Browse Articles

The architecture and energy transfer pathways of PSI–LHCI–LHCII in the phototrophic flagellate Euglena gracilis

Animal-origin-free method for generating blood vessel organoids

Sub-micron-resolution temperature mapping of Zn negative electrode for flow batteries

WNT inhibition activates interferon stimulated gene expression by alleviating epigenetic repression of endogenous retroviruses

Visuomotor decision-making through multifeature convergence in the larval zebrafish hindbrain

Abstract Animals continuously extract and evaluate diverse sensory information from the environment to guide behavior. Yet, how neural circuits integrate multiple, potentially conflicting, inputs remains poorly understood. Here, we use larval zebrafish to address this question, leveraging their robust optomotor response to coherent random dot motion and phototaxis towards light. We demonstrate that animals employ an additive behavioral algorithm of three visual features: motion coherence, luminance level, and changes in luminance. Using brain-wide two-photon imaging, we identify the loci of these computations, with the anterior hindbrain emerging as a multifeature integration hub. Through single-cell neurotransmitter and morphological analyses of functionally identified neurons, we characterize potential connections within and across computational nodes. These experiments reveal three parallel and converging computational pathways, matching our behavioral results. Our study provides a mechanistic brain-wide account of how a vertebrate brain integrates multiple features to drive sensorimotor decisions, bridging behavioral algorithms with their neural implementation.

Multivariable AI-based analysis of immune–lifestyle patterns associated with recurrent pregnancy loss: an exploratory retrospective study

Quantifying the trade-off between spring phenology and lethal frost risk: a meta-analysis

A comprehensive evaluation of lightweight deep learning models for tomato disease classification on edge computing environments

Abstract To achieve agricultural automation, deep learning applications for early and accurate disease detection in tomato plants have been extensively developed. However, there is a fundamental trade-off between computational efficiency and diagnostic accuracy in resource-constrained agricultural edge environments. This paper proposes an evaluation framework for seven architectures that represent standard, efficient, and hybrid CNN structures to assess their implementation potential. Through evaluations of explainability, computational efficiency, and diagnostic performance, seven lightweight architectures (ShuffleNetV2, MobileNetV3-Small, SqueezeNet, MobilePlantViT, DenseNet121, ResNet50, and VGG16) are thoroughly examined. Three significant findings are derived from experiments conducted on a subset of tomato diseases in the PlantVillage dataset. First, the MobilePlantViT architecture accurately strikes the ideal balance between efficiency and performance. Second, in order to quantitatively assess the explainability of XAI models (Grad-CAM, SHAP, and LIME) and identify the best option for edge devices, we propose the perturbation stability score (PSS) metric. Third, we test CPU inference measurements to better reflect the actual scenario and find that the hybrid design effectively leverages parallel computing. According to these findings, MobilePlantViT is the ideal architecture for applications that require operation on edge devices with limited resources and achieve high diagnosis accuracy (above 99.5%).

Long-range spatial extension of exciton states in van der Waals heterostructure

Abstract Narrow lines in photoluminescence (PL) spectra of excitons are characteristic of low-dimensional semiconductors. These lines correspond to the emission of exciton states in local minima of a potential energy landscape formed by fluctuations of the local exciton environment in the heterostructure. The spatial extension of such states was in the nanometer range. In this work, we present studies of narrow lines in PL spectra of spatially indirect excitons (IXs) in a MoSe 2 /WSe 2 van der Waals heterostructure. The narrow lines vanish with increasing IX density. The disappearance of narrow lines correlates with the onset of IX transport, indicating that the narrow lines correspond to localized exciton states. The narrow lines extend over distances reaching several micrometers and over areas reaching ca. ten percent of the sample area. This macroscopic spatial extension of the exciton states, corresponding to the narrow lines, indicates a deviation of the exciton energy landscape from random potential and shows that the excitons are confined in moiré potential with a weak disorder.

Integrated source apportionment, co-enrichment mechanisms analysis, and risk assessment of arsenic and fluoride in groundwater of large-scale irrigation districts in semi-arid regions

Atomic imaging for hydrogen and boron aggregates in boron-doped diamond by spectro-photoelectron holography

Caffeic acid suppresses cyclin D1 expression by directly binding to ribosomal protein S5 in colorectal cancer cells

Abstract Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, and dietary components such as coffee have been epidemiologically associated with a reduced risk of CRC. However, the molecular mechanisms underlying this effect remain elusive. In this study, we found that caffeic acid, a hydrolysate of chlorogenic acid abundant in coffee, significantly suppressed colony formation in human CRC cells. Chemical pull-down assays using nano-magnetic beads combined with mass spectrometry identified ribosomal protein S5 (RPS5) as a direct binding target of caffeic acid. Molecular dynamics simulations further supported the stability of the interaction between caffeic acid and a specific binding pocket on RPS5. Mechanistically, RNA interference-mediated knockdown of RPS5 induced G1 cell cycle arrest and downregulated cyclin D1 expression at both mRNA and protein levels, without affecting its promoter activity, suggesting a post-transcriptional regulatory mechanism of cyclin D1 by RPS5. These findings reveal a previously unrecognized RPS5-cyclin D1 axis targeted by caffeic acid and provide novel mechanistic insights into the potential chemopreventive effects of coffee against CRC.

Graphene quantum dot membranes with tailorable pores for efficient gas separation

On-site dissolved-gas analysis and electric-resistivity tomography as new tools to trace CO2 mineral sequestration in aquifers

Swapped and non-swapped TRAAK states co-exist in membranes at a ratio influenced by temperature

Abstract The potassium two-pore domain (K2P) ion channel TRAAK is expressed in the nervous system and regulates the fast action potential in membranes. Like all K2P channels, TRAAK possesses a distinct extracellular cap, which adopts a swapped and a non-swapped conformation 1,2 . However, the proportional representation of these two species within native membranes – and the trigger or stimulus associated with this conformational transition – are unknown. Here, we utilise Pulse Dipolar EPR Spectroscopy combined with heterologous single-subunit spin-labelling and monitor the complete conformational ensemble of TRAAK’s cap domain in membranes. We demonstrate the coexistence of the swapped and non-swapped states, quantify their populations within the TRAAK ensemble, and show that the swapped conformation dominates, with the ratio being influenced by temperature. Native lipid analysis shows that TRAAK selectively associates with and is activated by signalling lipids to the exclusion of membrane-dominant phosphatidylcholine lipids from its vicinity, forming a distinct microdomain. Our approach can identify the immediate lipid environment, detect and quantify cap state populations in homo-/hetero-K2P channels and link domain swapping to specific triggers.

Intestinal macrophages modulate synucleinopathy along the gut–brain axis

Hallucination-aware learning and latency optimization transformer (HALL-OPT) for real-time edge intelligence

COVID-19 pandemic perceived impacts on the Australian general population, a national survey exploring the role of socio-demographic and psychological factors

A score based likelihood ratio framework for deepfake image identification in forensic science

Abstract This paper proposes a score-based likelihood ratio system for forensic identification of deepfake images, addressing challenges in digital media identification due to rapid deepfake development. Built on the FaceForensics + + dataset, the system prevents data leakage via video-level splits (training, validation, selection, calibration, and test sets). Among six candidate models, the Capsule detector demonstrates the most robust performance (AUC = 0.983). Score distributions of real and fake samples are modeled using kernel density estimation, with optimal bandwidths selected through a two-stage search (real: 0.004, fake: 0.003). Extreme LR values are bounded using the empirical lower and upper bounds method (− 2.3634 to 1.9933), and PAV calibration is applied to optimize the calibration performance of the LR system. On the FF + + test set, the system exhibits favorable performancewith forensic practice expectations: low misleading evidence rates (RMEP = 0.069, RMED = 0.092), good error control (EER = 0.0804), and reduced decision loss after calibration (the cost log-likelihood ratio from 0.2899 to 0.1625). Generalization tests on five unseen datasets (Celeb-DF-v1/v2, DFDCP_methodA/B, UADFV) yield AUCs between 0.621 and 0.783—highest on UADFV (0.783), stable on DFDCP, weaker on Celeb-DF. The results show that at the moment, the technique shows potential for forensic-oriented deepfake identification, but requires further validation across diverse real-world scenarios before practical forensic application.

Supervised information gain-based feature selection for multimodal physiological signals in stress prediction