Open Research Europe

The queen bee is the central individual responsible for colony establishment, growth, and survival. Reliable confirmation of successful mating, continued queen presence, and normal reproductive performance is essential for effective colony management. We present a queen bee detection system based on an array of Hall-effect sensors and a miniature magnetic tag attached to the queen. The system is designed for continuous operation and real-time monitoring. A prototype was developed, constructed, and evaluated under both laboratory and field conditions. Field experiments conducted in an apiary demonstrated that the system can reliably detect queen bee passages through the hive entrance, enabling the identification of activities associated with mating flights. The results confirm the feasibility of Hall-effect sensing for automated, non-invasive queen bee monitoring and establish magnetic sensing as a promising new measurement modality for precision apiculture.

Malaria remains a severe health problem in endemic regions because people lack adequate diagnostic tools, leading to delayed medical care and elevated death rates. This research introduces a dual-mode artificial intelligence system that uses two complementary models to enhance malaria pre-screening and diagnosis. The patient-centered model uses multivariate logistic regression to analyze biosignals, including heart rate, body temperature, and oxygen saturation, collected through a wearable sensor prototype and a mobile interface for symptom analysis. The system enables patients to begin self-assessment to determine their level of need before scheduling a doctor's appointment. The clinician-centered model represents a customized convolutional neural network that uses annotated microscopy images of red blood cells to achieve 94.84% accuracy, 95.71% precision, 93.87% recall, 94.78% F1 score, and 0.84 Area Under Curve (AUC). The patient model achieved 94.6% accuracy and an AUC of 0.985 using a 70/30 train-test split. These systems work together to create a layered diagnostic system that can operate independently or together to detect malaria at an early stage, especially in areas with limited resources. The findings demonstrate that wearable biosignal data integration with image-based deep learning can produce dependable, scalable, and user-friendly systems for malaria pre-screening. Keywords - malaria diagnosis, artificial intelligence (AI), convolutional neural networks (CNN), wearable biosensors, multivariate logistic regression

Background: Sexual health vending machines have emerged as a promising approach to expanding access to contraception and other sexual health products on college campuses. This study evaluated patterns of use, perceived accessibility, and student attitudes towards the sexual health vending machine at Tufts University. Methods: This mixed-methods evaluation combined an online survey with vending machine refill data. Analyses included descriptive analysis, chi-square tests, and thematic analysis of open-ended responses. Results: The survey was open from October to December 2025 and included 118 respondents. Self-reported use suggests students engage with the vending machine on an as-needed basis. Users were significantly more likely to agree that it increased their likelihood of using a condom, compared with non-users. However, both groups expressed strong support for the vending machine, reporting that it improved access to sexual health resources, offered satisfactory product variety, and should be provided by the university. Although many students appreciated its visibility and role in reducing stigma, concerns about privacy and discretion were the most commonly reported reasons for non-use. Restock data indicated sustained year-round utilization, with expected decreases during vacation periods. Conclusion: Overall, these findings suggest that sexual health vending machines represent a scalable strategy for expanding access to sexual health resources in university settings. Offering a unique combination of immediacy, affordability, and convenience, this low-barrier resource may also promote preventive sexual health behaviors across campus, even among non-users, and support broader efforts to prevent sexually transmitted infections and unintended pregnancy among young adults.

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables the acquisition of diffraction patterns at every probe position in a dense array. For imaging applications this approach offers significant benefits in terms of spatial resolution and contrast enhancement. In this work, we present the development of a synchronous scan generator integrated with SerialEM software to enable automation of complex experimental protocols such as tomography. The proposed hardware functions as an interface between SerialEM, the scan controls of the microscope, a fast annular dark-field detector, and a synchronized trigger for a pixelated detector. Our previous implementation, named SavvyScan, relied on a dedicated computer equipped with a multichannel acquisition and signal-generation cards, as well as a separate microcontroller for synchronization. Here, we report a low-cost implementation based on a Red Pitaya board, utilizing direct programming of its embedded FPGA and Linux server components. We provide detailed instructions for system installation and operation, along with practical guidance for modifying the source code. System performance is validated through oscilloscope measurements and imaging of a replica grating sample. The utility of the approach is further demonstrated by generating a 3D electron tomogram of a cryogenic sample of mitochondria from a tilt series of shadow montage projections.

Small-angle X-ray or neutron scattering (SAXS/SANS) analysis of large unilamellar vesicles (LUVs) is often limited by high-dimensional bilayer models and the lack of dedicated, statistically rigorous workflows. Here, we introduce SAS_MoCa, an open-source Python package that integrates a compositional scattering density profile (SDP) description of lipid bilayers with a separated form factor (SFF) treatment of vesicle size and polydispersity, and couples these highly parameterized models to an adaptive thermodynamic simulated annealing algorithm formulated within a constrained Bayesian framework. SAS_MoCa enables users to incorporate quantitative prior information from, e.g., previous SAXS/SANS studies, dynamic light scattering, NMR, or molecular simulations, and returns full posterior parameter distributions, uncertainties (reported as medians and median absolute deviations) and correlations even from single SAXS curves. Validation on POPC, POPE and DMPC SAXS-only data demonstrates that the method yields reproducible structural parameters with uncertainties comparable to joint SAXS/contrast-variation SANS analyses. The modular architecture of SAS_MoCa facilitates extension to additional lipid systems and future joint SAXS/SANS or SANS-only applications.

Microscopes are essential tools for discoveries on a scale invisible to the unaided human eye. The development of immuno-fluorescence followed by molecular biology techniques and fluorescent fusion proteins have revolutionised the use of optical microscopy in bioscience. The quality of the data produced is dependent upon the sample, its preparation and the instrument used. However, instruments can degrade over time without easily visible changes to the produced images and, in turn, negatively impacts results. By testing instruments and doing comparisons between results over time and between different instruments, problems can be highlighted and corrective action can be taken. Using small fluorescent beads the point spread function (PSF) of the microscope can be recorded and the image resolution measured. Beads were prepared in a concentration matched to the field of view size and dried onto coverslips and mounted on slides. The beads were then imaged as 3D Z-stacks of sufficient size to fully enclose the PSF of the system. This data was uploaded to OMERO and processed using OMERO-metrics, an OMERO plugin developed for this purpose. This paper summarizes the development of workflows and protocols to enable this process, presents the results obtained and demonstrates the detection of significant instrument issues.

Botanical dietary supplements (like wheat, barley, teff, oats, white lupin, pumpkin seed, and chickpeas) may contain trace amounts of toxicants in addition to important micronutrients. Developing and validating a reliable protocol for the simultaneous quantification of Cu, Fe, Zn, Mo, Se, Mn, Pb, Al, Ni, and Cr using a PerkinElmer (NexIONTM2000 model) quadrupole ICP MS (including a He collision and reaction cell when needed) with closed vessel microwave digestion using (HNO3 + H2O2) was the aim of this study.The method was subsequently utilized in a sample survey, and the outcomes were evaluated against WHO/JECFA standards. From five study regions, twenty-seven farm-collected botanical powder samples representing seven species were acquired. To create one composite per species, field subsamples were cleaned, air dried, ground, and blended (nine subsamples per botanical: three grabs from each of three farms). HNO3/H2O2 was used to digest aliquots (0.250-0.500gm) in closed microwave containers. Internal standards, multi-point external calibration, procedural blanks, verified reference materials, matrix spikes, and duplicates were all used in ICP MSs multi-element quantitation. Method LODs/LOQs, accuracy (CRM recoveries), and precision (RSD) were calculated.The technique produced low LODs that were suitable for dietary evaluation (typical LOD ranges: Cu, Fe, Zn, Mn, Ni, Cr (0.001-0.01) mg/kg; Mo, Se, Pb, Al (0.002-0.05) mg/kg. For the majority of analytes, within-run RSDs were less than 5%, while CRM recoveries ranged from 88.9 to 110%. The concentrations of essential elements varied greatly (average mg/kg: Fe (280.7{+/-}25.6); Zn (6.0{+/-}0.541); Cu (2.8{+/-}0.269); Mn (398.3{+/-}23.8); {micro}gm/kg: Se (0.061{+/-}0.006); Mo (1.0 {+/-}0.022). Although some composites approached or exceeded conservative intake thresholds for Pb and Al under high consumption scenarios, toxic elements were generally low (mean mg/kg: Pb (0.062{+/-}0.007); Al(185.2{+/-}18.5); Ni(1.6{+/-}0.163); Cr(1.8{+/-}0.171).For the simultaneous nutritional and contaminant profiling of supplements derived from cereals and those not, the validated ICP- MS workflow with microwave HNO3 and H2O2 digestion is suitable. Accurate labeling and consumer safety can be supported by routine screening and supply chain controls.

Background: Public health initiatives increasingly target multiple overlapping high-risk groups to maximize impact. However, a common challenge in modelling these initiatives is to capture these overlapping risk factors, leading to potential misallocation of resources and biased effectiveness estimates. Using China's tuberculosis (TB) control program as an example, this study explores different possible frameworks to account for population heterogeneity and risk overlap. Methods: We examined four risk allocation frameworks: (i) Direct Summation (DS), a simple additive benchmark; (ii) Probabilistic Union Deduplication (PUD), using inclusion-exclusion principles; (iii) Risk population combination (RPC), modeling interaction effects; and (iv) Agent-Based Framework (ABF), a granular microsimulation. To show how these frameworks could be used in epidemiological modelling, we embedded each within a deterministic transmission model of TB epidemiology in China, to simulate the impact of China's National Tuberculosis Strategic Plan (NTSP). We explored each framework when implemented in both static and dynamic versions. We compared them using methodological principles and indicators of intervention cost (screening volume) and benefits (cases/deaths averted). Results: Under the static version, the detection yield of active cases followed a consistent hierarchy: DS > PUD > RPC {approx} ABF. The DS method systematically overestimated yields by double-counting overlapping populations, while PUD corrected for overlap but ignored interaction. The RPC and ABF methods provided the most granular estimates by incorporating Risk population combinations. Additionally, comparing static versus dynamic versions revealed that for the same multi-risk screening framework, mortality reductions remained stable and incidence reductions varied significantly. Conclusion: This study presents potential screening frameworks for overlapping risk populations. The RPC method offers optimal balance of real-world plausibility and computational efficiency. We propose the dynamic RPC method as the preferred tool for routine analysis where multimorbidity and intersectional risks exist, providing a robust evidence base for optimizing resource allocation in heterogeneous populations.

Wolbachia-mediated incompatible-insect technique (IIT) via wAlbB, wMel or wPip/wAlbA/wAlbB strains are promising approaches for suppressing wildtype Aedes mosquitoes and therefore Aedes-borne diseases. Yet, the effectiveness of this technique under climate change remains uncertain. Here, we evaluate the long-term robustness of male Wolbachia-infected mosquito releases to suppress wildtype Aedes aegypti and Ae. albopictus populations across future climate scenarios across diverse geographical regions. We compiled large publicly available datasets on Aedes abundance across Singapore, China, the European Union and the United States, historical and projected climatic conditions in these regions and conducted experiments to test the thermal stability of cytoplasmic incompatibility in Wolbachia-infected male Aedes aegypti and albopictus. A climatically-driven entomological model was developed and calibrated using a Bayesian approach to model observed Aedes population dynamics and infer area-specific climate-driven variation in mosquito life-history traits. We back-inferred historical mosquito abundance and projected mosquito abundance in future climate change scenarios incorporating experimental and locally inferred entomological parameters and then simulated the counterfactual implementation of IIT in these regions. We find that Aedes populations are projected to increase in most regions across all climate change scenarios from 2050-2100 even under high heat conditions in the absence of interventions. While we found that IIT can suppress wild-type populations effectively across all future scenarios and in high heat conditions, effectiveness was found to depend heavily on mosquito emigration rates, overflooding ratios, release intervals and release strategies Extensive robustness checks confirmed that the model reproduced historical temporal trends, captured the influence of individual parameters on outcome and was sensitive to changes in values of inferred parameters and implement policy. These findings demonstrate that IIT may be a robust vector control tool under future climate conditions.

The plasmodium of Myxogastoria (a group of amoeboid protists) species often crawls around the forest floor to feed while searching for places to form fruiting bodies for reproduction (sporulation). Certain environmental factors that trigger sporulation have been reported; however, other unknown factors are also expected. In this study, we reported field observations of Physarum rigidum and Fuligo septica. Inspired by the field observation, we examined the effects of multiple factors on sporulation in laboratory experiments using Physarum polycephalum. We found that:(1) there was a critical body size below which sporulation did not occur under our experimental conditions and (2) the plasmodium selected its sporulation sites from the available landscape of the experimental arena: dry and low sites for the majority and dry and high sites for the minority. Further analysis revealed that they preferred the edge area at the high site. We discuss the possible ecological importance of the threshold and location preference

Hand eczema has been described as having an increased prevalence in persons with increased frequency of hand washing. This study investigated the differences in the hand microbiome of persons with and without a history of eczema and secondly the sensitivity of these microbes to commercial liquid soap as a potential trigger for eczema flares. The study identified Staphylococcus to be the most populus genus on the hands in both groups, but the distribution of species was different. Additionally, there was no difference in the number of soaps that produced zones of inhibition but there were some differences in the overall sensitivity to the different soaps tested. Overall, it was determined that liquid soap can cause bactericidal effects on some species of the commensal microbiome, but further work is required to determine if this could be the cause of hand eczema.

The circadian system evolved under natural light-dark cycles, while modern humans spend much of their time indoors under electric lighting that differs substantially from daylight in intensity, spectral composition, and temporal structure. How such lighting environments influence circadian system has not been systematically examined in a diurnal animal model under ecologically relevant conditions. In this study, we used the diurnal Nile grass rat (Arvicanthis niloticus) to assess daily locomotor rhythms across four lighting conditions designed to approximate common human exposure scenarios: rectangular daylight (D65-R; [~]5,600 lux), semi-sigmoidal daylight mimicking natural intensity dynamics (D65-S; matched peak intensity with [~]50% lower cumulative energy), fluorescent indoor light (F12; [~]150 lux), and fluorescent light supplemented with a one-hour midday daylight pulse (F12+D65-P). Using a within-subject design (n = 8), male grass rats were housed under each condition for two weeks. D65-R produced the highest daytime activity levels and the strongest day/night activity ratio, consistent with robust circadian entrainment. Despite matching peak intensity, D65-S did not yield comparable circadian outcomes, indicating that cumulative photon exposure, rather than peak intensity alone, contributes to entrainment strength. Notably, the addition of a one-hour midday daylight pulse (D65-P) partially increased circadian amplitude under otherwise fluorescent conditions, with higher periodogram amplitude relative to F12 alone. A separate cohort of males was exposed to D65-R or F12 for six weeks (n = 10/condition) to assess physiological outcomes, including metabolic and reproductive measures. Compared with the D65-R group, F12 group showed higher diabetic rate (10% vs. 40%) and reduced sperm mobility (45{+/-}6 vs. 19{+/-}1 %), consistent with potential downstream correlates of circadian rhythm disruption. Together, these findings demonstrate that lighting conditions characteristic of indoor environments produce weaker circadian organization than daylight-equivalent lighting in a diurnal rodent, which underscore the importance of light quality in shaping circadian rhythms and downstream physiological processes.

Abstract Background: Despite considerable reductions in under-five mortality during the Millennium Development Goal era, progress towards Sustainable Development Goal (SDG) 3.2 remains uneven across Africa. Identifying countries at greatest risk of missing the target is essential for prioritizing interventions and resource allocation. Methods: A Bayesian spatial forecasting ecological study was conducted using 2024 country-level data from 49 African countries obtained from UNICEF. Spatial dependence was assessed using Global Moran's I and Local Indicators of Spatial Association. Bayesian structured additive regression models with Gaussian, Gamma, and Exponential likelihoods were fitted using Integrated Nested Laplace Approximation (INLA) and compared using the Deviance Information Criterion (DIC), Watanabe-Akaike Information Criterion (WAIC), and conditional predictive ordinates. Posterior exceedance probabilities were estimated, an SDG Failure Index (SFI) and a Priority Intervention Index (PII) were developed, and Bayesian posterior predictive simulations were performed to estimate country-specific probabilities of attaining SDG 3.2 by 2030. Results: Significant spatial clustering of under-five mortality was observed with (Moran's I = 0.355, p < 0.001), and hotspots in Benin, Cameroon, and Nigeria. The Gamma model provided the best fit (DIC = 114.92; WAIC = 111.71). Diarrhoea was the only significant predictor (posterior mean=0.030; 95% credible interval: 0.004-0.056). Twenty-three countries (46.9%) were classified as high risk, whereas only five (10.2%) had achieved SDG 3.2. West Africa recorded the highest mean mortality (7.05%) and North Africa the lowest (1.64%). Bayesian projections indicated that only five countries were likely to achieve SDG 3.2 by 2030, while 41 (83.7%) were unlikely to do so. Conclusion: Considerable geographical inequalities in under-five mortality persist across Africa, and most countries remain off-track for achieving SDG 3.2 by 2030. The integration of exceedance probability mapping, the SDG Failure Index, the Priority Intervention Index, and Bayesian probability forecasting provides a practical framework for monitoring progress and prioritizing countries requiring accelerated action towards achieving SDG 3.2.

Chemical imaging is a powerful branch of modern microscopy encumbered by a lack of flexible, high-throughput analysis tools. Bespoke analytical pipelines typically perform ratiometric analysis on two layers in a multispectral image to describe the relative composition of molecules in a sample. This strategy has been implemented across fields, spanning histopathology, cell biology, environmental science, and materials science. The commercialization of chemical imaging microscopes has facilitated the collection of large multispectral datasets, necessitating accessible ways to process them. This paper describes Multispectral Analysis Graphical User Interface (msaGUI), a desktop graphical user interface to analyze individual and batch datasets of multispectral images. Data is loaded as CSV, TSV, or TIFFs and processed through a user-defined sequence of modular image operations that can be flexibly combined, e.g. to reduce spectral crosstalk or background noise. After analysis, data is visualized as exportable images, histograms, and statistics. To yield publication-quality figures, outputted images are fully customizable. Written in Python with open-source libraries, the msaGUI program is packaged into an executable for Windows and Mac for a fully no-code application. Other operating systems are supported via the Python source code. In summary, msaGUI provides a rapid and user-friendly solution for analyzing and visualizing multispectral data.

Scalesia gordilloi is a critically endangered species endemic to San Cristobal Island in the Galapagos archipelago and represents one of the most unique and vulnerable lineages within the adaptive radiation of the genus Scalesia. Despite its evolutionary distinctiveness and conservation importance, no genomic resources have been available for this species. Here, we present the first high-quality reference genome of S. gordilloi, generated using Oxford Nanopore long-read sequencing. Across three PromethION R10.4.1 flow cells, we obtained 80.5 Gb of long reads (~25X coverage), which enabled a highly contiguous 3.61 Gb assembly composed of only 549 contigs and an N50 of 106.6 Mb. BUSCO completeness reached 98.6%, with assembly metrics comparable to other high-quality Asteraceae genomes. Repeat annotation revealed that 76.2% of the genome is composed of interspersed elements, dominated by LTR retrotransposons. Structural annotation resulted in 47,913 high-confidence protein-coding genes, consistent with expectations for large, repetitive Asteraceae genomes. This genome provides a critical foundation for conservation genomics, enabling assessments of genetic diversity, inbreeding, and adaptive potential in the species. It further establishes a framework for comparative genomics across the Scalesia radiation and supports future efforts to protect and restore one of the most threatened plant lineages of the Galapagos Islands.

Antimicrobial resistance (AMR) is a global threat to public health and development. Failure to address it could return society to a pre-antibiotic era with increased morbidity and mortality. Because human behaviour is crucial to AMR management, interventions modifying knowledge, attitudes, and practices are therefore essential. Modifying health-related behaviours presents a significant challenge, yet it is crucial for public health. Engaging populations during periods of shifting perceptions can address this challenge and ensure the sustainability of interventions. Adolescents and young people attending school represent a key demographic. The primary objective of this study is to evaluate student's awareness, perceptions, and behaviours concerning antimicrobial resistance (AMR) and hygiene, while enhancing and empowering children as agents of change within the community. In this study, students from Allied Health Sciences (AHS) across various disciplines were recruited to serve as peer educators for an evidence-informed educational workshop. A pilot delivery of these activities was conducted among a few students in a school before the final delivery was executed in three schools. Schools following a comparable educational board and curriculum were selected for inclusion in the study. A structured questionnaire was employed to assess the effects before and after the intervention. Statistically significant improvements were observed in participant's knowledge, attitudes, and practices (p < 0.001). Additionally, feedback was collected from participants, teachers, and the school nurse attending the session. By triangulating these findings, a notable immediate improvement was observed in students' knowledge, attitudes, and practices. This study provides evidence that employing multimodal teaching led by peer education is a valid and effective method for delivering health messages. It further underscores the mutual benefits for stakeholders (peer educators and peer learners) by offering a two-way learning opportunity. The benefits extend beyond academic and core scientific learning to include increased confidence as effective health educators and future-ready healthcare professionals.

BACKGROUND: Artificial intelligence (AI) medical scribes rely on speech-to-text (STT) models for transcription. Evaluations of STT models in non-English settings remain scarce. We benchmarked ten STT models on medical consultations from Latin American (LatAm) Spanish and assessed whether fine-tuning improves transcription accuracy. METHODS: Ten YouTube videos depicting medical consultations. Human transcriptions were the ground truth. Five open-source models were evaluated: Whisper Large, Whisper Large v3, Whisper Large v3 Turbo, Voxtral Mini 3B, and Canary 1B v2; and so were five close-source models: gpt-4o-transcribe, gpt-4o-mini-transcribe, gemini-2.5-pro, Eleven Labs, and Assembly AI. Whisper Large v3 was fine-tuned. One video was withheld from training. Performance assessed using Word Error Rate (WER), Character Error Rate (CER), BLEU Score, ROUGE-L, BERT Score, and Semantic Similarity on the one withheld video. RESULTS: None of the fine-tuning iterations outperformed the vanilla Whisper Large v3. With the withheld video, Gemini-2.5-pro was the close-source model with the best performance in four of six metrics. In comparison to the close-source models, the fine-tuned model never outperformed the other models (withheld video); conversely, in comparison to the close-source models, the fine-tuned model showed better performance across metrics, for instance: BLEU score (63% vs to 58% for the second-ranking model), BERT (89% vs to 86%), and semantic similarity (89% vs to 83%), CER (19% vs 20%). CONCLUSIONS: Whisper Large v3 and its fine-tuned variant are the best open-source STT models for transcribing medical conversations in LatAm Spanish. These findings provide an evidence base for developing AI medical scribes tailored to Spanish-speaking LatAm.

Light is a fundamental regulator of human physiology and behaviour. Whether prior light exposure shapes subsequent higher-order cognition and mood beyond the period of exposure remains unknown. We tested this in a within-subject, randomised crossover experiment in which 24 healthy young adult males completed a multimodal cognitive battery following 2 x15 min of full-spectrum light (FL; median 1,029 melanopic equivalent daylight illuminance [mEDI]) or standard indoor light (SL; median 234 mEDI), with all testing conducted under identical dim illumination. FL improved Digit-Symbol Substitution Test accuracy and promoted digit-directed gaze reallocation, consistent with more efficient associative encoding. On the Balloon Analogue Risk Task, FL reduced reward-seeking behaviour and suppressed backward-referencing gaze transitions linking current and prior-trial reward information. Mood declined following SL but remained stable after FL. Sustained attention, vigilance, and subjective sleepiness were unaffected. Our findings identify pre-task FL exposure as a selective primer of higher-order cognition and mood, independent of alertness.

Dengue fever risk and burden has increased globally in the past decade, with record-breaking outbreaks driving high case numbers, outbreaks increasing in existing transmission suitable regions, and occurring in new locations. A combination of global change processes, including climate change, have provided the environmental backdrop for introductions and resurgences of mosquito-transmitted dengue virus. Understanding shifts in exposure risk is integral to public health preparedness. This study provides global mapping of the thermal suitability of dengue transmission for CMIP6 climate scenarios, across a range of general circulation models (GCMs), and we created spatially explicit demographic projections of transmission risk using year-matched RCP-SSP frameworks for demographic and emissions scenarios. Globally, poleward shifts in projected distributions of suitability for transmission for both Ae. aegypti and Ae. albopictus suitability are shown in both the near term (2030s) and longer term (2050). Under a 'middle of the road' climate scenario (CMIP6 SSP2-4.5), regions in Africa and Asia are the major areas driving increases in year-round (12 months) population at risk (PAR) through 2050, with an anticipated net gain in 932 million people at risk for Ae. aegypti transmission and 24 million for Ae. albopictus, which includes multiple regions losing areas of year-round suitability as temperatures exceed the higher thermal boundary for transmission. In contrast, the estimated net increase in PAR for one or more months of transmission suitability at a global scale by 2050 is 3.29 billion people for Ae. aegypti transmission and 3.30 billion for Ae. albopictus transmission. This snapshot of a 'middle-of-the-road' combination of climate and demographic driven increases in potential dengue transmission exposure emphasizes the importance of both expanding suitability in new areas, and growing populations in areas approaching and becoming exposed year-round. Globalization, urbanization, and shipping will continue to provide the potential for introductions into newly suitable areas as season lengths increase, sparking outbreaks in unexposed populations. This is compounded and becomes ever more probable as the number of people and places at year-round risk also increases. This project provides all global gridded outputs for onward mapping and reuse, to add to the toolkit to anticipate and prepare for prevention and response to dengue in a changing world.

A major challenge in behavioral neuroscience is the lack of a unified software framework capable of implementing diverse paradigms across species and experimental setups. Researchers currently face a trade-off: they must either spend significant time developing custom, siloed solutions that hinder reproducibility, or incur substantial costs purchasing inflexible, closed systems. Here, we present Neurokraken, an open-source, Python-native platform designed to overcome these limitations. Neurokraken allows writing experiment progression entirely in standard python, while its core architecture automatically sets up a microcontroller for the connected hardware components and enables python side access with millisecond-precision timing and automatic logging. The system prioritizes ease of use and flexibility, enabling advanced series of events and conditions, the usage of python ecosystem code and packages within experiments, and the addition of any arduino-compatible electronic devices for custom experiments. As a result, users can easily create interactive virtual and real environments to engage, monitor, and record subjects. We present Neurokraken's versatility across a wide range of paradigms, for human and non-human primate psychophysics, and complex rodent behavior in both head-fixed and freely moving paradigms. Its modular design allows for rapid hardware reconfiguration, while a fully customizable user interface enables real-time monitoring and interactive experimental control without compromising timing precision. By uniting laboratory-grade precision with an accessible and flexible open-source philosophy, Neurokraken provides a single, powerful solution to design and execute next-generation behavioral experiments. We hope Neurokraken helps accelerate research, improve reproducibility throughout the neuroscience community, and make advanced behavioral experimentation more accessible through its substantial cost-efficiency.