MethodsX最新文献

This study describes a wet chemical extraction protocol for measuring the biogenic silica (bSi) in sediments from diverse marine environments. The protocol lists the reagents, materials, equipment, and sample preparation procedures, and provides a detailed explanation of the methods for examining the alkaline-leachable silicon (Si), and calculating bSi content. Although, the protocol was primarily developed for measuring bSi in sediments from the Chinese marginal seas, it was also validated using sediments from the Chesapeake Bay, the Atlantic Ocean, and the Southern Ocean. The protocol can be used to quantify bSi in recently deposited and aged sediments from the Holocene period. The protocol contributes to the ongoing efforts to minimize the methodological bias that exist in bSi quantification and the bSi burial flux evaluation, thereby assisting in our understanding of Si cycling in the modern ocean. • This protocol provides a step-by-step wet chemical extraction procedures and the measurement of dissolved Si in an alkaline solution using spectrophotometer. • This protocol is easy to set up and reproduce, and determines bSi content with high precision. • The protocol can be used to determine bSi in sediments of marginal seas and the open ocean.

The Radiographs are essential in clinical dentistry as they provide information that is invisible during an oral inspection. These images, however suffer from excess noise, low resolution, and poor contrast which impacts diagnosis accuracy. This study presents a two‑stage pipeline combining Enhanced Super‑Resolution GAN (ESRGAN) for radiograph enhancement followed by YOLOv8 for multi‑class dental anomaly detection. The proposed method involves the application of ESRGAN (Enhanced super-resolution generative adversarial network with adaptive dual perceptual loss) that improves image detail and sharpen resolution. A customized YOLOv8 object detection model which is trained to detect object and classify into six important dental conditions. The classification is Caries, Crown, Root Canal Treated (RCT), Restoration, Normal, and Badly Decayed teeth. The ESRGAN-enhanced images demonstrated high visual fidelity, achieving a Peak Signal-to-Noise Ratio (PSNR) of 28.7 dB and a Structural Similarity Index (SSIM) of 0.91. The proposed YOLOv8 model analyzes the images after being enhanced by ESRGAN. The YOLOv8 model was evaluated on 100 test images and achieved an overall mean Average Precision (mAP@0.5) of 56.9 % and mAP@0.5:0.95 of 41.6 %. The proposed model achieved Sensitivity (Recall) 0.942 and Specificity 0.919 for Crown detection. Detection of Caries and Badly Decayed teeth remained challenging, with lower sensitivity scores of 0.174 and 0.355, respectively. Specificity across classes ranged from 0.361 (RCT) to 0.887 (Caries), indicating variable false positive rates. The proposed pipeline demonstrated clinical potential by improving subtle structural visibility and supporting automated dental assessment. Future work will explore class‑specific augmentation and explainability tools to increase clinical utility. ESRGAN significantly improved the resolution and clarity of dental X-rays, enabling better visualization of fine details for accurate diagnosis. YOLOv8 effectively identified six dental conditions, achieving high accuracy for distinct classes like crowns and restorations.

Temperature treatment is commonly used to manipulate circadian rhythms in cells and tissue cultures. However, it is often laborious and error-prone in prolonged studies. We present the ThermoClock, an Arduino-based temperature regulation system designed for precise, automated temperature control in ex vivo and in vitro studies, particularly circadian rhythm research. Built with off-the-shelf components and open-source software, ThermoClock is easy to fabricate, costing approximately $450 and requiring under 10 h to assemble. Its modular design enables simultaneous control of multiple conditions, reducing manual intervention and user error. Individual ThermoClock modules use a Proportional-Integral-Derivative (PID) controller and off-the-shelf electronics to realize real time, precise temperature controls, while being cost-friendly and accessible to construct and operate. Assembled ThermoClock can operate up to five temperature modules, greatly enhancing experimental versatility and throughput. An Arduino script is provided to automate the temperature controls based on user-input temperature setpoint schedules. ThermoClock is designed to function in an incubator and shows significantly faster heating and cooling (p < 0.001) compared to a programmable incubator. It reaches the target temperature within five minutes after a setpoint change.

Smoking remains a persistent global health concern with complex behavioral dynamics influenced by memory and past experiences. This study formulates and analyses a fractional-order mathematical model of smoking behavior using the Caputo derivative to capture memory effects and non-local interactions. The well-posedness of the model is ensured through rigorous proofs of existence and uniqueness of solutions. To assess the system's resilience, Hyers-Ulam-Rassias stability is investigated under small perturbations. To address potential chaotic behavior, we implement chaos control techniques, stabilizing the system for reliable long-term predictions. A novel Newton polynomial-based numerical scheme is developed to efficiently approximate solutions, validated through extensive simulations. Our results demonstrate that fractional-order modeling provides deeper insights into smoking dynamics compared to classical approaches. Some key features of the proposed method include:•Investigating Hyers-Ulam-Rassias stability to analyze robustness against perturbations.•Applying chaos control techniques to manage and stabilize chaotic system behavior.•Developing and implementing a Newton polynomial-based numerical scheme for efficient solution approximation.