Heterogeneity in sporadic Parkinson's Disease (PD) is a critical problem that drives variable rates of progression and treatment response and complicates clinical trials. Access to large PD datasets that may help in clustering this heterogeneity is restricted by privacy and regulatory constraints. Simulated patients or digital twins may offer a solution. We developed a large language model (LLM)-framework to generate high-fidelity synthetic PD patients from the Parkinson's Progression Markers Initiative (PPMI) dataset based on the open-source Qwen3-8B-Base model. Using a relational, tree-structured representation and domain-specific fine-tuning, the model produces patient-level records with longitudinal clinical, imaging, and biomarker data. Fidelity was assessed through distributional similarity, correlation structure, and neurologist review. Utility was tested by training diagnostic classifiers, reproducing a published pharmacometric disease progression model applied to in silico trials, and by extracting a stringent dopamine-motor impairment relationship at early PD stages. Privacy was evaluated via identical match share, distance-to-closest-record, and membership inference attacks. These findings support the use of a dedicated LLM framework for patient simulation, contributing to the foundations of digital twins for PD in silico trials. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Protocols ### Funding Statement We acknowledge the funding from a research grant of the International Foundation of Artificial Intelligence and Big Data for Human Development, Bologna, Italy. This work was also partially funded by the European Union under grant agreement No. 101218531 (SydAi). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The institutional review board approved the PPMI protocol in all participating sites. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data used in the preparation of this article was obtained on 2024-06-18 from the Parkinson's Progression Markers Initiative (PPMI) database (https://www.ppmi-info.org/access-data-specimens/download-data), RRID:SCR_006431. For up-to-date information on the study, visit www.ppmi-info.org. PPMI data are available to qualified investigators upon registration and approval, according to study-specific data use agreements. The synthetic datasets generated in this work are derived from PPMI data. Code for LLM-based synthetic data generation, fine-tuning, and structured inference is publicly available at https://github.com/aindo-com/ppmi-llm, together with configuration files and full technical documentation of the implementation. No individual-level identifiable patient data are shared. The synthetic dataset is available upon reasonable request.
Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications.
Tolerance to thermal extremes is critical for the geographic distributions of ectotherm species, many of which are probably going to be modified by future climatic changes. To predict species distributions it is important to understand the potential of species to adapt to changing thermal conditions. Here, we tested whether the thermal tolerance traits of a common freeze-tolerant potworm were correlated with climatic conditions and if adaptation to extreme cold constrains the evolutionary potential for high temperature tolerance. Further, we tested if evolution of thermal tolerance traits is associated with costs in other fitness traits (body size and reproduction). Lastly, we tested if slopes of temperature-survival curves (i.e., the sensitivity distribution) are related to tolerance itself. Using 24 populations of the potworm, Enchytraeus albidus Henle (Enchytraeidae), collected from a wide range of climatic conditions, we established a common garden experiment in which we determined high and low temperature tolerance (using survival as endpoint), average reproductive output and adult body size. Heat tolerance was not related to environmental temperatures whereas lower lethal temperature was about 10 °C lower in Arctic populations than in populations from temperate regions. Reproduction was not related to environmental temperature, but was negatively correlated with cold tolerance. One explanation for the trade-off between cold tolerance and reproduction could be that the more cold-hardy populations need to channel energy to large glycogen reserves at the expense of less energy expenditure for reproduction. Adult body size was negatively related to environmental temperature. Finally, the slopes of temperature-survival curves were significantly correlated with critical temperature limits for heat and cold tolerance; i.e., slopes increased with thermal tolerance. Our results suggest that relatively heat-sensitive populations possess genetic variation, leaving room for improved heat tolerance through evolutionary processes, which may alleviate the effects of a warmer future climate in the Arctic. On the other hand, we observed relatively narrow sensitivity distributions (i.e., less variation) in the most heat tolerant populations. Taken together, our results suggest that both cold and heat tolerance can only be selected for (and improved) until a certain limit has been reached.
Zootaxa has been the leading journal on invertebrate systematics especially within Annelida. Our current estimates indicate annelids include approximately 20,200 valid species of polychaetes, oligochaetes, leeches, sipunculans and echiurans. We include herein the impact of Zootaxa on the description of new annelid species in the last two decades. Since 2001, there have been over 1,300 new annelid taxa published in about 630 papers. The majority of these are polychaetes (921 new species and 40 new genera) followed by oligochaetes (308 new species and 10 new genera) and leeches (21 new species). The numerous papers dealing with new polychaete species have provided us a clear picture on which polychaete families have had the most taxonomic effort and which authors and countries have been the most prolific of descriptions of new taxa. An estimated additional 10,000+ species remain to be described in the phylum, thus we urge annelid workers to continue their efforts and aid in training a new generation of taxonomists focused on this ecologically important group.
