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Rassegna settimanale sulle cure primarie italiane e sul movimento internazionale della Primary Health Care, prodotta con un approccio sperimentale di ragionamento aumentato: visione, temi e regole son…

Rassegna settimanale sulle cure primarie italiane e sul movimento internazionale della Primary Health Care, prodotta con un approccio sperimentale di ragionamento aumentato: visione, temi e regole sono di Angelo Rossi Mori, ricerca e prima stesura sono di Claude (Anthropic). Ogni numero comprende la rassegna completa, con le fonti identificate e un indicatore di affidabilità per ogni affermazione ({T} triangolata, {P} parziale, {L} limitata, {I} inferita), e una sintesi in sette temi con tre affermazioni ciascuno. Non è un documento validato: le notizie vanno verificate sulle fonti indicate prima di essere citate. I file sono in formato Word per restare modificabili. Numero 01: periodo 23 settembre-1 ottobre 2026, 21 pagine e 67 fonti. Commenti e segnalazioni: angelo.rossimori@cnr.it.

This is the readme for the model associated with the paper: Guet-McCreight A, Tripathy S, Sibille E, Hay E. Linking age changes in human cortical microcircuits to impaired brain function and EEG bioma…

This is the readme for the model associated with the paper: Guet-McCreight A, Tripathy S, Sibille E, Hay E. Linking age changes in human cortical microcircuits to impaired brain function and EEG biomarkers UPDATE: Added "Circuit_Simulation_NEURON9" folder for code updated to work in NEURON 9.0.2 and LFPy 2.3.5, including mod files (Modified 2026-09-24 by Valis Sowilo - github.com/ValisSowilo), circuit.py, and circuit_functions.py. Network Simulations: Simulation code associated with the L2/3 circuit used throughout the manuscript is in the /Circuit_Simulation/ directory. Note that this circuit model is adapted from https://doi.org/10.5281/zenodo.5770999. To run simulations, install all of the necessary python modules (see lfpy_env.yml), compile the mod files within the mod folder, and submit the simulations in parallel (e.g., see job_multirun_loop.sh). In job_multirun_loop.sh, the number at the end of the mpiexec command (see below - 1234) controls the random seed used for both the circuit variance (i.e., connection matrix, synapse placement, etc.) and the stimulus variance (i.e. Ornstein Uhlenbeck noise and stimulus presynaptic spike train timing). mpiexec -n 400 python circuit.py 1234 Here are some of the different configurations of parameters in circuit.py that we change to look at different conditions and levels of analysis. EEG simulations: tstop = 25000. rec_LFP = True rec_DIPOLES = True stimulate = 0 Brief stimulus simulations: tstop = 4500. rec_LFP = False rec_DIPOLES = False stimulate = 1 Stronger stimulus simulations: tstop = 4500. rec_LFP = False rec_DIPOLES = False stimulate = 2 Aging Parameters: OLDER_NMDA = 0 or 20 # Alters NMDA conductance (enter age in years relative to 50yrs) OLDER_SynLoss = 0 or 20 # Reduces PN2PN connection probability (enter age in years relative to 50yrs) OLDER_Proportion = 0 or 20 # Alters proportions (interneuron loss; enter age in years relative to 50yrs) Analysis and Dose Prediction: All code used for analysis and plotting of the circuit simulation results is found in the /Analysis_and_Plotting/ directory. Creation of subfolders for plots and analysis results may be necessary to run this code. In addition to spike simulation analysis, this folder also includes analysis of EEG power spectra (requires PSD generation by first running Plot_PSD_SavePSD2NPY_nperseg_80000.py) and oscillatory events (requires oscillatory event analysis generation by first running Plot_PSD_OEvents_thresh4.py).

1) Overview of GDPR from PROTON AG. 2) Chatting with Gemini about possible EU procedures for challenging Israeli non-compliance, first and foremost  - non-compliance with the Israeli Electronic S…

1) Overview of GDPR from PROTON AG. 2) Chatting with Gemini about possible EU procedures for challenging Israeli non-compliance, first and foremost  - non-compliance with the Israeli Electronic Signature Law.

Data associated with the manuscript Tumor-immune trajectory context connects static tissue architecture to clinical outcomes Preprint accessible: https://www.biorxiv.org/content/10.64898/2026.03.…

Data associated with the manuscript Tumor-immune trajectory context connects static tissue architecture to clinical outcomes Preprint accessible: https://www.biorxiv.org/content/10.64898/2026.03.26.714521v2 Includes spatial statistics of ABM simulations, time-delay embeddings, coordinates for TME state landscape

Complementary GitHub repository for the SMART-VERTIGO Dataset deposited on Zenodo (DOI: 10.5281/zenodo.23104512). This release includes repository documentation, citation metadata, and the associated …

Complementary GitHub repository for the SMART-VERTIGO Dataset deposited on Zenodo (DOI: 10.5281/zenodo.23104512). This release includes repository documentation, citation metadata, and the associated video-processing scripts and documentation.

Free optical Earth-observation missions such as Sentinel-2 provide frequent coverage of offshore wind farms, but their 10 m spatial resolution limits the analysis of floating platforms, which often oc…

Free optical Earth-observation missions such as Sentinel-2 provide frequent coverage of offshore wind farms, but their 10 m spatial resolution limits the analysis of floating platforms, which often occupy only a few pixels. This study presents a cluster-conditioned enhancement framework that maps Sentinel-2 RGB imagery to a nominal 0.75 m grid using a fixed WDSR super-resolution model and a genetic algorithm that optimizes interpolation, filtering, contrast, sharpening, and post-processing parameters. GEOSAT-2 imagery at 0.75 m resolution is used to define appearance-based clusters, validate the enhancement strategy under controlled downsampling, and provide structurally similar comparison images for real Sentinel-2 cases. The objective is based on feature recoverability and geometric matchability, measured primarily through geometrically consistent SIFT inliers, supported by perceptual and structural metrics. The method was evaluated using 42 GEOSAT-2 images and 82 real Sentinel-2 images over the WindFloat Atlantic floating offshore wind farm in Portugal. For real Sentinel-2 imagery, the GA-based enhancement increased the mean normalized inlier score from 0.168–0.169 for passive baselines to 0.418, corresponding to an improvement of approximately 2.5 times. Mean DISTS remained close to the passive baselines, while LPIPS increased, reflecting a trade-off in which sharper local structures improve feature recoverability but may depart from smoother perceptual similarity. These results indicate that GA-guided, cluster-conditioned enhancement can improve the geometric consistency of Sentinel-2 platform imagery and support downstream monitoring tasks for floating offshore wind farms.

    CoupledMD is a curated molecular-dynamics dataset of 207 active or active-like G protein-coupled receptor (GPCR)–G-protein complexes, spanning Class A and Class B receptors an…

    CoupledMD is a curated molecular-dynamics dataset of 207 active or active-like G protein-coupled receptor (GPCR)–G-protein complexes, spanning Class A and Class B receptors and the Gi/o, Gs, Gq/11 and G12/13 G-protein families. Each complex was simulated in a homogeneous POPC membrane for 500 ns in three computational repeats (621 production trajectories; 310.5 µs aggregate sampling), using the CHARMM36m/CGenFF force field.                                                                                                                                               This deposit contains the standardized reduced records — one matched retained-component PDB/XTC pair per system per repeat (2,500 frames at 200-ps spacing per trajectory, retaining the protein complex and bound ligands; membrane lipids, mobile ions and solvent removed) — together with a file-level manifest (SHA-256 checksums, atom and frame counts) and the Supplementary Data tables S1–S8 described in the accompanying Data Descriptor.                                                                                                                                                                                                                                                                        The dataset accompanies "CoupledMD: molecular dynamics trajectories of active GPCR–G-protein complexes" (Scientific Data). One repeat per system is also browsable via the CoupledMD portal (https://www.coupledmd.cn) and REST API. The full-system source trajectories (~14 TB) retain the membrane and ions but contain no water molecules; they are held on institutional storage at Shanghai Jiao Tong University School of Medicine with an off-site backup copy and can be requested through the portal's Data Access page (https://www.coupledmd.cn/#/data). Released under CC BY 4.0.  

This repository contains the Python implementation of the numerical model in "Capturing the Enforcer: A Dynamic Principal-Agent-Client Model of Corruption and Institutional Persistence." The model cha…

This repository contains the Python implementation of the numerical model in "Capturing the Enforcer: A Dynamic Principal-Agent-Client Model of Corruption and Institutional Persistence." The model characterizes a discrete-time dynamic principal-agent-client framework in which institutional integrity is an endogenous state variable eroded by corruption and restored by institutional resilience. The code solves the agent's dynamic optimization problem by value-function iteration on a discretized state-control grid, locates stationary institutional equilibria via Brent's method, and classifies their local stability. It reproduces Table 1 (baseline stationary equilibria) and Figures 2–7 of the paper, including the induced transition map, the optimal corruption, bribe, and reelection-probability schedules, simulated institutional trajectories, and the bifurcation analysis for institutional resilience, capture capacity, legal enforcement, social tolerance, and electoral incentives. Running institutional_capture_model.py with no modifications reproduces all reported results under the baseline calibration (Appendix B, Table B1) and writes the corresponding tables and figures to a results/ directory. Requirements: Python 3, NumPy, SciPy, pandas, Matplotlib.

This dataset provides climatological aggregation of aerosol remote sensing data over 22 years (2000-2021) in Maisach, Germany. Level 3 climatological datasets are obtained as aggregated products from …

This dataset provides climatological aggregation of aerosol remote sensing data over 22 years (2000-2021) in Maisach, Germany. Level 3 climatological datasets are obtained as aggregated products from the ACTRIS/EARLINET fully quality controlled (QC) aerosol optical products (i.e. Level 2 products). In particular, this release considers only data fully compliant to the ACTRIS/EARLINET QC procedure v4.0 [https://earlinet.eu/wp-content/uploads/2025/03/EARLINET_QC_v4_0_20250225.pdf]. Two types of data are released: profile values and integrated quantities. For each type of data, four different temporal aggregations are provided: seasonal, annual, normal seasonal, normal monthly.