Quantitative trait loci analysis for molecular markers linked to agricultural traits of Pleurotus ostreatus.

Pleurotus ostreatus is a global mushroom crop with nutritional and medicinal benefits. However, the genetic basis of several commercial traits remains unknown. To address this, we analyzed the quantitative trait loci (QTLs) for two representative cultivars, "Heuktari" and "Miso," with apparently distinct alleles. A genetic map with 11 linkage groups was constructed, in which 27 QTLs were assigned to 14 traits. The explained phenotypic variations in QTLs ranged from 7.8% to 22.0%. Relatively high LOD values of 6.190 and 5.485 were estimated for the pinheading period and the number of valid stipes, respectively. Some QTL-derived molecular markers showed potential enhancement rates of selection precision in inbred lines, especially for cap shape (50%) and cap thickness (30%). Candidate genes were inferred from the QTL regions and validated using qRT-PCR, particularly for the cysteine and glutathione pathway, in relation to cap yellowness. The molecular markers in this study are expected to facilitate the breeding of the Heuktari and Miso lines and provide probes to identify related genes in P. ostreatus.

Non-relevant segment recognition via hard example mining under sparsely distributed events.

We propose on/offline hard example mining (HEM) techniques to alleviate the degradation of the generalization performance in the sparse distribution of events in non-relevant segment (NRS) recognition and to examine their utility for long-duration surgery. Through on/offline HEM, higher recognition performance can be achieved by extracting hard examples that help train NRS events, for a given training dataset. Furthermore, we provide two performance measurement metrics to quantitatively evaluate NRS recognition in the clinical field. The existing precision and recall-based performance measurement method provides accurate quantitative statistics. However, it is not an efficient evaluation metric in tasks where false positive recognition errors are fatal, such as NRS recognition. We measured the false discovery rate (FDR) and threat score (TS) to provide quantitative values that meet the needs of the clinical setting. Finally, unlike previous studies, the utility of NRS recognition was improved by applying our model to long-duration surgeries, instead of short-length surgical operations such as cholecystectomy. In addition, the proposed training methodology was applied to robotic and laparoscopic surgery datasets to verify that it can be robustly applied to various clinical environments.

Deep learning-based surgical phase recognition in laparoscopic cholecystectomy.

Backgrounds/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot's triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score: 0.7761). Conclusions: Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.