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Stevenson Bech posted an update a month ago
Our work is early, however we demonstrate many successes, including L3 collaboration in room-scale VR, 1000’s of interacting objects, complex configurations such as stacking, and transparent coupling of haptic devices. These have been shown before, but each with a different technique; CBN supports them all within a single, unified system.In this paper, we report the results of the first international contest on mitosis detection in phase-contrast microscopy image sequences (https//www.iti-tju.org/mitosisdetection), which was held at the workshop of computer vision for microscopy image analysis (CVMI) in CVPR 2019. This contest aims to promote research on spatiotemporal mitosis detection under microscopy images. In this contest, we released a large-scale time-lapse phase-contrast microscopy image dataset (C2C12-16) for the mitosis detection task. Compared with the previous popular datasets (e.g., C2C12, C3H10), C2C12-16 contains more annotated mitotic events and more diverse cell culture environments. A total of ten different mitosis detection methods were submitted in the contest and evaluated on the test sets of four different cell culture environments in C2C12-16. In this benchmark, we describe all methods and conduct a thorough analysis based on their performances and discuss a feasible direction for mitosis detection. To the best of our knowledge, this is the first benchmark for the mitosis detection problem using a time-lapse phase-contrast microscopy spatiotemporal image sequence model.Neural architecture search (NAS) has emerged as a promising avenue for automatically designing task-specific neural networks. Existing NAS approaches require one complete search for each deployment specification of hardware or objective. This is a computationally impractical endeavor given the potentially large number of application scenarios. In this paper, we propose Neural Architecture Transfer (NAT) to overcome this limitation. NAT is designed to efficiently generate task-specific custom models that are competitive under multiple conflicting objectives. To realize this goal we learn task-specific supernets from which specialized subnets can be sampled without any additional training. The key to our approach is an integrated online transfer learning and many-objective evolutionary search procedure. A pre-trained supernet is iteratively adapted while simultaneously searching for task-specific subnets. We demonstrate the efficacy of NAT on 11 benchmark image classification tasks ranging from large-scale multi-class to small-scale fine-grained datasets. U0126 concentration In all cases, including ImageNet, NATNets improve upon the state-of-the-art under mobile settings ( ≤ 600M Multiply-Adds). Surprisingly, small-scale fine-grained datasets benefit the most from NAT. At the same time, the architecture search and transfer is orders of magnitude more efficient than existing NAS methods. Overall, experimental evaluation indicates that, across diverse image classification tasks and computational objectives, NAT is an appreciably more effective alternative to conventional transfer learning of fine-tuning weights of an existing network architecture learned on standard datasets. Code is available at https//github.com/human-analysis/neural-architecture-transfer.Antimicrobial resistance (AMR) is a global health and economic crisis. With too few antibiotics in development to meet current and anticipated needs, there is a critical need for new therapies to treat Gram-negative infections. One potential approach is the use of living predatory bacteria, such as Bdellovibrio bacteriovorus (small Gram-negative bacteria that naturally invade and kill Gram-negative pathogens of humans, animals and plants). Moving toward the use of Bdellovibrio as a ‘living antibiotic’ demands the investigation and characterization of these bacterial predators in biologically relevant systems. We review the fundamental science supporting the feasibility of predatory bacteria as alternatives to antibiotics.
The emergence of COVID-19 caused a new public health crisis, leading to major changes in daily life routines, often including physical activity (PA) levels. The main goal of this study was to analyze the differences in self-reported physical activity of people with complete spinal cord injuries between the time prior to the COVID-19 lockdown and the lockdown period itself.
A sample of 20 participants with complete thoracic spinal cord injuries completed the Physical Activity Scale for Individuals with Physical Disabilities before and during the COVID-19 lockdown.
The results showed differences between the pre-lockdown and lockdown measurements in total self-reported PA (
=-3.92;
<0.001;
=1.28), recreational PA (
=-3.92;
<0.001;
=1.18) and occupational PA (
=-2.03;
=0.042;
=0.55). Nevertheless, no differences were found in housework PA between the two time periods. Furthermore, the results showed differences in total minutes (z=-3.92;
<0.001; d=1.75), minutes spent on recreationtegies should be implemented within this population to lessen the effects of this physical inactivity stemming from the COVID-19 pandemic.
This scoping review examines the current research on the effect of cannabis upon pain intensity in spinal cord injury (SCI) pain. Chronic pain is a significant secondary condition following SCI, and traditional treatments (e.g. opioids, NSAIDs) are often criticized for providing inadequate relief. As a result, there is increasing interest in and use of cannabis and cannabinoid-based medications as an alternative means of pain control.
The purpose of this review was to examine the scientific evidence on the effect of cannabis/cannabinoids upon pain intensity in SCI by mapping the current literature.
Two hundred and fifty-two studies were identified by searching electronic databases for articles published through February 2020. In addition, reviewers scanned the reference lists of identified articles and examined clinicaltrials.gov for unpublished data in this area. Title, abstract, and full-text reviews were completed by two independent reviewers. Data extraction was performed by a single reviewer and verified by a second reviewer.