A Genome-Wide Association Study and Genomic Prediction for Fiber and Sucrose Contents in a Mapping Population of LCP 85-384 Sugarcane

Sugarcane (Saccharum spp. hybrids) is an economically important crop for both sugar and biofuel industries. Fiber and sucrose contents are the two most critical quantitative traits in sugarcane breeding that require multiple-year and multiple-location evaluations. Marker-assisted selection (MAS) could significantly reduce the time and cost of developing new sugarcane varieties. The objectives of this study were to conduct a genome-wide association study (GWAS) to identify DNA markers associated with fiber and sucrose contents and to perform genomic prediction (GP) for the two traits. Fiber and sucrose data were collected from 237 self-pollinated progenies of LCP 85-384, the most popular Louisiana sugarcane cultivar from 1999 to 2007. The GWAS was performed using 1310 polymorphic DNA marker alleles with three models of TASSEL 5, single marker regression (SMR), general linear model (GLM) and mixed linear model (MLM), and the fixed and random model circulating probability unification (FarmCPU) of R package. The results showed that 13 and 9 markers were associated with fiber and sucrose contents, respectively. The GP was performed by cross-prediction with five models, ridge regression best linear unbiased prediction (rrBLUP), Bayesian ridge regression (BRR), Bayesian A (BA), Bayesian B (BB) and Bayesian least absolute shrinkage and selection operator (BL). The accuracy of GP varied from 55.8% to 58.9% for fiber content and 54.6% to 57.2% for sucrose content. Upon validation, these markers can be applied in MAS and genomic selection (GS) to select superior sugarcane with good fiber and high sucrose contents

A Genome-Wide Association Study and Genomic Prediction for \u3ci\u3ePhakopsora pachyrhizi\u3c/i\u3e Resistance in Soybean

Soybean brown rust (SBR), caused by Phakopsora pachyrhizi, is a devastating fungal disease that threatens global soybean production. This study conducted a genome-wide association study (GWAS) with seven models on a panel of 3,082 soybean accessions to identify the markers associated with SBR resistance by 30,314 high quality single nucleotide polymorphism (SNPs). Then five genomic selection (GS) models, including Ridge regression best linear unbiased predictor (rrBLUP), Genomic best linear unbiased predictor (gBLUP), Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), Random Forest (RF), and Support vector machines (SVM), were used to predict breeding values of SBR resistance using whole genome SNP sets and GWAS-based marker sets. Four SNPs, namely Gm18_57,223,391 (LOD = 2.69), Gm16_29,491,946 (LOD = 3.86), Gm06_45,035,185 (LOD = 4.74), and Gm18_51,994,200 (LOD = 3.60), were located near the reported P. pachyrhizi R genes, Rpp1, Rpp2, Rpp3, and Rpp4, respectively. Other significant SNPs, including Gm02_7,235,181 (LOD = 7.91), Gm02_7234594 (LOD = 7.61), Gm03_38,913,029 (LOD = 6.85), Gm04_46,003,059 (LOD = 6.03), Gm09_1,951,644 (LOD = 10.07), Gm10_39,142,024 (LOD = 7.12), Gm12_28,136,735 (LOD = 7.03), Gm13_16,350,701(LOD = 5.63), Gm14_6,185,611 (LOD = 5.51), and Gm19_44,734,953 (LOD = 6.02), were associated with abundant disease resistance genes, such as Glyma.02G084100, Glyma.03G175300, Glyma.04g189500, Glyma.09G023800, Glyma.12G160400, Glyma.13G064500, Glyma.14g073300, and Glyma.19G190200. The annotations of these genes included but not limited to: LRR class gene, cytochrome 450, cell wall structure, RCC1, NAC, ABC transporter, F-box domain, etc. The GWAS based markers showed more accuracies in genomic prediction than the whole genome SNPs, and Bayesian LASSO model was the ideal model in SBR resistance prediction with 44.5% ~ 60.4% accuracies. This study aids breeders in predicting selection accuracy of complex traits such as disease resistance and can shorten the soybean breeding cycle by the identified markers

User-Centered Software Design: User Interface Redesign for Blockly–Electron, Artificial Intelligence Educational Software for Primary and Secondary Schools

According to the 2021 and 2022 Horizon Report, AI is emerging in all areas of education, in various forms of educational aids with various applications, and is carving out a similarly ubiquitous presence across campuses and classrooms. This study explores a user-centered approach used in the design of the AI educational software by taking the redesign of the user interface of AI educational software Blockly–Electron as an example. Moreover, by analyzing the relationship between the four variables of software usability, the abstract usability is further certified so as to provide ideas for future improvements to the usability of AI educational software. User-centered design methods and attribution analysis are the main research methods used in this study. The user-centered approach was structured around four phases. Overall, seventy-three middle school students and five teachers participated in the study. The USE scale will be used to measure the usability of Blockly–Electron. Five design deliverables and an attribution model were created and discovered in the linear relationship between Ease of Learning, Ease of Use, Usefulness and Satisfaction, and Ease of use as a mediator variable, which is significantly different from the results of previous regression analysis for the USE scale. This study provides a structural user-centered design methodology with quantitative research. The deliverables and the attribution model can be used in the AI educational software design. Furthermore, this study found that usefulness and ease of learning significantly affect the ease of use, and ease of use significantly affects satisfaction. Based on this, the usability will be further concretized to facilitate the production of software with greater usability

Self-compression of femtosecond pulses in normally dispersive media

Self-compression is a simple method to achieve ultrashort and ultraintense pulses. By solving a modified nonlinear Schrodinger equation considering the fifth-order susceptibility, it is found that self-compression appeared even in normally dispersive media owing to the negative fifth-order susceptibility inducing a mass of negative frequency chirp. Furthermore, negatively pre-chirped pulses help to achieve pulse self-compression at lower input peak intensity which will avoid the damage of media. The optimized-choosing of pre-chirp, input intensity and length of media are numerically analyzed. Proof-of-principle experiments successfully prove the above theoretical findings. It is expected that petawatt laser pulses with 25 fs/15 fs transform limited pulse duration can be self-compressed to about 10.7 fs/8.8 fs in normally dispersive media such as fused silica glass plates.Comment: 24 pages, 8 figures, 1 tabl

Prevalence of lactose intolerance in patients with diarrhea-predominant irritable bowel syndrome: data from a tertiary center in southern China

Background: Symptoms associated with lactose intolerance (LI) and diarrhea-predominant irritable bowel syndrome (IBS-D) are almost the same. These disease entities are difficult to differentiate clinically. In practice, differential diagnosis depends on self-reported patient milk intolerance. However, there is limited data on the prevalence of LI in China. The aim of this study was to investigate the prevalence of LI in IBS-D patients and asymptomatic healthy controls. Methods: Lactose malabsorption (LM) was diagnosed by a lactose hydrogen breath test (HBT) and was defined by peak breath H2 excretion over the baseline level of 65 20 ppm. LI-related symptoms were monitored for 8 h following lactose administration. LI was defined in LM patients with positive symptoms during the observation time. Patients with IBS-D were additionally asked if they were intolerant to milk. Results: A total of 109 eligible IBS-D patients (Rome III criteria) and 50 healthy controls were enrolled in this study. Except for hydrogen non-producers, the prevalence of LM did not differ between IBS-D patients and the control group (85%, 82/96 vs 72%, 34/47; P = 0.061). There was, however, a higher LI prevalence in IBS patients than in healthy subjects (45%, 43/96 vs 17%, 8/47; P = 0.002). Sensitivity, specificity, and positive and negative predictive values of self-reported milk intolerance for detecting LI were 58, 58, 53, and 63%, respectively. Conclusions: Prevalence of LI is significantly higher in IBS-D patients than in healthy subjects. Self-reported milk intolerance is a poor diagnostic predictor of LI

Deep Learning-Based Open Source Toolkit for Eosinophil Detection in Pediatric Eosinophilic Esophagitis

Eosinophilic Esophagitis (EoE) is a chronic, immune/antigen-mediated esophageal disease, characterized by symptoms related to esophageal dysfunction and histological evidence of eosinophil-dominant inflammation. Owing to the intricate microscopic representation of EoE in imaging, current methodologies which depend on manual identification are not only labor-intensive but also prone to inaccuracies. In this study, we develop an open-source toolkit, named Open-EoE, to perform end-to-end whole slide image (WSI) level eosinophil (Eos) detection using one line of command via Docker. Specifically, the toolkit supports three state-of-the-art deep learning-based object detection models. Furthermore, Open-EoE further optimizes the performance by implementing an ensemble learning strategy, and enhancing the precision and reliability of our results. The experimental results demonstrated that the Open-EoE toolkit can efficiently detect Eos on a testing set with 289 WSIs. At the widely accepted threshold of >= 15 Eos per high power field (HPF) for diagnosing EoE, the Open-EoE achieved an accuracy of 91%, showing decent consistency with pathologist evaluations. This suggests a promising avenue for integrating machine learning methodologies into the diagnostic process for EoE. The docker and source code has been made publicly available at https://github.com/hrlblab/Open-EoE

Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation

Eosinophilic esophagitis (EoE) is a chronic and relapsing disease characterized by esophageal inflammation. Symptoms of EoE include difficulty swallowing, food impaction, and chest pain which significantly impact the quality of life, resulting in nutritional impairments, social limitations, and psychological distress. The diagnosis of EoE is typically performed with a threshold (15 to 20) of eosinophils (Eos) per high-power field (HPF). Since the current counting process of Eos is a resource-intensive process for human pathologists, automatic methods are desired. Circle representation has been shown as a more precise, yet less complicated, representation for automatic instance cell segmentation such as CircleSnake approach. However, the CircleSnake was designed as a single-label model, which is not able to deal with multi-label scenarios. In this paper, we propose the multi-label CircleSnake model for instance segmentation on Eos. It extends the original CircleSnake model from a single-label design to a multi-label model, allowing segmentation of multiple object types. Experimental results illustrate the CircleSnake model's superiority over the traditional Mask R-CNN model and DeepSnake model in terms of average precision (AP) in identifying and segmenting eosinophils, thereby enabling enhanced characterization of EoE. This automated approach holds promise for streamlining the assessment process and improving diagnostic accuracy in EoE analysis. The source code has been made publicly available at https://github.com/yilinliu610730/EoE

Characterizing sources and emissions of volatile organic compounds in a northern California residence using space‐ and time‐resolved measurements

We investigate source characteristics and emission dynamics of volatile organic compounds (VOCs) in a single‐family house in California utilizing time‐ and space‐resolved measurements. About 200 VOC signals, corresponding to more than 200 species, were measured during 8 weeks in summer and five in winter. Spatially resolved measurements, along with tracer data, reveal that VOCs in the living space were mainly emitted directly into that space, with minor contributions from the crawlspace, attic, or outdoors. Time‐resolved measurements in the living space exhibited baseline levels far above outdoor levels for most VOCs; many compounds also displayed patterns of intermittent short‐term enhancements (spikes) well above the indoor baseline. Compounds were categorized as “high‐baseline” or “spike‐dominated” based on indoor‐to‐outdoor concentration ratio and indoor mean‐to‐median ratio. Short‐term spikes were associated with occupants and their activities, especially cooking. High‐baseline compounds indicate continuous indoor emissions from building materials and furnishings. Indoor emission rates for high‐baseline species, quantified with 2‐hour resolution, exhibited strong temperature dependence and were affected by air‐change rates. Decomposition of wooden building materials is suggested as a major source for acetic acid, formic acid, and methanol, which together accounted for ~75% of the total continuous indoor emissions of high‐baseline species