Search Results (1,198)

Optical density (OD)-based cell growth measurement is commonly used in high-throughput screening (HTS) during drug discovery or when deciphering the pharmaceutical mechanism of action. While resuspending the cells via a mixing step is often assumed to be necessary prior to OD measurement, its essentiality in HTS workflows has not been systematically verified. Here, through the measurement of the growth of several strains of the microbial yeast Schizosaccharomyces pombe cells, we compared the overall growth dynamics between samples that have been mixed and not mixed. Using statistical quantification by a two-tailed paired t-test followed by multiple comparison corrections, we concluded from the comparison of the doubling time of cells growing in the exponential phase that mixing did not significantly affect the biological interpretation compared to unmixed samples. Doubling time quantification between mixed and unmixed samples showed a difference of approximately 10% on average based on the assessment of the growth of eight strains. As such, if the experimental outcome can accommodate this level of variability, incorporating a mixing step before OD determination would not be necessary. These observations support the simplification of HTS processes, improving the cost efficacy and process efficiency of readouts, yet maintaining the accuracy of data acquisition. Full article

This paper presents a high-performance and resource-efficient Field Programmable Gate Array (FPGA)-based architecture for satellite telemetry transmission systems. The proposed design implements a flexible channel coding chain, including Reed–Solomon (R-S) encoding, convolutional encoding, symbol interleaving, pseudo-randomization, and Attached Synchronization Marker (ASM) insertion, in accordance with CCSDS recommendations. The architecture is fully integrated and configurable, allowing dynamic selection of coding schemes without requiring structural modifications. The system is implemented on a modern FPGA platform with a 32-bit AXI4-Stream interface at 110 MHz, reaching an effective throughput of up to 1.76 Gbps. Experimental results demonstrate reliable timing with positive setup and hold margins, allowing the system to operate at approximately 130 MHz. Power consumption is measured using Switching Activity Interchange Format (SAIF)-based switching activity, providing a realistic estimate of programmable logic power consumption. The total on-chip power is about 1.77 W for individual coding modes. It rises to 1.91 W in the concatenated setup, which is the worst-case scenario. The results show that the proposed architecture efficiently uses resources, runs reliably at high speeds, and exhibits predictable power consumption. This makes it well suited for high-reliability and energy-constrained satellite communication systems. resources are used. Full article

Irreversible covalent enzyme inhibitors, including targeted covalent inhibitors (TCIs) and mechanism-based enzyme inhibitors (MBEIs), play an increasingly important role in drug discovery. Their pharmacological behavior is governed by intrinsic inactivation parameters, typically described by the inactivation constant K_I, the maximal inactivation rate constant k_inact, and their ratio k_inact/K_I. However, no consensus exists regarding how these parameters should be experimentally determined and interpreted, particularly in high-throughput screening environments where IC₅₀ values are often used as primary descriptors. This article presents a critical survey of the kinetic methodologies employed to characterize irreversible enzyme inhibition. Continuous progress-curve analysis, discontinuous end-point assays, IC₅₀-based estimation strategies, direct mass-spectrometric monitoring of covalent modification, and numerical approaches required by pre-incubation protocols are examined and compared. Attention is given to the statistical robustness of parameter estimation under realistic experimental error, including bootstrap-based uncertainty analysis. For mechanism-based enzyme inhibitors, the kinetic consequences of branching between productive turnover and irreversible inactivation are analyzed, and limitations of classical half-life-based linearization methods are discussed. Intrinsic inactivation parameters are distinguished from protocol-dependent observables, and experimental conditions that may compromise reliable parameter extraction are identified. The objective is to clarify how irreversible inhibitors should be kinetically characterized when the goal is mechanistic understanding and rational drug design. By bridging classical enzymology with current discovery practices, this review provides practical guidance on what experimental data can legitimately support and where caution is required. Full article

This study presents a comparative analysis of hyperspectral image segmentation algorithms for fruit defect detection under different illumination conditions. The research evaluates the performance of four segmentation methods (Spectral Angle Mapper, Random Forest, Support Vector Machine, and Neural Network) using three distinct illumination modes (local, simultaneous and sequential). The experimental setup employed hyperspectral imaging to assess tomato fruit samples, with data acquisition performed across the 450–850 nm spectral range. Quantitative metrics, including accuracy, error rate, precision, recall, F1-score, and Intersection over Union (IoU), were used to evaluate algorithm performance. Key findings indicate that Random Forest demonstrated superior performance across most metrics, particularly under simultaneous illumination conditions. The highest accuracy was achieved by Random Forest under sequential illumination (0.9971), while the best combination of segmentation metrics was obtained under simultaneous illumination, with an F1-score of 0.8996 and an IoU of 0.8176. The Neural Network showed competitive results. The Spectral Angle Mapper proved sensitive to illumination variations but excelled in specific scenarios requiring minimal memory usage. By demonstrating that acquisition protocol optimization can substantially improve segmentation performance, our results support the development of accurate, non-contact, high-throughput inspection systems and contribute to reducing postharvest losses and improving supply chain quality control. Full article

Compared to antennas bearing a single port, MIMO antennas with several ports enable higher data throughput by exploiting spatial diversity. This capability is essential for next-generation implantable medical devices, where high channel capacity is a key requirement. A quad-element implantable MIMO antenna is designed and practically validated at 1420 MHz in this paper. It occupies a compact volume of $7\times 8\times 0.1 {\mathrm{mm}}^3$ ($5.6 {\mathrm{mm}}^3$). The compactness is realized by combining high-permittivity substrate (Rogers 3010 with relative permittivity of 10.2) with meandered radiator paths, which increase the effective current length while maintaining a small physical size. All antennas have very small mutual coupling with isolation of more than $31.78$ dB, which is mainly due to the spacing of 1 mm between the elements and the substrate, which is thin. The peak realized gain for each antenna element is $-27.3$ dBi. The simulation is performed within a capsule-like structure, which is embedded in the stomach tissue model. The experimental verification is carried out by embedding antenna within minced meat. The ECC, channel capacity, and link margin are also evaluated and found to be satisfactory. The proposed antenna ensures reliable communication performance, with the transmission range being as high as $2.5$ m, link margin being 15 dB, and the data rate being 120 Mb/s. The proposed antenna ensures a good level of ECC, which is less than $0.1$. The SAR is $52.3$ W/kg at 1420 MHz. This design is favorable for implants because of the small size, good impedance matching, high isolation, low correlation, good level of gain, and good link performance. Full article

Vehicular Ad Hoc Networks (VANETs) are a key enabler of intelligent transportation systems, supporting safety-critical and latency-sensitive applications through vehicle-to-vehicle and vehicle-to-infrastructure communications. However, high node mobility, rapidly changing network topologies, and heterogeneous wireless conditions pose significant challenges to traditional distributed networking approaches, particularly in terms of quality of service (QoS) stability and handover performance. Software-Defined Networking (SDN) offers promising solutions by enabling centralized control, programmability, and flexible deployment of network functions. This paper presents an SDN-enabled vehicular networking platform designed for realistic, system-level experimentation under dynamic mobility conditions. The proposed platform tightly couples microscopic vehicular mobility generated by SUMO with wireless network emulation in Mininet-WiFi, enabling real-time interaction between vehicle movement, wireless connectivity, and SDN control decisions, where a custom SDN controller implements mobility-aware traffic management and handover handling across roadside units. Extensive experimental scenarios evaluate throughput, packet loss, jitter, and end-to-end latency under varying traffic loads and mobility patterns. Results indicate that SDN-based centralized control improves QoS consistency relative to the unmanaged baseline configuration considered in this study. The proposed platform provides practical insights and a reproducible experimental framework for the design and evaluation of software-defined vehicular networking systems. Full article

Alzheimer’s disease (AD) represents an escalating global neuropharmacological crisis, with prevalence in high-growth demographic regions such as India projected to exceed 14 million by 2040. This study addresses the urgent need for high-potency, dual-site acetylcholinesterase (AChE) inhibitors through an integrated computational pipeline. We address the failure of mono-target paradigms by designing scaffolds capable of simultaneously anchoring the Catalytic Active Site (CAS) and the Peripheral Anionic Site (PAS). A robust GA-MLR QSAR model was developed from 115 quinoline analogs using 11,135 descriptors. Lead candidates were prioritized via cavity directed molecular docking (7XN1) and 100 ns molecular dynamics (MD) simulations. The five-descriptor model (R² = 0.7569, $Q_{LOO}^2$ = 0.7244) was validated by an external set of 8 experimental compounds ($R_{ext}^2$ = 0.8620). Lead Compound 19 emerged as a superior candidate (ΔG = −11.1 kcal/mol), exhibiting a stable MD trajectory (PL-RMSD ≈ 2.4 Å) and preserving essential Gly121-His447 catalytic anti-correlations. This study provides a statistically validated scaffold and computational mechanistic foundation for future in vitro experimental validation, advancing the high throughput screening of neuroprotective agents on a global scale. Full article

Distracted driving is one of the primary causes of road traffic accidents. Behavior recognition technology based on machine vision has emerged as a research hotspot due to its non-contact and high-efficiency nature. To address the challenges of complex lighting conditions in the driver’s cabin, low detection accuracy for small-scale keypoints, and the difficulty in effectively characterizing behavioral features, this paper proposes a distracted driving behavior recognition method based on an improved YOLOv8n-Pose model and multi-feature fusion. First, the original YOLOv8n-Pose model is optimized. A P2 detection layer is added to enhance the feature extraction capabilities for small-scale human keypoints, and the SE attention module is incorporated to improve the model’s robustness under complex lighting conditions. In addition, the loss function is replaced with focal loss to tackle the class imbalance problem, thus forming the YOLOv8n-PSF-Pose keypoint detection network. Subsequently, based on the coordinates of 12 human keypoints extracted by this network, a multi-dimensional feature vector is constructed, which takes joint angles as the core and integrates the relative distances between keypoints and the number of valid keypoints. Finally, a BP neural network is adopted to classify the constructed feature vectors, enabling the accurate recognition of six typical distracted driving behaviors (normal driving, drinking or eating, making phone calls, using mobile phones, operating vehicle infotainment systems, and turning around to fetch items). The experimental results show that the improved YOLOv8n-PSF-Pose model achieves an mAP50 of 93.8% in keypoint detection, which is 6.7 percentage points higher than the original model; the BP classification model based on multi-feature fusion achieves an F1-score of 97.7% in the behavior recognition task, which is significantly better than traditional classifiers such as SVM and random forest, and the image processing speed on the NVIDIA RTX 3090TI reaches a high throughput of 45 FPS. This proves that the proposed method achieves an excellent balance between accuracy and speed. This study provides an effective solution for the real-time and accurate recognition of distracted driving behaviors. Full article

This paper presents the design and experimental validation of an innovative robotic test stand for measuring camshaft cam geometry, intended to support preventive quality control in high-volume production. The proposed solution integrates a collaborative robot with a dedicated measurement setup to enable repeatable positioning of the inspected camshaft and automated acquisition of geometric features critical for functional performance. A complete measurement methodology was developed, including the measurement sequence, data acquisition procedure, and processing of the recorded signals to determine key cam geometry parameters. To verify the reliability of the proposed approach, measurement results obtained using the robotic stand were compared with reference data acquired using conventional metrology tools and standard inspection procedures. Experimental studies confirmed that the developed stand provides repeatable measurement results, enabling the stable identification of the examined geometric features across repeated trials. Moreover, a high level of agreement was observed between the measurement data obtained using the proposed method and the reference measurements, demonstrating the suitability of the cobot-based test stand for preventive quality control applications in industrial environments. The concept presented offers a scalable and flexible alternative to manual inspection and dedicated special-purpose gauges, with potential benefits in terms of inspection throughput and standardization of quality control workflows. The novelty of the approach lies in the indirect ultrasonic measurement model combined with a quadrant-based sensor orientation strategy and repeatable 90° camshaft indexing, enabling full-profile acquisition within the robot workspace. Full article

This study addresses the inefficiencies, subjectivity, and poor adaptability to lighting variations inherent in traditional candling methods used in large-scale egg incubation. We developed a high-throughput transmissive imaging system capable of capturing 30 eggs simultaneously. Based on this system, we propose PDA-YOLO, an enhanced YOLOv8-based object detection model featuring a position-decoupled attention strategy. Specifically, a lightweight C2f-SE module is integrated into the backbone to amplify subtle feature responses in low-contrast regions, while a CBAM is deployed prior to the detection head to mitigate background clutter through precise spatial attention. Experimental results on a self-constructed Hailan White egg dataset show that at the critical 60 h incubation stage, PDA-YOLO achieves a Recall of 91.5% and an mAP@0.5 of 97.4%, outperforming the YOLOv8 baseline while maintaining a real-time inference speed of 62.1 FPS. Grad-CAM visualizations confirm the model’s ability to focus on vascular textures and suppress noise. Furthermore, the model demonstrates robust performance under varying illumination (180–540 lumens), effectively mitigating missed detections in low light and recognition degradation from overexposure. This work provides a scalable, real-time solution for non-destructive, early-stage detection of poultry health and fertilization status in commercial hatcheries. Full article

Transient-source detection without relying on difference images still faces challenges in achieving high accuracy, especially under practical space-based astronomical survey conditions where the data volume is enormous, on-orbit transmission bandwidth is limited, and real-time response is required for rapid follow-up observations. To address these issues, this paper proposes a lightweight detection network that integrates multi-scale feature fusion with contextual feature extraction, enabling efficient real-time processing on resource-constrained edge devices. The proposed model enhances robustness to point-spread-function variations across observation conditions and to complex background environments, while simultaneously improving detection accuracy. To evaluate performance comprehensively, lightweight VGG and lightweight ResNet architectures and other baseline models—commonly used as baselines for transient-source detection—are adopted for comparison. Experimental results show that under the condition that the models have approximately the same number of parameters, the proposed network achieves the best accuracy, obtaining nearly 1% improvement compared with the best-performing baseline model. Based on this design, an ultra-lightweight version with only 7k parameters is further developed by incorporating a compact multi-scale module, improving accuracy by 1% over the version without the multi-scale structure. Moreover, through heterogeneous knowledge distillation and adaptive iterative training, the accuracy of the ultra-lightweight model is further increased from 93.3% to 94.0%. Finally, the model is deployed and validated on an AI hardware acceleration platform. The results demonstrate that the proposed method substantially improves inference throughput while maintaining high accuracy, providing a practical solution for real-time, low-latency, on-device transient-source detection under large data volume and limited transmission conditions. Specifically, the proposed models are trained offline on a high-performance GPU and subsequently deployed on the Fudan Microelectronics 7100 AI board to evaluate their real-world inference efficiency on resource-constrained edge devices. Full article

Chronic kidney disease (CKD) and kidney aging share many pathological and molecular features, with cellular senescence emerging as a potentially important contributor to disease progression. Senescent cells accumulate in the kidneys due to persistent stressors, contributing to chronic inflammation and fibrosis via the senescence-associated secretory phenotype (SASP). This review explores the intersection between CKD and renal aging, focusing on the mechanisms driving senescence, its impact on kidney function, and potential therapeutic interventions. We explore various senotherapeutic approaches, such as senolytics, senomorphics, and rejuvenating agents, and highlight the increasing role of artificial intelligence (AI) and machine learning (ML) in detecting and monitoring senescent cells, enabling high-throughput and precise assessment across experimental and clinical settings. Understanding these mechanisms offers new avenues for developing targeted treatments to slow CKD progression and improve patient outcomes. Full article

Spiral inertial microfluidic devices provide a simple, high-throughput approach for size-based particle separation; however, translating PDMS-optimized designs into monolithic, fully enclosed 3D-printed channels is often limited by printability and post-print channel clearing. In our previous PDMS study, a $400\times 120\mu \mathrm{m}$ spiral achieved high separation performance after computational optimization and experimental validation. To translate this high-performing PDMS concept into a faster and more cost-effective manufacturing approach, the same separation principle is transferred to a fully 3D-printed, closed-channel spiral device, and the geometry is re-optimized around manufacturability constraints. Printing trials showed that enclosed channels at $400\times 120\mu \mathrm{m}$ and $600\times 180\mu \mathrm{m}$ could not be cleared reliably due to trapped resin and frequent blockage, most often near the inner-outlet region. In contrast, $800\times 240\mu \mathrm{m}$ and $1200\times 360\mu \mathrm{m}$ channels were printed and flushed successfully, and $800\times 240\mu \mathrm{m}$ was selected as the smallest reproducibly functional cross-section. Particle-tracking simulations were then used to re-optimize spiral development length, showing that a 4-turn device provides limited collection for $12\mu \mathrm{m}$ targets (10%), intermediate lengths (5–7 turns) improve collection to 50%, and an 8-turn spiral achieves complete large-particle collection (100%) across tested target sizes (12–$24\mu \mathrm{m}$) while reducing small-particle crossover. Experimental validation of the 8-turn $800\times 240\mu \mathrm{m}$ device at $Q=6\mathrm{m}\mathrm{L} {\min }^{-1}$ using fluorescent polystyrene particles ($18\mu \mathrm{m}$ target; $6\mu \mathrm{m}$ background) yielded an average collection efficiency of 84% and an inner-outlet purity of 92%. Overall, these results demonstrate that spiral inertial separation can be retained in a monolithic 3D-printed format when the design is re-optimized around the smallest reliably clearable enclosed cross-section and sufficient spiral development length. Full article

One-dimensional hybrid organic–inorganic semiconductors enable band-edge engineering through reduced dimensionality and interfacial orbital hybridization. Nevertheless, the electronic physics of Cu/Ag-based systems has received limited attention. Here, we perform high-throughput first-principles calculations on 90 Cu/Ag halide HOISs derived from experimentally reported parent structures to elucidate bonding-dependent electronic behavior. We uncover a clear transition from electronically isolated inorganic chains in ionic hybrids to strongly hybridized band edges in covalent and mixed-bonding hybrid frameworks, where ligand p orbitals cooperatively couple with Cu-derived states and halogen p orbitals. This hybridization produces p-orbital-dominated band edges, enhanced dispersion, and light-hole effective masses along the 1D chains. Guided by this bonding-driven mechanism, we further identify four Cu-based compounds, which are helpful for tuning light-harvesting properties in low-dimensional hybrid semiconductors. Full article

Soil fumigation effectively mitigates replanting obstacles induced by intensive cultivation, yet its non-targeted biocidal effects can suppress beneficial microbial activity, potentially compromising agricultural sustainability. Microbial inoculation, as a strategy to supplement beneficial microorganisms, is often employed to restore soil microbial communities. However, in practice, commonly used exogenous microbial consortia exhibit poor adaptability in non-native environments, frequently resulting in limited efficacy. To address this limitation, we propose an ecological intervention based on the reintroduction of indigenous cultivable microorganisms: cultivable microbial communities were isolated from healthy adjacent soils and inoculated into fumigated soils affected by replanting obstacles. The experimental soil consisted of black soil under continuous cropping, collected from Northeast China. The three treatments were continuous cropping soil (control), fumigated continuous cropping soil and fumigated continuous cropping soil after inoculation of indigenous cultivable microorganisms. Using high-throughput sequencing and agronomic–chemical analyses, combined with cross-domain networks and procrustes analysis, we systematically assessed the ecological effects of this approach on microbial restoration and the alleviation of replanting obstacles. The results showed that indigenous cultivable microorganism inoculation significantly increased the richness of bacterial and fungal communities in fumigated soils within 21 days, extending microbial richness and diversity. Furthermore, inoculation accelerated the reconstruction of dominant microbial community structures, with the relative abundance of dominant species reaching up to 80%. Positive synergistic interactions between bacteria and fungi increased by approximately 10%, enhancing network stability. Key bacterial taxa, such as Paenibacillus and Mycobacterium, were significantly correlated with available potassium and phosphorus content, while Micromonospora, Massilia, and Flavisolibacter influenced plant fresh weight, total nitrogen, and potassium accumulation. Key fungal taxa, such as Cryptococcus and Phialemonium, were significantly associated with soil organic matter stability, maize photosynthetic efficiency, plant dry weight, and total phosphorus content. This study confirms the ecological adaptability and functionality of indigenous cultivable microorganisms in soil ecosystem restoration, offering a low-risk, highly effective localized intervention strategy for sustainable agriculture. Full article