dataesr/scientific-paragraphs-categorization huggingface dataset:
Data overview A total of 469 contrast images from 99 participants and 5 independent cohorts were used for training and testing the pattern to predict drug and food craving (two drug using cohorts, two of their matched controls, and another sample of drug users with no matched controls). All participants viewed images of drugs and food under two instructions conditions: a craving instruction and an instruction to use a cognitive strategy to reduce craving (see Methods). Contrast images were computed for the onset of the visual drug and food cues (see Figure  1a ), separately for each level of craving (1-5 Likert scale) for every participant (see Suppl. Figure  1 ) and were rescaled by the image-wise L2-norm to remove any differences in scale between participants and scanners.
The insula is connected to many regions of the NCS and has been previously associated with craving  53. Lesions in various insular locations have been shown to reduce the urge to use drugs and facilitate smoking cessation  54  , which could reflect the role of the insula in the interoceptive component of drug craving  55, 56. The NCS has positive weights (at uncorrected thresholds) in the mid and posterior insula, in line with these previous reports. However, the anterior insula also displayed negative weights in the NCS (at uncorrected thresholds), revealing a potentially more complex role of different insula subregions in craving. Further, the insula might be more prominent to bodily cues of withdrawal, craving, and negative affect  53  , as well as for nutrient-related reward signals  57  , whereas areas such as amygdala or vmPFC (which are more prominent in the NCS) are related to craving evoked by external cues  53  such as those employed in the present datasets.
Participants The present analysis used the pooled data from N=99 participants (33 female, MAGE=34. 1 years, SDAGE=10. 8) collected across three independent neuroimaging studies (five different participant groups, three scanners, see Figure  1  and Supplementary Table  1 ). Two additional participants in Study 1 were excluded in the original study and for the present analyses due to vomiting and not following task instructions. In Study 2, four additional participants were excluded in the original study and for the present analysis due to unanticipated claustrophobia and non-completion of the task, one due to providing false information during the screening, one due to no responses in some runs of the task, two due to having been scanned in the morning, and one due to excessive movement artefacts.
In Study 3, three participants in the user group were excluded due to not understanding or not completing the task, one due to high anxiety and large movement artefacts, one due to being a past but not current cocaine user, and one control participant due to high cocaine craving. Other details regarding the methods and procedures as well as other results from these data sets, focusing on the effects of regulation on behavior and univariate brain responses, are  59, 60  or will be (Schafer, Potenza, & Kober, unpublished data) reported elsewhere. Analyses reported here were not reported previously, and the three studies have not been previously combined. Across studies, participants were recruited using flyers and ads (in newspapers, online bulleting boards, etc. ) from communities around Yale and Columbia Universities.
After a jittered delay period (around 3 second), participants indicated how much they craved the drug or food at that moment ("How much do you want this?") on a 1-5 Likert scale, on which 1 indicated the lowest ("not at all") and 5 the highest ("very much") level of craving. Trials were separated by jittered intervals that followed an exponential distribution, during which a fixation cross was displayed. Prior work  [70] [71] [72]  including the results from the pooled datasets  59, 60  have confirmed that participants report less craving for food and drugs in the regulation (LATER) compared to the craving (NOW) condition.
fMRI data acquisition and preprocessing Data were collected on three different scanners at Columbia and Yale Universities using different acquisition parameters. Data underwent standard preprocessing in SPM (versions 5, 8, 12) including slice time correction, realignment, motion correction, warping, and smoothing with a 6mm FWHM kernel. No data censoring was used. Differences in acquisition and preprocessing across datasets are in fact helpful in the current context as they ensure that our pooled findings are not dependent on such details  30, 73.
fMRI single trial models For each participant, we first computed a first level general linear model (GLM) using SPM8 and custom scripts (canlab. github. org). These models contained separate regressors for trials in the same condition and rating level (1-5), per run (modeled at 8s duration each). One additional regressor was added to model activity related to ratings (3s) across all trials. Further, 24 movement regressors (estimates for displacement and rotation in three dimensions, their derivatives, squared movement estimates, and derivatives of squared movement estimates) and spike regressors (based on the identification of global outliers, coded as 1 for the outlier time point and zero for all other time points) were added as regressor of no interest to control for motion artifacts. Next, we averaged the resulting beta-images for each participant within each rating level.
It then performs the LASSO algorithm, which reduces the contribution of less important or more unstable components by shrinking their regression weights towards zero. Voxel weights can be reconstructed based on their scores for the different components, thus rendering the resulting classifier interpretable and applicable to new datasets. We used a 10-fold cross-validation procedure to evaluate the predictive accuracy of the classifier. Thus, we divided the data into 10 folds that were stratified by studies. Beta images of any given participant (corresponding to all levels of craving) were always held out in the same fold. In each iteration, the classifier was trained on the remaining data and then tested on the hold out data by calculating predicted level of craving (or "NCS response") as the dot product of the trained NCS and each held out beta-image.
Acknowledgments This work was funded by grants from  NIDA  ( R01 DA043690  to HK) and a  Campus France Marie-Sklodowska-Curie co-fund fellowship  ( PRESTIGE-2018-2-0023  to LK). Matlab code for analyses is available at: https://github. com/canlab. Collection of the datasets included herein was funded by  R01DA022541  (PI:  Kevin Ochsner ), P50AA012870 (PI:  John Krystal ), P20DA027844 (PI:  Marc Potenza ). We thank everyone who supported the collection of these data. We also thank  Nilofar Vafaie  for help with data management and transfer.
Data Availability Data, meta-data, and NCS weight maps are available for non-commercial aims at: https://github. com/canlab/Neuroimaging_Pattern_Masks/tree/master/Multivariate_signat ure_patterns/2022_Koban_NCS_Craving and at: https://doi. org/10. 6084/m9. figshare. 21174256. Code Availability Matlab code for analyses is available at: https://github. com/canlab. Custom code to train and apply the NCS is available at: https://github. com/canlab/Neuroimaging_Pattern_Masks/tree/master/Multivariate_signat.
Study 3 tested the ROC task (displaying cocaine and food cues) in 21 individuals with cocaine use disorder (Study 3a) and 18 matched non-users (Study 3b; see details in methods). (c)scale. A multi-level general linear model (GLM) confirmed a strong relationship between out-of-sample predicted and actual level of craving with a large effect size (! = 0. 38, STE = 0. 04, t(98) = 9. 21, p < 0. 0001, Cohen's d = 0. 93, see Figure3). The strength of the predictive performance varied across datasets, but was significant in all 5 cohorts, with effect sizes (Cohen's d) ranging from 0. 55-1. 48(seeTable2). Statistically controlling for white matter and ventricle signal did not alter these results (i. e. , the relationship between craving ratings and NCS remained significant, β = 0. 35, STE = 0. 05, t(97) = 6.
While our supplemental analyses show that the NCS is distinct from other signatures that predict other types of affect ratings, the discriminant validity of the NCS should be further evaluated in future studies. Another important future translating neuroscientific findings for human clinical use. It is also important for future translational applications of MRI-based neuromarkers, which will inevitably use different scanners, hardware, and processes that evolve over time-thus requiring a focus on large-scale patterns that are generalizable across studies, scanners, groups, different preprocessing protocols, and other factors. d_atlas. This repository includes multiple atlases and other meta-analytic and multivariate maps. Tools for manipulating and analyzing this and other atlases are in the CANlab Core Tools repository: https://github.
Observing phenotyping practices from an international cohort of 1,686 cases revealed heterogeneity of phenotype reporting among clinicians. Heterogeneity limited their exploitation for diagnosis as only 43% of symptom-gene associations in the cohort were available in public databases. We developed a symptom interaction model that summarized 16,600 terms into 390 groups of interacting symptoms and detected 3,222,053 novel symptom-gene associations. By learning phenotypic patterns in genetic diseases, symptom interaction modeling handled heterogeneity in phenotyping, to the extent of covering 98% of our cohort's symptom-gene associations. Using these symptom interactions improved the diagnostic performance in gene prioritization by 42% (median rank 80 to 41) compared to the best algorithms. Symptom interaction modeling will provide new discoveries in precision medicine by standardizing clinical descriptions.
Current algorithms address phenotyping heterogeneity using the ontology structure either to extract additional symptom-gene associations from literature or to evaluate the semantic similarity of symptoms  21. In contrast to these approaches, we used HPO as a dictionary of symptoms and considered relationships between symptoms only through their co-occurrence in genetic diseases found in HPO-structured and text-mined databases. Our algorithm uncovered the missing pieces of medical inductive reasoning in clinical descriptions through symptom similarity modeling and collaborative filtering using NMF methods  12. As such, projection into the symptoms interaction model dimension could provide a path to standardizing clinical descriptions. Moreover, the application of this algorithm is 13 reproducible and interpretable, and these features are fundamental in a medical context  22.
Long-standing aspirations are to be able to answer the question, "Have I seen a case 14. like that before?" among extensive clinical data, and to identify undescribed symptom-gene associations  26. Clinical description standardization using symptom interaction modeling may overcome several clinical bottlenecks in precision medicine. PhenoGenius is open-source, accessible through an interactive graph browser (https://github. com/kyauy/PhenoGenius), and a web app (https://phenogenius. streamlitapp. com/). This work paves the way for a set of tools to help identify new genes in disease, expand their clinical spectrum, and provide an easily interpretable clinical decision support system. If we can successfully deal with fuzzy phenotypic profiles and inductive medical reasoning in rare diseases, clinical data can be used for computational phenotype analysis, to improve the feasibility of precision medicine, and to support the adoption of genomic medicine.
Orphanet, and text-mined databases. NMF-based symptom-gene associations overlapped with 99% of similarity-based associations (1,497,601 of 1,506,469). medRxiv preprint doi: ; https://doi. org/10. 1101/2022. 07. 29. 22278181 this version posted October 31, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4. 0 International license. 8.
Moreover, 24 respondents claimed to participate in asynchronous and remote MBD reviews. This translates the influence of software engineering (e. g. SysML is a UML profile tailored for systems engineering) onto the development systems. For instance, the configuration management of models is relatively similar to the management of software code. Consequently, engineers make extensive use of tools such as Git that have been borrowed from the software development domain. According to our survey, a design review last between 1 and 2 hours and rarely exceed 2 hours, especially for non-experts in MBD. Figure  6  shows that only 10 % of respondents (n=8) always conduct a MBD review. Although all design reviews are not model-based, 40 % (n=24) of both experts and non-experts are used to often share models.
) in change management software (e. g. Jira). Figure  11  shows that the major difficulty encountered by MBD reviewers include the consideration of implicit interdependencies among the models that materialise different viewpoints on the product. In addition, two important difficulties are the necessity to master numerous software for accessing and consolidating the content of models and the loss of information when sharing screenshots of models in a deck of slides. The use Domain Specific Languages and the mushrooming of software are less frequent difficulties. Respondents mentioned further difficulties including interoperability and semantic alignment (i. e. domain specific language), the level of understanding and viewpoints differs among the attendance, people from hardware teams are often reluctant to MBSE/SysML diagrams.
E of ~40,000 isolates from a global dataset (see Methods), revealed these mutations to be exceedingly rare or completely absent from the database (Table  1 ). The most commonly identified SNP was at position 3,690,947 upstream of lpdA (identified in our study in strain MT49), at a frequency of 0. 0015 in this dataset (Table  1 ). One caveat is that genes in repetitive families (PE, PPE, PE-PGRS) were excluded from our analysis due to difficulties accurately resolving these regions with short-read sequencing data. It is possible that variants contributing to enhanced biofilm formation lie undetected in these regions. We observed two instances of convergent adaptation, i. e. the same locus being subject to repeated mutation. Interestingly each pair of convergent mutations appeared in the same sub-lineage.
A large (~120kb) duplication fixed in populations MT31 and MT55, which both belong to sub-lineage L4. 9. The second convergent mutation, an intergenic SNP upstream of NAD(P)H quinone reductase lpdA, within a transcription factor binding site (TFBS) and a non-coding RNA, occurred in MT49 and MT540 that both belong to sub-lineage L4. 4. 1. 1 (Table  1 ). Figure  5 : Trajectories of mutations that changed >=30% over the course of the experiment show just how few mutations were acquired. The mutations which became fixed almost always did so in the first or second sequenced passage, and we identified very few mutations which significantly declined in frequency. Mutation frequency data was calculated with Popoolation2 and plots were made using Lolipop (see Methods). Allele frequency dynamics for all variants that changed >30% over the course of the experiment are shown in Figure  5.
Regulatory pathways involved in biofilm formation Table  2 : Overlap between our genes of interest (GOI) and the transcription factors (TF) that regulate their expression. P-value calculated based on overlap of GOI with genes significantly differentially expressed by TF. Having identified two instances of convergent evolution at the level of individual loci, we investigated further for evidence of convergent evolution at the regulatory pathway level. Fourteen genes of interest (GOI) encompass the coding genes containing or adjacent to variants we identified as rising to fixation during the experiment (Table  1 ). Using the MTB Network Portal v2 database of transcription factor overexpression (TFOE) and ChIP-Seq data, we identified regulatory pathways that contained GOI. In Table  2  we list the transcription factors (TFs) that are predicted to affect expression of multiple GOI.
'Off target' phenotypic changes varied among strains; for example, planktonic growth rates decreased after passaging for MT540 whereas they increased for MT49, MT345, MT31 and MT55. Cell length increased for MT72 and MT55, whereas it decreased for MT345. Experimentally evolved populations generally manifest first-pass, large-effect mutations during initial adaptation  (Buskirk et al. , 2017) ; our results suggest that these mutations in M. tb are broad in their scope and variable in their effects. The latter phenomenon may be due to a large genetic target for biofilm growth, the impacts of hitchhiking mutations and/or genetic background of the strains. The mutations we identified here were either exceedingly rare or absent in our search of a database of almost 40,000 M. tb genomes. This suggests that the loci hit in our experiment are under strong purifying selection in M.
Genome assemblies were performed using an in-house reference-guided assembly pipeline (https://github. com/pepperell-lab/RGAPepPipe). Briefly, raw data was checked for quality with FastQC v0. 11. 8 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc) and trimmed using TrimGalore v0. 6. 4 (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore). Reads were mapped to the H37Rv reference genome using BWA-MEM v0. 7. 12  (Li, 2013) , and Samtools v1. 11  (Li et al. , 2009)  view and sort were used to process SAM and BAM files. Picard v1. 183 (http://broadinstitute. github. io/picard/) was used to remove duplicates and add read information. Reads were locally re-aligned using GATK v3. 5  (DePristo et al. , 2011)  and variants were identified using Pilon v1. 16  (Walker et al.
Identification of fixed SNPs To identify genetic changes occurring after repeated passaging, we used Popoolation2 v1. 201  (Kofler et al. , 2011b)  and breseq v0. 35. 0  (Deatherage and Barrick, 2014). BAM files from RGAPepPipe (see above) were filtered for quality and used with Samtools mpileup  (Li et al. , 2009)  to tabulate mutation frequencies across sequenced timepoints. The mpileup file was converted to a sync file using Popoolation2 and finally converted the sync file to a format suitable for downstream analysis using Python (code available at https://github. com/myoungblom/mtb_ExpEvo). Variants were filtered for quality by minimum coverage of 20X, minimum alternate allele count of 5 and a minimum allele frequency of 5%. We also removed variants in repetitive families of genes (PE, PPE, and PE-PGRS gene families) from analysis.
Structural variant detection To identify potential insertions, deletions, and duplications of interest in our genomic sequence data, we called structural variants using Pilon v1. 16  (Walker et al. , 2014)  as a part of RGAPepPipe. We removed all insertions, deletions, and duplications that were located entirely in regions annotated as repetitive genes (PE, PPE, and PE-PGRS gene families). We also removed variants that were identified in ancestral strains and/or that did not remain fixed in subsequent passages. Additionally, we manually curated potential variants by visually inspecting the alignments in IGV  (Robinson et al. , 2011; Thorvaldsdóttir et al. , 2013)  and removed variants that appeared to be mis-mappings or in regions of poor coverage. After curation of the data, we identified two large duplications that fixed in our datasets (Table  1 ).
Sliding window coverage plots to visualize the duplications in MT31 and MT55 were made using Samtools bedcov  (Li et al. , 2009)  using in-house scripts (code available at https://github. com/myoungblom/mtb_ExpEvo). Relative coverage was calculated by dividing each window coverage by the average coverage across the assembly -as given by BamQC  (Okonechnikov et al. , 2016)  -and plotted in R.
GO term enrichment analysis To identify possible functional enrichments in the genes within the large duplication that evolved in MT31 and MT55 (Table  1 ), we performed a GO term enrichment analysis. GO terms for all M. tb genes were downloaded from Quick GO (https://www. ebi. ac. uk/QuickGO/). We used the R package topGO (https://bioconductor. org/packages/release/bioc/html/topGO. html) to perform an enrichment analysis of the genes present in one or both duplications (a total of 172 genes). We calculated significance using a Fishers exact test and filtering for p-values < 0. 01 (Supplementary Table  2 ). We performed the same enrichment analysis on 100 randomly selected, contiguous sets of 172 genes to test the greater significance of our results (Supplementary Figure  2 ).
Genotype plots To better understand the structure of the population over passaging we created genotypes by clustering variants with similar trajectories throughout the experiment. Output from Popoolation2 as described above, was filtered for based on >= 30% frequency change between the final passage and ancestral populations, as well as filtering out all mutations within 1000bp of each other (we identified that these mutations are often the result of repetitive regions and/or poor mapping). Lolipop (https://github. com/cdeitrick/Lolipop) was used with default parameters to cluster mutations into genotypes and create plots.
Population genetics statistics Population genetics estimates (Tajima's D, pi and theta) were calculated as previously described  (O'Neill et al. , 2015). Briefly, we used Popoolation  (Kofler et al. , 2011a)  to estimate these parameters in sliding windows across the genome. To reduce biases caused by variable coverage we randomly subsampled read data without replacement to uniform coverage of 50X; we repeated this process 9 times to further reduce biases. Genome-wide averages were calculated by averaging all windows across the genome, across all replicate sub-sampled data. Scripts available at https://github. com/myoungblom/mtb_ExpEvo.
Network analysis Genes of interest (GOI) were defined as coding genes containing or just downstream of the fixed mutations we identified (Table  1 ). Network analysis of GOI was performed by accessing the transcription factor overexpression data available in the MTB Network Portal (http://networks. systemsbiology. net/mtb/content/TFOE-Searchable-Data-File). Transcription factors identified as significantly contributing to regulation of more than one of our GOIs were included in Table  2. We also used this same dataset to identify GOIs which are themselves regulators and affect expression of other GOIs (Table  2 ).
Fixed SNPs in global datasets We searched for the presence of our fixed mutations in publicly available sequencing data using a searchable COBS index of bacterial genomes curated from the European Nucleotide Archive  (Blackwell et al. , 2021). We used the Python interface to search the COBS index  (Bingmann et al. , 2019). Our query sequences included 50bp in either direction of the SNP of interest, with a k-mer matching threshold of 1. To determine the frequency of isolates in the COBS index with each SNP of interest, we needed to determine the number of M. tb genomes in the database: we searched the database using the full H37Rv 16S rRNA sequence (accession: NR_102810. 2) which returned 39,941 results -this total number of M. tb isolates was used to calculate frequency of our SNPs of interest (Table  1 ).