Random Lens Imaging

We call a random lens one for which the function relating the input light ray to the output sensor location is pseudo-random. Imaging systems with random lensescan expand the space of possible camera designs, allowing new trade-offs in optical design and potentially adding new imaging capabilities. Machine learningmethods are critical for both camera calibration and image reconstruction from the sensor data. We develop the theory and compare two different methods for calibration and reconstruction: an MAP approach, and basis pursuit from compressive sensing. We show proof-of-concept experimental results from a random lens made from a multi-faceted mirror, showing successful calibration and image reconstruction. We illustrate the potential for super-resolution and 3D imaging

Tiny images

The human visual system is remarkably tolerant to degradations in image resolution: in a scene recognition task, human performance is similar whether $32 \times 32$ color images or multi-mega pixel images are used. With small images, even object recognition and segmentation is performed robustly by the visual system, despite the object being unrecognizable in isolation. Motivated by these observations, we explore the space of 32x32 images using a database of 10^8 32x32 color images gathered from the Internet using image search engines. Each image is loosely labeled with one of the 70,399 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database represents a dense sampling of all object categories and scenes. With this dataset, we use nearest neighbor methods to perform objectrecognition across the 10^8 images

Scientific Visualization Using the Flow Analysis Software Toolkit (FAST)

Over the past few years the Flow Analysis Software Toolkit (FAST) has matured into a useful tool for visualizing and analyzing scientific data on high-performance graphics workstations. Originally designed for visualizing the results of fluid dynamics research, FAST has demonstrated its flexibility by being used in several other areas of scientific research. These research areas include earth and space sciences, acid rain and ozone modelling, and automotive design, just to name a few. This paper describes the current status of FAST, including the basic concepts, architecture, existing functionality and features, and some of the known applications for which FAST is being used. A few of the applications, by both NASA and non-NASA agencies, are outlined in more detail. Described in the Outlines are the goals of each visualization project, the techniques or 'tricks' used lo produce the desired results, and custom modifications to FAST, if any, done to further enhance the analysis. Some of the future directions for FAST are also described

Shining a Light on Intestinal Traffic

Inflammatory bowel disease (IBD), encompassing Crohn's disease and ulcerative colitis, is associated with enhanced leukocyte infiltration to the gut, which is directly linked to the clinical aspects of these disorders. Thus, leukocyte trafficking is a major target for IBD therapy. Past and emerging techniques to study leukocyte trafficking both in vitro and in vivo have expanded our knowledge of the leukocyte migration process and the role of inhibitors. Various strategies have been employed to target chemokine- and integrin-ligand interactions within the multistep adhesion cascade and the S1P/S1PR1 axis in leukocyte migration. Though there is an abundance of preclinical data demonstrating efficacy of leukocyte trafficking inhibitors, many have yet to be confirmed in clinical studies. Vigilance for toxicity and further research is required into this complex and emerging area of IBD therapy

Motion Deblurring in the Wild

The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown. Moreover, when pictures are taken in the wild, this task becomes even more challenging due to the blur varying spatially and the occlusions between the object. Due to the complexity of the general image model we propose a novel convolutional network architecture which directly generates the sharp image.This network is built in three stages, and exploits the benefits of pyramid schemes often used in blind deconvolution. One of the main difficulties in training such a network is to design a suitable dataset. While useful data can be obtained by synthetically blurring a collection of images, more realistic data must be collected in the wild. To obtain such data we use a high frame rate video camera and keep one frame as the sharp image and frame average as the corresponding blurred image. We show that this realistic dataset is key in achieving state-of-the-art performance and dealing with occlusions

Refining pathological evaluation of neoadjuvant therapy for adenocarcinoma of the esophagus

AIM: To assess tumour regression grade (TRG) and lymph node downstaging to help define patients who benefit from neoadjuvant chemotherapy.METHODS: Two hundred and eighteen consecutive patients with adenocarcinoma of the esophagus or gastro-esophageal junction treated with surgery alone or neoadjuvant chemotherapy and surgery between 2005 and 2011 at a single institution were reviewed. Triplet neoadjuvant chemotherapy consisting of platinum, fluoropyrimidine and anthracycline was considered for operable patients (World Health Organization performance status ? 2) with clinical stage T2-4 N0-1. Response to neoadjuvant chemotherapy (NAC) was assessed using TRG, as described by Mandard et al. In addition lymph node downstaging was also assessed. Lymph node downstaging was defined by cN1 at diagnosis: assessed radiologically (computed tomography, positron emission tomography, endoscopic ultrasonography), then pathologically recorded as N0 after surgery; ypN0 if NAC given prior to surgery, or pN0 if surgery alone. Patients were followed up for 5 years post surgery. Recurrence was defined radiologically, with or without pathological confirmation. An association was examined between t TRG and lymph node downstaging with disease free survival (DFS) and a comprehensive range of clinicopathological characteristics.RESULTS: Two hundred and eighteen patients underwent esophageal resection during the study interval with a mean follow up of 3 years (median follow up: 2.552, 95%CI: 2.022-3.081). There was a 1.8% (n = 4) inpatient mortality rate. One hundred and thirty-six (62.4%) patients received NAC, with 74.3% (n = 101) of patients demonstrating some signs of pathological tumour regression (TRG 1-4) and 5.9% (n = 8) having a complete pathological response. Forty four point one percent (n = 60) had downstaging of their nodal disease (cN1 to ypN0), compared to only 15.9% (n = 13) that underwent surgery alone (pre-operatively overstaged: cN1 to pN0), (P < 0.0001). Response to NAC was associated with significantly increased DFS (mean DFS; TRG 1-2: 5.1 years, 95%CI: 4.6-5.6 vs TRG 3-5: 2.8 years, 95%CI: 2.2-3.3, P < 0.0001). Nodal down-staging conferred a significant DFS advantage for those patients with a poor primary tumour response to NAC (median DFS; TRG 3-5 and nodal down-staging: 5.533 years, 95%CI: 3.558-7.531 vs TRG 3-5 and no nodal down-staging: 1.114 years, 95%CI: 0.961-1.267, P < 0.0001).CONCLUSION: Response to NAC in the primary tumour and in the lymph nodes are both independently associated with improved DFS

Local knowledge of the impacts of eucalyptus expansion on water security in the Ethiopian highlands

Lack of long-term hydrological monitoring makes it difficult to determine impacts of changing land use on the water dynamics for many catchments in Africa. Here we use local ecological knowledge (LEK) to explore the impacts of rapid expansion of eucalyptus agroforestry on water security in the Ethiopian highlands. Local knowledge about the impacts of changes in tree cover was collected from farmers (n = 30), extension staff (n = 2) and timber merchants (n = 2) in five kebeles within the Jeldu woreda. Jeldu has undergone significant land use change over the last forty years. The area was heavily deforested 20 years ago and farmers associate this time with a major change in the water dynamics. Recently the development of a new road to Goja, the main town, opened up the area as a source of timber for Addis Ababa. This has resulted in a substantial expansion of eucalyptus plots adjacent to roads on the upper plateau and in riparian areas where growth is accelerated. Poorer farmers have been displaced on to the sloping land (which used to be woodland) where there is now evidence of rapid soil degradation. The key findings were that farmers identified significant trade-offs at the plot scale between eucalyptus and adjacent crop fields. They also identified indicators suggesting the sudden increase in eucalyptus cover had accelerated declines in water availability at landscape scales. The study showed the value of using LEK for exploring immediate landscape scale dynamics in the absence of hydrological monitoring. Whilst there is a degree of uncertainty surrounding the impacts of eucalyptus, this research demonstrated local awareness associated of problems associated with unregulated expansion of eucalyptus woodlots on the water regulating capacity at immediate landscape scales in the Ethiopian highlands

Is convalescent plasma futile in COVID-19?:A Bayesian re-analysis of the RECOVERY randomized controlled trial

Background: Randomized trials are generally performed from a frequentist perspective, which can conflate absence of evidence with evidence of absence. The RECOVERY trial evaluated convalescent plasma for patients hospitalized with coronavirus disease 2019 (COVID-19) and concluded that there was no evidence of an effect. Re-analysis from a Bayesian perspective is warranted. Methods: Outcome data were extracted from the RECOVERY trial by serostatus and time of presentation. A Bayesian re-analysis with a wide variety of priors (vague, optimistic, sceptical, and pessimistic) was performed, calculating the posterior probability for: any benefit, an absolute risk difference of 0.5% (small benefit, number needed to treat 200), and an absolute risk difference of one percentage point (modest benefit, number needed to treat 100). Results: Across all patients, when analysed with a vague prior, the likelihood of any benefit or a modest benefit with convalescent plasma was estimated to be 64% and 18%, respectively. The estimated chance of any benefit was 95% if presenting within 7 days of symptoms, or 17% if presenting after this. In patients without a detectable antibody response at presentation, the chance of any benefit was 85%. However, it was only 20% in patients with a detectable antibody response at presentation. Conclusions: Bayesian re-analysis suggests that convalescent plasma reduces mortality by at least one percentage point among the 39% of patients who present within 7 days of symptoms, and that there is a 67% chance of the same mortality reduction in the 38% who are seronegative at the time of presentation. This is in contrast to the results in people who already have antibodies when they present. This biologically plausible finding bears witness to the advantage of Bayesian analyses over misuse of hypothesis tests to inform decisions