Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network.

BACKGROUND: In the management of cancer patients, determination of TNM status is essential for treatment decision-making and therefore closely linked to clinical outcome and survival. Here, we developed a tool for automatic three-dimensional (3D) localization and segmentation of cervical lymph nodes (LNs) on contrast-enhanced computed tomography (CECT) examinations. METHODS: In this IRB-approved retrospective single-center study, 187 CECT examinations of the head and neck region from patients with various primary diseases were collected from our local database, and 3656 LNs (19.5 ± 14.9 LNs/CECT, mean ± standard deviation) with a short-axis diameter (SAD) ≥ 5 mm were segmented manually by expert physicians. With these data, we trained an independent fully convolutional neural network based on 3D foveal patches. Testing was performed on 30 independent CECTs with 925 segmented LNs with an SAD ≥ 5 mm. RESULTS: In total, 4,581 LNs were segmented in 217 CECTs. The model achieved an average localization rate (LR), i.e., percentage of localized LNs/CECT, of 78.0% in the validation dataset. In the test dataset, average LR was 81.1% with a mean Dice coefficient of 0.71. For enlarged LNs with a SAD ≥ 10 mm, LR was 96.2%. In the test dataset, the false-positive rate was 2.4 LNs/CECT. CONCLUSIONS: Our trained AI model demonstrated a good overall performance in the consistent automatic localization and 3D segmentation of physiological and metastatic cervical LNs with a SAD ≥ 5 mm on CECTs. This could aid clinical localization and automatic 3D segmentation, which can benefit clinical care and radiomics research. RELEVANCE STATEMENT: Our AI model is a time-saving tool for 3D segmentation of cervical lymph nodes on contrast-enhanced CT scans and serves as a solid base for N staging in clinical practice and further radiomics research. KEY POINTS: • Determination of N status in TNM staging is essential for therapy planning in oncology. • Segmenting cervical lymph nodes manually is highly time-consuming in clinical practice. • Our model provides a robust, automated 3D segmentation of cervical lymph nodes. • It achieves a high accuracy for localization especially of enlarged lymph nodes. • These segmentations should assist clinical care and radiomics research.

Responses of maize hybrids to water stress conditions at different developmental stages: accumulation of reactive oxygen species, activity of enzymatic antioxidants and degradation in kernel quality traits.

Sustainable maize production under changing climatic conditions, especially heat and water stress conditions is one of the key challenges that need to be addressed immediately. The current field study was designed to evaluate the impact of water stress on morpho-physiological, biochemical, reactive oxygen species, antioxidant activity and kernel quality traits at different plant growth stages in maize hybrids. Four indigenous i.e., YH-5427, YH-5482, YH-5395, JPL-1908, and one multinational maize hybrid i.e., NK-8441 (Syngenta Seeds) were used for the study. Four stress treatments (i) Control (ii) 3-week water stress at pre-flowering stage (iii) 3-week water stress at anthesis stage (iv) 3-week water stress at grain filling/post-anthesis stage. The presence of significant oxidative stress was revealed by the overproduction of reactive oxygen species (ROXs) i.e., H2O2 (1.9 to 5.8 µmole g-1 FW) and malondialdehyde (120.5 to 169.0 nmole g-1 FW) leading to severe negative impacts on kernel yield. Moreover, a severe reduction in photosynthetic ability (50.6%, from 34.0 to 16.8 µmole m-2 s-1), lower transpirational rate (31.3%, from 3.2 to 2.2 mmol m-2 s-1), alterations in plant anatomy, reduced pigments stability, and deterioration of kernel quality was attributed to water stress. Water stress affected all the three studied growth stages, the pre-flowering stage being the most vulnerable while the post-anthesis stage was the least affected stage to drought stress. Antioxidant activity was observed to increase under all stress conditions in all maize hybrids, however, the highest antioxidant activity was recorded at the anthesis stage and in maize hybrids YH-5427 i.e., T-SOD activity was increased by 61.3% from 37.5 U mg-1 pro to 60.5 U mg-1 pro while CAT activity was maximum under water stress conditions 8.3 U mg-1 pro as compared to 10.3 U mg-1 pro under control (19.3%). The overall performance of maize hybrid YH-5427 was much more promising than other hybrids, attributed to its higher photosynthetic activity, and better antioxidant defense mechanism. Therefore, this hybrid could be recommended for cultivation in drought-prone areas.

Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT.

Background: Diagnosing a coronavirus disease 2019 (COVID-19) infection with high specificity in chest computed tomography (CT) imaging is considered possible due to distinctive imaging features of COVID-19 pneumonia. Since other viral non-COVID pneumonia show mostly a different distribution pattern, it is reasonable to assume that the patterns observed caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a consequence of its genetically encoded molecular properties when interacting with the respiratory tissue. As more mutations of the initial SARS-CoV-2 wild-type with varying aggressiveness have been detected in the course of 2021, it became obvious that its genome is in a state of transformation and therefore a potential modification of the specific morphological appearance in CT may occur. The aim of this study was to quantitatively analyze the morphological differences of the SARS-CoV-2-B.1.1.7 mutation and wildtype variant in CT scans of the thorax. Methods: We analyzed a dataset of 140 patients, which was divided into pneumonias caused by n=40 wildtype variants, n=40 B.1.1.7 variants, n=20 bacterial pneumonias, n=20 viral (non-COVID) pneumonias, and a test group of n=20 unremarkable CT examinations of the thorax. Semiautomated 3D segmentation of the lung tissue was performed for quantification of lung pathologies. The extent, ratio, and specific distribution of inflammatory affected lung tissue in each group were compared in a multivariate group analysis. Results: Lung segmentation revealed significant difference between the extent of ground glass opacities (GGO) or consolidation comparing SARS-CoV-2 wild-type and B.1.1.7 variant. Wildtype and B.1.1.7 variant showed both a symmetric distribution pattern of stage-dependent GGO and consolidation within matched COVID-19 stages. Viral non-COVID pneumonias had significantly fewer consolidations than the bacterial, but also than the COVID-19 B.1.1.7 variant groups. Conclusions: CT based segmentation showed no significant difference between the morphological appearance of the COVID-19 wild-type variant and the SARS-CoV-2 B.1.1.7 mutation. However, our approach allowed a semiautomatic quantification of bacterial and viral lung pathologies. Quantitative CT image analyses, such as the one presented, appear to be an important component of pandemic preparedness considering an organism with ongoing genetic change, to describe a potential arising change in CT morphological appearance of possible new upcoming COVID-19 variants of concern.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches-an international, multi-center comparative study.

Background: The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset. Methods: In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0-25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger et al., 2020) and consecutive quantitative assessment of lung attenuation. The best threshold was investigated by training and testing a linear regression of deep-learning and threshold-based results in a five-fold cross validation strategy. Results: Median CCS among 250 evaluated patients was 10 [6-15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97-0.98)]. Best attenuation threshold for identification of involved lung was -522 HU. While the relationship of deep-learning- and threshold-based quantification was linear and strong (r2 deep-learning vs. threshold=0.84), both automated quantification methods translated to the semi-quantitative CCS in a non-linear fashion, with an increasing slope towards higher CCS (r2 deep-learning vs. CCS= 0.80, r2 threshold vs. CCS=0.63). Conclusions: The manual semi-quantitative CCS underestimates the extent of COVID pneumonia in higher score ranges, which limits its clinical usefulness in cases of severe disease. Clinical implementation of fully automated methods, such as deep-learning or threshold-based approaches (best threshold in our multi-center dataset: -522 HU), might save time of trained personnel, abolish inter-reader variability, and allow for truly quantitative, linear assessment of COVID lung involvement.

Whole-Genome Identification of APX and CAT Gene Families in Cultivated and Wild Soybeans and Their Regulatory Function in Plant Development and Stress Response.

Plants coevolved with their antioxidant defense systems, which detoxify and adjust levels of reactive oxygen species (ROS) under multiple plant stresses. We performed whole-genome identification of ascorbate peroxidase (APX) and catalase (CAT) families in cultivated and wild soybeans. In cultivated and wild soybean genomes, we identified 11 and 10 APX genes, respectively, whereas the numbers of identified CAT genes were four in each species. Comparative phylogenetic analysis revealed more homology among cultivated and wild soybeans relative to other legumes. Exon/intron structure, motif and synteny blocks are conserved in cultivated and wild species. According to the Ka/Ks value, purifying selection is a major force for evolution of these gene families in wild soybean; however, the APX gene family was evolved by both positive and purifying selection in cultivated soybean. Segmental duplication was a major factor involved in the expansion of APX and CAT genes. Expression patterns revealed that APX and CAT genes are differentially expressed across fourteen different soybean tissues under water deficit (WD), heat stress (HS) and combined drought plus heat stress (WD + HS). Altogether, the current study provides broad insights into these gene families in soybeans. Our results indicate that APX and CAT gene families modulate multiple stress response in soybeans.

Spectral Detector CT-Derived Pulmonary Perfusion Maps and Pulmonary Parenchyma Characteristics for the Semiautomated Classification of Pulmonary Hypertension.

Objectives: To evaluate the usefulness of spectral detector CT (SDCT)-derived pulmonary perfusion maps and pulmonary parenchyma characteristics for the semiautomated classification of pulmonary hypertension (PH). Methods: A total of 162 consecutive patients with right heart catheter (RHC)-proven PH of different aetiologies as defined by the current ESC/ERS guidelines who underwent CT pulmonary angiography (CTPA) on SDCT and 20 patients with an invasive rule-out of PH were included in this retrospective study. Semiautomatic lung segmentation into normal and malperfused areas based on iodine density (ID) as well as automatic, virtual non-contrast-based emphysema quantification were performed. Corresponding volumes, histogram features and the ID SkewnessPerfDef-Emphysema-Index (δ-index) accounting for the ratio of ID distribution in malperfused lung areas and the proportion of emphysematous lung parenchyma were computed and compared between groups. Results: Patients with PH showed a significantly greater extent of malperfused lung areas as well as stronger and more homogenous perfusion defects. In group 3 and 4 patients, ID skewness revealed a significantly more homogenous ID distribution in perfusion defects than in all other subgroups. The δ-index allowed for further subclassification of subgroups 3 and 4 (p < 0.001), identifying patients with chronic thromboembolic PH (CTEPH, subgroup 4) with high accuracy (AUC: 0.92, 95%-CI, 0.85-0.99). Conclusion: Abnormal pulmonary perfusion in PH can be detected and quantified by semiautomated SDCT-based pulmonary perfusion maps. ID skewness in malperfused lung areas, and the δ-index allow for a classification of PH subgroups, identifying groups 3 and 4 patients with high accuracy, independent of reader expertise.

Biofortification of Cereals and Pulses Using New Breeding Techniques: Current and Future Perspectives.

Cereals and pulses are consumed as a staple food in low-income countries for the fulfillment of daily dietary requirements and as a source of micronutrients. However, they are failing to offer balanced nutrition due to deficiencies of some essential compounds, macronutrients, and micronutrients, i.e., cereals are deficient in iron, zinc, some essential amino acids, and quality proteins. Meanwhile, the pulses are rich in anti-nutrient compounds that restrict the bioavailability of micronutrients. As a result, the population is suffering from malnutrition and resultantly different diseases, i.e., anemia, beriberi, pellagra, night blindness, rickets, and scurvy are common in the society. These facts highlight the need for the biofortification of cereals and pulses for the provision of balanced diets to masses and reduction of malnutrition. Biofortification of crops may be achieved through conventional approaches or new breeding techniques (NBTs). Conventional approaches for biofortification cover mineral fertilization through foliar or soil application, microbe-mediated enhanced uptake of nutrients, and conventional crossing of plants to obtain the desired combination of genes for balanced nutrient uptake and bioavailability. Whereas, NBTs rely on gene silencing, gene editing, overexpression, and gene transfer from other species for the acquisition of balanced nutritional profiles in mutant plants. Thus, we have highlighted the significance of conventional and NBTs for the biofortification of cereals and pulses. Current and future perspectives and opportunities are also discussed. Further, the regulatory aspects of newly developed biofortified transgenic and/or non-transgenic crop varieties via NBTs are also presented.

Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT.

OBJECTIVE: Liver cancer is one of the most commonly diagnosed cancer, and energy-based tumor ablation is a widely accepted treatment. Automatic and robust segmentation of liver tumors and ablation zones would facilitate the evaluation of treatment success. The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT, and (2) segmentation of liver and ablation zones in both arterial and portal venous phase for after ablation treatment. MATERIALS AND METHODS: 252 CT images from 63 patients undergoing liver tumor ablation at a large University Hospital were retrospectively included; each patient had pre-treatment and post-treatment multi-phase CT images. 3D voxel-wise manual segmentation of the liver, tumors and ablation region by the radiologist provided reference standard. Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. Then, transfer learning was applied to adapt the base models on the clinical training-set, to obtain tumor and ablation segmentation models both for arterial and portal venous phase images. For modeling, 2D residual-attention Unet (RA-Unet) was employed for liver segmentation and a multi-scale patch-based 3D RA-Unet for tumor and ablation segmentation. RESULTS: On the independent test-set, the proposed method achieved a dice similarity coefficient (DSC) of 0.96 and 0.95 for liver segmentation on arterial and portal venous phase, respectively. For liver tumors, the model on arterial phase achieved detection sensitivity of 71%, DSC of 0.64, and on portal venous phase sensitivity of 82%, DSC of 0.73. For liver tumors >0.5cm3 performance improved to sensitivity 79%, DSC 0.65 on arterial phase and, sensitivity 86%, DSC 0.72 on portal venous phase. For ablation zone, the model on arterial phase achieved detection sensitivity of 90%, DSC of 0.83, and on portal venous phase sensitivity of 90%, DSC of 0.89. CONCLUSION: The proposed deep learning approach can provide automated segmentation of liver tumors and ablation zones on multi-phase (arterial and portal venous) and multi-time-point (before and after treatment) CT enabling quantitative evaluation of treatment success.

CRISPR-Based Crop Improvements: A Way Forward to Achieve Zero Hunger.

Zero hunger is one of the sustainable development goals set by the United Nations in 2015 to achieve global food security by 2030. The current harvest of crops is insufficient; feeding the world's population and meeting the goal of zero hunger by 2030 will require larger and more consistent crop production. Clustered regularly interspaced short palindromic repeats-associated protein (CRISPR-Cas) technology is widely used for the plant genome editing. In this review, we consider this technology as a potential tool for achieving zero hunger. We provide a comprehensive overview of CRISPR-Cas technology and its most important applications for food crops' improvement. We also conferred current and potential technological breakthroughs that will help in breeding future crops to end global hunger. The regulatory aspects of deploying this technology in commercial sectors, bioethics, and the production of transgene-free plants are also discussed. We hope that the CRISPR-Cas system will accelerate the breeding of improved crop cultivars compared with conventional breeding and pave the way toward the zero hunger goal.

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. PURPOSE: To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. STUDY TYPE: Retrospective. POPULATION: Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. ASSESSMENT: A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1 CE served as the reference standard. STATISTICAL TESTS: Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value <0.05 was considered statistically significant. RESULTS: In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 ± 4.24 cm3 ), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 ± 0.04 cm3 ) than detected BMs (0.96 ± 2.4 cm3 ). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. DATA CONCLUSION: Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. Level of Evidence 3 Technical Efficacy Stage 2.

Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage.

PURPOSE: To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH). METHODS: Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm3, 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared. RESULTS: In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1: 91.2%, reader 2: 86.5%, and reader 3: 86.5%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6%, reader 2: 97.6%,and reader 3: 96.0%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM). CONCLUSION: Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.

Harnessing the potential of plant transcription factors in developing climate resilient crops to improve global food security: Current and future perspectives.

Crop plants should be resilient to climatic factors in order to feed ever-increasing populations. Plants have developed stress-responsive mechanisms by changing their metabolic pathways and switching the stress-responsive genes. The discovery of plant transcriptional factors (TFs), as key regulators of different biotic and abiotic stresses, has opened up new horizons for plant scientists. TFs perceive the signal and switch certain stress-responsive genes on and off by binding to different cis-regulatory elements. More than 50 families of plant TFs have been reported in nature. Among them, DREB, bZIP, MYB, NAC, Zinc-finger, HSF, Dof, WRKY, and NF-Y are important with respect to biotic and abiotic stresses, but the potential of many TFs in the improvement of crops is untapped. In this review, we summarize the role of different stress-responsive TFs with respect to biotic and abiotic stresses. Further, challenges and future opportunities linked with TFs for developing climate-resilient crops are also elaborated.

Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning.

BACKGROUND: Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time-consuming and impeded by intra- and interrater variabilities. PURPOSE: To investigate the performance of a deep-learning model (DLM) to automatically detect and segment primary central nervous system lymphoma (PCNSL) on clinical MRI. STUDY TYPE: Retrospective. POPULATION: Sixty-nine scans (at initial and/or follow-up imaging) from 43 patients with PCNSL referred for clinical MRI tumor assessment. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE), and FLAIR at 1.0, 1.5, and 3.0T from different vendors and study centers. ASSESSMENT: Fully automated voxelwise segmentation of tumor components was performed using a 3D convolutional neural network (DeepMedic) trained on gliomas (n = 220). DLM segmentations were compared to manual segmentations performed in a 3D voxelwise manner by two readers (radiologist and neurosurgeon; consensus reading) from T1 CE and FLAIR, which served as the reference standard. STATISTICAL TESTS: Dice similarity coefficient (DSC) for comparison of spatial overlap with the reference standard, Pearson's correlation coefficient (r) to assess the relationship between volumetric measurements of segmentations, and Wilcoxon rank-sum test for comparison of DSCs obtained in initial and follow-up imaging. RESULTS: The DLM detected 66 of 69 PCNSL, representing a sensitivity of 95.7%. Compared to the reference standard, DLM achieved good spatial overlap for total tumor volume (TTV, union of tumor volume in T1 CE and FLAIR; average size 77.16 ± 62.4 cm3 , median DSC: 0.76) and tumor core (contrast enhancing tumor in T1 CE; average size: 11.67 ± 13.88 cm3 , median DSC: 0.73). High volumetric correlation between automated and manual segmentations was observed (TTV: r = 0.88, P < 0.0001; core: r = 0.86, P < 0.0001). Performance of automated segmentations was comparable between pretreatment and follow-up scans without significant differences (TTV: P = 0.242, core: P = 0.177). DATA CONCLUSION: In clinical MRI scans, a DLM initially trained on gliomas provides segmentation of PCNSL comparable to manual segmentation, despite its complex and multifaceted appearance. Segmentation performance was high in both initial and follow-up scans, suggesting its potential for application in longitudinal tumor imaging. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

The level of Cry1Ac endotoxin and its efficacy against H. armigera in Bt cotton at large scale in Pakistan.

A biophysical survey was conducted in 15 cotton-growing districts of Pakistan. Four hundred cotton growers were approached and inquired about the production technology of Bt cotton. Further, 25 strip tests using combo strips (Cry1Ac, Cry2Ab, Vip3Aa and Cp4, EPSPS gene) were performed at each farmer's field. Out of 10,000 total-tested samples, farmers claimed 9682 samples as Bt and 318 samples as non-Bt. After performing a strip test, 1009 and 87 samples were found false negative and false positive, respectively. Only 53 samples were found positive for Cry2Ab, 214 for EPSPS and none for Vip3Aa gene. Quantification of Cry endotoxin and bioassay studies were performed by taking leaves from upper, middle, and lower canopies, and fruiting parts at approximately 80 days after sowing from 89 varieties. Expression was highly variable among different canopies and fruiting parts. Moreover, Cry endotoxin expression and insect mortality varied significantly among varieties from 0.26 µg g-1 to 3.54 µg g-1 with mortality ranging from 28 to 97%, respectively. Highest Cry1Ac expression (3.54 µg g-1) and insect mortality (97%) were observed for variety FH-142 from DG Khan. Cry endotoxin expression varied significantly across various plant parts, i.e., IUB-13 variety from upper canopy documented 0.34 µg g-1 expression with 37% insect mortality in Layyah to 3.42 µg g-1 expression and 96% insect mortality from DG Khan. Lethal dose, LD95 (2.20 µg g-1) of Cry1Ac endotoxin was optimized for effective control of H. armigera. Our results provided evidence of practical resistance in H. armigera and way forward.

Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning.

In aneurysmal subarachnoid hemorrhage (aSAH), accurate diagnosis of aneurysm is essential for subsequent treatment to prevent rebleeding. However, aneurysm detection proves to be challenging and time-consuming. The purpose of this study was to develop and evaluate a deep learning model (DLM) to automatically detect and segment aneurysms in patients with aSAH on computed tomography angiography. In this retrospective single-center study, three different DLMs were trained on 68 patients with 79 aneurysms treated for aSAH (2016-2017) using five-fold-cross-validation. Their outputs were combined to a single DLM via ensemble-learning. The DLM was evaluated on an independent test set consisting of 185 patients with 215 aneurysms (2010-2015). Independent manual segmentations of aneurysms in a 3D voxel-wise manner by two readers (neurosurgeon, radiologist) provided the reference standard. For aneurysms > 30 mm3 (mean diameter of ~ 4 mm) on the test set, the DLM provided a detection sensitivity of 87% with false positives (FPs)/scan of 0.42. Automatic segmentations achieved a median dice similarity coefficient (DSC) of 0.80 compared to the reference standard. Aneurysm location (anterior vs. posterior circulation; P = .07) and bleeding severity (Fisher grade ≤ 3 vs. 4; P = .33) did not impede detection sensitivity or segmentation performance. For aneurysms > 100 mm3 (mean diameter of ~ 6 mm), a sensitivity of 96% with DSC of 0.87 and FPs/scan of 0.14 were obtained. In the present study, we demonstrate that the proposed DLM detects and segments aneurysms > 30 mm3 in patients with aSAH with high sensitivity independent of cerebral circulation and bleeding severity while producing FP findings of less than one per scan. Hence, the DLM can potentially assist treating physicians in aSAH by providing automated detection and segmentations of aneurysms.

Role of Genetics, Genomics, and Breeding Approaches to Combat Stripe Rust of Wheat.

Puccinia striiformis (Pst) is a devastating biotrophic fungal pathogen that causes wheat stripe rust. It usually loves cool and moist places and can cause 100% crop yield losses in a single field when ideal conditions for disease incidence prevails. Billions of dollars are lost due to fungicide application to reduce stripe rust damage worldwide. Pst is a macrocyclic, heteroecious fungus that requires primary (wheat or grasses) as well as secondary host (Berberis or Mahonia spp.) for completion of life cycle. In this review, we have summarized the knowledge about pathogen life cycle, genes responsible for stripe rust resistance, and susceptibility in wheat. In the end, we discussed the importance of conventional and modern breeding tools for the development of Pst-resistant wheat varieties. According to our findings, genetic engineering and genome editing are less explored tools for the development of Pst-resistant wheat varieties; hence, we highlighted the putative use of advanced genome-modifying tools, i.e., base editing and prime editing, for the development of Pst-resistant wheat.

Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method.

BACKGROUND: Aortic pulse wave velocity (PWV) is an indicator of aortic stiffness and is used as a predictor of adverse cardiovascular events. PWV can be non-invasively assessed using magnetic resonance imaging (MRI). PWV computation requires two components, the length of the aortic arch and the time taken for the systolic pressure wave to travel through the aortic arch. The aortic length is calculated using a multi-slice 3D scan and the transit time is computed using a 2D velocity encoded MRI (VE) scan. In this study we present and evaluate an automatic method to quantify the aortic pulse wave velocity using a large population-based cohort. METHODS: For this study 212 subjects were retrospectively selected from a large multi-center heart-brain connection cohort. For each subject a multi-slice 3D scan of the aorta was acquired in an oblique-sagittal plane and a 2D VE scan acquired in a transverse plane cutting through the proximal ascending and descending aorta. PWV was calculated in three stages: (i) a multi-atlas-based segmentation method was developed to segment the aortic arch from the multi-slice 3D scan and subsequently estimate the length of the proximal aorta, (ii) an algorithm that delineates the proximal ascending and descending aorta from the time-resolved 2D VE scan and subsequently obtains the velocity-time flow curves was also developed, and (iii) automatic methods that can compute the transit time from the velocity-time flow curves were implemented and investigated. Finally the PWV was obtained by combining the aortic length and the transit time. RESULTS: Quantitative evaluation with respect to the length of the aortic arch as well as the computed PWV were performend by comparing the results of the novel automatic method to those obtained manually. The mean absolute difference in aortic length obtained automatically as compared to those obtained manually was 3.3 ± 2.8 mm (p < 0.05), the manual inter-observer variability on a subset of 45 scans was 3.4 ± 3.4 mm (p = 0.49). Bland-Altman analysis between the automataic method and the manual methods showed a bias of 0.0 (-5.0,5.0) m/s for the foot-to-foot approach, -0.1 (-1.2, 1.1) and -0.2 (-2.6, 2.1) m/s for the half-max and the cross-correlation methods, respectively. CONCLUSION: We proposed and evaluated a fully automatic method to calculate the PWV on a large set of multi-center MRI scans. It was observed that the overall results obtained had very good agreement with manual analysis. Our proposed automatic method would be very beneficial for large population based studies, where manual analysis requires a lot of manpower.

Association of cardiovascular magnetic resonance-derived circumferential strain parameters with the risk of ventricular arrhythmia and all-cause mortality in patients with prior myocardial infarction and primary prevention implantable cardioverter defibrillator.

BACKGROUND: Impaired left ventricular (LV) contraction and relaxation may further promote adverse remodeling and may increase the risk of ventricular arrhythmia (VA) in ischemic cardiomyopathy. We aimed to examine the association of cardiovascular magnetic resonance (CMR)-derived circumferential strain parameters for LV regional systolic function, LV diastolic function and mechanical dispersion with the risk of VA in patients with prior myocardial infarction and primary prevention implantable cardioverter defibrillator (ICD). METHODS: Patients with an ischemic cardiomyopathy who underwent CMR prior to primary prevention ICD implantation, were retrospectively identified. LV segmental circumferential strain curves were extracted from short-axis cine CMR. For LV regional strain analysis, the extent of moderately and severely impaired strain (percentage of LV segments with strain between - 10% and - 5% and > - 5%, respectively) were calculated. LV diastolic function was quantified by the early and late diastolic strain rate. Mechanical dispersion was defined as the standard deviation in delay time between each strain curve and the patient-specific reference curve. Cox proportional hazard ratios (HR) (95%CI) were calculated to assess the association between LV strain parameters and appropriate ICD therapy. RESULTS: A total of 121 patients (63 ± 11 years, 84% men, LV ejection fraction (LVEF) 27 ± 9%) were included. During a median (interquartile range) follow-up of 47 (27;69) months, 30 (25%) patients received appropriate ICD therapy. The late diastolic strain rate (HR 1.1 (1.0;1.2) per - 0.25 1/s, P = 0.043) and the extent of moderately impaired strain (HR 1.5 (1.0;2.2) per + 10%, P = 0.048) but not the extent of severely impaired strain (HR 0.9 (0.6;1.4) per + 10%, P = 0.685) were associated with appropriate ICD therapy, independent of LVEF, late gadolinium enhancement (LGE) scar border size and acute revascularization. Mechanical dispersion was not related to appropriate ICD therapy (HR 1.1 (0.8;1.6) per + 25 ms, P = 0.464). CONCLUSIONS: In an ischemic cardiomyopathy population referred for primary prevention ICD implantation, the extent of moderately impaired strain and late diastolic strain rate were associated with the risk of appropriate ICD therapy, independent of LVEF, scar border size and acute revascularization. These findings suggest that disturbed LV contraction and relaxation may contribute to an increased risk of VA after myocardial infarction.

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer.

PURPOSE: To develop and validate a robust and accurate registration pipeline for automatic contour propagation for online adaptive Intensity-Modulated Proton Therapy (IMPT) of prostate cancer using elastix software and deep learning. METHODS: A three-dimensional (3D) Convolutional Neural Network was trained for automatic bladder segmentation of the computed tomography (CT) scans. The automatic bladder segmentation alongside the computed tomography (CT) scan is jointly optimized to add explicit knowledge about the underlying anatomy to the registration algorithm. We included three datasets from different institutes and CT manufacturers. The first was used for training and testing the ConvNet, where the second and the third were used for evaluation of the proposed pipeline. The system performance was quantified geometrically using the dice similarity coefficient (DSC), the mean surface distance (MSD), and the 95% Hausdorff distance (HD). The propagated contours were validated clinically through generating the associated IMPT plans and compare it with the IMPT plans based on the manual delineations. Propagated contours were considered clinically acceptable if their treatment plans met the dosimetric coverage constraints on the manual contours. RESULTS: The bladder segmentation network achieved a DSC of 88% and 82% on the test datasets. The proposed registration pipeline achieved a MSD of 1.29 ± 0.39, 1.48 ± 1.16, and 1.49 ± 0.44 mm for the prostate, seminal vesicles, and lymph nodes, respectively, on the second dataset and a MSD of 2.31 ± 1.92 and 1.76 ± 1.39 mm for the prostate and seminal vesicles on the third dataset. The automatically propagated contours met the dose coverage constraints in 86%, 91%, and 99% of the cases for the prostate, seminal vesicles, and lymph nodes, respectively. A Conservative Success Rate (CSR) of 80% was obtained, compared to 65% when only using intensity-based registration. CONCLUSION: The proposed registration pipeline obtained highly promising results for generating treatment plans adapted to the daily anatomy. With 80% of the automatically generated treatment plans directly usable without manual correction, a substantial improvement in system robustness was reached compared to a previous approach. The proposed method therefore facilitates more precise proton therapy of prostate cancer, potentially leading to fewer treatment-related adverse side effects.