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Design of a compact quasi-optical mode converter for a 105-GHz gyrotron using optimized perturbation technique
Numerically induced attentional biases in horizontal, vertical, and two-dimensional shapes
Clinical manifestations and severity of COVID-19 caused by Omicron among paediatric patients aged 0–17 years in Italy
Postprenylation processing of phosphodiesterase is critical for robust cone photoreceptor response
Study on biodegradation of used engine oil in a stirred batch bioreactor by ochrobactrum intermedium and Bacillus paramycoides isolates
Development and implementation of a model predictive control system for a solar parabolic trough plant influenced by an advanced meteorological disturbance model
Automated detection of corruption reports in text via deep reinforcement learning
The genetically encoded biosensor FEOX is a molecular gauge for cellular iron environment dynamics at single cell resolution
Abstract Iron-sensing is evolutionarily conserved among life on Earth, and mammalian cells are known to sense cellular iron through mechanisms similar to bacteria and marine invertebrates. While iron regulatory proteins (IRPs) function as RNA-binding proteins during iron-limiting conditions and reflect cellular iron status, we lack genetically-encodeable tools to quantify bioavailable intracellular iron status over developmental time and at single-cell resolution. In order to better understand the cellular environment that supports or restricts IRP-active conditions, particularly during complex and dynamic changes leading to differentiated states, we present a ratiometric genetically-encoded biosensor called FEOX. FEOX is a ratiometric fluorescent biosensor of the cellular iron environment based on a mammalian hemerythrin-like domain, acting as an iron-dependent ligand-based regulatory switch. Compared to increased IRP activity in response to iron-limitation, FEOX dynamics demonstrate decreased ratiometric fluorescence representing decreased cellular iron during iron-limitation. Using FEOX we are able to quantify the dynamics of the bioavailable cellular iron environment during early stem cell differentiation by ratiometric measurements at single-cell resolution. Results from FEOX and from an IRP activity sensor during stem cell pluripotency transition and early differentiation provide orthogonal support for an increased iron demand state. Using these genetically-encodeable tools will allow greater insight into cellular iron homeostasis within mammalian systems. IRPs function as RNA-binding proteins during iron-limiting conditions, regulating the stability and translation of messenger RNAs that are essential for cellular iron homeostasis. However, to better understand and study the cell and molecular regulators of the iron-limiting conditions required for IRP RNA-binding activity, new tools are needed to monitor iron-responsive alterations in the intracellular environment at single-cell resolution and over developmental time. We devised a genetically-encoded biosensor called FEOX that depends on molecular iron interactions independent of IRP function and directly gauges bioavailable cellular iron.
Experimental investigation of mechanical properties of sustainable silica sand reinforced AA6061 composites subjected to thermomechanical treatment
Abstract This study investigates the mechanical performance of sustainable AA6061 matrix composites reinforced with silica sand and subjected to thermomechanical treatments. The objective of this study is to assess silica sand as a cost-effective, eco-friendly alternative to traditional ceramic reinforcements in enhancing strength, hardness, and durability. Given the growing demand for sustainable materials in aerospace, automotive, and structural applications, this research explores the potential of silica sand to improve composite properties. The hypothesis is that silica sand, when uniformly dispersed in the AA6061 matrix and processed through rolling and peak ageing, can significantly enhance the mechanical properties. Composites were fabricated via stir casting with 2%, 4%, and 6% silica sand by weight, followed by low-temperature thermomechanical treatment. Mechanical testing included Brinell and Vickers hardness tests, tensile strength evaluation, microstructural analysis, and fracture surface examination. The results revealed consistent improvements with increased reinforcement. Compared to the as-cast composite, the 6% silica sand composite treated at 100 °C with 15% deformation exhibited a 118% increase in hardness and a 62% inrease in tensile strength. Fracture analysis revealed a mixed mode with predominantly brittle failure after treatment. These findings confirm that the combination of silica sand with suitable processing, can produce high-performance, sustainable aluminium composites.
DANet a lightweight dilated attention network for malaria parasite detection
Effects of fertilization depth on sugarcane quality and soil fertility
Structural determination of small proteins by cryo-EM using a coiled coil module strategy
Bending performance of reinforced concrete beams with partial waste glass aggregate replacement assessed by experimental, theoretical and digital image correlation analyses
Fault tolerant and quality of service aware routing algorithm based on priority technique for scalable network on chip architectures
Abstract Network on Chip (NoC) architectures are essential subsystems for on-chip communication. They use routers and simplified protocols modeled after public data networks to transport packets using complex routing algorithms from their source to their destination. Reliable communication can be severely hampered by component failures, such as malfunctioning routers or cables, which can interrupt packet transfer. Performance may be harmed by the narrow criteria used by traditional fault-tolerant routing algorithms to find reliable routes. In order to improve routing reliability and Quality of Service (QoS) in scalable NoC architectures, this paper suggests a novel, adaptive fault-tolerant routing algorithm that incorporates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision-making technique. The suggested approach dynamically assesses and ranks alternate routes to choose the best ones, even when there are failures, by utilizing path length and density information from nearby nodes. On 8 × 8 meshes with 10% link failures, the approach reduces average delay by ~ 8–12% compared to EDAR and increases throughput by ~ 2–5% compared to EDAR; on application-driven traces, it reduces delay by ~ 5–15% at nearly equal throughput. It reduces energy per flit by around 15–20% compared to EDAR, improves throughput by about 3–4%, and lowers delay by about 8–10% under transient, thermal, and voltage disturbances. The two-stage decision core maintains the improvements on 16 × 16 meshes and reroutes locally in about 3–5 cycles without adding a critical-path cost. Additionally, the approach ensures scalability for large-scale NoC implementations by introducing low hardware overhead. The suggested algorithm is a viable answer for next-generation NoC designs, meeting the requirements of high-performance, dependable, and scalable on-chip communication systems thanks to its combination of fault tolerance, QoS awareness, and resource efficiency.
Transcriptomic dynamics of cardiac remodeling after myocardial infarction
Microgravity-induced wet chemical etching of borosilicate glass enhances the process rate
Relative crash risk and road safety during rainfall in Texas from 2006 to 2021
Autism traits and mental well-being: the mediating role of social camouflaging and the moderating role of social exclusion and public stigma
Abstract There is a strong relationship between autism and mental health problems. Autistic individuals are more frequently exposed to stigma and social exclusion in social life, which may lead them to engage in socially desirable behaviors to camouflage themselves. Within this framework, the present study aimed to examine the relationship between autism traits and mental well-being, focusing on social camouflaging behaviors in the context of social exclusion and perceived stigma. The study sample consisted of 548 adults across Turkey, including 432 women (78.8%) and 116 men (21.2%). To test the proposed model, Model 4 and Model 21 developed by Hayes were used. Data were analyzed using SPSS 21 and PROCESS Macro. The findings indicated a negative and significant relationship between autism traits and mental well-being, with social camouflaging mediating this relationship. In addition, the relationship between autism traits and social camouflaging was found to be influenced by social exclusion, while the relationship between social camouflaging and mental well-being was moderated by perceived public stigma. The findings are expected to contribute to broadening perspectives in autism research.