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Potent acridone antimalarial against all three life stages of Plasmodium
Abstract Antimalarial therapeutics ideally should target all three major Plasmodium life cycle stages. Here we present an acridone antimalarial chemotype that is potent against blood, liver, and mosquito stages of malaria parasites. Attributes of lead candidate T111 include potent in vitro activity against cultured parasites, ex vivo activity against clinical isolates, oral single-dose cure in an asexual blood-stage rodent model, inhibition of sexual blood-stage parasites, activity against relapsing parasites in non-human primate liver cells, prevention of parasite development in mosquitoes, and synergy in combination with tafenoquine against blood- and liver-stage parasites. Analysis of parasites selected for resistance to T111 suggests inhibition of the mitochondrial electron transport chain, with a mechanism distinct from that of other antimalarials in use or under development. The safety profile of T111, including toxicology evaluations in rats, absence of hemolytic toxicity, and low genotoxicity and cardiotoxicity potential, demonstrates a favorable therapeutic index. Overall, T111 emerges as a promising candidate for treatment and prevention of malaria, with potential for single-dose cure of bloodstream infections, radical cure of liver infections, and interruption of transmission to mosquitoes.
Correction for Lazzaroni et al., Wolves respond differently to human cues as they expand into urban landscapes
Boosting underwater image quality: a deep learning approach to denoising and enhancement
Abstract Underwater image restoration is significant for various applications such as ecological evaluation, exploration, searching and rescue operations, and autonomous vehicle navigation. In underwater environments, spatial images are frequently degraded as a result of light scattering, absorption, sensor noise, and reduced contrast. This research proposes a whole framework with deep learning that simultaneously performs restoration and enhancement. At a single stage, solving the problems of underwater image degradation in a holistic approach. The core of the proposed approach in this study is a Denoising Convolutional Neural Network (DnCNN) architecture. Where the suppression of noise takes place with extreme focus on significant detail by an advanced non-local attention mechanism. For the further natural color restoration, multi-color space transformations RGB, LAB, and HSV. Which come into play for enhancing the contrast adjustment and color correction of contrast and a correction of colors enabling effective correction of deep-sea views. The framework takes advantage of both synthetically modified images and actual underwater images for model training which offers enhanced generalization for various settings. For the evaluation of the developed approach, two datasets of underwater images, EUVP and LSUI, were used. For the evaluation of the developed approach, two datasets of underwater images, EUVP and LSUI, were used. For the EUVP dataset, this model produces a PSNR of 30.77 dB, an SSIM of 0.892, RMSE of 0.065, and NIQE of 3.52. It produces a PSNR of 29.90 dB, SSIM of 0.881, RMSE of 0.071, and NIQE of 3.82 for the LSUI dataset. As mentioned earlier these results outperform the baseline DnCNN model and show consistent performance with varying underwater conditions. Accompanying the performance results, the model achieves high fidelity results while being lightweight, real-time processing.
Early life exposure to N-nitrosamine drives genotoxicity, mutagenesis, and tumorigenesis in DNA repair-deficient mice
The fitness costs of reproductive specialization scale inversely with organismal size
The evolution of reproductive specialization, in which somatic cells forfeit reproduction, represents a fundamental innovation in complex multicellular life. This specialization imposes a fitness cost: because somatic cells do not produce offspring, organisms that invest in soma have reduced fecundity. The magnitude of this cost might be expected to depend simply on the proportion of cells allocated to soma. Here, we show that these costs also decrease with the logarithm of organism size, because larger organisms require proportionally more cell divisions for development, diluting the rate at which reproductive costs compound across multicellular generations. We derive this result analytically and validate it with data from the volvocine green algae. When somatic cells provide a compensating survival benefit, a positive feedback emerges: larger organisms can afford greater somatic investment, which in turn favors further size increases. This size-scaling relationship helps explain the broad association between large organism size and multicellular complexity.
Spatial structure based deep feature fusion network for autism spectrum disorder classification
Hi-Compass: a depth-aware deep learning framework for predicting cell-type-specific 3D genome organization from single-cell to spatial resolution
Abstract Three-dimensional genome organization controls cell-type-specific gene expression through chromatin interactions, yet systematic analysis across diverse cellular contexts remains limited by experimental constraints. Here we present Hi-Compass, a depth-aware deep learning framework that predicts cell-type-specific chromatin organization using only chromatin accessibility data as cell-type-specific input. By dynamically accommodating variability in sequencing depth, Hi-Compass enables robust predictions across the full spectrum of data scales, from sparse single-cell to high-coverage bulk profiles. Benchmarking shows that Hi-Compass achieves superior concordance with experimental Hi-C data compared to existing methods, with particularly strong recovery of high-confidence chromatin loops. Applied to peripheral blood and embryonic heart datasets, Hi-Compass resolves cell-type-specific chromatin interactions and systematically links disease-associated variants to putative target genes. The framework further enables spatially resolved chromatin interaction prediction in hippocampal tissue and demonstrates cross-species applicability through fine-tuning to mouse systems. Hi-Compass expands the capacity to study three-dimensional genome regulation across biological scales and species.
Multichannel highly sensitive diamond quantum magnetometer
We demonstrate a highly sensitive real-time magnetometry method at two measurement points. This magnetometry method is based on the frequency-division multiplexing of continuous-wave optically detected magnetic resonance. We use two ensembles of nitrogen-vacancy (NV) centers separated by 3.6 mm to measure a magnetic field. A different bias field is applied to the two NV ensembles to resolve the resonance peak for each ensemble in the frequency space and enables the multiplexed magnetometry at the two points. The sensitivities achieved at the measurement points are 21 and 22pT/Hz. The proposed magnetometry method can be expanded to include more measurement points and shorter spacing. The capability of real-time measurement at numerous points with short spacing and high sensitivity is beneficial for various applications, including biomagnetic sensing, geophysical research, and material science.
A novel generalized orthopair fuzzy CURLI MCDM framework for smart art communication integrating emotional intelligence
Phonon-scattering-induced linear magnetoresistance in the quantum limit up to room temperature
Cerebellar contributions to action and cognition: Prediction, timescale, and continuity
The cerebellum is implicated in nearly every domain of human cognition, yet our understanding of how this subcortical structure contributes to cognition remains elusive. Efforts on this front have tended to fall into one of two camps. On one side are those who seek to identify a universal cerebellar transform, a single algorithm that can be applied across domains as diverse as sensorimotor learning, social cognition, and decision-making. On the other side are those who focus on functional specializations tailored for different task domains. In this perspective, we propose an integrated approach, one that recognizes functional specialization across different cerebellar subregions, but also builds on common constraints that help define the conditions that engage the cerebellum. Drawing on recurring principles from the cerebellum’s well-established role in motor control, we identify three core constraints: 1) Prediction—the cerebellum performs anticipatory, simulation-based computations; 2) Timescale—the cerebellum generates predictions limited to short intervals; and 3) Continuity—the cerebellum facilitates the manipulation of mental representations in a continuous, but not discrete manner. Together, these constraints define the boundary conditions underlying how the cerebellum supports cognition, and, just as importantly, specify the types of computations that should not depend on the cerebellum.
One-dimensional halide perovskite single crystals for optoelectronic applications
Metal halide perovskites have recently begun to flourish in the field of optoelectronics. However, the inherent instability and grain boundary defects of traditional three-dimensional (3D) and two-dimensional (2D) polycrystalline films remain significant obstacles hindering their commercialization. Consequently, one-dimensional (1D) halide perovskite single crystals (PSCs) have garnered considerable attention due to their unique “molecular wire” structures. This distinctive structural constraint endows 1D PSCs with exceptional physical properties: strong quantum and dielectric confinement effects, broadband emission driven by self-trapped excitons, significant optoelectronic anisotropy, and excellent environmental stability. This article reviews the recent advances in 1D halide PSCs. We systematically explore the fundamental crystal structures and their derived photophysical properties, with a focus on elucidating the mechanisms behind their high quantum yields and nonlinear optical responses. Furthermore, various single-crystal growth strategies, ranging from slow cooling crystallization and inverse temperature crystallization to space-confined synthesis, are critically analyzed. Finally, we summarize the cutting-edge applications of 1D PSCs in high-performance UV–vis photodetectors, x-ray detectors, light-emitting diodes, and emerging polarization-sensitive devices.
The impact of wrist flexion on task performance and compensatory movements in transradial prosthesis users
A molecular framework of the Rht-A1–TaLA1-D module controlling tiller angle in wheat
Photonic and electronic properties of two-dimensional GaN monolayer and AA/AA′ stacked structures: First-principles and quantum transport simulations
This paper investigates the electrical and optical properties of monolayer and bilayer gallium nitride (GaN) with different stacking configurations (AA and AA′) through first-principles calculations and quantum transport simulations. The analysis shows that the stacking arrangement significantly influences the optical and electrical properties of GaN, with the AA-stacking structure exhibiting enhanced stronger absorption coefficients and higher reflectivity. Based on these properties, a self-powered ultraviolet photodetector was designed. The AA-stacking structure demonstrates remarkable photocurrent response and excellent polarization sensitivity across a broad ultraviolet spectral range from 2 to 6.0 eV. These results suggest that tuning the stacking configuration can effectively improve the performance of photodetectors, offering new insights and strategies for the development of high-performance optoelectronic detectors in the future.