This study aimed to examine the acute health effects of wildfire smoke exposure on active-duty military personnel at Joint Base Lewis-McChord (JBLM).

The integration of artificial intelligence (AI) into clinical workflows is advancing even before full compliance with the European Union Cross-Border eHealth Network (MyHealth@EU) framework is achieved. While AI-based clinical decision support systems are automatically classified as high risk under the European Union's AI Act, cross-border health data exchange must also satisfy MyHealth@EU interoperability requirements. This creates a dual-compliance challenge: vertical safety and ethics controls mandated by the AI Act and horizontal semantic transport requirements enforced through Open National Contact Point (OpenNCP) gateways, many of which are still maturing toward production readiness. This paper provides a practical, phase-oriented tutorial that enables developers and providers to embed AI Act safeguards before approaching MyHealth@EU interoperability tests. The goal is to show how AI-specific metadata can be included in the Health Level Seven International Clinical Document Architecture and Fast Healthcare Interoperability Resources messages without disrupting standard structures, ensuring both compliance and trustworthiness in AI-assisted clinical decisions. We systematically analyzed Regulation (EU) 2024/1689 (AI Act) and the OpenNCP technical specifications, extracting a harmonized set of overlapping obligations. The AI Act provisions on transparency, provenance, and robustness are mapped directly onto MyHealth@EU workflows, identifying the points where outgoing messages must record AI involvement, log provenance, and trigger validation. To operationalize this mapping, we propose a minimal extension set, covering AI contribution status, rationale, risk classification, and Annex IV documentation links, together with a phase-based compliance checklist that aligns AI Act controls with MyHealth@EU conformance steps. A simulated International Patient Summary transmission demonstrates how Clinical Document Architecture/Fast Healthcare Interoperability Resources extensions can annotate AI involvement, how OpenNCP processes such enriched payloads, and how clinicians in another member state view the result with backward compatibility preserved. We expand on security considerations (eg, Open Worldwide Application Security Project generative AI risks such as prompt injection and adversarial inputs), continuous postmarket risk assessment, monitoring, and alignment with MyHealth@EU's incident aggregation system. Limitations reflect the immaturity of current infrastructures and regulations, with real-world validation pending the rollout of key dependencies. AI-enabled clinical software succeeds only when AI Act safeguards and MyHealth@EU interoperability rules are engineered together from day 0. This tutorial provides developers with a forward-looking blueprint that reduces duplication of effort, streamlines conformance testing, and embeds compliance early. While the concept is still in its early phases of practice, it represents a necessary and worthwhile direction for ensuring that future AI-enabled clinical systems can meet both European Union regulatory requirements from day 1., risks such as prompt injection and adversarial inputs), continuous postmarket risk assessment, monitoring, and alignment with MyHealth@EU's incident aggregation system. Limitations reflect the immaturity of current infrastructures and regulations, with real-world validation pending the rollout of key dependencies.

Gestational weight gain (GWG) is a critical determinant of maternal and neonatal health, yet its patterns and consequences in displaced populations remain understudied. This study examined the association between GWG and birth outcomes among Forcibly Displaced Rohingya (FDR) women in Cox's Bazar, Bangladesh. We conducted a longitudinal cohort study from October 2022 to October 2024, enrolling 2888 pregnant women at different stages of pregnancy. Among them, 301 were recruited in the first trimester and followed through the third trimester, with 231 neonatal outcomes recorded within 72 hours of delivery. Overall, 66.8% of women experienced inadequate GWG. Despite the high prevalence of inadequate GWG, mean birth weight (2.79 kg) and mean gestational age at delivery (38.6 weeks) were within favorable ranges. Inadequate GWG was more common in mothers aged 30-39 years ( = 0.061) but significantly less common in underweight mothers ( = 0.012). GWG was positively associated with neonatal birth weight, length, and weight-length ratio (WLR) Z score, but not with gestational age. After adjusting for confounding factors, inadequate GWG showed a significant independent association with lower birth length ( = 0.016). These findings highlight the need for targeted interventions in displaced populations.

High-operating-temperature (HOT) mid-wavelength and long-wavelength infrared photodetectors have emerged as critical enablers for eliminating bulky cryogenic cooling systems, offering transfromative potential in developing compact, energy-efficient infrared technologies with reduced size, weight, power, and cost. Focusing on infrared photodiodes, this review first discusses the fundamental mechanisms limiting performance at elevated operating temperatures. Subsequently, the progress in conventional epitaxial semiconductors, such as HgCdTe, InAsSb, and III-V type-II superlattice is reviewed, highlighting the evolution of device architectures designed to effectively suppress dark currents and approach background-limited performance. The review then surveys recent advancements in emerging material systems for HOT infrared photodiodes, including colloidal quantum dots, 2D materials, and amorphous or polycrystalline thin films. Finally, a comparative analysis of the high-temperature performance of devices from both conventional and emerging material systems is presented to enable benchmarked evaluation, followed by an outlook on future research directions.

Like many rodents, the water vole is able to reach high densities in meadows. During outbreaks, voles cause significant changes in plant communities. Although water voles consume a wide variety of plant species, dandelions have a unique position: they are selected by voles year-round and serve as a key resource during winter. Voles harvest all parts of the dandelion and store the roots in almost monospecific food stores. As dandelions are perennial plants that take years to grow, vole activity can significantly affect dandelion populations. Our aim was to estimate the influence of dandelion density on vole space use, particularly habitat selection during natal dispersal. We tested the hypothesis that voles select dandelion-rich plots for settlements. We also measured the variation in dandelion density due to new colonies' settlements to assess potential feedback effects. We hypothesized that voles decrease dandelion populations. To achieve that, we used a drone to monitor dandelions and voles over 2 years. We monitored 52 quadrats, each half a hectare, three times a year. We analyzed each image using remote sensing to locate voles and dandelions, and then examined the interactions between their locations over time. We found that dandelion-rich plots were more likely to colonize. In plots with low dandelion density, areas denser than the plot average were also more likely to be colonized. We observed a decrease in the number of dandelions after colony settlement. Finally, we found evidence that existing burrows were more likely to be reused by new voles if dandelions were still present. This study demonstrates that dandelion density is a strong criterion in habitat selection for water voles and that vole colonies rapidly deplete this resource after establishment. These findings provide insight into plant-herbivore interactions and offer valuable perspectives for further exploration of the plant hypothesis, particularly with respect to the dynamics of resource availability and its role in cyclic population fluctuations.

The human eye is highly advanced but limited by color blindness and poor adaptation to changing light. Artificial photodetectors attempt to mimic vision but often require complex processing to overcome these limitations. Thus, developing photodetectors that complement human vision is crucial to overcoming these limitations. Here, we report a CuInPS-based photodetector array with tunable photoresponse for in-sensor image processing, directly complementing human vision. Through ionic and electronic tuning, the photodetector shows both positive and negative correlations with light intensity and wavelength. It enhances signal-to-background ratio by 880% and suppresses noise by 1,170 times, allowing effective detection of weak signals under strong illumination. Moreover, taking advantage of the distinct photoresponse to red and green light, the photodetector could improve the contrast between red and green patterns up to 43%, offering potential aid for red-green color blindness. This work presents a vision-enhancing photodetector capable of compensating for human visual deficiencies without external computation.

New technologies from the field of mobile health (mHealth) are increasingly used to improve patient monitoring during rehabilitation. While in recent years, mobile phones, health apps, personal digital assistants, and smartwatches opened up new diagnostic and monitoring opportunities for patients, the development of innovative sensor devices, such as instrumented insoles, has now reached a sufficient level of usability with promising opportunities for clinical practice. According to research on the best method for monitoring recovery after musculoskeletal injury or surgery, the Patient-Reported Outcome Measurement Information System (PROMIS) and wearables such as instrumented insoles are among the most promising newer options. However, it is unknown how a patient's health perception and improvements in instrumented insole-derived gait parameters correlate after surgery for tibial or malleolar fractures.

Deep ultraviolet (DUV) photodetection usually relies on wide-bandgap semiconductors, which however face challenges in material growth and doping processes. In this work, we proposed and validated a photodetection scheme based on tunneling barrier modulation, achieving highly sensitive DUV photodetection. Using a two-dimensional van der Waals heterostructure, the device integrates MoS as the transporting layer for its high carrier mobility and low dark current, few-layered graphene (FLG) as the photon absorption layer, and hexagonal boron nitride (hBN) as the dielectric barrier. The device exhibits an photoresponsivity of 4.4 × 10A·W and specific detectivity of 1.4 × 10 for 250 nm DUV light, with a rejection ratio R/R exceeding 10 for visible light. Unlike conventional photodetectors, the cutoff wavelength is determined by the tunneling barrier rather than the material bandgap. Additionally, this photodetection scheme has been extended to a wide range of materials, utilizing different charge transporting layer (e.g., MoS, ReS), barrier layer (e.g., hBN, AlO), and photon absorption materials (e.g., FLG, PdSe, Au, Pd), showcasing its broad adaptability and potential for extensive application. Furthermore, the device has been successfully employed as a power meter for weak UV radiation (0.1 μW·cm) and for measuring solar UV irradiance with results matching the meteorological agency's weather reports. Overall, this work introduces an effective approach for developing high-performance DUV photodetectors, highlighting significant potential for applications in the optoelectronic market.

Vaccination has played a crucial role in mitigating the spread of COVID-19 and reducing its severe outcomes. While over 90% of Bangladesh's population has received at least one COVID-19 vaccine dose, the comparative effectiveness of homologous versus heterologous booster strategies, along with the complex interplay of factors within the population, remains understudied. This study aimed to compare antibody responses between these booster approaches.

Advances in symmetry-breaking engineering of heterointerfaces for optoelectronic devices have garnered significant attention due to their immense potential in tunable moiré quantum geometry and enabling polarization light detection. Despite several proposed approaches to breaking the symmetry of low-dimensional materials, there remains a lack of universal methods to create materials with prominent polarization detection capabilities. Here, we introduce a reliable strategy for manipulating the symmetry of low-dimensional materials through a programmable ferroelectric-doping patterns technique. This method introduces a spontaneous photocurrent and enables the detection of linearly polarization light in isotropic 2H-MoTe. The 2H-MoTe photodetector exhibits a significant short-circuit photocurrent intensity (J = 29.9 A/cm) and open-circuit voltage V of 0.12 V ( ~ 3 × 10V/cm). Under a specific bias, the polarization ratio transitions from 1 to ∞/-∞, shifting from a positive state (unipolar regime) to a negative state (bipolar regime). These findings underscore the potential of ferroelectric-doping patterns as a promising approach to creating composite materials with artificial bulk photovoltaic effect and achieving high-performance polarization light detection.

Perceptual learning refers to any change in discrimination abilities as a result of practice, a fundamental process that improves the organism's response to the external environment. Visual perceptual learning (vPL) is supposed to rely on functional rearrangements in brain circuity occurring at early stages of sensory processing, with a pivotal role for the primary visual cortex (V1). However, top-down inputs from higher-order visual areas (HVAs) have been suggested to play a key part in vPL, conveying information on attention, expectation and the precise nature of the perceptual task. A direct assessment of the possibility to modulate vPL by manipulating top-down activity in awake subjects is still missing. Here, we used a combination of chemogenetics, behavioral analysis and multichannel electrophysiological assessments to show a critical role in vPL acquisition and retention for neuronal activity in the latero-medial secondary visual cortex (LM), the prime source for top-down feedback projections reentering V1.

A key feature in the establishment of symbiosis between plants and microbes is the maintenance of the balance between the production of the small redox-related molecule, nitric oxide (NO), and its cognate scavenging pathways. During the establishment of symbiosis, a transition from a normoxic to a microoxic environment often takes place, triggering the production of NO from nitrite via a reductive production pathway. Plant hemoglobins [phytoglobins (Phytogbs)] are a central tenant of NO scavenging, with NO homeostasis maintained via the Phytogb-NO cycle. While the first plant hemoglobin (leghemoglobin), associated with the symbiotic relationship between leguminous plants and bacterial Rhizobium species, was discovered in 1939, most other plant hemoglobins, identified only in the 1990s, were considered as non-symbiotic. From recent studies, it is becoming evident that the role of Phytogbs1 in the establishment and maintenance of plant-bacterial and plant-fungal symbiosis is also essential in roots. Consequently, the division of plant hemoglobins into symbiotic and non-symbiotic groups becomes less justified. While the main function of Phytogbs1 is related to the regulation of NO levels, participation of these proteins in the establishment of symbiotic relationships between plants and microorganisms represents another important dimension among the other processes in which these key redox-regulatory proteins play a central role.

Among today's nonvolatile memories, ferroelectric-based capacitors, tunnel junctions and field-effect transistors (FET) are already industrially integrated and/or intensively investigated to improve their performances. Concurrently, because of the tremendous development of artificial intelligence and big-data issues, there is an urgent need to realize high-density crossbar arrays, a prerequisite for the future of memories and emerging computing algorithms. Here, a two-terminal ferroelectric fin diode (FFD) in which a ferroelectric capacitor and a fin-like semiconductor channel are combined to share both top and bottom electrodes is designed. Such a device not only shows both digital and analog memory functionalities but is also robust and universal as it works using two very different ferroelectric materials. When compared to all current nonvolatile memories, it cumulatively demonstrates an endurance up to 10 cycles, an ON/OFF ratio of ~10, a feature size of 30 nm, an operating energy of ~20 fJ and an operation speed of 100 ns. Beyond these superior performances, the simple two-terminal structure and their self-rectifying ratio of ~ 10 permit to consider them as new electronic building blocks for designing passive crossbar arrays which are crucial for the future in-memory computing.

Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff's law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.

Shale is a kind of sedimentary rock with an obvious bedding structure. The effect of the bedding plane on hydraulic fracture initiation, propagation, and complex fracture network formation is remarkable and a major problem in hydraulic fracturing and shale oil and gas development. In this study, a criterion is established to predict the evolution behavior of hydraulic fractures (HF) under different confining pressure differences and intersection angles. This criterion is intended to predict the types of interaction between HFs and bedding planes (BPs): penetrating, slipping, or dilating. The dependence of crossing on the intersection angle and the principal stress difference is quantitatively presented using the criterion. Meanwhile, 20 simulations with principal stress differences of 2, 4, 6, and 8 MPa and intersection angles of 30°, 45°, 60°, 75°, and 90° were simulated using the RFPA2D-Flow code. Simulation results exhibit good agreement with the criterion results for a wide range of angles. The investigation showed that HFs tend to penetrate BPs under high confining pressure differences and intersection angles and open BPs under low confining pressure differences and intersection angles. In addition to the above two forms, HFs slip due to shear. The criterion can provide relevant reference about the formation of complex fracture networks in shale layers.