Analysis of the early response to chemotherapy in lung cancer using apparent diffusion coefficient single-slice histogram

Purpose: To evaluate the application of apparent diffusion coefficient (ADC) values derived from diffusion-weighted imaging (DWI) using single-slice histogram analysis to study the chemotherapy responses in lung cancer.Methods: A total of 22 chemotherapy patients with advanced lung cancer from the Nanjing Drum Tower Hospital (Nanjing, China) were included in the study. We obtained DWI before and during chemotherapy, performed single-slice histogram analysis of ADC values, and assessed responses after 3 months of chemotherapy. Differences in ADC histogram parameters were compared between the responder and non-responder groups.Results: After therapy, we classified 13 as responders and 9 patients as non-responders. The recorded peak ADC value (ADCpeak) and lowest ADC value (ADClowest) did not show any significant difference in baseline ADClowest and ADCpeak between responders and non-responders. After chemotherapy, 13 responders had significant increase in ADClowest and ADCpeak compared with pre-treatment values (p < 0.001). ADClowest significantly increased in 9 non-responders (p < 0.05), although ADCpeak did not significantly increase. ADCpeak changes were significantly larger in the responder group than in the nonresponder group (p = 0.024). ADClowest changes after treatment were larger in the responder group than in the non-responder group, though not significantly.Conclusion: ADC values derived from single-slice histogram analysis may provide a useful and clinically feasible method for monitoring early chemotherapy response in patients with lung cancer.Keywords: Lung cancer, Chemotherapy, Apparent diffusion coefficient values, Diffusion-weighted imaging, Single-slice histogram analysi

Expression levels of apoptotic factors in a rat model of corticosteroid-induced femoral head necrosis

Purpose: To study the expression levels of apoptotic factors in corticosteroid-mediated femoral head necrosis (FHN) in rats. Methods: Sprague-Dawley (SD) rats (n = 60) bred adaptively for one week were randomly assigned to control and model groups (30 rats/group). A rat model of corticosteroid-induced femoral head necrosis was established. Then, 3 mL of blood drawn from the inferior vena cava of each rat was used for the assay of the expression levels of osteoprotegerin (OPG) and osteoclast differentiation factor (RANKL) in each group using enzyme-linked immunosorbent assay (ELISA). The caspase-3- and Bcl-2-+ve cells in each group were determined with immunohistochemical method. Results: Relative to control, serum OPG level of model group was significantly decreased, while the RANKL level was markedly raised (p < 0.05). The degree of empty lacunae in the model rats was markedly increased, relative to control. Caspase-3-+ve cells were more numerous in the model group than in control, while Bcl-2-positive cells were markedly decreased compared to control (p < 0.05). Conclusion: Apoptosis occurs in the rat model of femoral head necrosis. Glucocorticoids may regulate the apoptotic process by  upregulating caspase-3 and inhibiting Bcl-2. This provides a novel lead for FHN therapy. Keywords: Femoral head necrosis, Corticosteroid, Glucocorticoid, Apoptosi

The estimation of crop emergence in potatoes by UAV RGB imagery

Abstract Background Crop emergence and canopy cover are important physiological traits for potato (Solanum tuberosum L.) cultivar evaluation and nutrients management. They play important roles in variety screening, field management and yield prediction. Traditional manual assessment of these traits is not only laborious but often subjective. Results In this study, semi-automated image analysis software was developed to estimate crop emergence from high-resolution RGB ortho-images captured from an unmanned aerial vehicle (UAV). Potato plant objects were extracted from bare soil using Excess Green Index and Otsu thresholding methods. Six morphological features were calculated from the images to be variables of a Random Forest classifier for estimating the number of potato plants at emergence stage. The outputs were then used to estimate crop emergence in three field experiments that were designed to investigate the effects of cultivars, levels of potassium (K) fertiliser input, and new compound fertilisers on potato growth. The results indicated that RGB UAV image analysis can accurately estimate potato crop emergence rate in comparison to manual assessment, with correlation coefficient ($$r^{2}$$ r2 ) of 0.96 and provide an efficient tool to evaluate emergence uniformity. Conclusions The proposed UAV image analysis method is a promising tool for use as a high throughput phenotyping method for assessing potato crop development at emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency

Microbial Properties Depending on Fertilization Regime in Agricultural Soils with Different Texture and Climate Conditions: A Meta-Analysis

Over-fertilization has a significant impact on soil microbial properties and its ecological environment. However, the effects of long-term fertilization on microbial properties on a large scale are still vague. This meta-analysis collected 6211 data points from 109 long-term experimental sites in China to evaluate the effects of fertilizer type and fertilization duration, as well as soil and climate conditions, on the effect sizes on various microbial properties and indices. The organic fertilizers combined with straw (NPKS) and manure (NPKM) had the highest effect sizes, while the chemical fertilizers N (sole N fertilizer) and NPK (NPK fertilizer) had the lowest. When compared with the control, NPKM treatment had the highest effect size, while N treatment had the lowest effect size on MBN (111% vs. 19%), PLFA (110% vs. −7%), fungi (88% vs. 43%), Actinomycetes (97% vs. 44%), urease (77% vs. 25%), catalase (15% vs. −11%), and phosphatase (58% vs. 4%). NPKM treatment had the highest while NPK treatment had the lowest effect size on bacteria (123% vs. 33%). NPKS treatment had the highest while N treatment had the lowest effect sizes on MBC (77% vs. 8%) and invertase (59% vs. 0.2%). NPKS treatment had the highest while NPK treatment had the lowest effect size on the Shannon index (5% vs. 1%). The effect sizes of NPKM treatment were the highest predominantly in arid regions because of the naturally low organic carbon in soils of these regions. The effect sizes on various microbial properties were also highly dependent on soil texture. In coarse-textured soils the effect sizes on MBC and MBN peaked sooner compared with those of clayey or silty soils, although various enzymes were most active in silty soils during the first 10 years of fertilization. Effect sizes on microbial properties were generally higher under NPKM and NPKS treatments than under NPK or N treatments, with considerable effects due to climate conditions. The optimal field fertilizer regime could be determined based on the effects of fertilizer type on soil microorganisms under various climate conditions and soil textures. This will contribute to the microbial biodiversity and soil health of agricultural land. Such controls should be used for adaptation of fertilization strategies to global changes

EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography

This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a novel self-supervised method for recognizing standard views in pediatric echocardiography. EDMAE introduces a new proxy task based on the encoder-decoder structure. The EDMAE encoder is composed of a teacher and a student encoder. The teacher encoder extracts the potential representation of the masked image blocks, while the student encoder extracts the potential representation of the visible image blocks. The loss is calculated between the feature maps output by the two encoders to ensure consistency in the latent representations they extract. EDMAE uses pure convolution operations instead of the ViT structure in the MAE encoder. This improves training efficiency and convergence speed. EDMAE is pre-trained on a large-scale private dataset of pediatric echocardiography using self-supervised learning, and then fine-tuned for standard view recognition. The proposed method achieves high classification accuracy in 27 standard views of pediatric echocardiography. To further verify the effectiveness of the proposed method, the authors perform another downstream task of cardiac ultrasound segmentation on the public dataset CAMUS. The experimental results demonstrate that the proposed method outperforms some popular supervised and recent self-supervised methods, and is more competitive on different downstream tasks.Comment: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal Processing and Contro

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors

A phytophthora effector manipulates host histone acetylation and reprograms defense gene expression to promote infection

Immune response during pathogen infection requires extensive transcription reprogramming. A fundamental mechanism of transcriptional regulation is histone acetylation. However, how pathogens interfere with this process to promote disease remains largely unknown. Here we demonstrate that the cytoplasmic effector PsAvh23 produced by the soybean pathogen Phytophthora sojae acts as a modulator of histone acetyltransferase (HAT) in plants. PsAvh23 binds to the ADA2 subunit of the HAT complex SAGA and disrupts its assembly by interfering with the association of ADA2 with the catalytic subunit GCN5. As such, PsAvh23 suppresses H3K9 acetylation mediated by the ADA2/GCN5 module and increases plant susceptibility. Expression of PsAvh23 or silencing of GmADA2/GmGCN5 resulted in misregulation of defense-related genes, most likely due to decreased H3K9 acetylation levels at the corresponding loci. This study highlights an effective counter-defense mechanism by which a pathogen effector suppresses the activation of defense genes by interfering with the function of the HAT complex during infection

Case Studies of Environmental Visualization

The performance gap between simulation and reality has been identified as a major challenge to achieving sustainability in the Built Environment. While Post-Occupancy Evaluation (POE) surveys are an integral part of better understanding building performance, and thus addressing this issue, the importance of POE remains relatively unacknowledged within the wider Built Environment community. A possible reason that has been highlighted is that POE survey data is not easily understood and utilizable by non-expert stakeholders, including designers. A potential method by which to address this is the visualization method, which has well established benefits for communication of big datasets. This paper presents two case studies where EnViz (short for “Environmental Visualization”), a prototype software application developed for research purposes, was utilized and its effectiveness tested via a range of analysis tasks. The results are discussed and compared with those of previous work that utilized variations of the methods presented here. The paper concludes by presenting the lessons drawn from the five-year period of EnViz, emphasizing the potential of environmental visualization for decision support in environmental design and engineering for the built environment, and suggests directions for future development