E-TRoll: Tactile sensing and classification via a simple robotic gripper for extended rolling manipulations

Robotic tactile sensing provides a method of recognizing objects and their properties where vision fails. Prior work on tactile perception in robotic manipulation has frequently focused on exploratory procedures (EPs). However, the also-human-inspired technique of in-hand-manipulation can glean rich data in a fraction of the time of EPs. We propose a simple 3-DOF robotic hand design, optimized for object rolling tasks via a variable-width palm and associated control system. This system dynamically adjusts the distance between the finger bases in response to object behavior. Compared to fixed finger bases, this technique significantly increases the area of the object that is exposed to finger-mounted tactile arrays during a single rolling motion (an increase of over 60% was observed for a cylinder with a 30-millimeter diameter). In addition, this paper presents a feature extraction algorithm for the collected spatiotemporal dataset, which focuses on object corner identification, analysis, and compact representation. This technique drastically reduces the dimensionality of each data sample from 10×1500 time series data to 80 features, which was further reduced by Principal Component Analysis (PCA) to 22 components. An ensemble subspace k-nearest neighbors (KNN) classification model was trained with 90 observations on rolling three different geometric objects, resulting in a three-fold cross-validation accuracy of 95.6% for object shape recognition

Stain guided mean-shift filtering in automatic detection of human tissue nuclei

Background: As a critical technique in a digital pathology laboratory, automatic nuclear detection has been investigated for more than one decade. Conventional methods work on the raw images directly whose color/intensity homogeneity within tissue/cell areas are undermined due to artefacts such as uneven staining, making the subsequent binarization process prone to error. This paper concerns detecting cell nuclei automatically from digital pathology images by enhancing the color homogeneity as a pre-processing step. Methods: Unlike previous watershed based algorithms relying on post-processing of the watershed, we present a new method that incorporates the staining information of pathological slides in the analysis. This pre-processing step strengthens the color homogeneity within the nuclear areas as well as the background areas, while keeping the nuclear edges sharp. Proof of convergence for the proposed algorithm is also provided. After pre-processing, Otsu's threshold is applied to binarize the image, which is further segmented via watershed. To keep a proper compromise between removing overlapping and avoiding over-segmentation, a naive Bayes classifier is designed to refine the splits suggested by the watershed segmentation. Results: The method is validated with 10 sets of 1000 × 1000 pathology images of lymphoma from one digital slide. The mean precision and recall rates are 87% and 91%, corresponding to a mean F-score equal to 89%. Standard deviations for these performance indicators are 5.1%, 1.6% and 3.2% respectively. Conclusion: The precision/recall performance obtained indicates that the proposed method outperforms several other alternatives. In particular, for nuclear detection, stain guided mean-shift (SGMS) is more effective than the direct application of mean-shift in pre-processing. Our experiments also show that pre-processing the digital pathology images with SGMS gives better results than conventional watershed algorithms. Nevertheless, as only one type of tissue is tested in this paper, a further study is planned to enhance the robustness of the algorithm so that other types of tissues/stains can also be processed reliably

Ozone production in four major cities of China: sensitivity to ozone precursors and heterogeneous processes

Abstract. Despite a large volume of research over a number of years, our understandings of the key precursors that control tropospheric ozone production and the impacts of heterogeneous processes remain incomplete. In this study, we analyze measurements of ozone and its precursors made at rural/suburban sites downwind of four large Chinese cities – Beijing, Shanghai, Guangzhou and Lanzhou. At each site the same measurement techniques were utilized and a photochemical box model based on the Master Chemical Mechanism (v3.2) was applied, to minimize uncertainties in comparison of the results due to differences in methodology. All four cities suffered from severe ozone pollution. At the rural site of Beijing, export of the well-processed urban plumes contributed to the extremely high ozone levels (up to an hourly value of 286 ppbv), while the pollution observed at the suburban sites of Shanghai, Guangzhou and Lanzhou was characterized by intense in-situ ozone production. The major anthropogenic hydrocarbons were alkenes and aromatics in Beijing and Shanghai, aromatics in Guangzhou, and alkenes in Lanzhou. The ozone production was found to be in a VOCs-limited regime in both Shanghai and Guangzhou, and a mixed regime in Lanzhou. In Shanghai, the ozone formation was most sensitive to aromatics and alkenes, while in Guangzhou aromatics were the predominant ozone precursors. In Lanzhou, either controlling NOx or reducing emissions of olefins from the petrochemical industry would mitigate the local ozone production. The potential impacts of several heterogeneous processes on the ozone formation were assessed. The hydrolysis of dinitrogen pentoxide (N2O5), uptake of the hydroperoxyl radical (HO2) on particles, and surface reactions of NO2 forming nitrous acid (HONO) present considerable sources of uncertainty in the current studies of ozone chemistry. Further efforts are urgently required to better understand these processes and refine atmospheric models

Editorial: Distribution patterns, driving mechanisms and ecological service functions of urban plant biodiversity

Peer Reviewe

Do educational interventions aimed at nurses to support the implementation of evidence-based practice improve patient outcomes? A systematic review

Background: Numerous articles have sought to identify the impact of educational interventions for improving evidence-based practice (EBP) amongst nurses, most of these focus on skills and knowledge acquired. No systematic review has explored whether this educational input translates into improved patient outcomes. Objectives: To review the evidence on (1) The change in patient outcomes following educational interventions to support practising nurses in implementing EBP. (2) The instruments or methods used to determine whether EBP education improves patient outcomes. Methods: A systematic review following PRISMA guidance was conducted. Literature was comprehensive searched including 6 databases, journal handsearching, citation tracking, and grey literature websites. Studies were included if they reported an EBP educational intervention aimed at practising nurses and contained objective or self-reported measures of patient related outcomes. The quality of the included studies was assessed using a modified Health Care Practice R&D Unit (HCPRDU) tool. Because of the poor homogeneity of the included studies, the data were analysed by narrative synthesis. Results: Of the 4,284 articles identified, 18 were included: 12 pre–post studies, three qualitative studies, and three mixed-methods study designs. The level of quality was modest in the studies. The results of the EBP educational interventions on patient outcomes were assessed using three methods: individual projects to implement an evidence-based approach, qualitative approaches, and a questionnaire survey. The majority of the articles concluded there was a positive change in patient outcomes following an educational intervention to improve EBP; a wide range of context specific outcomes were described. Conclusion: Educational interventions for clinical nurses to support the implementation of EBP show promise in improving patient outcomes. However, the direct impact of EBP interventions on clinical outcomes is difficult to measure. Further testing and development is needed to improve the quality of studies and evaluation instruments in order to confirm the current findings

Co3O4 Nanocrystals on Graphene as a Synergistic Catalyst for Oxygen Reduction Reaction

Catalysts for oxygen reduction and evolution reactions are at the heart of key renewable energy technologies including fuel cells and water splitting. Despite tremendous efforts, developing oxygen electrode catalysts with high activity at low costs remains a grand challenge. Here, we report a hybrid material of Co3O4 nanocrystals grown on reduced graphene oxide (GO) as a high-performance bi-functional catalyst for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). While Co3O4 or graphene oxide alone has little catalytic activity, their hybrid exhibits an unexpected, surprisingly high ORR activity that is further enhanced by nitrogen-doping of graphene. The Co3O4/N-doped graphene hybrid exhibits similar catalytic activity but superior stability to Pt in alkaline solutions. The same hybrid is also highly active for OER, making it a high performance non-precious metal based bi-catalyst for both ORR and OER. The unusual catalytic activity arises from synergetic chemical coupling effects between Co3O4 and graphene.Comment: published in Nature Material

Particle dynamics near extreme Kerr throat and supersymmetry

The extreme Kerr throat solution is believed to be non-supersymmetric. However, its isometry group SO(2,1) x U(1) matches precisely the bosonic subgroup of N=2 superconformal group in one dimension. In this paper we construct N=2 supersymmetric extension of a massive particle moving near the horizon of the extreme Kerr black hole. Bosonic conserved charges are related to Killing vectors in a conventional way. Geometric interpretation of supersymmetry charges remains a challenge.Comment: V2: 10 pages; discussion in sect. 4 and 5 extended, acknowledgements and references adde

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

Effects of temperature and glycerol and methanol-feeding profiles on the production of recombinant galactose oxidase in Pichia pastoris

Optimization of protein production from methanol-induced Pichia pastoris cultures is necessary to ensure high productivity rates and high yields of recombinant proteins. We investigated the effects of temperature and different linear or exponential methanol-feeding rates on the production of recombinant Fusarium graminearum galactose oxidase (EC 1.1.3.9) in a P. pastoris Mut+ strain, under regulation of the AOX1 promoter. We found that low exponential methanol feeding led to 1.5-fold higher volumetric productivity compared to high exponential feeding rates. The duration of glycerol feeding did not affect the subsequent product yield, but longer glycerol feeding led to higher initial biomass concentration, which would reduce the oxygen demand and generate less heat during induction. A linear and a low exponential feeding profile led to productivities in the same range, but the latter was characterized by intense fluctuations in the titers of galactose oxidase and total protein. An exponential feeding profile that has been adapted to the apparent biomass concentration results in more stable cultures, but the concentration of recombinant protein is in the same range as when constant methanol feeding is employed. (c) 2014 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 30:728-735, 201