Since food habits can differ greatly between countries, only common food products are considered. These common food products are:  Beef, Chicken, Chard, Beans, Rice, Green beans, Carrot, Tomato, and Bread (roll). Most commonly used green leaves vary from one country to another (chard, collard greens, etc. ) but were considered as equivalent. For India, the "Chapatti/Roti " item was considered as equivalent to "Bread " for the other countries. Among questions concerning the socioprofessional status of respondents, participants were asked to input their individual and household weekly income. As these data were provided in the local currency, to avoid issues related to monetary conversion, all information related to income was normalized with respect to other participants from the same country.
, 1984 ). For all experiments reported in this work, the RF classifier has parameters selected after hyperparameter optimization  ( Pedregosa et al. , 2011 ) , using a total of 300 decision trees. Most classifiers, alongside their predictions, are also able to return a ranking of the relative importance of the variables in the problem, with the ones that best explain the variance in the results among the top. The Python code used in the experiments is publicly available on a GitHub repository. 3.
I understand that my name/my organisation's name will not appear in any reports, articles or presentation without my consent. I understand that data will be kept according to University guidelines for a minimum of 10 years after the end of the study. I agree to take part in the above study. " where an affirmative reply was required to enter the survey. They were able to withdraw from the survey at any time without giving a reason. According to your best guess, please srate how safe to eat the foods listed be-low are? i. e. how likely is it that eat-ing them will damage your health due to risks such as contamination, food poisoning, improper handling, food fraud, mislabeling etc. According to your best guess, please estimate the carbon footprint (grams of CO2) embodied in the food portions that you typically eat (from your pre-vious portion size answer).
To our knowledge, this study is the first to use such a high number of PIT tags (n = 1,063) in a medium-sized meandering gravel-bed river (90 m wide), and to compare field observations with expected travel distances inferred from previous literature using a probabilistic approach and with results from 2D hydrodynamic modeling to evaluate and discuss data accuracy. We investigate gravel mobility in the Ain River in Eastern France in the context of a gravel augmentation program to mitigate sediment deficit below a dam.
10. 1029/2022WR034127 3 of 19 sediments to the Rhône River. The mean annual discharge of the Lower Ain, recorded at the Chazey-sur-Ain gauging station downstream of our study site (Figure  1a ) over the 1959-2017 period, is 120 m 3 /s, and the 2-, 10-, and 50-year floods are ∼760, 1,150, and 1,500 m 3 /s, respectively. The low-flow discharge is 16 m 3 /s (information from the national discharge database, http://www. hydro. eaufrance. fr). The channel is 90 m wide on average and the mean slope is 0. 0011 m/m  (Rollet, 2007). Compared with other European rivers of this size, the Lower Ain remains unusually close to its natural state since it still features freely developing shifting meanders. It has been unaffected by in-channel gravel mining and very few embankments are found along its course  (Muhar et al.
Materials and Methods Bedload tracking using PIT tags was best suited for monitoring gravel augmentation in the Ain River than other methods, such as repeated topographic surveys, because the introduced volume of sediment was modest (1,300 m 3 ) and propagated downstream over a long reach. The detection of the sediment wave through conventional topography with decimetric vertical uncertainty would have been challenging. Thus, we focused on particle tracking. Data sets for this research (particle tracking, probabilistic and numerical modeling results) are freely available in the PANGAEA data repository  (Arnaud et al. , 2023).
During the wading surveys, the tracer positions and IDs were manually recorded using a GPS (Trimble GeoXH or GeoXT, XYZ accuracy <0. 5 m). During the 2015 boat surveys, a differential GPS (DGPS Trimble 5800 RTK, XYZ accuracy <0. 05 m) was used. The method was improved in 2017 by connecting the RFID reader to a tablet computer and a GPS (Geomax Zenith 35, XYZ accuracy <0. 05 m), which automatically recorded the tracer positions and IDs (NMEA track; ArpenGIS software). It suppressed errors in writing the tracer IDs and delays between the tracer detection beep signal and the manual DGPS logging, especially in areas where several tracers were detected in a short time. The boat surveys were performed longitudinally to keep the position of the antenna relatively fixed with respect to the onboard GPS and to allow for post-processing of XY tracer coordinates by subtracting the distance from the antenna.
Tracer Recovery Rates and Data Accuracy The 2014 partial tracking survey was performed by two operators for 1. 5 days only. Nevertheless, it allowed us to detect 329 tracers (including double reading positions). The 2015 survey required two to four operators over 5. 5 days, which allowed the detection of 825 tracers (also including multiple positions). The 2017 survey took the same time with two operators, and 617 tracers were detected (including multiple positions). Notably, a tracer can be detected by boat and wading searches several times within the same survey. We compared the position of each multiple detection of the same tracer to evaluate tracer geolocation accuracy (2014: n = 64 doubles, triplets, etc. ; 2015: n = 317; 2017: n = 238). For example, in 2017, 60% of the double reading positions were separated by less than 5 m, and 16% were separated by 5-10 m.
Data Analysis We measured the distances traveled by tracers by projecting the tracer positions onto the channel centerline using the linear referencing tool in ArcMap version 10. 8 (ESRI). We calculated two classical metrics based on the individual tracers found in successive surveys: the travel distance between surveys S i-1 and S i , and the cumulated travel distance from the initial seeding position (S 0 ). The travel distance L T (in m) of the tracer cloud centroid was also defined as: 𝐿𝐿𝑇𝑇 = 𝐶𝐶𝑇𝑇𝑇Si -𝐶𝐶𝑇𝑇𝑇Si-1 (1) where 𝐴𝐴 𝐴𝐴𝑇𝑇𝑇Si = 𝑛𝑛 stat ∑ 𝑡𝑡=1 𝑥𝑥𝑡𝑡𝑇Si∕𝑛𝑛stat is the longitudinal tracer cloud centroid (average) position on the channel centerline in survey S i , and x t , Si is the longitudinal tracer position in survey S i  (Arnaud et al. , 2017). The tracer cloud centroid metric uses all the tracer positions that were observed and inferred in a given survey, assuming that the tracer cloud is representative of the whole tracer sample  (Haschenburger, 2013).
Development of the 2D Model A 2D hydrodynamic model of the approximately 40 km-long main channel of the Lower Ain River between Pont d'Ain and the confluence with the Rhône River was constructed. The Barré-de-Saint-Venant equations were solved using the Rubar20 software  (Bessenasse et al. , 2004; El Kadi Abderrezzak et al. , 2009)  on a mesh aligned along the flow direction and made of quadrilaterals and triangles with an average cell area of 5. 5 m 2 (Figure  4a ). The topography of the floodplain, dry-bed areas, and the in-stream channel was obtained from topo-bathymetric aerial light detection and ranging (LiDAR) survey performed in August 2015 with an average of 18 points/m 2 for a low-flow discharge of 16 m 3 /s (Figure  4b ). The XYZ accuracy was estimated to be better than 0. 08 m for dry areas and better than 0.
Acknowledgments The research was funded by the  LTSER Zone Atelier Bassin du Rhône, Électricité de France (EDF) , and the  Rhône-Méditerranée-Corse Water Agency (Action   B15 ). The numerical modeling was funded additionally by the  EcoFlowS research  team in INRAE. The work was performed within the framework of the  EUR H2O'Lyon  ( ANR-17-EURE-0018 ) of the  Université de Lyon  through the " Investissements d'Avenir" program  operated by the  French National Research Agency. We thank the  Lower Ain River syndicate (SR3A)  for information on river restoration measures. We are grateful to  A. Barillier  ( EDF-CIH ) who provided topo-bathymetric LiDAR data processed by  D. Lague  ( University Rennes  1). We thank colleagues who helped with fieldwork and numerical modeling:  H. Capra ,  S. Monnet ,  L.
Data Availability Statement Data sets for this research are freely available under the terms of the Creative Commons Attribution 4. 0 International (CC BY) license in the PANGAEA data repository  (Arnaud et al. , 2023). The Rubar20 software is open-source (BSD 3 license) and the code is freely available at: https://gitlab. irstea. fr/theophile. terraz/rubar20. Information is also available at: https://riverhydraulics. inrae. fr/en/tools/numerical-modelling/2d-modelling-rubar20-and-rubar20ts/.
, 1986)  and the beginning of the Kapthurin Formation (~610 ka  Deino and McBrearty, 2002)  with many lacustrine phases of the Kapthurin Formation indicating alkaline and saline lakes  (Renaut et al. , 1999). Here, we present a new hydroclimate dataset from a lacustrine sequence (KSI) deposited between ~931 and 831 ka, coinciding with a paucity of sedimentary evidence at Turkana and other East African Rift System (EARS) basins. This study aims to explore EMPT hydroclimate variability within the Suguta-Turkana-Baringo Basin and understand its regional impacts.