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Organic crystal active waveguide as an all-angle signal receiver and transmission platform for encrypted visible light communication
A large-scale mosquito larviciding in Tanga Region, Tanzania, reduced mosquito densities to varying degrees across malaria transmission risk strata
Abstract In 2019, the Government of Tanzania endorsed a nationwide scale-up of mosquito larviciding. Prior to full implementation, a pilot project was conducted in the Tanga Region from June 2022 to April 2024. The intervention targeted three councils representing high, moderate, and low malaria epidemiological risk strata. Six rounds of larvicide application were conducted, each lasting eight weeks and scheduled according to local rainfall patterns. All mosquito breeding habitats identified by trained community members were treated using Bacillus thuringiensis israelensis and Bacillus sphaericus . Each intervention council was paired with a control council, and longitudinal entomological monitoring was conducted in 120 villages (60 intervention, 60 control). Larviciding was generally associated with lower densities of late-stage Anopheles larvae across strata, with Incidence Rate Ratios (IRRs) of 0.20 (95% CI: 0.12–0.35) in high-risk, 0.51 (95% CI: 0.26–1.01) in moderate-risk, and 0.40 (95% CI: 0.24–0.68) in low-risk areas. Adult Anopheles gambiae sensu lato densities were also reduced in moderate- and low-risk strata, while no significant reductions were observed in Anopheles funestus populations. These findings suggest that larviciding can reduce mosquito densities in varied ecological settings, though effectiveness may vary by species and transmission context. Optimizing implementation strategies may enhance the entomological impact.
Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska
Abstract Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.
Midbrain extracellular matrix and microglia are associated with cognition in aging mice
Abstract Synapse dysfunction is tightly linked to cognitive changes during aging. Emerging evidence suggests that microglia and the extracellular matrix (ECM) can potently regulate synapse integrity and plasticity. Yet the brain ECM, and its relationship with microglia, synapses, and cognition during aging remains virtually unexplored. In this study we combine ECM-optimized proteomic workflows with histological analyses in aging mice and discover regional differences in ECM composition and aging-induced ECM remodeling across basal ganglia nuclei. Moreover, we combine two distinct behavioral classification strategies with fixed-tissue confocal imaging and proteomic analysis and identify relationships between the hyaluronan- and proteoglycan-rich ECM and cognitive aging phenotypes. Finally, we provide evidence that aging midbrain microglia lose capacity to interact with and regulate the ECM, and that these aging-associated microglial changes are accompanied by local ECM accumulation and worse behavioral performance. Together, these observations indicate that changing microglia-ECM-synapse interactions contribute to cognitive functioning during healthy aging.
Effect of ice stunning versus electronarcosis on European sea bass (Dicentrarchus labrax) muscle structure, ultrastructure and quality traits
Neural correlates of postoperative pain in patients with rotator cuff tear following arthroscopic surgery: a resting-state fMRI study
The Panoptes system uses decoy cyclic nucleotides to defend against phage
Abstract Bacteria combat phage infection using antiphage systems and many systems generate nucleotide-derived second messengers upon infection that activate effector proteins to mediate immunity 1 . Phages respond with counter-defences that deplete these second messengers, leading to an escalating arms race with the host. Here we outline an antiphage system we call Panoptes that indirectly detects phage infection when phage proteins antagonize the nucleotide-derived second-messenger pool. Panoptes is a two-gene operon, optSE , wherein OptS is predicted to synthesize a nucleotide-derived second messenger and OptE is predicted to bind that signal and drive effector-mediated defence. Crystal structures show that OptS is a minimal CRISPR polymerase (mCpol) domain, a version of the polymerase domain found in type III CRISPR systems (Cas10). OptS orthologues from two distinct Panoptes systems generated cyclic dinucleotide products, including 2′,3′-cyclic diadenosine monophosphate (2′,3′-c-di-AMP), which we showed were able to bind the soluble domain of the OptE transmembrane effector. Panoptes potently restricted phage replication, but phages that had loss-of-function mutations in anti-cyclic oligonucleotide-based antiphage signalling system (CBASS) protein 2 (Acb2) escaped defence. These findings were unexpected because Acb2 is a nucleotide ‘sponge’ that antagonizes second-messenger signalling. Our data support the idea that cyclic nucleotide sequestration by Acb2 releases OptE toxicity, thereby initiating inner membrane disruption, leading to phage defence. These data demonstrate a sophisticated immune strategy that bacteria use to guard their second-messenger pool and turn immune evasion against the virus.
Multimodal single cell analyses reveal gene networks of planarian stem cell differentiation
Abstract Cell type identity is controlled by gene regulatory networks (GRNs), where transcription factors (TFs) regulate target genes (TGs) via open chromatin regions (OCRs), often specific to one or multiple cell types. Classic GRN discovery using perturbations is laborious and not easily scalable across the tree of life. Single-cell transcriptomics enables cell type-resolved gene expression analysis, but integrating perturbation data remains difficult. Here, we investigate planarian stem cell differentiation by integrating single-cell transcriptomics and chromatin accessibility data. The integrated analysis identifies gene networks matching known TF interactions and highlights TFs that may drive differentiation across multiple cell types. Our data reveals at least two major cell type supergroups linked by their regulatory logic, including alx3-1 + cells, comprising muscle, neurons and secretory cells, and hnf4 + cells, comprising gut phagocytes, goblet cells and parenchymal cells. We validated our data demonstrating high overlap between predicted targets and experimentally validated differentially regulated genes. Overall, our study integrates TFs, TGs and OCRs to reveal the regulatory logic of planarian stem cell differentiation, showcasing a comprehensive catalogue of GRN computational inferences that will be key to study this process.
Percutaneous extraction using sponge spicules for disease diagnosis and therapeutic drug monitoring
Spatio-temporal patterns and driving mechanisms of ecosystem services in mountainous regions: A multi-scale analysis of the Yanshan-Taihang mountain area
Neural correlates of trial outcome monitoring during long-term learning in primate posterior parietal cortex
Restorativeness and pleasantness shape tranquility in high-density urban residential soundscapes
Connections and causes of inter-model spread in boreal summer precipitation across monsoon regions in AMIP6 simulations
An 8×240 Gbps dense wavelength division multiplexing transmitter with lithium tantalate
Characterization of antibiotic resistance and biofilm formation in clinical Helicobacter pylori isolates from Ningxia, China
Flood Algorithm-Tuned PID-F Controller with a Modified Objective Function for Robust and Noise-resilient Speed Control of Nonlinear SparkIgnition Engines
Neuromorphic detection and cooling of microparticles in arrays
Abstract Micro-objects levitated in a vacuum are an exciting platform for precision sensing due to their low dissipation motion and the potential for control at the quantum level. Arrays of such sensors would offer increased sensitivity, directionality, and in the quantum regime the potential to exploit correlation and entanglement. We use neuromorphic detection via a single event based camera to record the motion of an array of levitated microspheres. We present a scalable method for arbitrary multiparticle tracking and control by implementing real-time feedback to simultaneously cool the motion of three uncoupled objects, a demonstration of neuromorphic sensing for real-time control at the microscale.