dataesr/scientific-paragraphs-categorization huggingface dataset:
The PDBx/mmCIF dictionary was extended with, three new data categories to provide the necessary semantic organisation to represent SIFTS annotations: "_pdbx_sifts_unp_segments", "_pdbx_sifts_xref_db_segments", and "_pdbx_sifts_xref_db". The "_pdbx_sifts_unp_segments" category displays the UniProtKB sequence segments that correspond to the PDB structure. The "_pdbx_sifts_xref_db_segments" category provides information about the cross-references between the PDB structure and other databases, such as Pfam, CATH, and SCOP2. Finally, the "_pdbx_sifts_xref_db" category displays per-residue annotations between the PDB structure, UniProtKB, and other data resources. Additionally, the "_atom_site" category was modified to integrate residue-level cross-reference data to the best mapped UniProtKB sequence.
The updated PDBx/mmCIF dictionary, including all the new and updated data categories and items, is publicly available at https://mmcif. wwpdb. org/dictionaries/mmcif_pdbx_v50. dic/ Index/. Augmenting the core SIFTS process. SIFTS (Structure Integration with Function, Taxonomy and Sequences) is a collaborative resource between the PDBe (Protein Data Bank in Europe) and UniProtKB teams at EMBL-EBI. It is designed to map the protein structures available in PDB to the protein sequences in UniProtKB at the individual residue level. The SIFTS mapping can facilitate transfer annotations from a variety of biological resources including the NCBI taxonomy database, IntEnz, GO, Pfam, InterPro, SCOP, CATH, PubMed, Ensembl, Homologene, and automatic Pfam domain assignments based on HMM profiles. The pipeline is run weekly by PDBe as part of the PDB release process.
The mapping between PDB protein structures and UniProtKB protein sequences is manually curated by PDB and UniProtKB annotators. SIFTS performs automatic sequence alignment and generates a residue-level mapping between aligned protein structures and sequences. The pipeline downloads and parses data from various biological resources, which is then loaded into the SIFTS database (Fig. 1 ). SIFTS database is queried to derive residue-level annotations for all these biological resources. The SIFTS process generates per-entry XML files, summary CSV and TSV files to distribute all the SIFTS annotations. The SIFTS database also powers all the SIFTS related PDBe API  29. To update PDBx/mmCIF files with residue-level annotations from SIFTS resources, a new process was added to the existing SIFTS pipeline.
For a given PDB entry, the new process reads all the relevant data from the SIFTS database and integrates it into the PDBx/mmCIF file. The integration of SIFTS data uses the extended PDBx/ mmCIF dictionary discussed earlier. The new process is implemented in Python and uses gemmi  77  to parse the PDBx/mmCIF file and write the SIFTS annotations in the corresponding categories. The process is executed as part of the PDBe weekly release pipeline, ensuring up-to-date SIFTS data in the PDBx/mmCIF files every Wednesday to coincide with the weekly PDB release. Currently, residue-level SIFTS annotations for UniProtKB, Pfam, SCOP2, and CATH databases are integrated in the PDBx/mmCIF files.
Acknowledgements This paper is dedicated to the fond memory of our dear collaborator and wwPDB member  John Westbrook. John  critically reviewed the new " SIFTS-specific " categories in PDBx/mmCIF data dictionary and provided valuable feedback. We also thank  Ezra Peisach  for his valuable comments while updating the PDBx/mmCIF data dictionary. Both John Westbrook  and  Ezra Peisach  are members of the RCSB Protein Data Bank, a co-founder of the wwPDB along with PDBj and PDBe. We also thank  EMBL  and  EMBL-EBI  for providing the necessary infrastructure to run this process weekly. This work was supported by funding from  EMBL  and funding awarded to PDBe by the  UK Biotechnology and Biological Research Council  ( BB/V004247/1 , PI: Sameer Velankar ) and RCSB PDB by the  NSF  ( DBI-2019297 , PI:  S.
Data availability We expanded the PDBe release pipeline with a process that adds SIFTS annotations to the PDBx/mmCIF files for individual structures in the PDB archive. The scientific community can download these PDBx/mmCIF files from the PDBe entry pages (https://pdbe. org/7dr0) and through direct URLs (https://www. ebi. ac. uk/pdbe/static/ entry/7o9f_updated. cif), using the PDBe download service (https://www. ebi. ac. uk/pdbe/download/api) or from the EMBL-EBI FTP area (https://ftp. ebi. ac. uk/pub/databases/msd/updated_mmcif/).
Code availability To assist users in utilising the updated PDBx/mmCIF files and SIFTS annotations, a Google Colab notebook is available at https://colab. research. google. com/github/PDBe-KB/sifts_data_analysis/blob/main/sifts. ipynb or via GitHub at https://github. com/PDBe-KB/sifts_data_analysis. This notebook provides information on how to parse, extract and filter SIFTS annotations from the updated PDBx/mmCIF files. Additionally, the notebook demonstrates how users can compare various numbering schemes of a given residue across different PDB structures of the same protein.
However, opposite genetic effects were also identified among psychiatric disorders  17  , which a b c d Fig. 1  | Overview of research questions and analyses. a, This study aims to answer three questions (Q1-Q3) about multiple neurobiological aspects of general psychopathology. b, We identified the NP factor in the IMAGEN dataset at ages 14 and 19 on the basis of task-based FC with a CPM (Q1; N = 1,750). c, We characterized the NP factor using multiple neurocognitive behaviors and genetic substrates (Q2). d, We checked the generalizability of the NP factor in multiple developmental periods using different fMRI states (Q3; N = 4,942). AN, anorexia nervosa; BN, bulimia nervosa; AUD, alcohol use disorder ; MDD, major depressive disorder; ADHD, attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder; CD, conduct disorder; ODD, oppositional defiant disorder; GAD, general anxiety disorder; Dep.
, depression; ED, eating disorder; SP, specific phobia; FPN, frontoparietal network; IFG, inferior frontal gyrus; mPFC, medial prefrontal cortex; SAL, salience network; SMF, superior medial frontal network; NP factor score, the connectivity strength of the NP factor; vPCun, ventral precuneus. https://doi. org/10. 1038/s41591-023-02317-4 comorbid mental disorders? (3) Could the NP factor be generalized to other developmental stages and clinical crossdisorder datasets?.
1a ). Second, we conducted longitudinal analyses to identify a sustainable transdiagnostic NP factor that was predictive of both externalizing and internalizing symptoms across adolescence and early adulthood (Fig. 1b  and Extended Data Fig. 1b ). Third, we characterized the NP factor in multiple neurobiological aspects (Fig. 1c ), including its neuroanatomical interpretation (that is, the brain networks involved), neurobehavioral relevance (with the corresponding task performance) and its associations with common environmental and behavioral risk factors. We also investigated candidate biological processes and genetic substrates underlying the crossdisorder NP factor. Finally, we assessed and confirmed the generalizability of the NP factor in other developmental periods and resting-state MRIs (from Adolescent Brain Cognitive Development (ABCD) and Human Connectome Project (HCP) cohorts) and in clinical datasets (from the ADHD-200 Sample (ADHD-200) and the STRATIFY/ESTRA Project (STRATIFY/ESTRA)) (Fig.
32, 95% CI = [0. 28,0. 36], t = 14. 12, P two-tailed = 5. 85 × 10 -43 ; for STRATIFY/ ESTRA at age 23, N = 305, r = 0. 03, 95% CI = [-0. 08,0. 14], t = 0. 52, P two-tailed = 0. 70; r difference = 0. 29, Z = 5. 04, P two-tailed = 4. 65 × 10 -7 ). Finally, we explored whether the NP factor identified during reward processing and inhibitory control could be generalized to predict symptoms on the basis of a highly related NP factor derived from the same FC using resting-state fMRI (N = 1,002, r = 0. 26, 95% CI = [0. 20,0. 31], t = 8. 51, P two-tailed = 2. 22 × 10 -16 ), that is, the most abundant fMRI data that are widely available for most population-based and clinical neuroimaging data, and considered as a nonspecific proxy of task-based FC  35. In the IMAGEN dataset, the NP factor established with resting-state connectivity showed a significant association with externalizing symptoms at age 19 (N = 931, r = 0.
063, 95% CI = [0. 01,∞), t = 1. 91, P one-tailed = 0. 014; Table  1 ). Additionally, for healthy adults from the population-based HCP dataset (average age of 29 years), the resting-state NP factor was significantly associated with externalizing symptoms (N = 1,081, r = 0. 075, 95% CI = [0. 03,∞), t = 2. 47, P one-tailed = 0. 007; Table  1 ). This association was further validated in the clinical ADHD-200 dataset, which showed significantly higher NP factor scores in individuals with ADHD (N = 292) compared with those in the control group (aged 11 years, N = 228, t = 3. 40, Cohen's d = 0. 30, 95% CI = [0. 15,∞), P one-tailed = 7. 25 × 10 -4 ; Table  2 ).
Nevertheless, more rigorous experimental studies are needed to clarify the causal mechanisms underlying this NP factor. Additionally, although we focused on a general neuropsychopathology in this study, factors of other more specific forms of psychopathology (such as externalizing, internalizing and thought disorder psychopatholgoies) should also play essential roles. Therefore, future studies are required to elucidate the dynamic interaction between the general and specific neuropsychopathologies that may further contribute to the development of psychiatric comorbidity. It should also be noted that, although the primary nonclinical, large-scale, population-based datasets used in this study were designed to represent the broader population (that is, no exclusion criterion was set for mental health status) from preadolescence to early adulthood (a critical development period in which the onset of most psychiatric disorders peaks  4, 5  ), the recruitment of these studies may still suffer from certain sampling biases (for instance, the IMAGEN sample was primarily recruited from middleclass schools and might underrepresent participants with the most severe psychiatric symptoms  50  ).
Therefore, despite the large sample sizes in our population-based data (IMAGEN and ABCD), these data may have lower base rates of psychiatric disorders, especially for extreme cases, than in the broader population. Therefore, future studies need to have longitudinal data from the same participants, with sufficient representations of the most severe symptoms, to verify whether the NP factor behaves in a dimensional manner (that is, either quantitatively or qualitatively differentiated between clinical participants and healthy controls) and could be extended to other developmental periods, such as middle and late adulthood. In conclusion, we established a transdiagnostic NP factor that could be generalized to multiple large-scale, population-based and clinical neuroimaging datasets and is persistent from preadolescence to early adulthood.
Image acquisition fMRI data were acquired at eight IMAGEN assessment sites with 3 T MRI scanners from different manufacturers (Siemens, Philips, General Electric, Bruker). The scanning variables were specifically chosen to be compatible with all scanners. The same scanning protocol was used at all sites. In brief, high-resolution T1-weighted 3D structural images were acquired for anatomical localization and coregistration with the functional time series. In addition, blood oxygen level-dependent (BOLD) functional images were acquired with gradient-echo, echo-planar imaging sequence. For all tasks, each volume consisted of 40 slices aligned to the anterior commission-posterior commission line (2. 4-mm slice thickness, 1-mm gap). The echo time was optimized (30 ms, with repetition time (TR) of 2,200 ms) to provide reliable imaging of the subcortical areas.
In the first step, we randomly divided the data into 50 folds, where one fold was left out as the testing dataset while the other 49 folds were used as the training dataset. Next, a vector of behavioral scores (for example, ADHD symptoms) was associated with the edge of the connectome (that is, the FC matrix) across participants from the training dataset, with site and handedness being included as covariates. Then, a default threshold  23  (that is, P < 0. 01 in our study) was applied to retain only edges that were significantly associated (either positively or negatively) with behavioral symptoms in the training dataset. Analyses were also repeated with three additional thresholds (for example, 0. 05, 0. 005 and 0. 001), demonstrating similar predictive performance (Supplementary Table  2 ).
Next, the sum of the weights of positive and negative edges (negative edges will be multiplied by -1 before summing up) was calculated for each individual and entered into a linear regression model to estimate the relationship between the summed edge strength and the observed behavior in the training dataset. In the testing dataset, the summed edge strength of each individual was submitted to the corresponding linear model estimated in the training dataset to generate the predicted behavior score. This process was repeated 50 times, with predicted behavior scores in each testing fold established on the basis of the remaining 49-fold data. Finally, Spearman's correlation was applied to estimate the model performance between predicted and actual behavior scores across all individuals. We repeated the CPM 1,000 times and continued further analyses using the edges selected in over 95% of models to select the most robust edges.
Generalization datasets To investigate whether the NP factor identified with the adolescent IMAGEN dataset using the task-based connectomes could be generalized into other developmental periods and fMRI states, we used multiple, large-scale, population-based datasets (ABCD cohort  33  and the HCP  64  ) and clinical case-control datasets (STRATIFY/ESTRA  34  and ADHD-200  65  ). https://doi. org/10. 1038/s41591-023-02317-4 ABCD cohort. The dataset used for this study was selected from the Annual Curated Data Release (https://data-archive. nimh. nih. gov/abcd) of the ABCD cohort, which recruited 11,875 children between 9 and 11 years of age from 21 sites across the USA  66. MRI data in the ABCD study were collected from different 3 T scanner platforms (Siemens Prisma, General Electric MR750 and Philips Achieva dStream).
To minimize the biases introduced by multiple platforms, we only included MRI data from the most frequent manufacturer, Siemens Prisma; data from this manufacturer comprised 5,968 participants from 13 sites. By examining the similarity of brain activations across these 13 sites, we further selected 2,326 participants with consistent activation patterns from 4 sites. After quality control  67  , 1,966 participants of the MID task and 1,837 participants of the SST were included in further analysis. ABCD has balanced sample sizes for boys and girls (based on self-reported sex) (Table  1 ). To construct the NP factor in the ABCD dataset, with the same positive-positive edges used to establish the NP factor in the IMAGEN cohort, we extracted the corresponding FC of reward anticipation and reward positive feedback from the MID task and FC of the stop success and stop failure from the SST.
The sum of FCs for the MID task and SST was the corresponding NP factor for the ABCD. For psychiatric symptoms, we used the Parent Child Behavior Checklist Scores (abcd_cbcls01) to assess the dimensional psychopathology in children  68. The summed scores of externalizing and internalizing symptoms were used in further analysis. The ABCD Parent Diagnostic Interview for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) provides a diagnostic output for common psychiatric disorders (abcd_ksad01). Diagnosis of ASD was provided from a clinical assessment questionnaire (abcd_screen01). Because the morbidity of SP (21. 5%) with abcd_ksad01 in the ABCD dataset was much higher than that of other pediatric epidemiologic investigations of SP (4. 8%)  69, 70  , we excluded this diagnostic information in the clinical relevance analysis.
HCP. The dataset used for this investigation was selected from the March 2017 public data release from the HCP, WU-Minn Consortium. HCP has balanced sample sizes for men and women (based on self-reported sex; Table  1 ). Our sample included 1,081 participants (aged 22-35 years, mean age 31 years) scanned on a 3 T Siemens connectome-Skyra scanner. More details of participants and collection and preprocessing of data are provided at the HCP website (http:// www. humanconnectome. org/). Externalizing symptoms were measured using the Achenbach Adult Self-Report (ASR) Syndrome Scales 72 (ASR_Computed_Externalizing_Adjusted_T). For all analyses of HCP data, we included site, handedness and sex as covariates. Stratify and ESTRA. STRATIFY and ESTRA recruited participants (ages 19-25) with alcohol use disorder or major depression (STRATIFY), anorexia nervosa or bulimia nervosa (ESTRA), and controls with no mental disorder diagnosis at three sites (Berlin, London and Southampton).
The proportions of men and women (based on self-reported sex) varied across different mental health disorder groups (Table  1 ). Furthermore, the protocol of both studies was harmonized to match the IMAGEN protocol. These datasets collected task-based neuroimaging data of the SST and MID task. After quality control (the same quality control procedures as with the ABCD dataset  67  ), 267 cases and 46 controls of the MID task and 380 cases and 64 controls of the SST were included in further analysis. For all analyses of Stratify and ESTRA data, we included site, handedness and sex as covariates. ADHD-200. ADHD-200 is a grassroots initiative dedicated to accelerating the scientific community's understanding of the neural basis of ADHD (aged 7-21 years). Males are predominant in the case group whereas both sexes (based on self-reported sex) are balanced in the control group (Table  1 ).
Polygenic risk scores To calculate the PRSs of depression, ADHD and intelligence, we used previously published GWASs of ADHD  28  , depression  29  and intelligence  30. The discovery depression GWAS consisted of 135,458 cases and 344,901 controls, the ADHD study consisted of 20,183 cases and 35191 controls and the IQ study included 269,867 individuals. We then used PRSice software (http://prsice. info/) to calculate the corresponding PRS. The clumping process was applied to retain only SNPs with the smallest P value for each linkage disequilibrium block (combined with a sliding window process to exclude any less significant SNPs with an r 2 < 0. 1 in 250-kb windows). PRSs were calculated at P value thresholds between 0 and 0. 5 in increments of 0. 01, and we used the mean PRSs of depression, ADHD and intelligence for subsequent analyses  73.
Acknowledgements This work received support from the following sources: the  National Natural Science Foundation of China  ( T2122005  and  81801773  to T. J. , 82150710554 to G. S. ),  Ministry of Education (MOE) Frontiers Center for Brain Science  (to C. X. ),  National Key R&D Program of China  ( 2022CSJGG1000 ,  2019YFA0709501 ,  2021YFC2501402  and  2018YFC1312900  to T. J. ; 2019YFA0709502 and  2018YFC1312904  to J. F. ), the  Shanghai Pujiang Project  ( 18PJ1400900  to T. J. ),  Guangdong Key Research and Development Project  ( 2018B030335001  to J. F. ), the  European Union -funded  FP6  Integrated Project IMAGEN (reinforcement-related behavior in normal brain function and psychopathology; LSHM-CT-2007-037286 to G. S. ), the  Horizon 2020 -funded  European Research Council   Advanced Grant for STRATIFY  (brain network-based stratification of reinforcement-related disorders; 695313 to G.
org/10. 1038/s41591-023-02317-401EV0711  to G. S. ), the  European Union  (grant  101057429  to G. S. ) and  UK Research and Innovation  (grant  10038599  and  10041392  to S. D. ) funded project environMENTAL,  Forschungsnetz AERIAL  ( 01EE1406A  and  01EE1406B  to G. S. ),  Forschungsnetz IMAC-Mind  ( 01GL1745B  to G. S. ), the  Deutsche Forschungsgemeinschaft  ( SM 80/7-2 ,  SFB 940 ,  TRR 265  and  NE 1383/14-1  to G. S. ), and  NIH -funded  ENIGMA  project ( 5U54EB020403-05  and  1R56AG058854-01  to S. D. ). Further support was provided by grants from the  L'Agence nationale de la recherche (ANR)  ( ANR-12-SAMA-0004  to M. -L. P. M. , and  AAPG2019 -GeBra  and  ANR-18-NEUR00002-01 -ADORe  to J. -L. M. ); the  Eranet Neuron  ( AF12-NEUR0008-01 -WM2NA  to J. -L. M. ); the  Fondation de France  ( 00081242  to J.
Data availability IMAGEN data are available from a dedicated database at https://ima-gen2. cea. fr. STRATIFY/ESTRA data are available from the IMAGEN database at https://imagen2. cea. fr. ABCD data are available from a dedicated database at https://abcdstudy. org/. HCP data are available from a dedicated database at https://www. humanconnectome. org/. ADHD-200 data are available from a dedicated database at http:// fcon_1000. projects. nitrc. org/indi/adhd200. Shen 268 parcellation is available at https://www. nitrc. org/frs/?group_id=51.
There are different strategies that could be used for screening the genomic loci to predict the safe loci for transgene knock-in; i) the traditional gene trapping method which relies on random integration of reporter construct followed by isolating the cells with higher expression  [36, 37]  or whole genome sequencing to predict the reporter integration sites that are supporting the high expression of the reporter  [38] , ii) random integration of the transgene by homologous recombination followed by in-vivo imaging to identify the permissive loci for the generation of stably-expressed reporter animal  [29] , iii) Mining of the genomic  [3, 7, 14, 39, 40] , the epigenomic  [13, 41] , and the transcriptomic  [41]  data based on registered data in bioinformatic databases, iv) Comparative genomics for screening and detecting the similarity between GSH in one species and other species based on homologous sequences  [12, 18, 21, 22].
The alignment was excluded if only one flanking gene around the validated GSH locus was similar to that in the chicken genome (Fig. 1C-a ). If there was no similarity, the region was not considered as a potential GSH (Fig. 1C-c ). The presence of possible coding or non-coding genes in the potential GSH locus was evaluated by NCBI's genome data viewer or UCSC genome browser (https:// genome. ucsc. edu) (Fig. 1D ). Chicken Hi-C data was used to study whether the potential GSH is contained within an individual TADs (Fig. 1E  and Additional file 1). Hi-C data was visualized by Juicebox software (version 1. 11. 08) which is available at the following link: https:// github. com/ aiden lab/ Juice box/ wiki/ Downl oad. Coordinates of defined TAD boundaries are shown in Additional file 2 and coordinates of the annotated genes are shown in Additional file 3.
The coordinate system of the map corresponds to the genome version Gal-Gal5. After visualizing the data by Juicebox, the chromosome containing the genes of interest (DRG1, EIF4ENIF1, (See figure on next page. ) Fig. 1  A schematic pipeline for the prediction of genomic safe harbor loci in the chicken genome. Seven steps were followed to predict the chicken genomic safe harbor loci. Step 1) A few validated GSH loci in the human, murine, and porcine genomes were nominated (A). Step 2) Flanking genes surrounding the validated GSH loci were analyzed in the chicken genome using the genome data viewer of NCBI (B). Step 3) Gene arrangement around the validated GSH loci was compared to the potential GSH locus in the chicken genome (C). Step 4) If one flanking gene was similar, the pairwise alignment was excluded and the process was followed from step 5 (C-a).