Critical Care Ultrasonography and Its Application for COVID-19

Ultrasound has developed as an invaluable tool in diagnosis and proper management in the intensive care unit (ICU). Application of critical care ultrasonography is quite distinct from the routine comprehensive diagnostic ultrasound exam, because the urgent setting mandates a goal-directed approach. Performing accurate and efficient critical care ultrasound requires ultrasound providers to first understand the pathophysiology of the disease and related imaging findings, and then follow the protocols to perform a focused ultrasound exam. In the ongoing coronavirus disease 2019 (COVID-19) pandemic, ultrasound plays an essential role in diagnosing and monitoring critically ill COVID-19 patients in the ICU. Our review focuses on the basics and clinical application of critical care ultrasound in diagnosing common lung disease, COVID-19 pulmonary lesions, pediatric COVID-19, and cardiovascular dysfunction as well as its role in ECMO and interventional ultrasonography

Chinese Expert Consensus on Critical Care Ultrasound Applications at COVID-19 Pandemic

The spread of new coronavirus (SARS-Cov-2) follows a different pattern than previous respiratory viruses, posing a serious public health risk worldwide. World Health Organization (WHO) named the disease as COVID-19 and declared it a pandemic. COVID-19 is characterized by highly contagious nature, rapid transmission, swift clinical course, profound worldwide impact, and high mortality among critically ill patients. Chest X-ray, computerized tomography (CT), and ultrasound are commonly used imaging modalities. Among them, ultrasound, due to its portability and non-invasiveness, can be easily moved to the bedside for examination at any time. In addition, with use of 4G or 5G networks, remote ultrasound consultation can also be performed, which allows ultrasound to be used in isolated medial areas. Besides, the contact surface of ultrasound probe with patients is small and easy to be disinfected. Therefore, ultrasound has gotten lots of positive feedbacks from the frontline healthcare workers, and it has played an indispensable role in the course of COVID-19 diagnosis and follow up

No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search Results

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-$k$ results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.Comment: 24 pages, 21 figures, 6 tables, submitted to The VLDB Journal for possible publicatio

Part Detector Discovery in Deep Convolutional Neural Networks

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called "part detector discovery" (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_discovery and https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision (ACCV) 201

LinkedIn for Searching Better Job Opportunity: Passive Jobseekers’ Perceived Experience

LinkedIn is a famous online social networking platform for the jobholders, jobseekers and employers although their purposes vary. Passive jobseekers are those who already have jobs but are searching for better alternatives. In this paper, we tried to identify the passive jobseekers’ perceived experience towards LinkedIn as a job searching platform. We used Focused Group Discussion (FGD) method to unmask the perception of 150 respondents from Bangladesh and India who use LinkedIn as professional social networking site frequently. After summarizing the FGD results, it was revealed that LinkedIn is the most preferred source of job search tool among the passive job candidates in terms of all aspects such as information availability, accuracy, relevance, reliability, timeliness and cost effectiveness. In addition, the authors revealed that the participants did not care much about privacy, fairness and ethics on LinkedIn as a professional social networking site (SNS). We expect that the results of this qualitative study will be helpful as a groundwork for further research. We also hope that the results will aid the recruiters to efficiently attract competent passive candidates as well as the job seekers to utilize LinkedIn in finding better job opportunity

A Genome-wide Association Study Coupled With Machine Learning Approaches To Identify Influential Demographic And Genomic Factors Underlying Parkinson’s Disease

Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson\u27s disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson\u27s disease. Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. We first applied correlation and GWAS analyses to find the top demographic and genomic factors associated with PD, respectively. We further developed and compared a variety of machine learning (ML) models for predicting PD. From the developed ML models, we performed feature importance analysis to reveal the predictability of each demographic or the genomic input feature for PD. Finally, we performed gene set enrichment analysis on our GWAS results to identify PD-associated pathways. Results: In our study, we identified both novel and well-known demographic and genetic factors (along with the enriched pathways) related to PD. In addition, we developed predictive models that performed robustly, with AUC = 0.89 for demographic data and AUC = 0.74 for genomic data. Our GWAS analysis identified several novel and significant variants and gene loci, including three intron variants in LMNA (p-values smaller than 4.0e-21) and one missense variant in SEMA4A (p-value = 1.11e-26). Our feature importance analysis from the PD-predictive ML models highlighted some significant and novel variants from our GWAS analysis (e.g., the intron variant rs1749409 in the RIT1 gene) and helped identify potentially causative variants that were missed by GWAS, such as rs11264300, a missense variant in the gene DCST1, and rs11584630, an intron variant in the gene KCNN3. Conclusion: In summary, by combining a GWAS with advanced machine learning models, we identified both known and novel demographic and genomic factors as well as built well-performing ML models for predicting Parkinson\u27s disease

Trans-lymphatic Contrast-Enhanced Ultrasound in Combination with Blue Dye Injection is Feasible for Detection and Biopsy of Sentinel Lymph Nodes in Breast Cancer

Objective: The best method for sentinel lymph node biopsy (SLNB) in early-staged breast cancer (EBC) remains controversial. This study aimed to evaluate a novel method by combining trans-lymphatic contrast-enhanced ultrasound (TLCEUS) with blue dye injection as a guidance of SLNB. Methods: TLCEUS was performed in 88 patients with newly diagnosed EBC. Methylene blue dye was percutaneously injected into enhanced sentinel lymph nodes (SLNs) under ultrasound guidance, followed by standard SLNB and axillary lymph node dissection. Enhancement patterns and the arriving time (AT) of contrast agent within SLNs were evaluated. Histopathological examination of dissected nodes was performed to confirm metastasis. Results: A total of 95 enhanced SLNs were identified and biopsied in 86 of 88 patients with identification rate of 97.7%. The specificity was 75.0%, sensitivity was 83.3%, and false-negative rate was 16.7%. Contrast-enhanced SLNs with type I, type II, and type III patterns had a metastatic positive rate of 11.4% (5/44), 57.1% (12/21) and 80.0% (24/30), respectively. Metastatic positive SLNs showed a mean AT of 61.6 ± 58.7 s while metastatic negative SLNs showed a mean AT of 41.3 ± 19.9 s, which was statistically significantly different. Conclusion: The TLCEUS/blue dye method can be used as an alternative to the radioisotope/blue dye method for its feasibility and accuracy