An increasing number of companies are contributing to open source software (OSS) projects by assigning their employees to advance their business objectives. These paid developers collaborate with volunteer contributors, but the differing motivations of these two groups can sometimes lead to conflicts, which might endanger the OSS project's sustainability. This article presents a multi-method comparative study of paid developers and volunteers in Rust, currently one of the most popular open source programming languages. We compare volunteers and paid developers through contribution behavior, social collaboration, and long-term participation. Then, we solicit volunteers’ perceptions of paid developers and explore the emotions caused when volunteers transition to paid roles. We find that core paid developers tend to contribute more frequently; peripheral paid developers contribute bigger commits and focus more on implementing features; both core and peripheral paid developers collaborate more with volunteers but less intensively than expected; and being paid correlates positively with becoming a long-term contributor. Our study also reveals existing unfamiliarity and prejudices among volunteers towards paid developers, and that volunteer-to-paid transitions can evoke negative community sentiments. This study suggests that the dichotomous view of paid vs. volunteer developers is too simplistic and that further subgroups could be identified. Contributing organizations should become more sensitive to how OSS communities perceive them when they attempt to get involved and make improvements.

In modern cognitive warfare, adversaries deliberately target human cognition, emotion, belief, trust, and decision-making processes, seeking to destabilize democratic societies through disinformation and divergent media campaigns. This article argues that the growing accessibility and vulnerability of the human emotional brain to external influence in a technologically connected world has important repercussions for global defense and security strategy. Recent EU/NATO strategic documents emphasize the need to strengthen resilience, counter hybrid/cognitive threats, and protect societies against disinformation and manipulation. Resilience to cognitive warfare, however, depends on distributed societal capacities for emotional literacy, deliberation, and comprehension—grounded in emotional and cognitive superiority, political culture, robust democratic institutions, and an informed public. Deep security crises and prolonged military conflicts arouse strong negative emotions among affected individuals, groups, and societies. Accordingly, this article proposes Emotionally Based Strategic Communications (EBSC) as a scientifically and ethically grounded approach for applying cognitive neuroscience and artificial intelligence (AI) to design emotionally resonant, legitimate, and strategically aligned communications, aiming to strengthen societal cohesion, counter adversary narratives, and build societal resilience against cognitive threats. EBSC provides tools for identifying and transforming dominant emotional states within target populations through the intentional design of structured multimodal narratives, language, imagery, and symbolic framing, with the aim of positively reconfiguring collective emotions without coercion. EBSC is conceptualized as a Large Language Model (LLM)–based systematic approach to strategic communications, which senses the emotional climate of target populations via social-sentiment analysis algorithms applied to various open digital sources; interprets and contextualizes this emotional climate; conducts design and development of appropriate output messages; delivers these messages across mass media; assesses their impact; and adapts them in a real-time closed loop, under supervision of accountable human decision-makers. The article calls for integrating the proposed closed-loop, LLM-based EBSC approach into the European defense ecosystem and strategic communications policy, aligned with EU frameworks on resilience and counter-disinformation. Such integration may offer a means of bridging cognitive neuroscience and AI into operational, scientifically informed, and emotionally resonant strategic communications that counter adversary narratives, prepare the public to resist disinformation and psychological pressure, and strengthen trust, cohesion, and overall societal resilience among EU/NATO allies.

Physical inactivity is the fourth largest cause of global mortality. Health organizations have requested a tool to objectively measure physical activity because many specific and causal relationships between activity and health outcomes are not clearly understood. Existing activity monitors are either unsuitable for large-scale use or have substantial error. We present OpenMetabolics, a biomechanically-informed activity monitor that employs a smartphone in a pants pocket which measures leg motion to estimate energy expenditure. OpenMetabolics uses a data-driven machine learning model to capture the relationship between underlying leg muscle activity and energy expended during common physical activities. OpenMetabolics estimated energy expenditure with 18% cumulative error across all real-world activities, approximately two times lower than existing tools. We developed a pocket motion artifact correction model to accurately monitor energy expenditure when the smartphone is in a pocket of various types of clothing. A week-long, at-home monitoring study highlighted individual and population-level activity patterns across various timescales. We have made the data, code, and smartphone application open source. This accurate and accessible activity monitor could be deployed for large-scale studies with many patient populations to relate activity to health outcomes, inform health policy, and develop interventions.

BACKGROUNDPublic Health Intelligence (PHI) aims to detect health threats early for a timely and effective response. The PHI team at the Robert Koch Institute (RKI) uses the Epidemic Intelligence from Open Sources (EIOS) system in combination with other sources for detecting signals of international public health threats relevant to Germany. However, while EIOS is increasingly used for PHI worldwide, it is rarely evaluated.AIMWe designed and conducted an attribute-based evaluation to assess EIOS's performance for international PHI in 2023 and to identify areas for improvement.METHODSWe adapted surveillance system attributes and designed attribute-specific data collection methods. We conducted a mixed-method evaluation combining prospective and retrospective operational data collection with feedback from PHI officers.RESULTSDuring 2 weeks in July 2023, the PHI team reported 20 signals: 16 detected using EIOS and four from other sources. Increasing the number of EIOS sources increased timeliness and sensitivity slightly but caused a 35-fold increase in articles to screen (35,546 vs 1,138). The team found EIOS flexible and simple for signal detection but identified challenges in simplicity of signal documenting and reporting and in completeness of EIOS sources screened by the team.CONCLUSIONThe current use of EIOS proved sensitive and timely. However, PHI must balance sensitivity, timeliness and resource requirements. To maintain this balance, we strongly recommend regular evaluations of the use of EIOS for PHI. Our evaluation offers practical guidance for other PHI teams. We recommend integrating EIOS with an event management system to facilitate signal documentation and reporting.

The study aimed to address the principles of circularity and sustainable development in the construction sector in the context of post-war reconstruction. General information on the classification of rubble was collected by analysing analytical reports, laws and regulatory and planning documents. The method of generalisation and systematisation was used to study the existing approaches to forming a comprehensive assessment of the efficiency of rubble processing technologies. Statistical data on the volume of destruction were processed using statistical analysis and data mining. The technical and technological analysis was used to assess the technical feasibility of sorting, crushing and further processing of construction materials. The analysis determined that as of 01.05.2024, a total of 4073 multi-apartment buildings in administrative districts in Kharkiv were destroyed. In the Saltivskyi administrative district, located in the eastern part of Kharkiv, 865 residential units were destroyed, while in the Kyivskyi district in the north-east of the city, 823 were destroyed. The study analysed the destruction in the Kharkiv region, the social and infrastructural burden in the Lviv region, and the specific environmental and transit conditions in the Zakarpattia region. The total direct economic damage caused to Ukraine since the beginning of the full-scale invasion has amounted to more than 155 billion USD. The massive destruction resulted in significant volumes of construction waste, which poses a substantial environmental threat and needs to be addressed immediately. An analysis of information from open sources and the media showed that the application of an integrated approach to the management of rubble based on the principles of the circular economy not only minimised the environmental impact of massive infrastructure destruction but also contributed to the efficient use of secondary resources in the process of restoring the affected areas. The practical significance of the study is determined by the possibility of applying the results obtained to develop effective strategies for managing construction waste in the context of post-war reconstruction, in particular through the introduction of systems for sorting, recycling and reuse of secondary materials at the national and regional levels

ABSTRACT Deepfake detection models achieve high accuracy, yet their interpretability remains underexplored. This study presents a unified evaluation pipeline for post hoc visual explanations, grounded in the Co‐12 attributes of explanation quality and operationalised through a structured framework that assesses Coherence, Composition, Correctness, Completeness, Compactness, Covariate completeness and Continuity. Applying this pipeline to 16 representative explanation methods reveals systematic differences across methodological categories. CAM‐based approaches demonstrate strong spatial coherence and temporal continuity; gradient‐based techniques such as Guided Backprop and LRP yield compact and accurate attributions; and redistribution‐based methods including ExcitationBP and Deep Taylor maintain high consistency across evaluation conditions. In contrast, perturbation‐based approaches such as SHAP and LIME exhibit weaker localisation and reduced temporal stability. By enabling controlled, attribute‐level comparison of explanation methods, the proposed pipeline bridges conceptual interpretability frameworks and empirical analysis, offering practical guidance for the development and deployment of interpretable deepfake detectors in forensic and auditing applications. The source code is publicly available at: https://github.com/junxinchenieee/EAI‐Deepfake‐Detection .

Animal-borne tracking devices (bio-loggers) are established instruments for researching animal behaviour. However, commercial animal trackers are rather standardized and not perfectly adapted to species-specific requirements. Although species-specific solutions are developed, customization effort is high and requires detailed engineering know-how. Furthermore, the development process brings multiple challenges across the process chain and uncertainties for untested species may require iterative refinements in the early design phase. This interdisciplinary study provides a vision of how to enable mass customization of animal trackers through a web-based design platform. The platform involves biologists in engineering processes, makes custom designs accessible to the community, and enhances reusability. Knowledge-based engineering and design automation algorithms are central platform elements, and they automate engineering processes from requirements to the electronic component selection and generation of 3D-printable housing geometries. The animal tracker housings are manufactured using low-cost 3D-printing (additive manufacturing), which offers high flexibility in terms of producible geometry and batch size. Furthermore, this study presents a design automation prototype that implements core functions of the vision to demonstrate the feasibility of automatically generated animal trackers. The software architecture of the design automation prototype and the intermediate algorithm steps are described as open source. To demonstrate the functionality of the design automation prototype, the animal tracker housings of three species are successfully generated and produced. The algorithms take less than 50 seconds to generate the three housings. This demonstrates, how the automation eliminates bottlenecks in the development process and thus greatly reduces efforts for customized animal trackers. The full realisation of the vision can eventually empower biologists to design animal trackers without the involvement of engineers.

Best practices for work from home: A qualitative survey in open source and distributed software development

FEAOF: A transferable framework applied to prediction of hERG-related cardiotoxicity.

Increasing developers’ code accountability perceptions in open source software development

The pathologies pancreatic ductal adenocarcinomas, inflammatory lesions of the pancreas, postpancreatectomy reactive tissue, and recurrent pancreatic ductal adenocarcinomas all express fibroblast activation protein and are hardly distinguishable by static PET using [68Ga]Ga-labeled fibroblast activation protein inhibitors (FAPIs) combined with CT. Dynamic imaging allows full [68Ga]-Ga-FAPI kinetic profile analysis, highlighting differences among these pathologies. Here, we applied a voxel-level digital biopsy approach combined with network analysis and clustering to characterize healthy, nonmalignant pathologic, and malignant pathologic kinetic signatures. Methods: This monocentric, retrospective study included 47 patients (>18 y) with morphologically unclear pancreatic lesions on CT or MRI and supplemental [68Ga]Ga-FAPI-46 PET/CT in a primary (31 patients) or recurrent (16 patients) setting. Lesions were classified according to biopsy results (primary cases) or CT appearance and clinical course (recurrent cases). Digital biopsy samples (300 voxels) of pancreatic lesions and control organs (muscle, fat, kidneys, liver, and blood) were taken and then masked and imported into an open source visual analytics application. Voxel networks were created with multiple digital biopsy samples from a single scan or digital biopsy samples combined from multiple scans, with a minimum Pearson correlation value of 0.7. A k-nearest-neighbor edge reduction was applied before Markov clustering. Datasets were then unmasked for interpretation. Static PET parameters (SUVmax and SUVmean) and time to peak of pancreatic lesions and control tissues were extracted from isotropic volumes and analyzed by a t test (threshold for significance, P = 0.05). Results: This work created 47 individual networks and 2 combined networks. Within individual networks, voxels tended to arrange and cluster within the sampled volume of interest (VOI; left and right kidneys strongly coclustered). Networks typically arranged into healthy controls, elimination organs, and pathologic (malignant and nonmalignant) regions. Pathologies tended to cluster with high purity (>95% from the same VOI), with multiple clusters per VOI, indicating intralesional heterogeneity. Our analysis approach could differentiate between malignant and nonmalignant pathologies in the primary and recurrence settings. This differentiation was driven by slower FAPI clearance within malignant voxels. Conclusion: The kinetics of [68Ga]Ga-FAPI-46 across the different tissues, coupled with this sampling and analysis approach, allowed the separation and identification of healthy, nonmalignant pathologic, and malignant pathologic clusters and kinetic features that may facilitate diagnosis and warrant further investigation.

We introduce a statistical approach for pattern recognition in multivariate spatial transcriptomics data. Our algorithm constructs a projection of the data onto a low-dimensional feature space which is optimal in maximising Moran's I, a measure of spatial dependency. This projection mitigates non-spatial variation and outperforms principal components analysis for pre-processing. Patterns of spatially variable genes are well represented in this feature space, and their projection can be shown to be a denoising operation. Our framework does not require any parameter tuning, and it furthermore gives rise to a calibrated, powerful test of spatial gene expression. The algorithm is implemented in the open source software R and is available at https://github.com/IMSBCompBio/SpaCo.

Abstract Introduction Contrast-enhanced (CE) angiography is currently the gold-standard for the evaluation of the aorta in magnetic resonance imaging (MRI). While modern gadolinium-containing contrast agents (CA) have a favorable safety profile, there is great interest in contrast-free techniques due to financial, environmental and safety-issues. Contrast-free steady-state-free-precession (SSFP) sequences for MR-angiography are available but have not been systematically compared to the gold standard of CE-angiography. Methods Between February 2022 and January 2024, a total of 54 patients with an indication for CA angiography of the thoracic aorta have been prospectively included for an intra-individual comparison of CE-angiography with SSFP-angiography before and after CA application. Reformatting and co-registering into corresponding vessel cross-sections, signal to noise ratio (SNR) of the lumen in the ascending aorta as well as maximum diameter measurements at seven prespecified locations (aortic root, sino-tubular Junction (STJ), ascending aorta, proximal aortic arch, medial aortic arch, distal aortic arch, descending thoracic aorta) were performed using an open source DICOM viewing and processing software (Horos, www.horosproject.org). ANOVA was performed to assess mean differences between techniques for SNR. Two-way-ANOVA with post-hoc tests was performed to assess differences in maximum diameter measurements between techniques at all seven measurement locations. A Bland-Altman-analysis was performed for maximum diameter measurements with CE-angiography and SSFP native at the aortic root. Results Five patients had to be excluded due to either incomplete data acquisition or insufficient image quality in one of the three sequences. In the remaining 49 patients, the mean SNR was lower in the CE-angiography compared to both the SSFP-angiography before and after CA application (14.86 vs. 21.3 vs. 22.25, p<0.001). While two-way-ANOVA showed statistically significant differences in maximum diameter measurements between the three techniques, the absolute difference was clinically not considered (CE-angio - SSFP native: 0.413 mm, P-value = 0.108 CE-angio - SSFP post CA: 0.558 mm P-value = 0.021). The estimated marginal means for segment-wise measurements are shown in Figure 1. Bland-Altman-Analysis showed minimal bias and acceptable limits of agreement between CE-angiography and SSFP native at the aortic root (Figure 2). Discussion Both SSFP acquisitions showed better signal to noise ratio compared to the CE-angiography, which is partly due to the longer acquisition time. Diameter measuerements showed clinically insignificant lower values for both SSFP acquisitions compared to the CE-angiography, which can be explained by the lower resolution of the CE-angiography with partial-volume-effects. Conclusion SSFP-angiography both before and after CA application is a feasible alternative to CE-angiography in MRI.Estimated Marginal Means Bland-Altman-Plot

Abstract In recent years, multiple Type Ia supernovae (SNe Ia) have been observed with “bumps” in their rising light curves shortly after the explosion. Here, we present SN 2021qvo: an SN Ia that exhibits a clear early bump in photometry obtained by the Young Supernova Experiment. Photometric and spectroscopic observations of SN 2021qvo show that it has a broader light curve, higher peak luminosity, shallower Si ii λ 5972 pseudoequivalent width, and lower ejecta velocities than normal SNe Ia, which are all consistent with the characteristics of the 2003fg-like (often called “super-Chandrasekhar”) SN subtype. Including SN 2021qvo, just four known 2003fg-like SNe Ia have sufficient prepeak data to reveal a rising light-curve bump, and all four have bump detections. A host-galaxy analysis reveals that SN 2021qvo exploded in a low-mass galaxy log ( M * / M ⊙ ) = 7.8 3 − 0.24 + 0.17 , also consistent with other members of this class. The current leading early bump 2003fg-like SN Ia progenitor model involves an interaction between the circumstellar material (CSM) and the SN ejecta. We test the validity of this theory by modeling the early bump and subsequent light-curve evolution of SN 2021qvo with the Modular Open Source Fitter for Transients. We find that the bump can be modeled with a best-fit CSM mass in the range M CSM = 3.31−8.51 × 10 −3 M ⊙ . SN 2021qvo adds to the small but growing number of 2003fg-like SNe Ia with rising light-curve bumps; as the number of these SNe Ia with CSM estimates continues to grow, population-level inferences about the CSM distribution will be able to constrain the progenitor scenario for these SNe Ia.

Remote sensing science is expected to produce spatio-temporal indicators to help societies to address major global challenges. In this regard, we have implemented the CHOVE-CHUVA web platform to monitor socio-environmental dynamics in the Brazilian Amazon state of Mato Grosso. Result of a long-term collaboration between research labs, local NGOs, and administrations, this Space for Climate Observatory initiative relies on two major pillars: (1) visualizing and computing spatio-temporal indices derived from Earth Observation data and (2) collecting citizen information as part of collaborative science. A major asset of the platform is to gather, visualize, and process data covering a wide range of themes such as land status, land use, climate, natural vegetation, agriculture, and hydrology. The collaborative information refers to land use types that are still unusual in Mato Grosso, i.e., forest restoration and low-carbon agricultural practices. The implementation of the platform was based on a French open source geospatial data infrastructure named PRODIGE. Prospects for enhancing the platform include integrating new thematic information, making better use of raw Earth Observation data, improving interactions with end-users to better capture their interpretation of socio-environmental dynamics, and improving the platform's efficiency to update data and process large study areas.

Onboarding of ESA missions to the Ground Segment Engineering Framework: an open source MBSE framework for ESA mission and science ground segments

Protein structures are crucial in understanding the function, mechanism and disease-causing variants of proteins within any living cell. A number of experimental techniques are employed by researchers to determine such structures. Through structure inspection in molecular viewers, combined with supporting biochemical and biophysical experiments, scientists are able to identify the function of a protein, its reaction mechanism and effects caused by sequence variation. These detailed findings, supported by experimental results, are documented by being described in the scientific literature and by making the accompanying data open source. However, it has become increasingly difficult for a reader, in particular a non-expert, to access the correct additional information and assess the validity of the conclusions drawn based on experimental results. A reader is often required to resort to a number of different software packages to access the different data types. Here, we present a first-of-its-kind implementation of an artificial intelligence- and text-mining-supported software tool that allows the association of mentions in the text of one or more specific protein residues with their corresponding counterparts in the respective protein structure or structures. Our application allows a researcher to explore a residue of interest in the context of a publication and its respective protein structure, supported by its experimental evidence, in a single view. We describe model implementation, annotation extraction, downstream processing, dissemination and visualization at the IUCr and PDBe. The application presented is primarily aimed at readers of IUCr publications and users visiting the PDBe entry pages. However, we believe that in the future our application will be a valuable tool for reviewers of new submissions to IUCr journals and may even be useful as a curation tool involving the authors of a publication as annotation validators.

The study aim is to analyze the origins and main development stages of military psychophysiology. Methods: Our research is based on archives and open scientific sources with a focus on the main stages in the formation and development of military psychophysiology among military specialists. Results and discussion. Our findings reveal the origins and key stages in the development of military psychophysiology, representing the stage-wise evolution of an integral framework for professional psychological assessment and support among the military since the first Russian school of medical research in military psychophysiology was launched. By identifying specific historical stages in research and education efforts, the study analyzes the experience of leading Russian research teams in such areas as adaptability to military work, mental health of various military categories, as well as professional psychological assessment and medical support in peace and wartime. Conclusion. Our study shows that throughout its history the formation and development of military psychophysiology in Russia is was largely shaped by the challenges of the time. These challenges determined the intensity of research into the most pressing issues of military medicine relevant for a particular historical period. In the 1980s these efforts allowed to establish an integral framework for professional psychological assessment and medical psychological (or psychophysiological) support among the military throughout their professional career.

Despite the considerable expansion of bioimage analysis as a subfield of biomedical sciences, there is an ongoing need for comprehensive image analysis pipelines to address specific biological inquiries. In the tumor microenvironment, the extracellular matrix (ECM) plays a pivotal role in cancer progression, promoting tumor cell adaptability, intratumor heterogeneity, and therapeutic resistance. In neuroblastoma (NB), the ECM glycoprotein vitronectin (VN) has been associated with more aggressive tumors. Three-dimensional (3D) hydrogels are an emerging biomimetic tool with significant potential for studying the role of ECM elements and testing new mechano-drugs such as cilengitide, a potential therapeutic agent to treat high-risk NB due to its ability to inhibit VN activity in cells. To gain a more detailed understanding of 3D-grown NB cell dynamics, we developed DANEELpath, an open-source image analysis toolkit. DANEELpath integrates deep learning techniques, specific segmentation of individual and cluster cells through mathematical morphology pipelines, and extraction of spatial features within whole-slide images. Thanks to its versatility, DANEELpath is adaptable to address different biological questions and has significant potential for use in a variety of research fields and model systems, which could help advance biomedical discovery.

Understanding the relative orientation of protein secondary structure elements is crucial for elucidating their tertiary organization, function, and interactions. Here, we introduce HelixSide, a comprehensive method for systematically quantifying geometrical metrics of helical secondary structures, including widely used measures, such as tilt and kink angles. Additionally, to characterize the orientation of secondary structure motifs relative to each other or to the helical axis, we introduce a new quantity, the side angle. HelixSide computes these metrics at both single-residue and whole-protein levels, revealing local and global conformational features of the system. We demonstrate the method's utility through case studies of two well-characterized single-pass transmembrane proteins: insulin receptor and glycophorin A. These analyses showcase HelixSide's ability to capture tertiary structural characteristics and compare conformational states. HelixSide is open source and available on GitHub at https://github.com/SakariPirnes/helixside. It is applicable to experimental structures, theoretical models, and molecular dynamics trajectories of membrane and soluble proteins, and can be used as a featurization tool for machine learning.