Browse Articles

Exon inclusion signatures enable accurate estimation of splicing factor activity

Postpartum Persistence of Ebola Virus in Breast Milk

Giant energy storage and dielectric performance in all-polymer nanocomposites

Development of Quantum dot-based enzyme biosensor for the detection of dopamine in urine

Abstract Dopamine (DA), a neurotransmitter released by the hypothalamus, plays a significant role in maintaining mental well-being. Abnormal DA level leads to neurological disorders such as depression and schizophrenia. Consequently, DA is commonly monitored in urine using various analytical methods as a non‑invasive approach for assessing its physiological status. The available methods for DA detection are laborious and time-consuming. To circumvent this issue, we developed a Quantum dot-based enzyme biosensor for the rapid, sensitive detection of DA. Fluorescence quenching of QDs was in proportion with the DA concentration and was found to be linear with an R 2  = 0.99, with p  < 0.05. The biosensor used to detect DA in urine samples in the range of 1.2 µM-8 µM, with R² = 0.97 and p  < 0.05, and a limit of detection (LOD) of 1.2 µM in a urine sample (1:100). Spiking and recovery analysis in urine showed 94–98% recovery with p  < 0.05. The developed method showed specificity towards detecting DA in the presence of common interfering factors such as uric acid and ascorbic acid. The results show that dopamine-specific quenching is consistent and concentration-dependent, effectively distinguishing the target from background components in complex samples. This approach provides a promising platform for reliable DA monitoring in clinical diagnostics.

Synthesis of monodisperse InSb colloidal quantum dots by monomer concentration control for short-wave infrared photodetectors

Abstract InSb colloidal quantum dots combine a low bulk bandgap (0.17 eV) with a large exciton Bohr radius, enabling access to short-wave infrared wavelength within the quantum confinement regime, alongside strong covalent bonding, complementary metal-oxide semiconductor compatibility, and restriction of hazardous substances compliance. However, prior one-pot and hot-injection approaches yield broad size distributions and weak excitonic absorption, while continuous-injection methods improve spectral features but are restricted to small dot sizes (<1.2 μm excitonic peaks). Here, we introduce a monomer-concentration-controlled approach that produces narrow-size-dispersed InSb quantum dots tunable from 950 to 1900 nm with the sharpest excitonic absorption peaks reported to date. Their monodisperse nature allowed the emergence of heavy hole-light hole splitting evident in their optical absorption spectra. Leveraging these high-quality nanocrystals, we demonstrate short-wave infrared photodetectors achieving external quantum efficiencies of 22% at 1500 nm and 19% at 1580 nm, extending the spectral reach of III-V colloidal quantum dot photodetectors at this wavelength range.

Impact of high relative dose intensity on effectiveness and treatment continuity of IO-TKI therapy in Japanese advanced renal cell carcinoma

Genetic alternative splicing regulation mapping of cartilage and synovium reveals tissue-specific mechanisms of joint-related traits

The impact of land use types on soil physicochemical properties in Dandi District, Ethiopia

Endogenous VEGF signaling acts as a guardian of human primed pluripotency

TinyML pipeline for efficient crack classification in UAV-based structural health inspections

Abstract Structural health monitoring (SHM) of civil, aerospace, and energy infrastructure increasingly relies on UAVs with vision sensors for efficient inspections. Crack classification is a central task, yet cloud-based inference introduces bandwidth, power, connectivity, and privacy challenges that limit its practicality. This study presents a fully self-contained Tiny Machine Learning (TinyML) pipeline for onboard crack classification on a milliwatt-level STM32H7 microcontroller. Using MobileNetV1x0.25 as the baseline, we systematically evaluate the full measurement pipeline, including image capture, preprocessing, and inference on a low-power embedded system. Two preprocessing strategies, a handcrafted sequence (grayscale, contrast, denoise, median, binarization) and a greedy algorithm-based composite method, are compared. Four compression techniques, namely post-training quantization (PTQ), quantization-aware training (QAT), pruning, and weight clustering, are assessed individually and in combination. The optimized pipeline achieves an F1-score of 0.938, an improvement of 11.4% over state-of-the-art deployments. At the same time, it requires only 2.9 MB RAM and 309 KB flash, with an end-to-end latency of 461.6 ms and an energy cost of 623.16 mJ per inference. On a DJI Mini 4 Pro UAV, continuous operation reduces flight time by just 1.31 minutes (4%), compared to 8 minutes (24%) when using Jetson-based platforms. Overall, this work delivers a reproducible benchmark for UAV-based SHM, demonstrating a practical balance of accuracy, resource efficiency, and energy consumption, and advancing the feasibility of on-device crack classification in highly resource-constrained environments.

mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data

N1-Methylpseudouridine directly modulates translation dynamics

Cycloastragenol attenuates osteoarthritis by restoring chondrocyte senescence via the NRF2/NF-κB signaling axis

DEUP1 functions as a scaffold for basal foot integrity and planar polarity in multiciliated cells

Statistics reach a ‘crisis point’: nations struggle with a critical lack of data

Internal conversion dominates the excited state dynamics and fluorescence efficiency of mono-substituted TPE-BODIPY dyes

Brachiopod genome unveils the evolution of BMP signalling in bilaterian body patterning

Abstract The molecular control and ancestral state of dorsal–ventral patterning within spiralians remain unclear due to the remarkable diversity across species. Here we present a chromosome-level genome for the brachiopod Lingula anatina and apply functional transcriptomics to study dorsal–ventral patterning under BMP signalling control. We uncover asymmetrical activation of BMP signalling at the dorsal side of the gastrula, governed by ventral chordin expression and a ‘seesaw’ of BMP ligands. Using small-molecule drugs and recombinant proteins, we show that high BMP activity inhibits genes typically associated with neural patterning during gastrula and larval stages, similar to deuterostomes and non-spiralian protostomes. Our findings suggest deep conservation of this mechanism across all three major bilaterian clades, supported by striking similarities in BMP-regulated gene sets between brachiopods and Xenopus . We argue that the spiralian ancestor retained the ancestral bilaterian mechanism, although downstream network components have undergone developmental system drift.

Decoding real-world visual scenes from alpha and gamma band flicker evoked oscillations in human EEG

Abstract Current approaches to investigate the role of neural oscillations in natural scene processing have been limited to artificial stimuli and long data collection. We present a new way to decode real-world scenes participants are viewing from the steady-state visually evoked potentials (SSVEPs) evoked while wearing flickering LCD glasses. We discovered that SSVEP responses from real world scenes are surprisingly complex and have distinct waveform shapes: they differ markedly across scenes and participants but are consistent within individuals, even across multiple days. SSVEP shape varies greatly between stimuli, but is reliable, meaning that decoding works even with a single electrode. Decoding is highly accurate with 5–10 s of data and was still above chance level with less than a second of data. Decomposing the SSVEPs into frequency bands showed that the information about the visual scene is present across all of the harmonics of the flicker frequency: optimal decoding used the broadband signals, but with 40 Hz (gamma band) showing the highest amount of information after band-pass filters. These findings implicate a broad range of oscillations in encoding real-world scenes, with a particular importance for 40 Hz. The SSVEP’s temporal profile is a rich source of information for decoding.

Visualising reaction complexes in amine-based unloaded and CO2-loaded carbon capture solutions

Abstract In power generation and industries where CO 2 emissions are unavoidable, carbon capture, utilisation, and storage is an important tool to offset climate change. Many carbon capture agents are blends of aqueous amines, which absorb CO 2 and are then thermally regenerated. The physical interactions between solutes play a crucial role in their reactivity and energy requirements for regeneration. Atomically resolved, experimentally derived information about the structure of these solutions, however, has yet to be reported. In this work, we report the structure of two model carbon capture solvents, aqueous sodium and potassium glycinate, in the unloaded and CO 2 -loaded state by performing structural refinement on H/D isotopically varied neutron diffraction data. This allows us to quantify the structure, frequency, and EPSR-derived pair interaction energetic stability of intermolecular interactions present. Such methodology can be readily applied to other carbon capture solutions, providing unparalleled insight and facilitating their large-scale modelling and rational design.

Genome-wide association study of resistance to taro leaf blight and yield traits in taro (Colocasia esculenta (L.) Schott)

Abstract Taro leaf blight (TLB), the most destructive disease of taro caused by the oomycete Phytophthora colocasiae . An effective strategy for controlling this disease is the development of resistant cultivars. In this study, a genome-wide association study (GWAS) was conducted on 265 taro genotypes to identify genomic regions and putative candidate genes associated with TLB resistance and yield traits. The field experiments were conducted at the National Root Crops Research Institute (NRCRI), Umudike, in 2021/2022 and 2022/2023 planting seasons. TLB severity, plant height, vigor, number of suckers, corm weight, cormel weight, and total tuber weight were evaluated. A genome-wide association mapping detected 18 single nucleotide polymorphisms (SNPs) markers significantly associated with TLB, number of suckers, corm weight, cormel weight, and total tuber weight. Gene annotation of significant SNP loci identified 6, 1, 2, 2, and 1 putative candidate genes associated with TLB resistance, number of suckers, corm weight, cormel weight, and total tuber weight respectively. Co-localization of a significant SNP 83978367 associated with corm weight and total tuber weight on chromosome 11 is possibly an indication of pleiotropic effects or the presence of closely linked genes controlling these traits. GWAS failed to detect significant associations in plant height and vigor. This study provides a basis for research on genetic control of TLB resistance, number of suckers, and yield-associated traits and thus provides resources for the ongoing effort to develop high-yielding, and disease resistant taro cultivars.