Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://apc.cs.princeton.edu

Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://apc.cs.princeton.edu

Macroporous nanowire nanoelectronic scaffolds for synthetic tissues

available in PMC 2013 April 11.The development of three-dimensional (3D) synthetic biomaterials as structural and bioactive scaffolds is central to fields ranging from cellular biophysics to regenerative medicine. As of yet, these scaffolds cannot electrically probe the physicochemical and biological microenvironments throughout their 3D and macroporous interior, although this capability could have a marked impact in both electronics and biomaterials. Here, we address this challenge using macroporous, flexible and free-standing nanowire nanoelectronic scaffolds (nanoES), and their hybrids with synthetic or natural biomaterials. 3D macroporous nanoES mimic the structure of natural tissue scaffolds, and they were formed by self-organization of coplanar reticular networks with built-in strain and by manipulation of 2D mesh matrices. NanoES exhibited robust electronic properties and have been used alone or combined with other biomaterials as biocompatible extracellular scaffolds for 3D culture of neurons, cardiomyocytes and smooth muscle cells. Furthermore, we show the integrated sensory capability of the nanoES by real-time monitoring of the local electrical activity within 3D nanoES/cardiomyocyte constructs, the response of 3D-nanoES-based neural and cardiac tissue models to drugs, and distinct pH changes inside and outside tubular vascular smooth muscle constructs.National Institutes of Health (U.S.) (Director’s Pioneer award)McKnight Foundation (Technological Innovations in Neurosciences Award)Boston Children's Hospital (Biotechnology Research Endowment)National Institutes of Health (U.S.) (DE013023)National Institutes of Health (U.S.) (DE016516

Gut microbiota and acylcarnitine metabolites connect the beneficial association between estrogen and lipid metabolism disorders in ovariectomized mice

Decreased estrogen level is one of the main causes of lipid metabolism disorders and coronary heart disease in women after menopause. Exogenous estradiol benzoate is effective to some extent in alleviating lipid metabolism disorders caused by estrogen deficiency. However, the role of gut microbes in the regulation process is not yet appreciated. The objective of this study was to investigate the effects of estradiol benzoate supplementation on lipid metabolism, gut microbiota, and metabolites in ovariectomized (OVX) mice and to reveal the importance of gut microbes and metabolites in the regulation of lipid metabolism disorders. This study found that high doses of estradiol benzoate supplementation effectively attenuated fat accumulation in OVX mice. There was a significant increase in the expression of genes enriched in hepatic cholesterol metabolism and a concomitant decrease in the expression of genes enriched in unsaturated fatty acid metabolism pathways. Further screening of the gut for characteristic metabolites associated with improved lipid metabolism revealed that estradiol benzoate supplementation influenced major subsets of acylcarnitine metabolites. Ovariectomy significantly increased the abundance of characteristic microbes that are significantly negatively associated with acylcarnitine synthesis, such as Lactobacillus and Eubacterium ruminantium group bacteria, while estradiol benzoate supplementation significantly increased the abundance of characteristic microbes that are significantly positively associated with acylcarnitine synthesis, such as Ileibacterium and Bifidobacterium spp. The use of pseudosterile mice with gut microbial deficiency greatly facilitated the synthesis of acylcarnitine due to estradiol benzoate supplementation and also alleviated lipid metabolism disorders to a greater extent in OVX mice. IMPORTANCE Our findings establish a role for gut microbes in the progression of estrogen deficiency-induced lipid metabolism disorders and reveal key target bacteria that may have the potential to regulate acylcarnitine synthesis. These findings suggest a possible route for the use of microbes or acylcarnitine to regulate disorders of lipid metabolism induced by estrogen deficiency

A novel method for metal–diamond composite coating deposition with cold spray and formation mechanism

This paper describes the application of cold spray to the deposition of a diamond grade pre-coated with Cu and Ni. This is the first time that pre-coated diamond powders are used as the sole feedstock without the addition of binders (ductile phases) in cold spraying. The experimental results showed that it was possible to manufacture thick metal–diamond composite coatings onto an Al alloy substrate with high diamond fraction in the coating and without phase change. Results from this paper also have demonstrated a new methodology for the deposition of metal–diamond/ceramic composite coating with the cold spray technique

An integrated transcriptomic and computational analysis for biomarker identification in gastric cancer

This report describes an integrated study on identification of potential markers for gastric cancer in patients’ cancer tissues and sera based on: (i) genome-scale transcriptomic analyses of 80 paired gastric cancer/reference tissues and (ii) computational prediction of blood-secretory proteins supported by experimental validation. Our findings show that: (i) 715 and 150 genes exhibit significantly differential expressions in all cancers and early-stage cancers versus reference tissues, respectively; and a substantial percentage of the alteration is found to be influenced by age and/or by gender; (ii) 21 co-expressed gene clusters have been identified, some of which are specific to certain subtypes or stages of the cancer; (iii) the top-ranked gene signatures give better than 94% classification accuracy between cancer and the reference tissues, some of which are gender-specific; and (iv) 136 of the differentially expressed genes were predicted to have their proteins secreted into blood, 81 of which were detected experimentally in the sera of 13 validation samples and 29 found to have differential abundances in the sera of cancer patients versus controls. Overall, the novel information obtained in this study has led to identification of promising diagnostic markers for gastric cancer and can benefit further analyses of the key (early) abnormalities during its development

Brief research report pesticide occupational exposure leads to significant inflammatory changes in normal mammary breast tissue

Studies have documented the high occurrence of several tumors, including female breast cancer, in populations occupationally exposed to pesticides worldwide. It is believed that in addition to direct DNA damage, other molecular alterations that indicate genomic instability are associated, such as epigenetic modifications and the production of inflammation mediators. The present study characterized the profile of inflammatory changes in the breast tissue of women without cancer occupationally exposed to pesticides. In samples of normal breast tissue collected during biopsy and evaluated as negative for cancer by a pathologist, oxidative stress levels were assessed as inflammatory markers through measurements of lipoperoxides and total antioxidant capacity of the sample (TRAP) by high-sensitivity chemiluminescence, as well as levels of nitric oxide (NOx) metabolites. The levels of inflammation-modulating transcription factors PPAR-γ (peroxisome proliferator-activated receptor gamma) and NF-κB (nuclear factor kappa B) were also quantified, in addition to the pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α) and interleukin 12 (IL-12). The levels of lipoperoxides, TRAP, and NOx were significantly lower in the exposed group. On the other hand, PPAR-γ levels were increased in the breast tissue of exposed women, with no variation in NF-κB. There was also a rise of TNF-α in exposed women samples without significant variations in IL-12 levels. These findings suggest an inflammatory signature of the breast tissue associated with pesticide exposure, which may trigger mechanisms related to mutations and breast carcinogenesis

A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations

Genome-wide association studies (GWAS) have become the preferred experimental design in exploring the genetic etiology of complex human traits and diseases. Standard SNP-based meta-analytic approaches have been utilized to integrate the results from multiple experiments. This fundamentally assumes that the patterns of linkage disequilibrium (LD) between the underlying causal variants and the directly genotyped SNPs are similar across the populations for the same SNPs to emerge with surrogate evidence of disease association. We introduce a novel strategy for assessing regional evidence of phenotypic association that explicitly incorporates the extent of LD in the region. This provides a natural framework for combining evidence from multi-ethnic studies of both dichotomous and quantitative traits that (i) accommodates different patterns of LD, (ii) integrates different genotyping platforms and (iii) allows for the presence of allelic heterogeneity between the populations. Our method can also be generalized to perform gene-based or pathway-based analyses. Applying this method on real GWAS data in type 2 diabetes (T2D) boosted the association evidence in regions well-established for T2D etiology in three diverse South-East Asian populations, as well as identified two novel gene regions and a biologically convincing pathway that are subsequently validated with data from the Wellcome Trust Case Control Consortium