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Long-term editing of brain circuits using an engineered electrical synapse
Abstract Electrical signalling across distinct populations of brain cells underpins cognitive and emotional function. However, approaches that selectively regulate electrical signalling between two cellular components of a mammalian neural circuit remain sparse. Here we engineered an electrical synapse composed of two connexin proteins 1 found in Morone americana (white perch fish)—connexin 34.7 and connexin 35—to accomplish mammalian circuit modulation. By exploiting protein mutagenesis, devising a new in vitro system for assaying connexin hemichannel docking, and performing computational modelling of hemichannel interactions, we uncovered a structural motif that contributes to electrical synapse formation. Targeting this motif, we designed connexin 34.7 and connexin 35 hemichannels that dock with each other to form an electrical synapse but not with other major connexins expressed in the mammalian central nervous system. We validated this electrical synapse in vivo using worms ( Caenorhabditis elegans ) and mice ( Mus musculus ). We demonstrate that it can strengthen communication across neural circuits composed of pairs of distinct cell types and modify behaviour accordingly. Thus, we establish ‘long-term integration of circuits using connexins’ (LinCx) for precision circuit editing in mammals.
Association between metabolic indices and the prevalence of sarcopenia in older adults: a cross-sectional study
Abstract Sarcopenia—the progressive decline in skeletal muscle mass and function—is a major hurdle for aging societies, often leading to physical disability and a loss of independence. In this study, we evaluated 162 participants over the age of 60 to determine if simple metabolic tools, specifically the Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), and Cardiometabolic Index (CMI), could help identify individuals with sarcopenia. We found that aging involves a problematic shift in body composition: even when a person’s weight or BMI remains stable, visceral fat often accumulates while muscle tissue wastes away. Our analysis shows that VAI and LAP are strongly and independently associated with sarcopenia. Notably, these indices provide a much clearer picture of health risks than BMI alone, as BMI fails to account for how fat is actually distributed. We also observed distinct sex-related differences; while men typically showed higher lipid accumulation and muscle strength, the link between muscle mass and metabolic health was more pronounced in women. Ultimately, incorporating VAI and LAP into routine geriatric assessments could serve as useful tools for identifying muscle loss in routine assessments. This would enable clinicians to implement targeted dietary and exercise interventions sooner, which is essential for helping older adults maintain their functional capacity.
Tiny hubs of metabolic activity optimize nitrogen use in maize
RCAF for patient-level thyroid ultrasound malignancy prediction under leakage-free evaluation and calibration
Abstract Accurate differentiation between benign and malignant thyroid nodules on ultrasound remains clinically important, yet interpretation is operator-dependent and subject to inter-observer variability. Recent deep learning studies report strong performance for thyroid ultrasound classification, but many prior approaches remain centred on image-level prediction, with limited emphasis on patient-level baselines, calibration-aware evaluation, and mask-related shortcut analysis. To address these gaps, we present a patient-level thyroid ultrasound malignancy prediction framework centred on a Region-Aware Context-Aware Fusion (RCAF) model evaluated under a strict leakage-free protocol. RCAF combines lesion-focused and context-preserving frame representations through a dual-branch design with gated fusion, followed by attention-based multiple instance learning (AttnMIL) for patient-level aggregation. The framework incorporates development-only probability calibration and threshold selection before single-shot evaluation on an untouched independent test cohort. Experiments on the public ThyroidXL benchmark show that RCAF outperforms stronger fair patient-level baselines, including image-only, transformer-based, and lesion-only comparators. Calibration analysis improves probability reliability, threshold analysis demonstrates stable behaviour under clinically relevant operating conditions, and shortcut sensitivity experiments show that naive mask concatenation produces shortcut-prone gains, whereas RCAF degrades by only 0.001 ROC-AUC under within-patient mask permutation, supporting principled region-aware reasoning. For cross-domain assessment, RCAF was evaluated on TN5000, a Chinese thyroid ultrasound dataset acquired under different imaging conditions; following domain adaptation and 8-view test-time augmentation, the model achieved AUC = 0.914 [0.879–0.948] on a class-balanced validation subset. Overall, RCAF constitutes a strong patient-level thyroid ultrasound classification framework, with encouraging cross-domain adaptability. Broader prospective multi-centre validation remains necessary before clinical deployment.
Use of organ transplant solution to preserve skeletal muscle for cellular and spatial transcriptomic analyses
A vibration control method for chatter mitigation in milling process based on sliding mode control and reinforcement learning
Abstract Chatter is a self-excited vibration phenomenon that limits the productivity, surface quality and tool life of milling operations. In this study, an active chatter mitigation strategy is proposed by integrating sliding mode control (SMC) with reinforcement learning (RL) and an active vibration damper (AVD). A two-degree-of-freedom milling model is first formulated to describe tool vibration in the feed and normal directions, including regenerative cutting-force effects, nonlinear force components and damper-friction dynamics. A continuous-time sliding mode controller is then developed to provide robust suppression of chatter under bounded nonlinearities and modelling uncertainties. To reduce the conservative switching action and improve adaptive compensation, an actor-critic reinforcement learning component is incorporated as a bounded auxiliary control signal. The RL agent uses vibration states, sliding variables and previous control information to learn compensation forces that reduce residual chatter while penalising excessive control effort and abrupt force variations. A Lyapunov-based boundedness theorem is established to show that the sliding surface, vibration error and closed-loop milling states remain uniformly ultimately bounded when the switching gain dominates the lumped uncertainty and bounded RL compensation. Numerical simulations are conducted using cutting and structural parameters extracted from established nonlinear milling chatter studies. The proposed SMC-RL controller is compared with an uncontrolled case and a conventional PID controller. The results show that the proposed method achieves faster vibration decay and lower residual chatter in both vibration directions. Based on the mean squared error indicator, the proposed controller achieves vibration attenuation of 86.27% in the x -direction and 87.09% in the y -direction, outperforming the PID benchmark. These findings demonstrate that combining SMC robustness with RL-based adaptive compensation provides a promising framework for active chatter suppression in high-productivity milling processes.
Process optimization and characterization of levan from Sporosarcina globispora MTCC 4776
Abstract This study aims to explore Sporosarcina globispora MTCC 4776 to produce levan using submerged fermentation. The assessment of carbon and nitrogen sources revealed that a moderate concentration of sucrose and tryptone improved levan production. Eight of the independent variables, concentration of sucrose, tryptone, CaCl₂·2H₂O, K₂HPO₄, MgSO₄·7H₂O, % of inoculum, incubation time and agitation speed was assessed with twelve experimental runs through Plackett–Burman design. Concentration of sucrose, tryptone and agitation speed were found to be significant. These factors were further optimized using Central Composite Design, yielding a maximum of 1.41 g/L of levan, which is a 6.2-fold increase compared to unoptimized conditions. The extracted levan was characterized for its structure and physicochemical properties by FTIR, 1 H/ 13 C-NMR, TGA/DTG, DSC, and XRD. These results confirmed the presence of a β-(2 → 6)- linked fructofuranosyl chain with excellent thermal stability and a semi-crystalline structure. In the future, studies on the purification and application of levan will be conducted to assess the sample’s potential.
Cascaded adaptive load frequency control for single area and double area power systems considering wind penetration
Abstract This study investigates the application of a cascaded adaptive controller for Load Frequency Control (LFC) in single-area and two-area power systems. The controller is a combination between adaptive PI controller and PID controller thus the term cascaded controller. The main objective is to evaluate the controller’s performance under various operating conditions through comparison with a conventional controller and adaptive controller. The case studies in the single-area power system shall be four case studies to be examined: (1) a conventional system without renewable energy integration, (2) a system with wind power introduced as a disturbance source, and (3) and (4) modified versions of the first two cases excluding time-delay effects. Similarly, the two-area power system is analyzed using four case studies, for the first and second scenarios, a static load change is applied independently to each area, while the third and fourth scenarios extend these cases by considering dynamic, time-varying load changes. For all scenarios, disturbances are introduced in one area, and their effects on tie-line power flow are analyzed. An optimization algorithm is utilized to determine the optimal gain parameters for each controller configuration. MATLAB/Simulink is employed for system simulation, and the system responses are assessed in the time domain. Simulation results demonstrate that the proposed cascaded adaptive controller exhibits superior performance and robustness, particularly in disturbance rejection and frequency stability enhancement. As there is an improvement in the system response by minimizing overshoot, reducing oscillations, and achieving faster settling times—outperforming traditional PI, standalone AFOPI, and PID controllers indicating that it can be utilized in different power systems.
Depth-augmented diffusion policy with pseudo-depth for robust robotic manipulation
Isogeny-based post-quantum proxy signature for Internet of Things
Seroprevalence of hepatitis E virus antibodies among slaughtered pigs in Nigeria: an abattoir-based multi-state survey
Silver nanoparticle hydrogen peroxide composite mitigates resistant Escherichia coli dissemination driven by poultry waste biosecurity failures
Abstract This two-phase study evaluated biosecurity vulnerabilities across 100 commercial poultry farms via a field survey and an in vitro assessment of hydrogen peroxide (H 2 O 2 ) versus a silver nanoparticle–hydrogen peroxide (AgNPs– H 2 O 2 ) composite. Survey metrics revealed critical baseline deficiencies: 80% of farmers lacked antimicrobial resistance (AMR) awareness, 90% practiced no litter treatment, and 70% of untreated waste was sold directly to aquaculture. Molecular analysis of 192 litter samples verified a 72.4% E. coli prevalence, with Multiple Antibiotic Resistance (MAR) indices peaking at 0.90. Phenotypic profiling showed high resistance to ampicillin (89.9%) and amoxicillin-clavulanic acid (61.2%), whereas colistin demonstrated 100% susceptibility. A significant co-resistance was identified between imipenem and tetracycline (φ = 0.65, p_adj < 0.001) and between cefotaxime and ceftazidime (φ = 0.58, p_adj < 0.001), P < 0.01) after Benjamini-Hochberg FDR correction. Concurrently, the AgNPs– H 2 O 2 composite exhibited superior efficacy with a Minimum Inhibitory Concentration (MIC) of 3.125 µg/mL, proving four-fold more potent than standalone H 2 O 2 . Time-kill kinetics demonstrated complete bacterial reduction within 24 h ( P < 0.001), successfully suppressing the post-6-hour regrowth observed with H 2 O 2 alone. While this study is limited by its regional geographical scope and the lack of molecular characterization for specific resistance genes, it conclusively identifies veterinary supervision deficits ( P < 0.0001) as a driver of extensively drug-resistant (XDR) transmission. Ultimately, the AgNPs– H 2 O 2 composite offers a promising in vitro One Health biosecurity strategy, achieving complete bacterial reduction under controlled conditions at a 75% lower concentration than standalone H 2 O 2 , pending field validation.
Sustainable construction materials performance evaluation of PMSWA-enhanced solid blocks through response modeling
Temporal shifts and relationships of emotions in social and mass media: A case study of the “Reiwa Rice Riot” in Japan
Abstract In Japan, severe rice shortages in 2024 sparked widespread public controversy across both news media and social platforms, culminating in what has been termed the “Reiwa Rice Riot.” This study proposes a framework to analyze the temporal dynamics and directional relationships of emotions expressed on X (formerly Twitter) and in news articles, using the “Reiwa Rice Riot” as a case study. While recent studies have shown that emotions are dynamically associated across social and mass media, the patterns and pathways of such emotional shifts remain insufficiently understood. To address this gap, we applied a machine learning–based emotion classification grounded in Plutchik’s eight basic emotions to analyze posts from X and domestic news articles. Our findings suggest that emotional shifts on X tended to precede those in news media in a temporal sense. Furthermore, in both media platforms, fear was initially the most dominant emotion, but over time intersected with anticipation which ultimately became the prevailing emotion. Our findings suggest that patterns in emotional expressions on social media may serve as a lens for understanding temporal shifts in emotions during social crises and the temporal precedence of emotions across social and news media.
Improved hybrid islanding detection using data fusion, adaptive back propagation neural network and support vector machine with ROCPAD and IBRPV
Abstract A new hybrid islanding detection method (IDM) is proposed in this study to enhance the precision and effectiveness of islanding detection in hybrid microgrids (HMGs), especially in light of the increasing integration of renewable energy sources (RES) into power networks. The IDM presents a novel data-driven approach that combines data fusion techniques with an adaptive backpropagation neural network (ABPNN) and support vector machine. By utilizing feature datasets such as the rate of change of phase angle difference (ROCPAD), intermittent-bilateral reactive power variation (IBRPV), and frequency, the IDM aims to accurately identify islanding events within HMGs. The approach can be summarized in two main stages: The data set for training the ABPNN is cleaned first by offline data preprocessing, using k-means clustering and logic operation techniques. Subsequently, the trained neural network is used to classify the online testing data into different categories by support vector machines so that islanding and non-islanding events can be identified in real scenarios in real time. The results of the proposed IDM show significant improvements in the accuracy of islanding detection, the rapid identification of islanding events, and the prevention of nuisance tripping occurrences. The IDM achieves a zero non-detection zone (NDZ) and exhibits minimal impact on power quality, making it a highly promising solution for islanding detection in HMG environments.