Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image

In this paper, we propose a deep convolutional recurrent neural network that predicts action sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g. first-order logic) with continuous motion planning such as nonlinear trajectory optimization. Due to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we develop a neural network which, based on an initial image of the scene, directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. A key aspect is that our method generalizes to scenes with many and varying number of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene in images as input to the neural network, instead of a fixed feature vector. Results show runtime improvements of several magnitudes. Video: https://youtu.be/i8yyEbbvoEkComment: Robotics: Science and Systems (R:SS) 202

Anchor Collaboratives: Building Bridges with Place-Based Partnerships and Anchor Institutions

In January 2019, the Anchor Collaborative Network (ACN) was initiated to build a shared movement of anchor institution collaboralions that are working to accelerate equitable, inclusive strategies that respond to local needs and challenges. This report presents information and examples to show the current slate of the field. ACN Working Groups plan to produce case studies, toolkits and other educational information to further assist other cities, anchor institutions, and partner organizations lo learn about and advance anchor mission work

Learning to solve sequential physical reasoning problems from a scene image

In this article, we propose deep visual reasoning, which is a convolutional recurrent neural network that predicts discrete action sequences from an initial scene image for sequential manipulation problems that arise, for example, in task and motion planning (TAMP). Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g., first-order logic) with continuous motion planning such as nonlinear trajectory optimization. The action sequences represent the discrete decisions on a symbolic level, which, in turn, parameterize a nonlinear trajectory optimization problem. Owing to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we introduce deep visual reasoning: based on a segmented initial image of the scene, a neural network directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. Our method generalizes to scenes with many and varying numbers of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene and the goal in (segmented) images as input to the neural network, instead of a fixed feature vector. We show that the framework can not only handle kinematic problems such as pick-and-place (as typical in TAMP), but also tool-use scenarios for planar pushing under quasi-static dynamic models. Here, the image-based representation enables generalization to other shapes than during training. Results show runtime improvements of several orders of magnitudes by, in many cases, removing the need to search over the discrete action sequences.DFG, 390523135, EXC 2002: Science of Intelligence (SCIoI

Glycolysis Inhibition of Autophagy Drives Malignancy in Ovarian Cancer: Exacerbation by IL-6 and Attenuation by Resveratrol.

peer reviewedCancer cells drive the glycolytic process towards the fermentation of pyruvate into lactate even in the presence of oxygen and functioning mitochondria, a phenomenon known as the "Warburg effect". Although not energetically efficient, glycolysis allows the cancer cell to synthesize the metabolites needed for cell duplication. Autophagy, a macromolecular degradation process, limits cell mass accumulation and opposes to cell proliferation as well as to cell migration. Cancer cells corrupt cancer-associated fibroblasts to release pro-inflammatory cytokines, which in turn promote glycolysis and support the metastatic dissemination of cancer cells. In mimicking in vitro this condition, we show that IL-6 promotes ovarian cancer cell migration only in the presence of glycolysis. The nutraceutical resveratrol (RV) counteracts glucose uptake and metabolism, reduces the production of reactive oxygen species consequent to excessive glycolysis, rescues the mitochondrial functional activity, and stimulates autophagy. Consistently, the lack of glucose as well as its metabolically inert analogue 2-deoxy-D-glucose (2-DG), which inhibits hexokinase 2 (HK2), trigger autophagy through mTOR inhibition, and prevents IL-6-induced cell migration. Of clinical relevance, bioinformatic analysis of The Cancer Genome Atlas dataset revealed that ovarian cancer patients bearing mutated TP53 with low expression of glycolytic markers and IL-6 receptor, together with markers of active autophagy, display a longer overall survival and are more responsive to platinum therapy. Taken together, our findings demonstrate that RV can counteract IL-6-promoted ovarian cancer progression by rescuing glycolysis-mediated inhibition of autophagy and support the view that targeting Warburg metabolism can be an effective strategy to limit the risk for cancer metastasis

ITIH5 as a multifaceted player in pancreatic cancer suppression, impairing tyrosine kinase signaling, cell adhesion and migration

Inter-alpha-trypsin inhibitor heavy chain 5 (ITIH5) has been identified as a metastasis suppressor gene in pancreatic cancer. Here, we analyzed ITIH5 promoter methylation and protein expression in The Cancer Genome Atlas (TCGA) dataset and three tissue microarray cohorts (n = 618), respectively. Cellular effects, including cell migration, focal adhesion formation and protein tyrosine kinase activity, induced by forced ITIH5 expression in pancreatic cancer cell lines were studied in stable transfectants. ITIH5 promoter hypermethylation was associated with unfavorable prognosis, while immunohistochemistry demonstrated loss of ITIH5 in the metastatic setting and worsened overall survival. Gain-of-function models showed a significant reduction in migration capacity, but no alteration in proliferation. Focal adhesions in cells re-expressing ITIH5 exhibited a smaller and more rounded phenotype, typical for slow-moving cells. An impressive increase of acetylated alpha-tubulin was observed in ITIH5-positive cells, indicating more stable microtubules. In addition, we found significantly decreased activities of kinases related to focal adhesion. Our results indicate that loss of ITIH5 in pancreatic cancer profoundly affects its molecular profile: ITIH5 potentially interferes with a variety of oncogenic signaling pathways, including the PI3K/AKT pathway. This may lead to altered cell migration and focal adhesion formation. These cellular alterations may contribute to the metastasis-inhibiting properties of ITIH5 in pancreatic cancer.</p

Human cerebral malaria and Plasmodium falciparum genotypes in Malawi

<p>Abstract</p> <p>Background</p> <p>Cerebral malaria, a severe form of <it>Plasmodium falciparum </it>infection, is an important cause of mortality in sub-Saharan African children. A Taqman 24 Single Nucleotide Polymorphisms (SNP) molecular barcode assay was developed for use in laboratory parasites which estimates genotype number and identifies the predominant genotype.</p> <p>Methods</p> <p>The 24 SNP assay was used to determine predominant genotypes in blood and tissues from autopsy and clinical patients with cerebral malaria.</p> <p>Results</p> <p>Single genotypes were shared between the peripheral blood, the brain, and other tissues of cerebral malaria patients, while malaria-infected patients who died of non-malarial causes had mixed genetic signatures in tissues examined. Children with retinopathy-positive cerebral malaria had significantly less complex infections than those without retinopathy (OR = 3.7, 95% CI [1.51-9.10]).The complexity of infections significantly decreased over the malaria season in retinopathy-positive patients compared to retinopathy-negative patients.</p> <p>Conclusions</p> <p>Cerebral malaria patients harbour a single or small set of predominant parasites; patients with incidental parasitaemia sustain infections involving diverse genotypes. Limited diversity in the peripheral blood of cerebral malaria patients and correlation with tissues supports peripheral blood samples as appropriate for genome-wide association studies of parasite determinants of pathogenicity.</p

Evaluating the effects of increasing physical activity to optimize rehabilitation outcomes in hospitalized older adults (MOVE Trial): Study protocol for a randomized controlled trial

Background: Older adults who have received inpatient rehabilitation often have significant mobility disability at discharge. Physical activity levels in rehabilitation are also low. It is hypothesized that providing increased physical activity to older people receiving hospital-based rehabilitation will lead to better mobility outcomes at discharge. Methods/Design: A single blind, parallel-group, multisite randomized controlled trial with blinded assessment of outcome and intention-to-treat analysis. The cost effectiveness of the intervention will also be examined. Older people (age &gt;60 years) undergoing inpatient rehabilitation to improve mobility will be recruited from geriatric rehabilitation units at two Australian hospitals. A computer-generated blocked stratified randomization sequence will be used to assign 198 participants in a 1:1 ratio to either an 'enhanced physical activity' (intervention) group or a 'usual care plus' (control) group for the duration of their inpatient stay. Participants will receive usual care and either spend time each week performing additional physical activities such as standing or walking (intervention group) or performing an equal amount of social activities that have minimal impact on mobility such as card and board games (control group). Self-selected gait speed will be measured using a 6-meter walk test at discharge (primary outcome) and 6 months follow-up (secondary outcome). The study is powered to detect a 0.1 m/sec increase in self-selected gait speed in the intervention group at discharge. Additional measures of mobility (Timed Up and Go, De Morton Mobility Index), function (Functional Independence Measure) and quality of life will be obtained as secondary outcomes at discharge and tertiary outcomes at 6 months follow-up. The trial commenced recruitment on 28 January 2014. Discussion: This study will evaluate the efficacy and cost effectiveness of increasing physical activity in older people during inpatient rehabilitation. These results will assist in the development of evidenced-based rehabilitation programs for this population. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12613000884707(Date of registration 08 August 2013); ClinicalTrials.gov Identifier NCT01910740(Date of registration 22 July 2013)