Identification of 2,4-Di-tert-butylphenol as a Novel Agonist for Insect Odorant Receptors.

Odorant molecules interact with odorant receptors (ORs) lining the pores on the surface of the sensilla on an insect's antennae and maxillary palps. This interaction triggers an electrical signal that is transmitted to the insect's nervous system, thereby influencing its behavior. Orco, an OR coreceptor, is crucial for olfactory transduction, as it possesses a conserved sequence across the insect lineage. In this study, we focused on 2,4-di-tert-butylphenol (DTBP), a single substance present in acetic acid bacteria culture media. We applied DTBP to oocytes expressing various Drosophila melanogaster odor receptors and performed electrophysiology experiments. After confirming the activation of DTBP on the receptor, the binding site was confirmed through point mutations. Our findings confirmed that DTBP interacts with the insect Orco subunit. The 2-heptanone, octanol, and 2-hexanol were not activated for the Orco homomeric channel, but DTBP was activated, and the EC50 value was 13.4 ± 3.0 µM. Point mutations were performed and among them, when the W146 residue changed to alanine, the Emax value was changed from 1.0 ± 0 in the wild type to 0.0 ± 0 in the mutant type, and all activity was decreased. Specifically, DTBP interacted with the W146 residue of the Orco subunit, and the activation manner was concentration-dependent and voltage-independent. This molecular-level analysis provides the basis for novel strategies to minimize pest damage. DTBP, with its specific binding to the Orco subunit, shows promise as a potential pest controller that can exclusively target insects.

YOLOv5 based object detection in reel package X-ray images of semiconductor component.

The industrial manufacturing landscape is currently shifting toward the incorporation of technologies based on artificial intelligence (AI). This transition includes an evolution toward smart factory infrastructure, with a specific focus on AI-driven strategies in production and quality control. Specifically, AI-empowered computer vision has emerged as a potent tool that offers a departure from extant rule-based systems and provides enhanced operational efficiency at manufacturing sites. As the manufacturing sector embraces this new paradigm, the impetus to integrate AI-integrated manufacturing is evident. Within this framework, one salient application is AI deep learning-facilitated small-object detection, which is poised to have extensive implications for diverse industrial applications. This study describes an optimized iteration of the YOLOv5 model, which is known for its efficacious single-stage object-detection abilities underpinned by PyTorch. Our proposed "improved model" incorporates an additional layer to the model's canonical three-layer architecture, augmenting accuracy and computational expediency. Empirical evaluations using semiconductor X-ray imagery reveal the model's superior performance metrics. Given the intricate specifications of surface-mount technologies, which are characterized by a plethora of micro-scale components, our model makes a seminal contribution to real-time, in-line production assessments. Quantitative analyses show that our improved model attained a mean average precision of 0.622, surpassing YOLOv5's 0.349, and a marked accuracy enhancement of 0.865, which is a significant improvement on YOLOv5's 0.552. These findings bolster the model's robustness and potential applicability, particularly in discerning objects at reel granularities during real-time inferencing.

Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms.

This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms. As a result, the HS-derived feature set trained with a support vector machine yielded an accuracy of 0.785 and a weighted F1 score of 0.782, which outperformed traditional methods. These findings underscore the competitive edge of metaheuristic-based selection, demonstrating its potential in advancing sarcopenia diagnosis. This study advocates for further exploration of metaheuristic-based feature selection's pivotal role in future sarcopenia research.