Novel transforaminal approach allows surgical decompression of an atlantoaxial band in dogs: a cadaveric study and clinical cases.

OBJECTIVES To describe a novel transforaminal approach for surgical excision of the atlantoaxial (AA) band and examine its feasibility, safety, and mechanical advantages in an ex vivo study and clinical cases. SAMPLES 26 canine cadavers and 2 canine patients with AA bands. PROCEDURES The transforaminal approach via the first intervertebral foramen was designed to avoid damaging the dorsal AA ligament (DAAL) and dorsal laminas to maintain joint stability. The cadaveric study started on December 2020 and lasted 3 months. The ligamentum flavum (LF) was removed using a novel approach; then, gross examination was conducted to verify the potential damage to the spinal cord and associated structures and the adequacy of LF removal. Subsequently, the ex vivo tension test of the DAAL was conducted to establish whether the approach induced mechanical damage to the ligaments. Finally, 2 dogs diagnosed with an AA band were surgically treated with the transforaminal approach. RESULTS In the cadaveric study, postsurgical evaluation verified the subtotal removal of LF without damage to the dura mater. There were no significant differences in the mechanical properties of the DAAL, including the ultimate strength (P = .645) and displacement (P = .855), between the surgical and intact groups during the ex vivo tension test. In clinical cases, clinical signs and neurologic grades improved until the final follow-up. CLINICAL RELEVANCE The described surgical procedure using a transforaminal approach appears to sufficiently permit the removal of an AA band while reducing damage to the DAAL and spinal cord. Our study highlights the feasibility of the transforaminal approach

EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images

Light field cameras capture both the spatial and the angular properties of light rays in space. Due to its property, one can compute the depth from light fields in uncontrolled lighting environments, which is a big advantage over active sensing devices. Depth computed from light fields can be used for many applications including 3D modelling and refocusing. However, light field images from hand-held cameras have very narrow baselines with noise, making the depth estimation difficult. any approaches have been proposed to overcome these limitations for the light field depth estimation, but there is a clear trade-off between the accuracy and the speed in these methods. In this paper, we introduce a fast and accurate light field depth estimation method based on a fully-convolutional neural network. Our network is designed by considering the light field geometry and we also overcome the lack of training data by proposing light field specific data augmentation methods. We achieved the top rank in the HCI 4D Light Field Benchmark on most metrics, and we also demonstrate the effectiveness of the proposed method on real-world light-field images.Comment: Accepted to CVPR 2018, Total 10 page

Case report: Primary chronic calcaneal bursitis treated with subtotal bursectomy in a cat.

A 6-year-old, female spayed Bengal cat with a bodyweight of 6.4 kg was presented with swelling of the bilateral calcaneal region and weight-bearing hindlimb lameness with a 4-month history of unsuccessful conservative therapy. On orthopedic examination, a cyst-like mass around the calcaneal tendon was palpated. Palpating the mass and flexing the tarsal joint triggered pain. Through ultrasonography and magnetic resonance imaging, an inflamed or fluid-accumulated lesion was suspected around the calcaneal tendon, but there was no evidence of calcaneal tendonitis. Swollen calcaneal bursae were removed surgically. Histopathologic examination revealed fibrosis and an edematous feature. The cat was diagnosed with bilateral chronic primary calcaneal bursitis based on history, clinical signs, and diagnostic results. Hence, subtotal bursectomy was performed. At 4 weeks postoperatively, the cat had no pain around the tarsal joints and was ambulating normally. Radiographic and ultrasonographic exams revealed no recurrence of swelling or inflammation in the calcaneal region. Thirteen-month follow-up confirmed acceptable function and no relapse of clinical signs. The inflammation of calcaneal bursa alone can be the primary cause of hindlimb lameness in cats. A cat with hindlimb lameness and swelling on the calcaneal region should be assessed with the possibility of primary calcaneal bursitis. Subtotal calcaneal bursectomy can be considered as an effective treatment for primary chronic bursitis

VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting

Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sorting. In industrial waste sorting, one of the biggest challenges is the extreme diversity of the input stream depending on factors like the location of the sorting facility, the equipment available in the facility, and the time of year, all of which significantly impact the composition and visual appearance of the waste stream. These changes in the data are called ``visual domains'', and label-efficient adaptation of models to such domains is needed for successful semantic segmentation of industrial waste. To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting. Our challenge incorporates a fully-annotated waste sorting dataset, ZeroWaste, collected from two real material recovery facilities in different locations and seasons, as well as a novel procedurally generated synthetic waste sorting dataset, SynthWaste. In this competition, we aim to answer two questions: 1) can we leverage domain adaptation techniques to minimize the domain gap? and 2) can synthetic data augmentation improve performance on this task and help adapt to changing data distributions? The results of the competition show that industrial waste detection poses a real domain adaptation problem, that domain generalization techniques such as augmentations, ensembling, etc., improve the overall performance on the unlabeled target domain examples, and that leveraging synthetic data effectively remains an open problem. See https://ai.bu.edu/visda-2022/Comment: Proceedings of Machine Learning Researc

Impact of successful restoration of sinus rhythm in patients with atrial fibrillation and acute heart failure: Results from the Korean Acute Heart Failure registry

Background: Restoring and maintaining sinus rhythm (SR) in patients with atrial fibrillation (AF) failed to show superior outcomes over rate control strategies in prior randomized trials. However, there is sparse data on their outcomes in patients with acute heart failure (AHF).Methods: From December 2010 to February 2014, 5,625 patients with AHF from 10 tertiary hospitals were enrolled in the Korean Acute Heart Failure registry, including 1,961 patients whose initial electrocardiogram showed AF. Clinical outcomes of patients who restored SR by pharmacological or electrical cardioversion (SR conversion group, n = 212) were compared to those of patients who showed a persistent AF rhythm (AF persistent group, n = 1,662).Results: All-cause mortality both in-hospital and during the follow-up (median 2.5 years) were significantly lower in the SR conversion group than in the AF persistent group after adjustment for risk factors (adjusted hazard ratio [HR]; 95% confidence interval [CI] = 0.26 [0.08–0.88], p = 0.031 and 0.59 [0.43–0.82], p = 0.002, for mortality in-hospital and during follow-up, respectively). After 1:3 propensity score matching (SR conversion group = 167, AF persistent group = 501), successful restoration of SR was associated with lower all-cause mortality (HR [95% CI] = 0.68 [0.49–0.93], p = 0.015), heart failure rehospitalization (HR [95% CI] = 0.66 [0.45–0.97], p = 0.032), and composite of death and heart failure rehospitalization (HR [95% CI] = 0.66 [0.51–0.86], p = 0.002).Conclusions: Patients with AHF and AF had significantly lower mortality in-hospital and during follow-up if rhythm treatment for AF was successful, underscoring the importance of restoring SR in patients with AHF

Optimization of ZnO Nanorod-Based Surface Enhanced Raman Scattering Substrates for Bio-Applications

Nanorods based on ZnO for surface enhanced Raman spectroscopy are promising for the non-invasive and rapid detection of biomarkers and diagnosis of disease. However, optimization of nanorod and coating parameters is essential to their practical application. With the goal of establishing a baseline for early detection in biological applications, gold-coated ZnO nanorods were grown and coated to form porous structures. Prior to gold deposition, the grown nanorods were 30-50 nm in diameter and 500-600 nm in length. Gold coatings were grown on the nanorod structure to a series of thicknesses between 100 and 300 nm. A gold coating of 200 nm was found to optimize the Rhodamine B model analyte signal, while performance for rat urine depended on the biomarkers to be detected. These results establish design guidelines for future use of Au-ZnO nanorods in the study and early diagnosis of inflammatory diseases

Machine Learning Optimization of Parameters for Noise Estimation

In this paper, a fast and effective method of parameter optimization for noise estimation is proposed for various types of noise. The proposed method is based on gradient descent, which is one of the optimization methods used in machine learning. The learning rate of gradient descent was set to a negative value for optimizing parameters for a speech quality improvement problem. The speech quality was evaluated using a suite of measures. After parameter optimization by gradient descent, the values were re-checked using a wider range to prevent convergence to a local minimum. To optimize the problem's five parameters, the overall number of operations using the proposed method was 99.99958% smaller than that using the conventional method. The extracted optimal values increased the speech quality by 1.1307%, 3.097%, 3.742%, and 3.861% on average for signal-to-noise ratios of 0, 5, 10, and 15 dB, respectively

Zico: Efficient GPU Memory Sharing for Concurrent DNN Training

GPUs are the workhorse in modern server infrastructure fueling advances in a number of compute-intensive workloads such as deep neural network (DNN) training. Several recent works propose solutions on sharing GPU resources across multiple concurrent DNN training jobs, but none of them address rapidly increasing memory footprint introduced by such job co-locations, which greatly limit the effectiveness of sharing GPU resources. In this paper, we present Zico, the first DNN system that aims at reducing the system-wide memory consumption for concurrent training. Zico keeps track of the memory usage pattern of individual training job by monitoring its progress on GPU computations and makes memory reclaimed from the job globally sharable. Based on this memory management scheme, Zico automatically decides a strategy to share memory among concurrent jobs with minimum delay on training while not exceeding a given memory budget such as GPU memory capacity. Our evaluation shows that Zico outperforms existing GPU sharing approaches and delivers benefits over a variety of job co-location scenarios

Magnetically recoverable hybrid TiO2 nanocrystal clusters with enhanced photocatalytic activity

We report a facile route to the synthesis of hybrid nanoclusters consisting of Fe3O4/SiO2/TiO2 core/shell structured nanoparticles and TiO2 nanocrystals. The hybrid nanoclusters were synthesized by a solvothermal reaction and produced well-defined anatase crystalline TiO2 without a calcination process. Enhanced photocatalytic activity was achieved due to the high crystallinity and large surface area of the nanoclusters. Furthermore, the nanoclusters could be recovered from the suspension simply by applying an external magnetic field. The recovered nanoclusters were found to maintain their initial photocatalytic activity after at least ten cycles of use