Dimension- and shape-dependent thermal transport in nano-patterned thin films investigated by scanning thermal microscopy

Scanning thermal microscopy (SThM) is a technique which is often used for the measurement of the thermal conductivity of materials at the nanometre scale. The impact of nano-scale feature size and shape on apparent thermal conductivity, as measured using SThM, has been investigated. To achieve this, our recently developed topography-free samples with 200 and 400 nm wide gold wires (50 nm thick) of length of 400–2500 nm were fabricated and their thermal resistance measured and analysed. This data was used in the development and validation of a rigorous but simple heat transfer model that describes a nanoscopic contact to an object with finite shape and size. This model, in combination with a recently proposed thermal resistance network, was then used to calculate the SThM probe signal obtained by measuring these features. These calculated values closely matched the experimental results obtained from the topography-free sample. By using the model to analyse the dimensional dependence of thermal resistance, we demonstrate that feature size and shape has a significant impact on measured thermal properties that can result in a misinterpretation of material thermal conductivity. In the case of a gold nanowire embedded within a silicon nitride matrix it is found that the apparent thermal conductivity of the wire appears to be depressed by a factor of twenty from the true value. These results clearly demonstrate the importance of knowing both probe-sample thermal interactions and feature dimensions as well as shape when using SThM to quantify material thermal properties. Finally, the new model is used to identify the heat flux sensitivity, as well as the effective contact size of the conventional SThM system used in this study

Topography-free sample for thermal spatial response measurement of scanning thermal microscopy

A novel fabrication technique is described for the production of multimaterial, lithographically defined, topography-free samples for use in experiments to investigate the nature of contrast in scanning probe microscopy (SPM). The approach uses a flat sacrificial substrate as the base for fabrication, which is deleted in the final step. This leaves an exposed, flat surface with patterns of materials contrast defined during the lithography stages. In the example application presented, these are designed to challenge the detection ability of a scanning thermal microscopy (SThM) probe, although many other applications can be envisioned. There are many instances in SPM where images can exhibit topographically induced artifacts. In SThM, these can result in a change of the thermal signal which can easily be misinterpreted as changes in the sample thermal conductivity or temperature. The elimination of these artifacts through postprocessing requires a knowledge of how the probe responds thermal features of differing sizes. The complete sample fabrication process, followed by successful topographic/thermal scanning is demonstrated, showing sub-1.5 nm topography with a clear artifact-free thermal signal from sub-100 nm gold wires. The thermal spatial resolution is determined for the sample materials and probe used in this study to be in the range of 35–75 nm

Sampling-based path planning for multi-robot systems with co-safe linear temporal logic specifications

© 2017, Springer International Publishing AG. This paper addresses the problem of path planning for multiple robots under high-level specifications given as syntactically co-safe linear temporal logic formulae. Most of the existing solutions use the notion of abstraction to obtain a discrete transition system that simulates the dynamics of the robot. Nevertheless, these solutions have poor scalability with the dimension of the configuration space of the robots. For problems with a single robot, sampling-based methods have been presented as a solution to alleviate this limitation. The proposed solution extends the idea of sampling methods to the multiple robot case. The method samples the configuration space of the robots to incrementally constructs a transition system that models the motion of all the robots as a group. This transition system is then combined with a Büchi automaton, representing the specification, in a Cartesian product. The product is updated with each expansion of the transition system until a solution is found. We also present a new algorithm that improves the performance of the proposed method by guiding the expansion of the transition system. The method is demonstrated with examples considering different number of robots and specifications

Identification of Extracellular Segments by Mass Spectrometry Improves Topology Prediction of Transmembrane Proteins

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins

Metagenomic study of the viruses of African straw-coloured fruit bats: detection of a chiropteran poxvirus and isolation of a novel adenovirus

Viral emergence as a result of zoonotic transmission constitutes a continuous public health threat. Emerging viruses such as SARS coronavirus, hantaviruses and henipaviruses have wildlife reservoirs. Characterising the viruses of candidate reservoir species in geographical hot spots for viral emergence is a sensible approach to develop tools to predict, prevent, or contain emergence events. Here, we explore the viruses of Eidolon helvum, an Old World fruit bat species widely distributed in Africa that lives in close proximity to humans. We identified a great abundance and diversity of novel herpes and papillomaviruses, described the isolation of a novel adenovirus, and detected, for the first time, sequences of a chiropteran poxvirus closely related with Molluscum contagiosum. In sum, E. helvum display a wide variety of mammalian viruses, some of them genetically similar to known human pathogens, highlighting the possibility of zoonotic transmission

Cellular sheddases are induced by Merkel Cell Polyomavirus Small Tumour Antigen to mediate cell dissociation and invasiveness

Merkel cell carcinoma (MCC) is an aggressive skin cancer with a high propensity for recurrence and metastasis. Merkel cell polyomavirus (MCPyV) is recognised as the causative factor in the majority of MCC cases. The MCPyV small tumour antigen (ST) is considered to be the main viral transforming factor, however potential mechanisms linking ST expression to the highly metastatic nature of MCC are yet to be fully elucidated. Metastasis is a complex process, with several discrete steps required for the formation of secondary tumour sites. One essential trait that underpins the ability of cancer cells to metastasise is how they interact with adjoining tumour cells and the surrounding extracellular matrix. Here we demonstrate that MCPyV ST expression disrupts the integrity of cell-cell junctions, thereby enhancing cell dissociation and implicate the cellular sheddases, A disintegrin and metalloproteinase (ADAM) 10 and 17 proteins in this process. Inhibition of ADAM 10 and 17 activity reduced MCPyV ST-induced cell dissociation and motility, attributing their function as critical to the MCPyV-induced metastatic processes. Consistent with these data, we confirm that ADAM 10 and 17 are upregulated in MCPyV-positive primary MCC tumours. These novel findings implicate cellular sheddases as key host cell factors contributing to virus-mediated cellular transformation and metastasis. Notably, ADAM protein expression may be a novel biomarker of MCC prognosis and given the current interest in cellular sheddase inhibitors for cancer therapeutics, it highlights ADAM 10 and 17 activity as a novel opportunity for targeted interventions for disseminated MCC

New evidence on the tool-assisted hunting exhibited by chimpanzees (Pan troglodytes verus) in a savannah habitat at Fongoli, Sénégal

For anthropologists, meat eating by primates like chimpanzees (Pan troglodytes) warrants examination given the emphasis on hunting in human evolutionary history. As referential models, apes provide insight into the evolution of hominin hunting, given their phylogenetic relatedness and challenges reconstructing extinct hominin behaviour from palaeoanthropological evidence. Among chimpanzees, adult males are usually the main hunters, capturing vertebrate prey by hand. Savannah chimpanzees (P. t. verus) at Fongoli, Sénégal are the only known nonhuman population that systematically hunts vertebrate prey with tools, making them an important source for hypotheses of early hominin behaviour based on analogy. Here, we test the hypothesis that sex and age patterns in tool-assisted hunting (n=308 cases) at Fongoli occur and differ from chimpanzees elsewhere, and we compare tool-assisted hunting to the overall hunting pattern. Males accounted for 70% of all captures but hunted with tools less than expected based on their representation on hunting days. Females accounted for most toolassisted hunting. We propose that social tolerance at Fongoli along with the tool-assisted hunting method, permits individuals other than adult males to capture and retain control of prey, which is uncommon for chimpanzees. We assert that tool-assisted hunting could have similarly been important for early hominins

Merkel Cell Polyomavirus Small T Antigen Drives Cell Motility via Rho-GTPase-Induced Filopodium Formation

Cell motility and migration is a complex, multistep, and multicomponent process intrinsic to progression and metastasis. Motility is dependent on the activities of integrin receptors and Rho family GTPases, resulting in the remodeling of the actin cytoskeleton and formation of various motile actin-based protrusions. Merkel cell carcinoma (MCC) is an aggressive skin cancer with a high likelihood of recurrence and metastasis. Merkel cell polyomavirus (MCPyV) is associated with the majority of MCC cases, and MCPyV-induced tumorigenesis largely depends on the expression of the small tumor antigen (ST). Since the discovery of MCPyV, a number of mechanisms have been suggested to account for replication and tumorigenesis, but to date, little is known about potential links between MCPyV T antigen expression and the metastatic nature of MCC. Previously, we described the action of MCPyV ST on the microtubule network and how it impacts cell motility and migration. Here, we demonstrate that MCPyV ST affects the actin cytoskeleton to promote the formation of filopodia through a mechanism involving the catalytic subunit of protein phosphatase 4 (PP4C). We also show that MCPyV ST-induced cell motility is dependent upon the activities of the Rho family GTPases Cdc42 and RhoA. In addition, our results indicate that the MCPyV ST-PP4C interaction results in the dephosphorylation of β1 integrin, likely driving the cell motility pathway. These findings describe a novel mechanism by which a tumor virus induces cell motility, which may ultimately lead to cancer metastasis, and provides opportunities and strategies for targeted interventions for disseminated MCC

Random-phase approximation and its applications in computational chemistry and materials science

The random-phase approximation (RPA) as an approach for computing the electronic correlation energy is reviewed. After a brief account of its basic concept and historical development, the paper is devoted to the theoretical formulations of RPA, and its applications to realistic systems. With several illustrating applications, we discuss the implications of RPA for computational chemistry and materials science. The computational cost of RPA is also addressed which is critical for its widespread use in future applications. In addition, current correction schemes going beyond RPA and directions of further development will be discussed.Comment: 25 pages, 11 figures, published online in J. Mater. Sci. (2012

A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data

BACKGROUND: As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. METHODS: In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN) algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. RESULTS: We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM) algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. CONCLUSION: Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets