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Replication data for: Cortico-Cortical Transfer of Socially Derived Information Gates Emotion Recognition
No full textData and statistical analyses underlying figures and supplemental figures appearing in the related scientific article. Article abstract: Emotion recognition and the resulting responses are important for survival and social functioning. However, how socially derived information is processed for reliable emotion recognition is incompletely understood. Here, we reveal an evolutionarily conserved long-range inhibitory/excitatory brain network mediating these socio-cognitive processes. Anatomical tracing in mice revealed the existence of a subpopulation of somatostatin (SOM) GABAergic neurons projecting from the medial prefrontal cortex (mPFC) to the retrosplenial cortex (RSC). Through optogenetic manipulations and Ca2+ imaging fiber photometry in mice and functional imaging in humans, we demonstrate the specific participation of these long-range SOM projections from the mPFC to the RSC, and an excitatory feedback loop from the RSC to the mPFC, in emotion recognition. Notably, we show that mPFC-to-RSC SOM projections are dysfunctional in mouse models relevant to psychiatric vulnerability and can be targeted to rescue emotion recognition deficits in these mice. Our findings demonstrate a cortico-cortical circuit underlying emotion recognition
Replication Data for: How accurately can we estimate spontaneous body kinematics from video recordings? Effect of movement amplitude on OpenPose accuracy
No full textThese data support the study "How accurately can we estimate body kinematics from video recordings? Effect of movement amplitude on OpenPose accuracy". We analyzed a dataset comprising 46 human participants exhibiting spontaneous movements of varying amplitude. The dataset consists of raw body kinematics
Replication Data for: On the allosteric puzzle and pocket crosstalk through computational means
No full textThe files contain the simulation data of the paper: "On the allosteric puzzle and pocket crosstalk through computational means". In particular:
1) file "trajs_pocketcorrel.tar.gz": once uncompressed, the obtained folder has three sub-folders containing MD simulation data for three different systems, specifically i) the A2A receptor, ii) the androgen receptor (AR), and iii) the epidermal growth factor receptor (EGFR) kinase domain. For each system, the "md_inputs" folder contains structure and topology to conduct the simulations, while the "md_outputs" contains the generated trajectories in xtc format, with no solvent, that we used for our analyses of pocket correlation.
2) file "pocketcorrel_analysis.tar.gz": once uncompressed, it contains the material necessary to conduct pocket correlation analyses on MD trajectory data. The three folders refer to three different systems, and the Jupyter Notebook ("CleanedNotebook_correlation_analyses_EGFR.ipynb") is intended for use with the data in the folders, which contain preprocessed data ("analysis" subfolder) to conduct the analysis through the Notebook and input files ("md_inputs", same files as in trajs_pocketcorrel.tar.gz) to perform the MD simulations. The conda environment with all the packages necessary to run the Jupyter Notebook can be created with "conda env create -f environment.yml".
Note that the Notebook shows the analyses on the EGFR system, but can be straightforwardly adapted to the other two systems as well.
The file readme.txt reports all of the above information; a file README.md is also contained inside the file pocketcorrel_analysis.tar.gz, with a short description of its content (same information as the point 2 above)
Replication Data for: Regularized Bennett and Zwanzig free energy estimators
No full textThe file contains the simulation data of the paper: "Regularized Bennett and Zwanzig free energy estimators." Once the file is uncompressed in the input folder there is a file called "lambda.txt" which contains the set of lambda values and the files "harmonic%.6f.txt" (the float in the file name is the corresponding lambda value) which contains the potential values for each time step. Assuming a 0-based indexing, column 2 is the Uf potential values, column 3 is Ur, column 5 is Ur−Uf. In this simulation Ur is the full force field and Uf is the Debye crystal. The unit is kJ/mol. The folder contains pre-computed results and they can be computed by using the python source in the git repository: https://gitlab.iit.it/SDecherchi/regularizedba
Replication data for: Spontaneous dyadic behavior predicts the emergence of interpersonal neural synchrony
No full textThese data support the study Spontaneous dyadic behavior predicts the emergence of interpersonal neural synchrony. We recorded neural activity (EEG) and human behavior (full-body kinematics, eye movements and facial expressions) while dyads of participants were instructed to look at each other without speaking or making co-verbal gestures. The dataset consist of processed EEG and behavioral data (body kinematics, eye gaze and facial expressions)
Page images for "The Specchieri MarVen Dataset: an Abbreviation-rich Dataset in Venetian Idiom"
No full textThis dataset contains the images of the first 80 folios of the Marigold MS. Classe IV 35. historical manuscript used to create the dataset presented in the paper Ferro, S., Pasquariello, D., Pelillo, M., Traviglia, A. (2023). The Specchieri MarVen Dataset: an Abbreviation-Rich Dataset in Venetian Idiom. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14234. Springer, Cham. https://doi.org/10.1007/978-3-031-43153-1_44
Resulting files from Pocketron analysis for: Molecular Dynamics and Machine Learning Give Insights on the Flexibility/Activity Relationships in Tyrosine Kinome
No full textResulting files from the pocket analysis for each of the 43 simulated Tyrosine Kinases. The MD trajectories were analyzed through the Pocketron algorithm, implemented in BiKi Life Sciences suite. To run the analysis, the default Pocketron values for small and big probes were used, respectively 1.4 and 3.0 Å, together with a trajectory stride of 10 for frame skipping and 4 water molecules as minimum pocket volume. The deposited files can be used to visualize (via VMD) the resulting set of pockets and to plot each pocket volume and accessible surface as a function of simulation time. A reference PDB file for each Tyrosine Kinase is also provided together with the list of the atom numbers forming each pocket. The splitMatrix and mergeMatrix files report the probability of a splitting or merging event between pockets to occurr. The 43 Tyrosine Kinases of the study, in their active or/and inactive form, have been associated to the relative PDB id.
Please, read the README_Tyrosine Kinome Pocketron analysis_20230712.txt file for further information on the files
Replication data for "The Specchieri MarVen Dataset: an Abbreviation-rich Dataset in Venetian Idiom"
No full textDespite the release of numerous datasets for training models in historical handwritten text recognition, there is still a significant need for more diverse and extensive data. This dataset release aims to contribute to bridging this gap. It comprises 159 pages from an Early Modern age volume part of the Venetian 'Marigold' collection. It contains various abbreviations that are key to transcribing for a complete understanding of the content. To accommodate different research needs, the dataset is released in two versions: one with 'expanded' abbreviations and another without abbreviations -- where the abbreviations are removed --, aligning with the choices made for other released datasets. Additionally, the dataset encompasses two distinct writing styles.
For this reason, three separate splits for training and evaluating machine learning models are released: one with a combination of both styles and two individual splits for each style
Resulting Clustering Medoids for: Molecular Dynamics and Machine Learning Give Insights on the Flexibility/Activity Relationships in Tyrosine Kinome
No full text10 clustering-resulting medoids for each of the 43 simulated Tyrosine Kinases. The MD trajectories were clustered through the k-medoids algorithm, implemented in BiKi Life Sciences suite. For cluster generation, the RMSD matrix of the entire segment of the A-loop including the DFG-motif was used. The collected medoids could be well used as an atlas of conformations and pockets for virtual screening and docking campaigns. Each of the 43 Tyrosine Kinases of the study, in its active or/and inactive form, has been associated to its PDB id.
Please, read the README_Tyrosine Kinome clustering medoids_20230712.txt file for further information
Replication data for: Interpersonal synchronization of spontaneously generated body movements
No full textThese data support the study "Interpersonal synchronization of spontaneously generated body movements". We measured spontaneous movements while dyads of participants were asked to simply look at each other. The dataset consists of raw body kinematics