The automatic extraction and recognition of news captions and annotations can be of great help locating topics of interest in digital news video libraries. To achieve this goal, we present a technique, called Video OCR (Optical Character Reader), which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, we apply an interpolation filter, multiframe integration and character extraction filters. Character segmentation is performed by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using text-like properties and the use of a language-based postprocessing technique to increase word recognition rates. The overall recognition results are satisfactory for use in news indexing. Performing Video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.

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Broccoli sprout extract containing sulforaphane (BSE-SFN) has been shown to inhibit ultraviolet radiation-induced damage and tumor progression in skin. This study evaluated the toxicity and potential effects of oral BSE-SFN at three dosages. Seventeen patients who each had at least 2 atypical nevi and a prior history of melanoma were randomly allocated to 50, 100, or 200 μmol oral BSE-SFN daily for 28 days. Atypical nevi were photographed on days 1 and 28, and plasma and nevus samples were taken on days 1, 2, and 28. Endpoints assessed were safety, plasma and skin sulforaphane levels, gross and histologic changes, IHC for phospho-STAT3(Y705), Ki-67, Bcl-2, HMOX1, and TUNEL, plasma cytokine levels, and tissue proteomics. All 17 patients completed 28 days with no dose-limiting toxicities. Plasma sulforaphane levels pooled for days 1, 2, and 28 showed median postadministration increases of 120 ng/mL for 50 μmol, 206 ng/mL for 100 μmol, and 655 ng/mL for 200 μmol. Median skin sulforaphane levels on day 28 were 0.0, 3.1, and 34.1 ng/g for 50, 100, and 200 μmol, respectively. Plasma levels of proinflammatory cytokines decreased from day 1 to 28. The tumor suppressor decorin was increased from day 1 to 28. Oral BSE-SFN is well tolerated at daily doses up to 200 μmol and achieves dose-dependent levels in plasma and skin. A larger efficacy evaluation of 200 μmol daily for longer intervals is now reasonable to better characterize clinical and biological effects of BSE-SFN as chemoprevention for melanoma. .

INTRODUCTION: Chest imaging is necessary for diagnosis of COVID-19 pneumonia, but current risk stratification tools do not consider radiographic severity. We quantified radiographic heterogeneity among inpatients with COVID-19 with the Radiographic Assessment of Lung Edema (RALE) score on Chest X-rays (CXRs). METHODS: We performed independent RALE scoring by ≥2 reviewers on baseline CXRs from 425 inpatients with COVID-19 (discovery dataset), we recorded clinical variables and outcomes, and measured plasma host-response biomarkers and SARS-CoV-2 RNA load from subjects with available biospecimens. RESULTS: We found excellent inter-rater agreement for RALE scores (intraclass correlation co-efficient=0.93). The required level of respiratory support at the time of baseline CXRs (supplemental oxygen or non-invasive ventilation [n=178]; invasive-mechanical ventilation [n=234], extracorporeal membrane oxygenation [n=13]) was significantly associated with RALE scores (median [interquartile range]: 20.0[14.1-26.7], 26.0[20.5-34.0] and 44.5[34.5-48.0], respectively, p<0.0001). Among invasively-ventilated patients, RALE scores were significantly associated with worse respiratory mechanics (plateau and driving pressure) and gas exchange metrics (PaO2/FiO2 and ventilatory ratio), as well as higher plasma levels of IL-6, sRAGE and TNFR1 levels (p<0.05). RALE scores were independently associated with 90-day survival in a multivariate Cox proportional hazards model (adjusted hazard ratio 1.04[1.02-1.07], p=0.002). We validated significant associations of RALE scores with baseline severity and mortality in an independent dataset of 415 COVID-19 inpatients. CONCLUSION: Reproducible assessment of radiographic severity revealed significant associations with clinical and physiologic severity, host-response biomarkers and clinical outcome in COVID-19 pneumonia. Incorporation of radiographic severity assessments may provide prognostic and treatment allocation guidance in patients hospitalized with COVID-19.

Given a large collection of images, very few of which have labels given a priori, how can we automatically assign the labels of the remaining majority, and make suggestion for images that may need brand new labels distinct from existing ones? Popular automatic labeling techniques usually scale super linearly with the size of the image set, and/or their performances degrade if limited images bear initial labels. In this paper, we propose QMAS, an efficient solution to the following problems: (i) low-labor labeling (L3) -given a collection of images, very few of which are already labeled with keywords, find the most suitable labels for the remaining ones; and (ii) mining and attention routing -with the same input set, output a number of top representative images and top outliers. We present experimental evaluation on three data sets of proprietary and public satellite images up to a size of 2.25GB. QMAS scales linearly with the number of images, obtaining better or equal accuracy while being up to 40 times faster than its baseline algorithm. With limited numbers of initial labels available, QMAS achieves a significant accuracy margin over the baseline approach. The application of QMAS to recommend representatives and spot outliers is also illustrated. The proposed framework could be generalized to solve similar content-based annotation and mining problems on other multi-modal databases.