Climate change impacts on Allium crop production: Insights from long-term observations in South Korea.

Climate change is an imminent threat, particularly affecting agricultural productivity, which relies heavily on weather conditions. Understanding the specific impacts of climate change on key crops is crucial for developing effective adaptation strategies to ensure food security. The growth patterns of onions and garlic were observed at over ten different locations in South Korea, and the yield data from the past 40 years were analyzed. The yield was significantly correlated with temperature and strongly affected by the frequent and unexpected patterns of precipitation. The increase in mean temperature during winter and the spatial and temporal concentration of precipitation are expected to be the most influential factors for Allium crop production in the future. In addition, the yields of onions and garlic can serve as good indicators for predicting the impacts of weather on agricultural productivity, given their extended cultivation periods and significant correlations with temperature and precipitation. As climate change scenarios become available, the results of this study can serve as a basis for predicting changes in agricultural production in the future and identifying opportunities to adapt cultivation systems for food security.

Correlation between reproductive performance and sow body weight change during gestation.

This study investigated the correlation between piglet performance and sow body weight change (BWC) during two gestational periods: 35-70, 70-105, and 35-105 days. A cohort of 70 sows was evaluated for BWC, backfat thickness change (BFC), caliper score change (CALC), feed intake, and weaning-to-estrus interval (WEI). The collected data were then analyzed according to the two specified periods. Our findings highlighted that piglet birth weight, weaning weight, and average daily weight gain (ADG) correlated with sow body characteristics, including BFC and CALC. The strongest correlation was observed with BWC. Piglet mortality was intimately associated with BFC. Piglet birth weight, weaning weight, and ADG showed a positive correlation with sow BWC, particularly during the 35-70 day period. Furthermore, sows displaying a higher BWC during the 70-105 day period, and also exhibiting a higher BW gain from 35-70 days, registered greater piglet weight gains and higher weaning weights. These trends became more apparent as the sow's BWC increased during the 70-105 day period. Piglet mortality increased when the sow exhibited a lower BWC during both the 35-70 and 70-105 day periods. No significant observations were found concerning the number of stillborn piglets, live-born piglets, or weaned piglets, and no interaction effects were detected between these periods. In conclusion, our findings underscore the significance of sow BWC during the early stages of gestation (d 35-70) for enhancing piglet performance from birth to weaning.

Pathogenicity, Host Resistance, and Genetic Diversity of Fusarium Species under Controlled Conditions from Soybean in Canada.

Fusarium spp. are commonly associated with the root rot complex of soybean (Glycine max). Previous surveys identified six common Fusarium species from Manitoba, including F. oxysporum, F. redolens, F. graminearum, F. solani, F. avenaceum, and F. acuminatum. This study aimed to determine their pathogenicity, assess host resistance, and evaluate the genetic diversity of Fusarium spp. isolated from Canada. The pathogenicity of these species was tested on two soybean cultivars, 'Akras' (moderately resistant) and 'B150Y1' (susceptible), under greenhouse conditions. The aggressiveness of the fungal isolates varied, with root rot severities ranging from 1.5 to 3.3 on a 0-4 scale. Subsequently, the six species were used to screen a panel of 20 Canadian soybean cultivars for resistance in a greenhouse. Cluster and principal component analyses were conducted based on the same traits used in the pathogenicity study. Two cultivars, 'P15T46R2' and 'B150Y1', were consistently found to be tolerant to F. oxysporum, F. redolens, F. graminearum, and F. solani. To investigate the incidence and prevalence of Fusarium spp. in Canada, fungi were isolated from 106 soybean fields surveyed across Manitoba, Saskatchewan, Ontario, and Quebec. Eighty-three Fusarium isolates were evaluated based on morphology and with multiple PCR primers, and phylogenetic analyses indicated their diversity across the major soybean production regions of Canada. Overall, this study contributes valuable insights into host resistance and the pathogenicity and genetic diversity of Fusarium spp. in Canadian soybean fields.

Development of a virtual reality system usability questionnaire (VRSUQ).

Virtual reality (VR) has gained significant attention as a technology that provides immersive experiences similar to the real world. In order for a VR system to be accepted, usability needs to be guaranteed. Accordingly, VR-related researchers are continuing their efforts to improve VR systems by conducting usability evaluations. However, existing studies have limitations in that they cannot comprehensively evaluate the detailed properties of VR systems by using questionnaires developed for general product usability evaluation or focusing only on some usability aspects of VR systems. This suggests it may be difficult to fully capture usability issues in a VR system, and that it is necessary to develop a usability evaluation tool that reflects the specific characteristics of the VR system. Therefore, this study develops and proposes the Virtual Reality System Usability Questionnaire (VRSUQ). In the development of the questionnaire, items were structured based on a literature review and discussions with experts. To account for the diverse characteristics of VR systems, the validity of the questionnaire was verified through both exploratory and confirmatory factor analysis, utilizing data obtained from three distinct experimental studies that employed different VR systems. In addition, by comparing the results of VRSUQ with the results from the System Usability Scale, which is widely used for perceived usability evaluation, alternative possibilities for using VRSUQ are presented. Further testing on various VR platforms is needed to ensure the reliability and validity of VRSUQ, and as results from using VRSUQ are accumulated, it is expected to be widely used as a more powerful and robust VR-specific perceived usability evaluation tool.

Multislice computed tomography demonstrating mental nerve paresthesia caused by periapical infection: A case report.

Components derived from an infected lesion within the bone can spread through various passages in the mandible, particularly via the mental foramen. Radiologically, the spread of infection is typically nonspecific and challenging to characterize; however, multislice computed tomography (MSCT) can effectively detect pathological changes in soft tissues and the bone marrow space. This report describes the case of a 55-year-old woman who experienced mental nerve paresthesia due to a periapical infection of the right mandibular second premolar. MSCT imaging revealed increased attenuation around the periapical lesion extending into the mandibular canal and loss of the juxtamental foraminal fat pad. Following endodontic treatment of the tooth suspected to be the source of the infection, the patient's symptoms resolved, and the previous MSCT imaging findings were no longer present. Increased bone marrow attenuation and obliteration of the fat plane in the buccal aspect of the mental foramen may serve as radiologic indicators of inflammation spreading from the bone marrow space.

High-quality chromosome-level genome assembly of Nicotiana benthamiana.

Nicotiana benthamiana is a fundamental model organism in plant research. Recent advancements in genomic sequencing have revealed significant intraspecific genetic variations. This study addresses the pressing need for a precise genome sequence specific to its geographic origin by presenting a comprehensive genome assembly of the N. benthamiana LAB strain from the Republic of Korea (NbKLAB). We compare this assembly with the widely used NbLAB360 strain, shedding light on essential genomic differences between them. The outcome is a high-quality, chromosome-level genome assembly comprising 19 chromosomes, spanning 2,762 Mb, with an N50 of 142.6 Mb. Comparative analyses revealed notable variations, including 46,215 protein-coding genes, with an impressive 99.5% BUSCO completeness score. Furthermore, the NbKLAB assembly substantially improved the QV from 33% for NbLAB360 to 49%. This refined chromosomal genome assembly for N. benthamiana, in conjunction with comparative insights, provides a valuable resource for genomics research and molecular biology. This accomplishment forms a strong foundation for in-depth exploration into the intricacies of plant genetics and genomics, improved precision, and a comparative framework.

Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images.

Nasal endoscopy is routinely performed to distinguish the pathological types of masses. There is a lack of studies on deep learning algorithms for discriminating a wide range of endoscopic nasal cavity mass lesions. Therefore, we aimed to develop an endoscopic-examination-based deep learning model to detect and classify nasal cavity mass lesions, including nasal polyps (NPs), benign tumors, and malignant tumors. The clinical feasibility of the model was evaluated by comparing the results to those of manual assessment. Biopsy-confirmed nasal endoscopic images were obtained from 17 hospitals in South Korea. Here, 400 images were used for the test set. The training and validation datasets consisted of 149,043 normal nasal cavity, 311,043 NP, 9,271 benign tumor, and 5,323 malignant tumor lesion images. The proposed Xception architecture achieved an overall accuracy of 0.792 with the following class accuracies on the test set: normal = 0.978 ± 0.016, NP = 0.790 ± 0.016, benign = 0.708 ± 0.100, and malignant = 0.698 ± 0.116. With an average area under the receiver operating characteristic curve (AUC) of 0.947, the AUC values and F1 score were highest in the order of normal, NP, malignant tumor, and benign tumor classes. The classification performances of the proposed model were comparable with those of manual assessment in the normal and NP classes. The proposed model outperformed manual assessment in the benign and malignant tumor classes (sensitivities of 0.708 ± 0.100 vs. 0.549 ± 0.172, 0.698 ± 0.116 vs. 0.518 ± 0.153, respectively). In urgent (malignant) versus nonurgent binary predictions, the deep learning model achieved superior diagnostic accuracy. The developed model based on endoscopic images achieved satisfactory performance in classifying four classes of nasal cavity mass lesions, namely normal, NP, benign tumor, and malignant tumor. The developed model can therefore be used to screen nasal cavity lesions accurately and rapidly.

Pan-chloroplast genomes for accession-specific marker development in Hibiscus syriacus.

Hibiscus syriacus L. is a renowned ornamental plant. We constructed 95 chloroplast genomes of H. syriacus L. cultivars using a short-read sequencing platform (Illumina) and a long-read sequencing platform (Oxford Nanopore Technology). The following genome assembly, we delineate quadripartite structures encompassing large single-copy, small single-copy, and inverted repeat (IRa and IRb) regions, from 160,231 bp to 161,041 bp. Our comprehensive analyses confirmed the presence of 79 protein-coding genes, 30 tRNA genes, and 4 rRNA genes in the pan-chloroplast genome, consistent with prior research on the H. syriacus chloroplast genome. Subsequent pangenome analysis unveiled widespread genome sequence conservation alongside unique cultivar-specific variant patterns consisting of 193 single-nucleotide polymorphisms and 61 insertions or deletions. The region containing intra-species variant patterns, as identified in this study, has the potential to develop accession-specific molecular markers, enhancing precision in cultivar classification. These findings are anticipated to drive advancements in breeding strategies, augment biodiversity, and unlock the agricultural potential inherent in H. syriacus.

Respiratory Epithelial Adenomatoid Hamartoma at an Unusual Location: A Case Report and Literature Review.

Respiratory epithelial adenomatoid hamartoma (REAH) in the head and neck is a rare benign lesion containing glandular tissue covered with ciliated respiratory epithelium. In the head and neck, REAH of the nasal cavity, paranasal sinuses, and nasopharynx have been reported in literature. Due to rareness of REAH and insufficient knowledge of its imaging features, the diagnosis can be challenging when we encounter a non-specific cystic mass at an uncommon site in the head or neck. Here, we report the case of a pathologically confirmed REAH showing a cystic mass centered at the buccal space (retromaxillary fat pad) with CT and MRI findings.

Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits.

OBJECTIVE: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). METHODS: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. RESULTS: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. CONCLUSION: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.