As light is transmitted from subject to observer it is absorbed and scattered by the medium it passes through. In mediums with large suspended particles, such as fog or turbid water, the effect of scattering can drastically decrease the quality of images. In this paper we present an algorithm for removing the effects of light scattering, referred to as dehazing, in underwater images. Our key contribution is to propose a simple, yet effective, prior that exploits the strong difference in attenuation between the three image color channels in water to estimate the depth of the scene. We then use this estimate to reduce the spatially varying effect of haze in the image. Our method works with a single image and does not require any specialized hardware or prior knowledge of the scene. As a by-product of the dehazing process, an up-to-scale depth map of the scene is produced. We present results over multiple real underwater images and over a controlled test set where the target distance and true colors are known.
Disclosure of Conflict of Interest: C.M. O'Connor and G. Narla are named inventors on a US provisional patent application concerning compositions and methods for treating high grade subtypes of uterine cancer. C.M. O'Connor, T.K. Suhan, K.P. Zawacki, and J. Sangodkar are consultants for RAPPTA Therapeutics. G. Narla is chief scientific officer at RAPPTA Therapeutics, is an SAB member at Hera BioLabs, reports receiving commercial research support from RAPPTA Therapeutics, and has ownership interest (including patents) in RAPPTA Therapeutics. D. Zamarin reports research support to his institution from Astra Zeneca, Plexxikon, and Genentech; personal/consultancy fees from Synlogic Therapeutics, GSK, Genentech, Xencor, Memgen, Immunos, Celldex, Calidi, and Agenus. D. Zamarin is an inventor on a patent related to use of oncolytic Newcastle Disease Virus for cancer therapy.Research.
Background and Objectives:To compare surgical outcomes of overweight and obese patients with acute appendicitis who have undergone single-port extracorporeal laparoscopically assisted appendectomy (SP) with those who have had conventional 3-port laparoscopic appendectomy (TP).Methods:This single-center retrospective chart review included patients 21 years of age and younger with a preoperative diagnosis of appendicitis who underwent laparoscopic appendectomy from January 2010 through December 2015. Cases of gangrenous and perforated appendicitis were excluded. Subgroup analyses of patients with acute appendicitis were performed. Operative time (OT), length of stay (LOS), and cost were compared between groups stratified by body mass index (BMI) and operative technique.Results:A total of 625 appendectomies were performed—457 for acute appendicitis. Sixty-eight patients were overweight. The SP technique (n = 30) had shorter OT (median minutes, 41 vs 68; P < .001), lower cost (median , $5741 vs $8530; P < .001), and shorter LOS (median hours, 16 vs 19; P = .045) than the TP technique had (n = 38). Seventy patients were obese: 19 were treated with SP and 51 with TP. LOS did not differ significantly between the SP and TP groups, but subjects treated with SP had shorter OT (median minutes, 39 vs 63; P < .001) and lower cost (median, $6401 vs $8205; P = .043).Conclusions:The SP technique for acute appendicitis was found to have a significantly shorter OT and lower cost in all weight groups. There were minimal differences in LOS. SP should be considered in patients with acute appendicitis, regardless of their weight.
SP has comparable surgical outcomes in adolescent, adult, and pediatric patients.
TULAA had a shorter OT and was less costly than conventional TPLA. TULAA should be considered as the first surgical approach at treating appendicitis in children.
Abstract. Using image hierarchies for visual categorization has shown to have a number of important benefits. For instance it enables a significant gain in efficiency (e.g., logarithmic with the number of categories [1,2]). Moreover, arranging visual data in a hierarchical structure echoes the way how humans organize data and enables the construction of a more meaningful distance metric for image classification [3] (see figure 1). However, a critical question still remains controversial: would structuring data in a hierarchical sense also help classification accuracy? While our intuition suggests that the answer may be positive, up to date no method have shown conclusive results that can demonstrate the correctness of this claim for the most general case of large scale databases. In this paper we address this question and show that the hierarchical structure of a database can be indeed successfully used to enhance classification accuracy using a sparse approximation framework. We propose a new formulation for sparse approximation problem where the goal is to discover the sparsest path within the hierarchical data structure that best represents the query object. Extensive quantitative and qualitative experimental evaluation on a number of branches of the Imagenet database [4] as well as on the Caltech 256 [2] demonstrate our theoretical claims and show that our approach produces the best categorization results (in term of a number of hierarchical-based distance functions) over a number of competing large scale classification schemes that do not exploit the hierarchical structure of the database.
Purpose: Devimistat (CPI-613®) is a novel inhibitor of tumoral mitochondrial metabolism. We investigated effect of devimistat in vitro and in a phase 1b clinical trial in patients with advanced biliary tract cancer (BTC). Patients and Methods: Cell viability assays of devimistat +/- GC were performed and effect of devimistat on mitochondrial respiration via oxygen consumption rate (OCR) was evaluated. A phase 1b/2 trial was initiated in patients with untreated advanced BTC. In phase 1b, devimistat was infused over 2 hours in combination with GC on days 1 and 8 every 21 days with a primary objective to determine recommended phase 2 dose (RP2D). Secondary objectives included safety, overall response rate (ORR), progression-free survival (PFS) and overall survival (OS). Results: In vitro, devimistat with GC had synergistic effect on two cell lines. Devimistat significantly decreased OCR at higher doses and in arms with divided dosing. In the phase 1b trial, 20 patients received a median of 9 cycles (range 3-19). One DLT was observed and the RP2D of devimistat was determined to be 2000 mg/m2 in combination with GC. Most common grade 3 toxicities included neutropenia (n=11, 55%), anemia (n=4, 20%), and infection (n=3, 15%); with no grade 4 toxicities. After a median follow-up of 15.6 months, ORR is 45% and median PFS is 10 months (95% CI, 7.1 – 14.9). Median OS is not yet estimable. Conclusion: Devimistat in combination with GC is well tolerated and has an acceptable safety profile in patients with untreated advanced BTC.
Abstract-This paper reports on a method for tracking a camera system within an a priori known map constructed from co-registered 3D light detection and ranging (LIDAR) and omnidirectional image data. Our method pre-processes the raw 3D LIDAR and camera data to produce a sparse map that can scale to city-size environments. From the original LIDAR and camera data we extract visual features and identify those that are most robust to varying viewpoint. This allows us to include only the visual features that are most useful for localization in the map. Additionally, we quantize the visual features using a vocabulary tree to further reduce the map's file size. We then use vision-based localization to track the vehicle's motion through the map. We present results on urban data collected with Ford Motor Company's autonomous vehicle testbed. In our experiments the map is built using urban data from winter 2009, and localization is performed using data collected in fall 2010 and winter 2011. This demonstrates our algorithm's robustness to temporal changes in the environment.
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