Browse Articles

Joint attention GAN for medical report generation with clinical style preservation

Depletion of the Protein Hydration Shell with Increasing Temperature Observed by Small-Angle X-ray Scattering and Molecular Simulations

Effects of bisphosphonates after denosumab discontinuation and treatment effect heterogeneity using causal machine learning

pH-Isomerizable Acylhydrazone-Based Ionizable Lipids for Spleen-Targeted mRNA Vaccines

A qualitative study of question-posing anxiety in Chinese postgraduates in UK TESOL programs

Enzyme-Responsive Self-Evolving Hydrogel for Osteochondral Regeneration through Mechanosignaling Pathway

Irritability in autism examined through network analysis of phenotypic and physiological correlates

An adaptive, energy-efficient and secure routing protocol for zone-related mobile Ad-hoc networks using reinforcement learning

Abstract The rapid surge of Mobile Ad Hoc Networks (MANETs) stimulates the need for adaptive, intelligent, and secure routing mechanisms to ensure seamless communication in dynamic environments. Traditional routing protocols are battling with security threats such as wormhole attacks that disrupt routing and degrade network performance. To address these challenges, this study outlines a state-of-the-art technique, the Reinforcement Learning-Based Secure Routing Protocol (RLSRP), which leverages adaptive k-hop clustering and deep Q-Networks (DQN) to fine-tune routing decisions dynamically while mitigating security risks. RLSRP unwaveringly measures network condition by evaluating latency variations and anomalies to spot suspicious nodes, thereby enhancing route stability. The protocol implements zone-related clustering where nodes within each zone collaborate to optimise routing paths based on real-time conditions, ensuring energy-efficient communication. Current research investigated deep reinforcement learning methodologies to improve security in zone-related MANETs and ensure efficient data routing in large-scale environments. A detailed simulation-based evaluation depicts the potency of the proposed RLSRP model when compared with other reinforcement learning-based routing protocols. Using a large-scale setup–scaling up to 10 million nodes–with Dask and TensorFlow, the results show that RLSRP consistently outperforms FSSAM, Cluster-RL, and Reputation-based Q-learning in terms of Packet Delivery Ratio (exceeding 99%), reduced latency, and improved energy efficiency. These findings reported RLSRP as a secure and scalable solution for practical MANET routing.

Stepwise Hydration Reveals Conformational Switching in Chiral Prolinol

Development of green silicone rubber/waste sludge composites with Bi2O3 extracted from olive leaves for radiation shielding application

Boron-Enabled Stereoselective Synthesis of Polysubstituted Housanes

Radiographic and clinical outcomes at 12 months following full endoscopic interlaminar decompression for grade 1 degenerative lumbar spondylolisthesis

Signatures of correlation of spacetime fluctuations in laser interferometers

Abstract Spacetime fluctuations (SFs), a common feature of proposed gravity models, could be detected using laser interferometers. To advance this effort, we provide the correspondence between expected interferometer output signals and gravity models. We consider three classes of SFs, characterised by the decay behaviours and symmetries of their two-point correlation functions. For each, we identify the low- and high-frequency behaviour of the outputs and their dependence on the interferometer’s length. Capturing these requires sensitivity over a broad frequency range that spans the light-round-trip frequency, as provided by laboratory-scale setups, whereas detecting the presence or absence of SFs can be done with high narrowband sensitivity at the light-round-trip frequency. Our approach applies to interferometers with arm cavities, such as the km-long LIGO detectors, and those without, like laboratory-scale setups QUEST and GQuEST. Finally, we constrain the strength and correlation scale of SFs by comparing our modelled signals with experimental data.

Analyzing multiple mediators in multiple single-mediator models leads to wrong conclusions

Targeting TRAF3IP2 disrupts cellular energetics through inhibition of NAMPT in triple negative breast cancer

Abstract Triple-negative breast cancer (TNBC) is characterized by extensive metabolic alterations that enable its sustained growth therapeutic resistance. Nicotinamide phosphoribyltransferase (NAMPT) catalyzes the first and rate-limiting step in the nicotinamide dinucleotide (NAD) salvage pathway. Elevated NAMPT is associated with increased aggressiveness and poor prognosis in multiple cancers. Previously, we showed the role of TRAF3IP2 in TNBC tumorigenesis. Here, we aim to show that the anti-tumorigenic effects resulting from TRAF3IP2 inhibition are driven in part by decreases in cellular energetics in TNBC cells. Results show that inhibition of TRAF3IP2 leads to significant decrease in NAMPT expression, reduced NAD and ATP production, and disruption of TNBC bioenergetics through cell line-specific alterations in glycolysis and mitochondrial function. Notably, the established MDA-MB-231 line and the patient-derived 4IC model exhibited distinct OCR responses, underscoring metabolic heterogeneity across TNBC models. Additionally, this study showed that targeting TRAF3IP2 disrupts cellular energetics by affecting AMPK/LKB1 and mTOR signaling pathways and increasing reactive oxygen species (ROS) levels, ultimately leading to reduced cell viability and increased apoptosis. These findings suggest that TRAF3IP2 plays a critical role in maintaining TNBC bioenergetics and represents a potential target for therapeutic intervention.

A flexible photoacoustic retinal prosthesis

Advancing evaluation of AI systems when humans make the decisions

Woody plants composition, structure and regeneration status of Muger Zala natural forest, Central Ethiopia

Unveiling mechanistic patterns of copper-catalyzed radical bond formation through linear free energy relationship

The variability of evolvability: Properties of dynamic fitness landscapes determine how phenotypic variability evolves

The magnitude and shape of phenotypic variation depends on properties of the genotype-to-phenotype (GP) map, which itself can evolve over time. The evolution of GP maps is particularly interesting in variable environments, as GP maps can evolve to bias variation in the direction of past selection, increasing the evolvability of the population over time. However, the degree and manner in which environmental variation shapes GP maps and influences evolutionary dynamics may depend on properties of the fitness landscape. To explore how evolutionary dynamics are affected by variable environments across a wide range of different pairs of fitness landscapes, we evolved GP maps to produce spatial-temporal gene expression patterns that matched two-dimensional patterns generated by different elementary cellular automata rules. We found remarkable variation in how populations evolved in variable environments. In some cases, changing the environment helped populations find higher fitness peaks; in others, it hindered them. The evolution of evolvability also depended on the fitness landscape pair. In some experiments, the ability to generate adaptive phenotypic variation upon environment change increased over time, while in some others, populations found shared areas between fitness landscapes. On the other hand, environmental variability consistently resulted in higher fitness landscape exploration, average fitness, and mutational robustness compared to evolution in static environments, which we hypothesize are tightly connected. In conclusion, work presented here sheds light on important general consequences of environmental variability, while also demonstrating dependency on properties of fitness landscapes, which future research on the evolution of evolvability should consider.