Lung Segmentation from Chest X-rays using Variational Data Imputation

Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.Comment: Accepted to be presented at the first Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37th International Conference on Machine Learning (ICML). Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE

Single-atom tailoring of platinum nanocatalysts for high-performance multifunctional electrocatalysis

Platinum-based nanocatalysts play a crucial role in various electrocatalytic systems that are important for renewable, clean energy conversion, storage and utilization. However, the scarcity and high cost of Pt seriously limit the practical application of these catalysts. Decorating Pt catalysts with other transition metals offers an effective pathway to tailor their catalytic properties, but often at the sacrifice of the electrochemical active surface area (ECSA). Here we report a single-atom tailoring strategy to boost the activity of Pt nanocatalysts with minimal loss in surface active sites. By starting with PtNi alloy nanowires and using a partial electrochemical dealloying approach, we create single-nickel-atom-modified Pt nanowires with an optimum combination of specific activity and ECSA for the hydrogen evolution, methanol oxidation and ethanol oxidation reactions. The single-atom tailoring approach offers an effective strategy to optimize the activity of surface Pt atoms and enhance the mass activity for diverse reactions, opening a general pathway to the design of highly efficient and durable precious metal-based catalysts

Single-atom tailoring of platinum nanocatalysts for high-performance multifunctional electrocatalysis

Platinum-based nanocatalysts play a crucial role in various electrocatalytic systems that are important for renewable, clean energy conversion, storage and utilization. However, the scarcity and high cost of Pt seriously limit the practical application of these catalysts. Decorating Pt catalysts with other transition metals offers an effective pathway to tailor their catalytic properties, but often at the sacrifice of the electrochemical active surface area (ECSA). Here we report a single-atom tailoring strategy to boost the activity of Pt nanocatalysts with minimal loss in surface active sites. By starting with PtNi alloy nanowires and using a partial electrochemical dealloying approach, we create single-nickel-atom-modified Pt nanowires with an optimum combination of specific activity and ECSA for the hydrogen evolution, methanol oxidation and ethanol oxidation reactions. The single-atom tailoring approach offers an effective strategy to optimize the activity of surface Pt atoms and enhance the mass activity for diverse reactions, opening a general pathway to the design of highly efficient and durable precious metal-based catalysts

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

We conducted a systematic review of the current status of machine learning (ML) algorithms’ ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study’s design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this

Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review

We conducted this Systematic Review to create an overview of the currently existing Artificial Intelligence (AI) methods for Magnetic Resonance Diffusion-Weighted Imaging (DWI)/Fluid-Attenuated Inversion Recovery (FLAIR)—mismatch assessment and to determine how well DWI/FLAIR mismatch algorithms perform compared to domain experts. We searched PubMed Medline, Ovid Embase, Scopus, Web of Science, Cochrane, and IEEE Xplore literature databases for relevant studies published between 1 January 2017 and 20 November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Five studies fit the scope of this review. The area under the curve ranged from 0.74 to 0.90. The sensitivity and specificity ranged from 0.70 to 0.85 and 0.74 to 0.84, respectively. Negative predictive value, positive predictive value, and accuracy ranged from 0.55 to 0.82, 0.74 to 0.91, and 0.73 to 0.83, respectively. In a binary classification of ±4.5 h from stroke onset, the surveyed AI methods performed equivalent to or even better than domain experts. However, using the relation between time since stroke onset (TSS) and increasing visibility of FLAIR hyperintensity lesions is not recommended for the determination of TSS within the first 4.5 h. An AI algorithm on DWI/FLAIR mismatch assessment focused on treatment eligibility, outcome prediction, and consideration of patient-specific data could potentially increase the proportion of stroke patients with unknown onset who could be treated with thrombolysis

Genome-Wide Association Study Provides Insight into the Genetic Control of Plant Height in Rapeseed (Brassica napus L.)

Plant height is a key morphological trait of rapeseed. In this study, we measured plant height of a rapeseed population across six environments. This population contains 476 inbred lines representing the major Chinese rapeseed genepool and 44 lines from other countries. The 60K Brassica Infinium® SNP array was utilized to genotype the association panel. A genome-wide association study (GWAS) was performed via three methods, including a robust, novel, nonparametric Anderson-Darling (A-D) test. Consequently, 68 loci were identified as significantly associated with plant height (P 0.1), we found plausible candidates orthologous to the documented Arabidopsis genes involved in height regulation. One significant association found by GWAS colocalized with the established height locus BnRGA in rapeseed. Our results provide insights into the genetic basis of plant height in rapeseed and may facilitate marker-based breeding