124 research outputs found
sj-docx-1-evb-10.1177_11769343211041382 – Supplemental material for Genome-Wide Phylogenetic Analysis, Expression Pattern, and Transcriptional Regulatory Network of the Pig C/EBP Gene Family
No full textSupplemental material, sj-docx-1-evb-10.1177_11769343211041382 for Genome-Wide Phylogenetic Analysis, Expression Pattern, and Transcriptional Regulatory Network of the Pig C/EBP Gene Family by Chaoxin Zhang, Tao Wang, Tongyan Cui, Shengwei Liu, Bing Zhang, Xue Li, Jian Tang, Peng Wang, Yuanyuan Guo and Zhipeng Wang in Evolutionary Bioinformatics</p
Image-based deep learning approaches for plant phenotyping
No full textDoctor of PhilosophyDepartment of Computer ScienceDoina CarageaThe genetic potential of plant traits remains unexplored due to challenges in available phenotyping methods. Deep learning could be used to build automatic tools for identifying, localizing and quantifying plant features based on agricultural images. This dissertation describes the development and evaluation of state-of-the-art deep learning approaches for several plant phenotyping tasks, including characterization of rice root anatomy based on microscopic root cross-section images, estimation of sorghum stomatal density and area based on microscopic images of leaf surfaces, and estimation of the chalkiness in rice exposed to high night temperature based on images of rice grains.
For the root anatomy task, anatomical traits such as root, stele and late metaxylem were identified using a deep learning model based on Faster Region-based Convolutional Neural Network (Faster R-CNN) with the pre-trained VGG-16 as backbone. The model was trained on root cross-section images of roots, where the traits of interest were manually annotated as rectangular bounding boxes using the LabelImg tool. The traits were also predicted as rectangular bounding boxes, which were compared with the ground truth bounding boxes in terms of intersection over union metric to evaluate the detection accuracy. The predicted bounding boxes were subsequently used to estimate root and stele diameter, as well as late metaxylem count and average diameter. Experimental results showed that the trained models can accurately detect and quantify anatomical features, and are robust to image variations. It was also observed that using the pre-trained VGG-16 network enabled the training of accurate models with a relatively small number of annotated images, making this approach very attractive in terms of adaptations to new tasks.
For estimating sorghum stomatal density and area, a deep learning approach for instance segmentation was used, specifically a Mask Region-based Convolutional Neural Network (Mask R-CNN), which produces pixel-level annotations of stomata objects. The pre-trained ResNet-101 network was used as the backbone of the model in combination with the feature pyramid network (FPN) that enables the model to identify objects at different scales. The Mask R-CNN model was trained on microscopic leaf surface images, where the stomata objects have been manually labeled at pixel level using the VGG Image Annotator tool. The predicted stomata masks were counted, and subsequently used to estimate the stomatal area. Experimental results showed a strong correlation between the predicted counts/stomatal area and the corresponding manually produced values. Furthermore, as for the root anatomy task, this study showed that very accurate results can be obtained with a relatively small number of annotated images.
Working on the root anatomy detection and stomatal segmentation tasks showed that manually annotating data, in terms of bounding boxes and especially pixel-level masks, can be a tedious and time-consuming job, even when a relatively small number of annotated images are used for training. To address this challenge, for the task of estimating chalkiness based on images of rice grains exposed to high night temperatures, a weakly supervised approach was used, specifically, an approach based on Gradient-weighted Class Activation Mapping (Grad-CAM). Instead of performing pixel-level segmentation of the chalkiness in rice images, the weakly supervised approach makes use of high-level annotations of images as chalky or not-chalky. A convolutional neural network (e.g., ResNet-101) for binary classification is trained to distinguish between chalky and not-chalky images, and subsequently the gradients of the chalky class are used to determine a heatmap corresponding to the chalkiness area and also a chalkiness score for a grain. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics showed that Grad-CAM can accurately identify chalky grains and detect the chalkiness area. The results also showed that the models trained on polished rice cannot be transferred between polished and unpolished rice, suggesting that new models need to be trained and fine-tuned for other types of rice grains and possibly images taken under different conditions.
In conclusion, this dissertation first contributes to the field of deep learning by introducing new and challenging tasks that require adaptations of existing deep learning models. It also contributes to the field of agricultural image analysis and plant phenotyping by introducing fully automated high-throughput tools for identifying, localizing and quantifying plant traits that are of significant importance to breeding programs. All the datasets and models trained in this dissertation have been made publicly available to enable the deep learning community to use them and further advance the state-of-the-art on the challenging tasks addressed in this dissertation. The resulting tools have also been made publicly available as web servers to enable the plant breeding community to use them on images collected for tasks similar to those addressed here.
Future work will focus on the adaptation of the models used in this dissertation to other similar tasks, and also on the development of similar models for other tasks relevant to the plant breeding community, to the agriculture community at large
Thermal Performance and Energy Conservation Effect of Grain Bin Walls Incorporating PCM in Different Ecological Areas of China
No full textChina, as one of the largest grain production countries, is faced with a storage loss of at least 20 billion kilograms each year. The energy consumption from grain bin buildings has been rising due to the preferred environmental demand for the long-term storage of grain in China. A prefabricated phase change material (PCM) plate was incorporated into the bin walls to reduce energy consumption. The physical model of PCM bin walls was numerically simulated to optimize the latent heat and phase change temperature of PCMs for ecological grain storage area. The thermal regulating performance of the prefabricated PCM plate on the grain bin wall was optimized. It was indicated that a higher value of latent heat of the PCM is more suitable for the hotter region for storing grain in bins in this paper. The energy saving did not increase in the same proportion as the increase in latent heat, suggesting a diminishing return. In this study, the optimal latent heat ranged from 180 to 250 kJ/kg. The values of phase change temperature were selected as 31 °C, 28 °C, and 28 °C for Guangzhou, Zhengzhou, and Harbin cities, respectively, corresponding to hot, warm, and cold climates. The percentages of energy saving were 12.5%, 14.8%, and 17.5% with the corresponding phase change temperatures, which showed an advantage of the PCM used in grain bin walls
Electrochemiluminescence Studies of Phosphorescent Dopant and Host Molecules of Polymer Light-emitting Diodes
No full textPreparation and Characterization of the Forward Osmosis Membrane Modified by MXene Nano-Sheets
No full textThe Forward Osmosis (FO) membrane was the core of FO technology. Obtaining a high water flux while maintaining a low reverse solute flux has historically been considered the gold standard for a perfect FO membrane. In a thin-film composite FO membrane, the performance of the membrane was determined not only by the material and structure of the porous support layer but also by the structural and chemical properties of the active selective layer. Researchers have selected numerous sorts of materials for the FO membranes in recent years and have produced exceptional achievements. Herein, the performance of the modified FO membrane constructed by introducing new two-dimensional nanomaterial MXene nano-sheets to the interfacial polymerization process was investigated, and the performance of these modified membranes was investigated using a variety of characterization and testing methods. The results revealed that the MXene nano-sheets played an important role in improving the performance of the FO membrane. Because of the hydrophilic features of the MXene nano-sheets, the membrane structure may be tuned within a specific concentration range, and the performance of the modified FO membrane has been significantly enhanced accordingly. The optimal membrane water flux was boosted by around 80%, while its reverse solute flux was kept to a minimum of the resultant membranes. It showed that the addition of MXene nanosheets to the active selective layer could improve the performance of the FO membrane, and this method showed promising application prospects
Effect of Virtual Reality Technology on Attention and Motor Ability in Children With Attention-Deficit/Hyperactivity Disorder: Systematic Review and Meta-Analysis
No full textAbstract
BackgroundAttention-deficit/hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children and virtual reality (VR) has been used in the diagnosis and treatment of ADHD.
ObjectiveThis paper aims to systematically evaluate the effect of VR technology on the attention and motor ability of children with ADHD.
MethodsThe intervention method of the experimental group was VR technology, while the control group adopted non-VR technology. The population was children with ADHD. The outcome indicators were attention and motor abilities. The experimental design was randomized controlled trial. Two researchers independently searched PubMed, Cochrane Library, Web of Science, and Embase for randomized controlled trials related to the effect of VR technology on ADHD children’s attention and motor ability. The retrieval date was from the establishment of each database to January 4, 2023. The PEDro scale was used to evaluate the quality of the included literature. Stata (version 17.0; StataCorp LLC) was used for effect size combination, forest map-making, subgroup analyses, sensitivity analyses, and publication bias. GRADEpro (McMaster University and Evidence Prime Inc) was used to evaluate the level of evidence quality.
ResultsA total of 9 literature involving 370 children with ADHD were included. VR technology can improve ADHD children’s attention (Cohen dPdPPdPdPdPdP
ConclusionsVR technology can improve attention and motor ability in children with ADHD. Immersive VR technology has the best attention improvement effect for informally diagnosed children with ADHD
Construction of Method for Rapid Detection of <em>Vibrio Parahaemolyticus</em> Using the Quantitative Real-Time PCR Based on the ToxR Gene
No full textComorbidity and drug resistance of smear-positive pulmonary tuberculosis patients in the yi autonomous prefecture of China: a cross-sectional study
No full textAbstract Background Tuberculosis (TB) has a high morbidity and mortality rate, and its prevention and treatment focus is on impoverished areas. The Liangshan Yi Autonomous Prefecture is a typical impoverished area in western China with insufficient medical resources and high HIV positivity. However, there have been few reports of TB and drug resistance in this area. Methods We collected the demographic and clinical data of inpatients with sputum smear positive TB between 2015 and 2021 in an infectious disease hospital in the Liangshan Yi Autonomous Prefecture. Descriptive analyses were used for the epidemiological data. The chi-square test was used to compare categorical variables between the drug-resistant and drug-susceptible groups, and binary logistic regression was used to analyse meaningful variables. Results We included 2263 patients, 79.9% of whom were Yi patients. The proportions of HIV (14.4%) and smoking (37.3%) were higher than previously reported. The incidence of extrapulmonary TB (28.5%) was high, and the infection site was different from that reported previously. When drug resistance gene detection was introduced, the proportion of drug-resistant patients became 10.9%. Patients aged 15–44 years (OR 1.817; 95% CI 1.162–2.840; P < 0.01) and 45–59 years (OR 2.175; 95% CI 1.335–3.543; P < 0.01) had significantly higher incidences of drug resistance than children and the elderly. Patients with a cough of ≥ 2 weeks had a significantly higher chance of drug resistance than those with < 2 weeks or no cough symptoms (OR 2.069; 95% CI 1.234–3.469; P < 0.01). Alcoholism (OR 1.741; 95% CI 1.107–2.736; P < 0.05) and high bacterial counts on sputum acid-fast smears (OR 1.846; 95% CI 1.115–3.058; P < 0.05) were significant in the univariate analysis. Conclusions Sputum smear-positive TB predominated in Yi men (15–44 years) with high smoking, alcoholism, and HIV rates. Extrapulmonary TB, especially abdominal TB, prevailed. Recent drug resistance testing revealed higher rates in 15–59 age group and ≥ 2 weeks cough duration. Alcohol abuse and high sputum AFB counts correlated with drug resistance. Strengthen screening and supervision to curb TB transmission and drug-resistant cases in the region
An Efficient Synthesis of an Optically Active (‐)‐(3R,4R,5R)‐4‐(1‐Hydroxyl‐isopropyl)‐3‐acetyloxyl‐butyrolactone.
No full text- …
