Automatic design of mechanical metamaterial actuators

Mechanical metamaterial actuators achieve pre-determined input\u2013output operations exploiting architectural features encoded within a single 3D printed element, thus removing the need for assembling different structural components. Despite the rapid progress in the field, there is still a need for efficient strategies to optimize metamaterial design for a variety of functions. We present a computational method for the automatic design of mechanical metamaterial actuators that combines a reinforced Monte Carlo method with discrete element simulations. 3D printing of selected mechanical metamaterial actuators shows that the machine-generated structures can reach high efficiency, exceeding human-designed structures. We also show that it is possible to design efficient actuators by training a deep neural network which is then able to predict the efficiency from the image of a structure and to identify its functional regions. The elementary actuators devised here can be combined to produce metamaterial machines of arbitrary complexity for countless engineering applications

Comparative analysis of metabolic and transcriptomic features of Nothobranchius furzeri

Some species have a longer lifespan than others, but usually lifespan is correlated with typical body weight. Here, we study the lifetime evolution of the metabolic behaviour of Nothobranchius furzeri, a killifish with an extremely short lifespan with respect to other fishes, even when taking into account rescaling by body weight. Comparison of the gene expression patterns of N. furzeri with those of zebrafish Danio rerio and mouse (Mus musculus) shows that a broad set of metabolic genes and pathways are affected in N. furzeri during ageing in a way that is consistent with a global deregulation of chromatin. Computational analysis of the glycolysis pathway for the three species highlights a rapid increase in the metabolic activity during the lifetime of N. furzeri with respect to the other species. Our results highlight that the unusually short lifespan of N. furzeri is associated with peculiar patterns in the metabolic activities and in chromatin dynamics

Nucleosomes in serum as a marker for cell death

The concentration of nucleosomes is elevated in blood of patients with diseases which are associated with enhanced cell death. In order to detect these circulating nucleosomes, we used the Cell Death Detection-ELISA(Plus) (CDDE) from Roche Diagnostics (Mannheim, Germany) (details at http:\textbackslash{}\textbackslash{}biochem.roche.com). For its application in liquid materials we performed various modifications: we introduced a standard curve with nucleosome-rich material, which enabled direct quantification and improved comparability of the values within (CVinterassay:3.0-4.1%) and between several runs (CVinterassay:8.6-13.5%), and tested the analytical specificity of the ELISA. Because of the fast elimination of nucleosomes from circulation and their limited stability, we compared plasma and serum matrix and investigated in detail the pre-analytical handling of serum samples which can considerably influence the test results. Careless venipuncture producing hemolysis, delayed centrifugation and bacterial contamination of the blood samples led to false-positive results; delayed stabilization with EDTA and insufficient storage conditions resulted in false-negative values. At temperatures of -20 degreesC, serum samples which were treated with 10 mM EDTA were stable for at least 6 months. In order to avoid possible interfering factors, we recommend a schedule for the pre-analytical handling of the samples. As the first stage, the possible clinical application was investigated in the sera of 310 persons. Patients with solid tumors (n = 220; mean = 361 Arbitrary Units (AU)) had considerably higher values than healthy persons (n = 50; mean = 30 AU; P = 0.0001) and patients with inflammatory diseases (n = 40; mean = 296 AU; p = 0.096). Within the group of patients with tumors, those in advanced stages (UICC 4) showed significantly higher values than those in early stages (UICC 1-3) (P = 0.0004)

Identifying inhibitors of epithelial–mesenchymal plasticity using a network topology-based approach

Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges, such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial-mesenchymal plasticity (EMP)-an important arm of phenotypic plasticity-through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameter-agnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity correlates positively with the number of positive feedback loops in a given network. These results pave a way toward an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis-by reducing the number of positive feedback loops

Co-circulation of a novel phlebovirus and Massilia virus in sandflies, Portugal

Free PMC Article: www.ncbi.nlm.nih.gov/pmc/articles/pmid/26497645/Background: In Portugal, entomological surveys to detect phleboviruses in their natural vectors have not been performed so far. Thus, the aims of the present study were to detect, isolate and characterize phleboviruses in sandfly populations of Portugal. Findings: From May to October 2007–2008, 896 female sandflies were trapped in Arrábida region, located on the southwest coast of Portugal. Phlebovirus RNA was detected by using a pan-phlebovirus RT-PCR in 4 out of 34 Phlebotomus perniciosus pools. Direct sequencing of the amplicons showed that 2 samples exhibited 72 % nucleotide identity with Arbia virus, and two showed 96 % nucleotide identity with Massilia virus. The Arbia-like virus (named Alcube virus) was isolated in cell culture and complete genomic sequences of one Alcube and two Massila viruses were determined using next-generation sequencing technology. Phylogenetic analysis demonstrated that Alcube virus clustered with members of the Salehabad virus species complex. Within this clade, Alcube virus forms a monophyletic lineage with the Arbia, Salehabad and Adana viruses sharing a common ancestor. Arbia virus has been identified as the most closely related virus with 20-28 % nucleotide and 10-27 % amino acid divergences depending on the analysed segment. Conclusions: We have provided genetic evidence for the circulation of a novel phlebovirus species named Alcube virus in Ph. perniciosus and co-circulation of Massilia virus, in Arrábida region, southwest of Portugal. Further epidemiological investigations and surveillance for sandfly-borne phleboviruses in Portugal are needed to elucidate their medical importance.This work was partially funded by the FCT project “New arboviruses isolated in Portugal. Risk assessment and public health application" (PTDC/SAU-SAP/119199/2010)

Integrative analysis of pathway deregulation in obesity

Obesity is a pandemic disease, linked to the onset of type 2 diabetes and cancer. Transcriptomic data provides a picture of the alterations in regulatory and metabolic activities associated with obesity, but its interpretation is typically blurred by noise. Here, we solve this problem by collecting publicly available transcriptomic data from adipocytes and removing batch effects using singular value decomposition. In this way we obtain a gene expression signature of 38 genes associated to obesity and identify the main pathways involved. We then show that similar deregulation patterns can be detected in peripheral markers, in type 2 diabetes and in breast cancer. The integration of different data sets combined with the study of pathway deregulation allows us to obtain a more complete picture of gene-expression patterns associated with obesity, breast cancer, and diabetes

Multi-Granular Optical Cross-Connect: Design, Analysis, and Demonstration

A fundamental issue in all-optical switching is to offer efficient and cost-effective transport services for a wide range of bandwidth granularities. This paper presents multi-granular optical cross-connect (MG-OXC) architectures that combine slow (ms regime) and fast (ns regime) switch elements, in order to support optical circuit switching (OCS), optical burst switching (OBS), and even optical packet switching (OPS). The MG-OXC architectures are designed to provide a cost-effective approach, while offering the flexibility and reconfigurability to deal with dynamic requirements of different applications. All proposed MG-OXC designs are analyzed and compared in terms of dimensionality, flexibility/reconfigurability, and scalability. Furthermore, node level simulations are conducted to evaluate the performance of MG-OXCs under different traffic regimes. Finally, the feasibility of the proposed architectures is demonstrated on an application-aware, multi-bit-rate (10 and 40 Gbps), end-to-end OBS testbed

Topography of epithelial-mesenchymal plasticity

The transition between epithelial and mesenchymal states has fundamental importance for embryonic development, stem cell reprogramming, and cancer progression. Here, we construct a topographic map underlying epithelial-mesenchymal transitions using a combination of numerical simulations of a Boolean network model and the analysis of bulk and single-cell gene expression data. The map reveals a multitude of metastable hybrid phenotypic states, separating stable epithelial and mesenchymal states, and is reminiscent of the free energy measured in glassy materials and disordered solids. Our work not only elucidates the nature of hybrid mesenchymal/epithelial states but also provides a general strategy to construct a topographic representation of phenotypic plasticity from gene expression data using statistical physics methods