dataesr/scientific-paragraphs-categorization huggingface dataset:
Looking at their dynamic properties can bring us precious information on their function: (a) A protein complex with fully folded partners, target 29 from the CAPRI score-set  [119] , tRNA m7G methylation complex from yeast (pdb code 2vdu) with the catalytic unit Trm8 (in cyan) and its partner Trm82 (in magenta); (b) A protein complex comprising disordered regions, p150 unit from the eukaryotic initiation factor 4F (in magenta) folds upon binding the translational initiation factor A4 (in cyan), but its N-terminal tail remains disordered (pdb 1rf8); (c) A protein complex with active interfaces, cryo-EM structure of a microtubule fragment with GDP (in orange) bound at the interface between the tubulin α (in cyan) and β (in magenta) chains. (pdb 3j6f). All graphical representations were made with VMD  [95].
Conclusions and Perspectives The dynasome, i. e. , the ensemble of mobility patterns that can be presented by a protein, has been considered a central element in our understanding of proteins for over a decade, as it was often described as the missing link between structure and function  [167]. This led to the creation of the MoDel (for Molecular Dynamics Extended Library, http://mmb. irbbarcelona. org/MoDEL/, accessed on 30 November 2021) dataset, which comprises over 1700 trajectories, from atomistic MD simulations of soluble monomeric proteins  [168]. Numerous additional databases storing data from biomolecular simulations now exist. They deal with specific protein families such as GPCR (GPCRmd, https://submission. gpcrmd. org/home/, accessed on 30 November 2021)  [169] , or specific systems, such as the BioExcel-CV19 platform (https://bioexcel-cv19.
bsc. es/#/, accessed on 30 November 2021), which provides web access to atomistic-MD trajectories for macromolecules involved in the COVID-19 disease. However, we are still missing similar initiatives for protein-protein assemblies. This is of importance, as such initiatives play a key role for the development of artificial intelligence and machine learning (AI/ML)-based approaches, since they rely on the availability of large datasets. The year 2021 has shown very promising work using AI/ML strategies for the prediction of protein assemblies  [170] , notably when investigating SARS-CoV-2-related proteins  [171, 172] , thus highlighting the need for similar large repositories dedicated to protein interfaces. As high-performance computing facilities now render long simulations increasingly accessible worldwide, it is high time for us to address the fourth dimension of macromolecular assemblies, namely time, and start investigating their dynamic properties in a more systematic fashion, if we want to decipher the protein social network in the cell.
Figure2. Protein interfaces take many shapes. Looking at their dynamic properties can bring us precious information on their function: (a) A protein complex with fully folded partners, target 29 from the CAPRI score-set [119] , tRNA m7G methylation complex from yeast (pdb code 2vdu) with the catalytic unit Trm8 (in cyan) and its partner Trm82 (in magenta); (b) A protein complex comprising disordered regions, p150 unit from the eukaryotic initiation factor 4F (in magenta) folds upon binding the translational initiation factor A4 (in cyan), but its N-terminal tail remains disordered (pdb 1rf8); (c) A protein complex with active interfaces, cryo-EM structure of a microtubule fragment with GDP (in orange) bound at the interface between the tubulin α (in cyan) and β (in magenta) chains. (pdb 3j6f).
Contributions: E. K. , C. P. and S. S. -M. contributed to the conception, the bibliography search, the figures realization, and the manuscript writing. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the "Initiative d'Excellence" program from the French State (Grant "DYNAMO", ANR-11-LABX-0011-01) and the EMBO Installation Grant awarded to E. K. (project # 4421).
Related Link (All Sites Were Accessed on 28 January 2022) GROMACS https://manual. gromacs. org/documentation/2021/reference-manual/analysis. html VMD https://www. ks. uiuc. edu/Research/vmd/ PYTRAJ/CPPTRAJ https://amber-md. github. io/pytraj/latest/index. html MDTraj https://www. mdtraj. org/1. 9. 5/index. html MDAnalysis https://www. mdanalysis. org ProLIF https://github. com/chemosim-lab/ProLIF interfacea https://github. com/JoaoRodrigues/interfacea/tree/master gRINN grinn. readthedocs. io ProDy http://prody. csb. pitt. edu/ MD-TASK https://md-task. readthedocs. io/ Data Availability Statement: Not applicable.
Author Contributions: E. K. , C. P. and S. S. -M. contributed to the conception, the bibliography search, the figures realization, and the manuscript writing. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the "Initiative d'Excellence" program from the French State (Grant "DYNAMO", ANR-11-LABX-0011-01) and the EMBO Installation Grant awarded to E. K. (project # 4421).
Finally, commonly-used tools, such as Neurosynth  [40] , are not expressive enough to represent complex hypotheses of specific functional associations in the LPFC. For example, it is arguably difficult and arduous to query a meta-analytic database on the probability that a topic is associated with a study given activation in one region and the simultaneous absence of activation in any spatially-anterior region (similarly posterior, superior or homologous). The expressivity limitation becomes challenging when performing a comprehensive meta-analysis with tens of topics at a time as well as regions that are consistently coactivated by several tasks. Here, we overcome these challenges using a recently-introduced query language, called Neu-roLang  [42]  to perform a comprehensive coordinate-based meta-analysis on 14,371 articles from the Neurosynth database  [40] , along with a gradient-mapping technique  [26]  to identify the organizing principles of activity in the LPFC.
NeuroLang puts forth first-order probabilistic logic programming as a structured and more expressive formalism to represent neuroscience hypotheses and solve complex queries on large databases  [42]. For instance, we can succinctly express queries of functional specificity in the likes of: "What is the probability that empathy is present given activation in the rostral LPFC and there does not exist any activation reported in caudal or middle LPFC?". NeuroLang also brings the power of probabilistic reasoning to deal with elements of uncertainty in heterogeneous data, such as in peak locations, between-regions coactivation, and the presence of terms, all in a single unifying framework. Most importantly, however, a meta-analysis performed using NeuroLang is highly reproducible, that is, the same queries used by one study can be used by future studies to validate the results as more data becomes available.
By leveraging the expressivity of NeuroLang to perform this meta-analysis, we identify a principal rostrocaudal gradient and a subsidiary dorsoventral gradient that respectively explain the most and second-most variance in meta-analytic connectivity in the LPFC in both hemispheres. Moreover, we find that the principal gradient captures a spectrum of increasing abstraction in patterns of network connectivity and specific topic associations from caudal to rostral LPFC, while supporting a proposed domain-general role for middle LPFC regions. Finally, a gradient-based meta-analysis of inter-hemispheric asymmetries reveals the relative dominance of language and memory in the left LPFC as well as the relative dominance of inhibitory control and error processing in the right LPFC.
LPFC In the first analysis, we infer the extent to which LPFC regions agree in the spatial distribution of meta-analytic connectivity patterns across thousands of studies found in the Neurosynth database. In other words, our goal is to identify the main profiles of variation (i. e. gradients) in whole-brain meta-analytic connectivity within the LPFC  [25, 26]. Towards achieving this goal, we need to reduce the high-dimensionality of voxel-level data to increase interpretability of our findings and alleviate computational burdens. To do this, we project voxel-level data onto 1024 functional regions from the Dictionaries of Functional Modes (DiFuMo) probabilistic atlases covering the entire brain  [43]. The DiFuMo is a set of continuously-valued brain atlases derived from thousands of subjects across 27 studies, including a total of 2192 task-based and resting-state functional magnetic resonance imaging (fMRI) sessions publicly available on OpenNeuro  [44].
Unlike spatially-constrained clusters, a DiFuMo atlas does not ignore the overlap among neuronal populations, allowing voxels to be grouped into multiple functional regions with varying weights. To this end, we write NeuroLang logic program (Program available here) that infers the conditional probability of a brain region to be reported active given activation in a LPFC region, as well as the conditional probability that the brain region is active given no activation in the LPFC region. The program then computes the logarithm with base 10 of the odds ratio (LOR) of these two hypotheses for every LPFC-brain region pairs, creating a N × M meta-analytic connectivity matrix, where N denotes the number of regions in the LPFC and M the number of regions in the entire brain. The LOR captures the amount of evidence in favor of specific coactivation between each LPFC region and brain regions, as compared to the evidence favoring independent activation of each.
After constructing the coactivation matrix, we apply an unsupervised non-linear dimensionality reduction method known as diffusion embedding  [26, 45]  to the resultant meta-analytic connectivity matrix in each hemisphere, separately, using the BrainSpace toolbox  [46]. The resultant low-dimensional embedding identifies the position of each LPFC region along unidimensional axes, known as gradients, each representing a direction of variation in meta-analytic connectivity. The axis that accounts for the greatest amount of variance in meta-analytic connectivity is called the principal gradient, which will be the focus of the next analyses. Technical details on formulating the NeuroLang logic program that infers the meta-analytic connectivity matrix along with details on diffusion embedding are found in the Materials and Methods section.
e. quintile bins) and their overlap with canonical large-scale brain networks (Figure  3 ). For this purpose, we write a NeuroLang logic program (Program available here) that first performs a multilevel kernel density analysis  [47]  using a uniform kernel of 10 mm radius at the study level, and then projects the resulting binary activation maps (1 map per study) onto 1024 functional regions. This yields a mapping between brain regions and each study in the database, wherein each region has a probability of being reported by a study, which depends on the location of the reported voxels (further details are provided in Materials and Methods). However, this is not the case for quintile bins along the principal gradient, which are labels and not continuously valued regions. Therefore, we set the program to consider activation reported in a quintile bin if at least one peak is reported within it or within its near vicinity (< 3mm).
Right Hemisphere Neurosynth Topics A two-headed arrow along the horizontal axis signifies a coactivation among quintile bins in a given range and potentially any region within but not outside the range. Note that the exact order of topics varies between hemispheres, but the general profile of topic distribution is comparable. We conduct this segregation-based meta-analysis using 38 topics expertly-chosen from an original set of 100 topics (version-5 of topic modelling) from Neurosynth  [49]. These topics cover broad cognitive and behavioral domains mainly studied in cognitive neuroscience. We set the NeuroLang logic program (Program available here) to infer the probability that a topic is present given activation in bin a (a ∈  [1, 5]   queries the database on the probability that a topic is present in a study given coactivation of bins 1 and 4 (and potentially any region in between) and there exists no activation in any bin outside the quintile range  [1, 4].
In the event where a = b, the program queries the database on the presence of a topic given activation constrained in only one quintile bin at a time. We illustrate this visually in Figure  4 , for example, the coactivation of bins 1 and 4 (and potentially any bins in between) is represented by the "1 < -> 4" notation on the horizontal axis, and the sole activation of bin 3 is represented by the "3" notation. Concurrently, the program infers the probability of the opposite event by selecting the studies that do not match a criteria imposed by a given segregation query. By computing the LOR of the two opposing hypotheses, we obtain a measure of the evidence in favor of specific associations between each topic and spatially-constrained activation patterns along the principal gradient of the LPFC.
Gradient-based meta-analysis of inter-hemispheric asymmetries reveals lateralized associations with topics of language, memory, inhibitory control, and error processing Our final analysis aims at contrasting the two hemispheres in terms of specific topic associations in a gradient-like fashion. More precisely, we compare homologous quintile bins in both hemispheres in terms of there specific topic associations given unilateral activation. For this purpose, we write a NeuroLang logic program (Program available here) that solves inter-hemispheric segregation queries to infer the probability "that a topic is present in a study 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; "the probability that a Neurosynth topic is present in a study given activation in a left LPFC quintile bin and there exists no reported activation in the entire right LPFC".
Integrating ontologies, such as the Cognitive Atlas  [48] , will arguably improve the ability of automated meta-analyses to differentiate fine-grained cognitive constructs. In fact, NeuroLang is well equipped to integrate ontologies into meta-analyses, and this will be our next step towards improving the precision of meta-analytic queries. A second limitation is that our meta-analysis is based on an automatically-generated coordinatebased dataset. Coordinate-based meta-analytic datasets suffer from information loss due to the relative sparseness of reported results  [63] , with peak activations being sensitive to statistical methods adopted in each study, notably thresholding  [47]. Moreover, with small sample sizes per study, potential "publication bias", or the tendency of authors and journals to only publish positive or "statistically significant" results  [36] , might impact the reliability of the current findings.
Conclusion In conclusion, the present study provides quantitative meta-analytic evidence for organizing gradients in the LPFC of humans. The LPFC appears to be organised along two spatial gradients, rostrocaudal and dorsoventral, that respectively explain the most and second-most variance in meta-analytic connectivity. We also reveal that the dominant gradient captures a spectrum of increasing abstraction in network connectivity and specific structure-function associations, grounding a popular class of hypotheses on comprehensive empirical evidence and supporting recent revised views on the functional properties of different LPFC regions. Importantly, we overcome the limitations of previous large-scale attempts using a novel domain-specific query language, called NeuroLang, to formulate expressive queries on the largest coordinate-based meta-analysis database to date.
Data and Software We use the latest version of the Neurosynth database  [40]  last updated in July 2018 to include 14,371 publications with more than 500,000 activation coordinates covering the whole brain. Each study in the database is represented by a PubMed ID, peak activation coordinates and weighted topic associations. Activation coordinates are either reported in MNI space or are transformed from Talairach space before analysis. To examine the structurefunction associations in the LPFC, we use the set of 100 Neurosynth topic terms (version 5) previously generated by applying latent Dirichlet allocation to the abstracts of articles in the database  [49]. Out of the 100 topics, we include 38 topics that we believe represent coherent cognitive functions, excluding those that correspond to subject populations (e.
g. brain disorders, age, sex), brain anatomy, imaging modalities and analysis techniques. Finally, all analyses and visualizations are implemented in python. In particular, we use the Neu-roLang (https://github. com/NeuroLang/NeuroLang) package to perform all meta-analysis steps and the BrainSpace package (https://github. com/MICA-MNI/BrainSpace) to estimate a low-dimensional embedding of meta-analytic connectivity patterns in the LPFC  [46]. All python notebooks and data files used in this study will be publicly available to be openly accessed and used on https://osf. io/ur7ej/quickfiles.
Representing heterogeneous data in a single framework with NeuroLang The goal behind developing NeuroLang is to create a universal language that reduces the likelihood of miscommunication within the cognitive neuroscience community by enabling databases, hypotheses, and questions to be defined in a formal, shareable and reproducible manner. This is believed to be a critical step towards advancing the field of cognitive neuroscience  [65]. In this study, we represent various data types coming from heterogeneous sources, such as peak coordinates, topic models, anatomical masks, and brain atlases in a single framework. More precisely, these data and the relationships among them can be represented as facts and  structured in rows. Each row is a sequence of k elements representing a piece of data, such as the MNI coordinates of a reported peak in a study, and can be implicitly assigned a probability that quantifies the level of uncertainty in this data.
Fact tables represent explicit information present in the data, while rule tables represent inferred relationships among the different data elements. The goal is to declare these tables as predicates in a probabilistic logic program that solves complex queries on them. For a survey on probabilistic databases and probabilistic programming the reader is referred to  [67]. To concretely showcase how we represent data in NeuroLang, we start with the Neurosynth database. the database includes studies that report peak activations coordinates in standard space (Figure  6A ). In NeuroLang, we represent these peaks in a fact table called PeakReported. This table contains a row (x, y, z, study) for each peak with coordinates (x, y, z) that has been reported active by a study. Also, the studies themselves are represented in a fact table called Study that contains a row for each study containing a single element, (study), representing its PubMed identifier.
Similarly to Neurosynth, we assume each study within the database to be an independent equiprobable sample of neuroscientific knowledge  [40]. This assumption is represented by another fact table we call SelectedStudy, which simply assigns a uniform probability (1/N , N = 14, 370) for each study to be selected in any possible world of events. In other words, this assumption allows the studies to have a similar weight in the meta-analysis  [40, 42]. Further, the spatial uncertainty surrounding the reported location of each peak in a given study can be represented in a rule table, named VoxelReported. In this rule, a multilevel kernel density analysis (MKDA)  [47]  assumes each peak's 10mm neighboring voxels to be equivalently reported  [47]. Then, the VoxelReported table contains a row (x, y, z, study) for each voxel at location (x, y, z) and falls within 10 mm Euclidean distance from a peak reported by a study.
Being based on Datalog  [68] , a fully declarative logic programming language designed to solve queries on large databases, the NeuroLang program that computes VoxelReported is written as follows: The answer states that "a voxel at location (x, y, z) in grey matter is considered active in a study if it is situated within 10 mm radius from a peak reported by study at location (x2, y2, z2)". Here, GreyMatterVoxel is also a fact table representing a grey matter mask in MNI space. This table contains a row (x, y, z) for each brain voxel having at least 25% chance of being found in grey matter. The distance variable is estimated using the built-in function EUCLIDEAN, which computes the distance between two locations in standard space. The Neurosynth database also includes topics that have been derived using latent Dirichlet allocation applied to the abstracts of the articles  [65].
Each study in the database has a loading value on each topic, which can be considered as a proxy to the probability that a topic is present in a study. This weighted topic-study association is represented in a probabilistic fact table named TopicInStudy. This table contains a row (topic, study) for each topic present in a study, and the study has a non-zero loading on the topic. The reason for calling this table probabilistic is that the study-on-topic loading is implicitly embedded as a measure of uncertainty in the presence of a topic in a given study. Anatomical masks and functional atlases can also be represented in NeuroLang. For instance, we represent the LPFC mask described previously in a fact table called LateralPFCVoxel. This table contains a row (x, y, z) for each voxel belonging to the LPFC mask.
Moreover, we represent the 1024 functional regions from DiFuMo in a fact which contains a row (r, x, y, z, w) for each voxel at location (x,y,z) in MNI space having a non-zero weight w on a DiFuMo region r. Similar to case of topic-study association, the voxel-on-region weight can be used as a measure of uncertainty in a voxel belonging to a region. This is achieved by first scaling the weight of every voxel in each region to the maximum weight value in that region. In this sense, the voxel with the maximum loading on a region will have a probability of 1 of belonging to it. This creates a probabilistic fact table RegionVoxel, named this way as it does not contain the weight variable explicitly, but implicitly. This probabilistic table can be used by a NeuroLang program to infer other probabilistic tables.
RegionVolume(r, count(x, y, z)) :-RegionVoxelWeighted(r, x, y, z, w) VolumeOfOverlapWithMask(r, count(x, y, z)) :-RegionVoxelWeighted(r, x, y, z, w) & LateralPFCVoxel(x, y, z) LateralPFCRegion(r) :- RegionVolume(r, v0) & VolumeOfOverlapWithMask(r, v) & (v/v0 > 0. 5) ans(r) :-LateralPFCRegion(r) We start by declaring the predicates to be used in solving the query. These predicates are the RegionVolume and VolumeOfOverlapWithMask. These encode the total volume and the volume of overlap with the LPFC mask of each brain region r, respectively. The LateralPFCRegion is the final answer of this program and states that : "A brain region r belongs to the LPFC if its volume of overlap, v, with an LPFC mask makes up more than 50% of its total volume, v0". Volume variables v and v0 are estimated by the built-in function count(x, y, z), which simply counts the number of voxels in a brain region.