dataesr/scientific-paragraphs-categorization huggingface dataset:
Inferring the meta-analytic connectivity matrix using NeuroLang To infer a whole-brain meta-analytic connectivity profile for each LPFC region, we query the database on the probability that a brain region is reported active given the presence as well as when given the absence of activation in a LPFC region. This gives us a measure of specificity in the meta-analytic connectivity between each LPFC region and every brain region. To infer these probabilities, we write a NeuroLang program that first projects the voxels reported active in each study onto the 1024 functional regions to determine which ones are reported by the study (step 2 in Figure  6B ). In this context, the program regards the reporting of a brain region by a study as a probabilistic event rather than a certain one. That is, if a voxel reported active has a normalized weight w on a region r, then the region is assigned a probability w of being reported by the study.
A positive LOR indicates more evidence for a brain region to be reported active given activation in a LPFC region, a negative LOR implies more evidence for a brain region to be reported active given no activation in the LPFC region, and a LOR equal to 0 implies that the evidence is inconclusive for either hypotheses. The program that infers the meta-analytic connectivity matrix is as follows: In order for the program to solve the query, we need to declare some useful predicates. First, we get the maximum weight in each DiFuMo brain region in RegionMaxWeight using the built-in function max(w). This will be used to declare the probabilistic table RegionVoxel which implicitly incorporates the normalized weight w/W of each voxel (x, y, z) on each region r. This is represented by the (:: w/W) notation after RegionVoxel(r, x, y, z).
For instance, ProbabilityOfCoactivation is a rule table that encodes the probability (PROB) of activation being reported in brain region r given that activation is also reported in LPFC region r2. Likewise, ProbabilityOfNoCoactivation is another rule table that encodes the probability (PROB) of activation being reported in brain region r given that activation is not reported in LPFC region r2. The SelectedStudy table sets the program to assign an equal weight (1/N , N = 14, 370) to all the studies in the meta-analysis. Finally, the MetaAnalyticConnectivityMatrix rule table is inferred by computing the "LOR" of the two hypotheses as a measure of evidence of meta-analytic connectivity between each LPFC region and every brain region.
Each of these tables includes a row (bin, x, y, z) for each voxel at location (x, y, z) in MNI space and belonging to a quintile bin. Moreover, we declare another fact table Bin whose rows contain only the labels of the quintile bins (i. e. , bin1 to bin5). We write a NeuroLang program that infers the conditional probability of a brain region to be reported active given activation reported in a bin as well as when given no bin activation. Specifically,the program infers the LOR of these two hypotheses as a measure of evidence for coactivation between each brain region and each quintile bin along the principal gradient. A cortical coactivation pattern for each quintile bin is then constructed by recovering the brain regions that exhibit at least threefold the evidence (or LOR > 0. 5) of being reported active when given activation in a bin relative to no bin activation.
The NeuroLang program that infers coactivation patterns of quintile bins in the left LPFC is as follows: As in the program of the previous section, we first declare the predicates that will be used to find the answer to our query. We set the program to consider activity in a quintile bin to be reported by a study if at least one peak activation is reported within the bin or within its near vicinity (distance < 3). The program stores the results in the rule table LeftBinActivation, which includes a row (bin, study) for each bin in which activation is reported by a study. The program then derives the studies that do not report activity within each bin using the negation operator and stores them in another rule table NotLeftBinActivation. This rule table includes a row for each bin wherein no activation has been reported by a study.
Similarly as the program in the previous section, the program here considers activation reporting in individual brain regions as a probabilistic rather than deterministic event depending on the location of active voxels within each region, and stores the results in the rule table BrainRegionActivation. The program then infers the conditional probabilities of the two hypotheses and stores them in the rule tables ProbabilityOfCoactivation and ProbabilityOfNoCoactivation. Finally, the answer to our query CoactivationPatternOfBin is derived by estimating the "LOR" as a measure of evidence in favor of coactivation between each brain region r and each bin and thresholding it at LOR > 0. LeftBinActivation.
The LOR of these two hypotheses gives us a measure of evidence in favor of association between a topic and patterns of activity along the principal gradient. The NeuroLang program that infers specific structure-function associations in the left LPFC using segregation queries is as follows: & TopicPresentGivenNotSegregationQuery(topic, bin1, bin2, p0) & LOR = log10(p1/(1 -p1))/(p0/(1 -p0)) ans(topic, bin1, bin2, LOR) :-TopicAssociationMatrix(topic, bin1, bin2, LOR) We first declare the studies that report activations in each quintile bin of the left LPFC principal gradient in a rule table LeftBinActivation. Then, we declare a segregation query which first identifies the studies that report coactivation between each pair of quintile bins along the principal gradient, bin1 and bin2, under the conditions that (bin2 >= bin1) and there exists no activation reported in any bin3, such that (bin3 < bin1 | bin3 > bin2).
The results are represented in the tables TopicPresentGivenSegregation and TopicPresentGivenNotSegrega Finally, the answer to our query, represented in the rule table TopicBinsAssociationMatrix, is derived by computing the "LOR" of the two hypotheses as a measure of evidence in favor of specific association between each topic topic and activation in a quintile range [bin1, bin2] along the principal gradient. To ensure that the results are not driven by a single choice of studies, we run this NeuroLang program 1000 times using random sub-samples of the Neurosynth database (80% of the dataset) in each run. This procedure creates an empirical distribution for each probability estimation from which we consider the 95 th percentile as a point estimate of interest. The NeuroLang program that infers specific topic associations of coactivation patterns within the right LPFC is as follows:  ans(topic, bin1, bin2, LOR) :-TopicAssociationMatrix(topic, bin1, bin2, LOR) Finally, we write a program that performs inter-hemispheric segregation queries to infer the probability "that a topic is present given activation in a right LPFC quintile bin and there exists no reported activation in the entire left LPFC".
The program also infers the probability of the opposite hypothesis; "a topic is present given activation in a left LPFC quintile bin and there exists no reported activation in the entire right LPFC". The NeuroLang program that infers hemisphere-specific topic-bin associations is as follows: In this program, we define the predicates LeftBinActivation and RightBinActivation that store the studies reporting activation in each quintile bin of the principal gradient in the left and right LPFC, respectively. Then we declare the inter-hemispheric segregation queries using the negation operator and the existential quantifier, ~exists, and stores the results in OnlyLeftBinActivation and OnlyRightBinActivation. Subsequently, the program infers the conditional probabilities that a topic is present in a study when given activation either in a left or a right quintile bin.
15 reuse, remix, or adapt this material for any purpose without crediting the original authors. share, this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally bioRxiv preprint doi: ; https://doi. org/10. 1101/2022. 01. 21. 477198 this version posted January 23, 2022. The copyright holder has placed Neurosynth database Lateral PFC Mask 500k Activation Peaks 14,370 Articles NeuroLang Functional Atlas 38 Topics Working Memory Empathy. Perception Cued Attention Meta-Analytic Connectivity Matrix LPFC Regions Log Odds Ratio Brain Regions.
General description of deamidation Deamidation is a peculiar post-translational modification (PTM) because it has the specificity to be either enzymatically catalyzed (in bacteria or viruses as reviewed in  [1] ) or chemically catalyzed in any proteinaceous sample, even outside cells. In the latter case, deamidation falls in the category of spontaneous and irreversible PTMs, just like protein oxidation or carbonylation for example. Deamidation occurs in peptides and proteins, and removes the amino group on the lateral chain of the neutral polar residues asparagine (Asn, N) and glutamine (Gln, Q). It is a very frequently observed modification in proteins. Indeed, with an occurrence of 4. 3% for Asn and 4. 2% for Gln in eukaryotic proteins (source: the Biological Magnetic Resonance Data Bank), and if an average length of 400 residues is assumed in the ~10,000 different proteins estimated in a typical eukaryotic cell, one may calculate ~400,000 potential deamidation sites  [1]  vs ~640,000 for phoshorylation  [2].
Collagens are very abundant proteins in bodies, and notably in bones (85-90%) where they largely exceed non collagenous proteins (0-15%). In addition, these organic samples are naturally confined in the mineral inorganic phase (mainly calcium phosphate) that composes 50-70% of bones, thus accounting for a remarkable preservation in prehistoric samples, even temperate. Owing to the highly ordered arrangement in triple helix of Type I collagen, deamidation rate is also kept considerably slow. Different MS-derived tools have been developed to tailor the measurement of deamidation to archaeology specific needs. A method called ZooMS (for Zoology Mass Spectrometry), developed by M. Collins and M. Buckley  [89] , is widely used for the taxonomic identification of bones: the MALDI-ToF-MS finger printing analysis of collagen type I provides spectrometric patterns that are compared and matched to a reference database.
Variations in amino acid sequences from different taxa allow robust, fast and cost-effective species identification. The levels of deamidation in archaeological collagen can be further measured from the same ZooMS analyses. The usefulness of deamidation for absolute dating of bone samples was unfortunately disproved by most studies: Orlando et al. compared the proportion of deamidated sites in collagen samples from a Middle Pleistocene horse bone (see suppl data in  [81] ) to Late Pleistocene mammoth samples previously analyzed by Cappellini et al. [82] : Dent mammoth (-11,000 years) and La Sena mammoth (-19,000 years) preserved in temperate environments show ~80% deamidation, while the Siberian mammoth (-43,000 years) and the Middle Pleistocene horse (-780,000 to -560,000 years) preserved in permafrost show ~53% deamidation, in spite of their age difference.
This proves that thermal variations over time at the burial site tamper with deamidation rates, and preclude their use to reflect chronological age. In some cases, the expected deamidation rates were corrected by a metric called "thermal age"  [90]  to account for the environmental history of fossils in different places (temperature, hydration, burial depth…); better correlation of deamidation rates with chronological age was achieved  [90, 91]. The Collins lab further designed a new software called DeamiDATE 1. 0  [92]  which relies on site specific deamidation rather than bulk deamidation to compare samples. In DeamiDATE the relative deamidation of samples determined by MS/MS analysis is weighted by the half-time specific to each X-N-Y or X-Q-Y combination as calculated by Robinson and Robinson under controlled pH and temperature conditions (37 • C, pH 7.
Large peptides/proteins are more likely than small molecules to harbor complex three-dimensional structure and include many functional groups; as a consequence, the former are more susceptible to spontaneous modifications (including conformational and chemical changes)  [100]. Today, machine learning models have been refined to the point that algorithms now allow to compute complex datasets and assist protein design and development with unprecedented accuracy: as a result, spontaneous degradation may be averted in therapeutic proteins  [101]. Deamidation, isomerization and racemization have clearly been identified as spontaneous processes potentially challenging the biological activity of proteins because they proceed at physiological pH with rates relevant to the time-scale of protein production  [29].
Acknowledgements This work was supported by the  CNRS, Université de Bordeaux  (to UMR 5095). M. P. is supported by  Ligue Nationale Contre le Cancer , A. B-L is recipient of a PhD fellowship from the  Ministère  de l'enseignement supérieur, de la recherche et de l'innovation, A. M. is supported by  CNRS Innovation. The authors are deeply indebted to  Dr Stéphane Chaignepain  for sharing his insight on mass spectrometry.
The 'ASPICov: An automated pipeline for identification of SARS-Cov2 nucleotidic variants' paper represents a pipeline to detect and identify SARS-Cov2 mutations by analyzing raw reads data. The pipeline is written in Nextflow DSL, making it reproducible in different computing environments, which is an undeniable asset. Starting with raw data, the pipeline outputs results in a user-friendly HTML report, combining the figures and quality reports of various tools incorporated within the pipeline. ASPICov comes across as a handy and helpful tool to aid researchers in identifying and analyzing whole genome variations of SARS-Cov2. The tool appears to be under active development, hence a few hiccups may appear during running and testing, such as errors due to script permissions or problems with the download of the test dataset.
Scenarios serve science by testing the sensitivity of a system and/or society to adapt to the future. In this study, we present a new land use scenario methodology called ScenaLand. This methodology aims to develop plausible and contrasting land use and management (LUM) scenarios, useful to explore how LUM (e. g. soil and water conservation techniques) may affect ecosystem services under global change in a wide range of environments. Sce-naLand is a method for constructing narrative and spatially explicit land use scenarios that are useful for end-users and impact modellers. This method is innovative because it merges literature and expert knowledge, and its low data requirement makes it easy to be implemented in the context of inter-site comparison, including global change projections. Sce-naLand was developed and tested on six different Mediterranean agroecological and socioeconomic contexts during the MASCC research project (Mediterranean agricultural soil conservation under global change).
2011)  and their combination has been set with the recent accomplishment of CMIP6 climate and SSP datasets  (O'Neill et al. 2016; Hurtt et al. 2020). At the regional scale, SSPs can be downscaled and contextualized according to the local socioeconomic context, and climate change scenarios can be downscaled with various statistical and numerical methods from global climate models (GCMs) to regional climate models (RCMs) and validated with local climate measurements  (Maraun 2016). For example, the European scale project IMPRESSIONS (Impacts and Risks from high-end scenarios: Strategies for innovative solutions) has been successful to design regional-scale scenarios and to involve stakeholders from more than four European countries  (Kok et al. 2019). While climate scenarios are often used in a wide range of environmental projects, SSPs require some regional hypothesis to address a specific environmental problem at a local scale.
Despite the satisfactory results of ScenaLand, some improvements might be needed for the validation of LUM techniques. For example, in the MASCC project, the SWC practices have only been biophysically studied and mostly based on expert knowledge, literature review, and existing databases such as the WOCAT (World Overview of Conservation Approaches and Technologies). In some other cases, such as Macieira and Tleta, previous stakeholder workshops also provided stronger validations by local experts than in the other sites. However, it would be interesting to carry out a cost-benefit analysis using for instance the PESERA-DESMICE (Desertification Mitigation Cost-Effectiveness) approach (Fernández and Vega 2016) or evaluate the cost-benefits of soil conservation techniques  (Prats et al. 2012 (Prats et al.
Acknowledgements This work and the postdoctoral grant of AV Pastor benefits from the financial support of the  MASCC  project through  ARIMNET2 , an  ERA-NET  funded by the  European Union 's  Seventh Framework Program  for research, technological development and demonstration under grant agreement no. 618127. In addition, MMH is beneficiary of a  Serra Hunter fellowship  ( UB-LE-9055 ) funded by the  Generalitat de Catalunya. JJK received the financial support through  CESAM  ( UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020 ), from  FCT/MCTES  through national funds (OE). The authors would like to thank  Marie Castellazi  for her valuable contribution and shared experience in building land use scenarios,  Dominique Rollain  for his valuable contribution to the Roujan scenarios and  Francesc Gallart  for his valuable contribution to the  Can Vila study case.
Methods Since 2007, the Rouen University Hospital Department of Biomedical Informatics (RUH DBI) has developed a crosslingual health termino-ontology server (HeTOP; URL: www. hetop. eu) containing 91 termino-ontologies in 55 languages  [2]. A simpler and more compact access to drug information was created based on the HeTOP instantiation: all the drug information is accessible in a unique screen. A formal evaluation was built with three different areas. The first axis uses the Likert's scale. This method is a psychometric tool that allows to measure perception of users about the HeTOP drug server. By marking the line, they attribute a rating on the following three aspects of the tool: 1) interest in terms of student's skills, evaluating the potential tool aid to the student's competence 2) pedagogic interest, evaluating the potential added value of the tool for teaching, and 3) the display simplicity, evaluating the usability of the tool.
We also consider a full battery of eight-digit product dummies, as well as year dummies in our production function estimations. Indeed, quantity in the data is measured in units (kilograms, litres, number of items, etc. ) that are specific to each eight-digit product and so quantity TFP a it can be reasonably compared across firms and time only within an eight-digit product category. For similar reasons, also λ it can be reasonably compared across firms and time only within an eight-digit product category. In terms of production function estimations we are forced, by sample constraints, to run a single estimation across the whole manufacturing firms sample instead of by two-digit industry groupings. As explained afterwards, we need firms to be in both the Prodcom and ARDx 13  data sets, described below, while also requiring information on one and two period lags for all variables.
Composition effects and weighting There are reasonable concerns about composition effects as the ARDx firm sample changes over time, and particularly so from 2007 to 2008 when the ONS switched from producing the Annual Respondents Database to the Annual Business Survey. Therefore we present, as baseline, results for what we label the 'within sample' which compares the mean of within-firm changes between t -1 and t. The within sample is thus composed of firms present in the data in both t -1 and t and, if manufacturing firms, also producing the same product in both years. The within sample allows minimising the impact of sample composition effects, including those related to different units of measurement for the products of manufacturing firms. We show below that the within sample accounts for the lion's share of overall firm revenue in both t -1 and t.
Data The core data required to estimate firm-level revenue TFP using standard methods comprise revenue, employment costs, intermediate inputs and capital stock. For these variables we turn to the Office for National Statistics (ONS) Annual Respondents Database X (ARDx). 14 The ARDx is a recently-created dataset for researchers using ONS data via the Virtual Microdata Library and the Secure Data Service. It combines and standardizes data and variables across the period 1998-2017 from two surveys, the Annual Business Survey (ABS) which has been carried out since 2009, and its predecessor, the Annual Business Inquiry (ABI), which was carried out 1998-2008 and used to create the Annual Respondents Database. These are the largest business surveys in the UK and have been used by many UK productivity researchers including Barnett et al.
(2014), Harris and Moffat (2017) and Riley, Bondibene,  and Young (2013). The ABI and ABS are similar in sampling method, structure and questions, and the ARDx was created to provide researchers with a consistent data set across time. The ARDx covers around two-thirds of UK economic activity, comprising most SIC 2007 sections, except parts of sections A (agriculture) and K (finance), and all of O (public administration and defence), T (activities of households) and U (extraterritorial organizations). The sample frame of the ABS is the Inter-Departmental Business Register (IDBR), a register of firms from HM Revenue and Customs data on VAT and PAYE details. The sample is stratified by SIC 2007 activities (at the four-digit level), employment size and country (England & Wales, and Scotland). A sample of 62,000 of the 2.
1 m firms on the IDBR is drawn annually. All firms in the largest employment categories in each cell are selected. Firms in each of the cells including smaller businesses are drawn for two consecutive years only, and then not reselected for at least 2 years afterwards. For the smallest (0-9) employment category, firms are only selected in a single year, and then not again for at least 3 years afterwards to ensure that the compliance burden on firms is proportionate. Since we require lagged values of variables in our estimations, we drop these firms and focus on firms with at least 10 employees. Estimation of quantity TFP and demand for the manufacturing sector requires data on quantities sold and prices, information that is available in the Products of the European Community (Prodcom) dataset.
10. 61: 'Cotton yarn (other than sewing thread)' and 26. 20. 16: 'Input or output units, whether or not containing storage units in the same housing'. The latter example highlights how it can often be necessary to work with eight-digit data rather than the already quite detailed six-digit level, in order to be confident to compare reasonably similar items. The former example highlights instead how a eight-digit product breakdown can be very precise in terms of narrowing down product definitions and so working at this level of disaggregation allows us to take into account rich differences in technology, demand and degree of competition across finely-defined products. Around 20,000 firms a year, representing at least 90% of the value of production in each four-digit industry, are surveyed to construct the Prodcom dataset using the IDBR as the sample frame.