Browse Articles

BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction

Increased brightness of fluorescent uridine, qU, inside single- and double-stranded RNA

Abstract Fluorescent base analogues enable studies of nucleic acids and by expanding their range of properties the scope of potential applications broadens. The recent photophysical characterisation of quadracyclic uridine (qU) in its monomeric ribonucleotide form revealed a high fluorescence quantum yield of 27%, with an interesting dual-band character emission. In this study, we show that incorporation of qU into RNA results in loss of the dual-band character but with further increased quantum yields and longer fluorescence lifetimes, in both single- and double-stranded RNA. Additionally, we find that neighbouring bases of qU affect its photophysical properties, with quantum yields varying between 33 and 68% in single-strands and 30–51% in duplexes. Furthermore, circular dichroism and duplex melting analyses of the qU-labelled duplexes were compared to unlabelled counterparts, with results suggesting that A-form RNA conformation is maintained, albeit with destabilisation caused by qU. Spectral features indicate that incorporated qU predominantly exists in its iminol tautomer, incapable of Watson-Crick base-pairing, which likely strongly contributes to the observed destabilisation. Overall, we report one of the highest brightness values (reaching 6 000 M − 1 cm − 1 ) to date for an RNA-incorporated fluorescent nucleobase, highlighting qU as a valuable label for applications such as fluorescence microscopy that require strong emission.

Collective route memories emerge through differential forgetting of navigational information in homing pigeons

Abstract Better decision-making in larger groups than smaller groups or individuals has been observed across various taxa. While this phenomenon is thought to result from the pooling of independent information in collective decision-making, an alternative mechanism is the better retention of learned information in larger groups: collective memory. We investigated the emergence of collective memory and its role in collective intelligence by training homing pigeons to navigate home in pairs and testing their retention of learned routes. In a treatment with an eight-week forgetting period between training and memory testing, pairs flew closer to their learned routes than solo-tested birds, likely through differential retention of information across pairs. However, better memory retention in pairs did not translate into better homing efficiency, perhaps because the forgetting period was too short to generate a sufficient drop in efficiency. A second treatment demonstrated that extra training and a shorter forgetting period abolished the difference between paired and solo memory performance. These findings demonstrate that differential retention of information across group members can lead to the emergence of collective memory in animals. This has implications for a wide range of contexts in which the interplay of learning and memory shape individual and collective behaviour.

A study on the mechanism of Buyang Huanwu Decoction in treating epilepsy by regulating adenosine levels

Treatment management algorithm for natural frozen embryo transfer cycles using a real-time ovulation prediction machine learning model

Abstract Our aim was to develop an AI-based NC-FET Treatment Management Algorithm (NTMA), decision-support system that predicts ovulation in real time to manage and optimize natural frozen embryo transfer cycle (NC-FET) scheduling. The algorithm was developed using a “teacher-student” machine learning approach and was trained on a total of 3,975 labeled NC-FET, including 3,432 training cycles and 543 test cycles. A second test group included 166 documented ovulation cycles (documented follicular rupture and LH surge in two consecutive days of ultrasound scans). The algorithm showed high ovulation detection accuracy in both tests’ groups particularly one day before and the day of ovulation (95.4% and 94.6% in the labeled test group and 95.5% and 95.3% in the documented test group, respectively). Most influential predictive features of the algorithm included LH levels, the estrogen/progesterone ratio, and leading follicle size during the monitored test days. The NTMA yielded 92.04% correct prediction 7 in identifying ovulation with an average of 3.1 tests per cycle. We propose an AI-based algorithm for the complete management of a NC-FET. The algorithm shows high accuracy in predicting the time of ovulation and therefore can serve as a useful decision support tool for clinicians in their daily practice. Prospective studies are warranted to validate these results.

Association study of optineurin gene polymorphisms T34T and M98K with normal tension glaucoma in a Turkish cohort

Hybrid prediction system for reliable multi-seasonal sustainable energy generation under meteorological and environmental volatility

Assessing farmers’ knowledge, attitudes, health risk perceptions, and practices toward pesticide use in Morocco

Microwave assisted synthesis and antimicrobial evaluation of novel Thiazolidinedione pyrrole hybrids with antiviral potential and comprehensive computational modeling studies

Carrot extracellular nanovesicles as carotenoid carriers in an in vitro macular degeneration model

Federated learning with continual update for privacy-preserving clinical event prediction across distributed hospitals using MCN-GNN

Abstract Federated Learning (FL) enables accurate and secure Clinical Event Prediction (CEP) across distributed hospitals. However, the prevailing works overlooked the catastrophic forgetting during the global update. Therefore, a Meta Experience Polynomial Decay-based Replay (MEPDR)-centric continual update is proposed. Initially, the hospitals (local model) register and log into the blockchain. Then, to train the CEP model, data collection, pre-processing, and feature extraction are performed. Further, the Temporal-Causal Graph (TCG) is constructed. Afterward, the node matrix is created, and the CEP is done using Mean-Centering Normalization-based Graph Neural Network (MCN-GNN). The model’s gradients are further preserved using the Homomorphic Robust Log Scaling-based Encryption (HRLSE). Next, the hospitals are authenticated using the Exponential Probing Digital Signature Algorithm (ExPrDSA). Thereafter, in the global model, the aggregation is performed using the Calinski–Harabasz Index with Zhonghua Distance-based K-Means Clustering (CHIZD-KMC), followed by global CEP. After that, during the global update, the MEPDR-based continual learning is carried out in each local model. Also, the transactions are stored in the blockchain to enhance traceability. Thus, the proposed system effectively predicted the clinical events with an accuracy of 98.97%, outperforming existing works.

Utilizing l-arginine as an eco-friendly absorbent for hydrogen sulfide mitigation in produced water

Exogenous benzyladenine reinforces the antioxidant activity, phytochemical content, and macronutrients of Tagetes erecta plants

Abstract Growth regulators have become an effective tool for enhancing the cultivation of medicinal and aromatic plants by promoting vegetative development and increasing the accumulation of bioactive metabolites. Benzyladenine (BA), a synthetic cytokinin, is widely recognized for its ability to influence growth and secondary metabolism in ornamental species; however, dose response evaluations under open field conditions remain limited for Tagetes erecta . This study examined the effects of foliar BA applications at 0, 50, 75, and 100 ppm, applied weekly over three intervals, on vegetative growth, pigment composition, antioxidant-related metabolites, and macronutrient status. BA treatments significantly improved all measured parameters relative to untreated plants, with responses showing a clear concentration dependent trend. The 100 ppm treatment produced the most pronounced enhancements, yielding maximum vegetative growth (72.133 cm height, 177.80 g fresh weight, 74.26 g dry weight), highest pigment levels (8.35 mg/100 g FW chlorophyll a, 5.88 mg/100 g FW chlorophyll b, 5.12 mg/100 g FW carotenoids), improved mineral content (3.15% nitrogen, 0.454% phosphorus, 2.92% potassium), and elevated antioxidant properties (25.844% antioxidant activity, 94.2 mg/100 g FW total phenols, 2.884 mg/100 g FW total flavonoids). These findings demonstrate that BA, particularly at 100 ppm, effectively enhances growth performance and secondary metabolite accumulation in T. erecta , providing field-based evidence for the role of cytokinins in optimizing the agronomic and phytochemical potential of this economically important ornamental species.

DUSP6 promotes motility, invasion, and tumorigenicity of thyroid cancer cells via IL8-induced neutrophil extracellular traps

Identification and characterization of 13 gene families encoding enzymes involved in flavonoid biosynthesis in barley and their roles under abiotic stress

Age-dependent perception of floral emissions and the role of CO2 in regulating nectar-seeking in mosquitoes

Abstract Mosquitoes require access to nectar for energy and reproduction, a behaviour which varies depending on adult maturation and gonotrophic cycle. To locate and discriminate among nectar resources, mosquitoes may make use of various floral emanations, including volatile organic compounds and carbon dioxide (CO 2 ). In order to identify the bioactive volatile organic compounds (VOCs) in one of the preferred host plants of Anopheles mosquitoes, Lantana camara , combined electrophysiological and chemical analyses were performed using ectopically expressed Anopheles coluzzii odorant receptors (Ors) in the empty neuron system of Drosophila. When presented as a synthetic odour blend, and controlled for the detected ratio and emission rate of individual VOCs, the blend elicited an age- and dose-dependent attraction of An. coluzzii and of the closely-related Anopheles arabiensis. Lantana camara demonstrated a differential circadian emission of CO 2 , which directly correlated with the volume of nectar secreted. Behavioural assays designed to determine the role of ecologically-relevant concentrations of CO 2 in regulating nectar seeking, demonstrated a context-dependency, emphasizing that CO 2 is used for close-range floral discrimination during foraging. This study demonstrates a mechanism regulating the detection and perception of ecologically-relevant information by mosquitoes during sugar seeking.

Hybrid Vigenere and elliptic curve cryptography technique over the finite field $$\mathbb{F}_{256}$$

KNEESENSE: a low-cost wearable system with hydraulic filament sensing for real-time knee rehabilitation monitoring

Buffering and energy-absorbing characteristics of disc spring composite monomer under impact dynamic load

Junction opener enables CAR T cell treatment of solid tumors

Abstract Physical barriers presented by solid tumors limit CAR T cell efficacy. Thus, technologies to increase CAR T cell access to solid tumors could have massive implications in advancing CAR T cell therapies. Here we demonstrate that treatment of solid tumor-bearing mice with our Junction Opener technology and CAR T cells enhances tumor control - potentially enabling CAR T cell immunotherapy for solid tumors.