SeVuc: A study on the Security Vulnerabilities of Capsule Networks against adversarial attacks

Capsule Networks (CapsNets) preserve the hierarchical spatial relationships between objects, and thereby bear the potential to surpass the performance of traditional Convolutional Neural Networks (CNNs) in performing tasks like image classification. This makes CapsNets suitable for the smart cyber-physical systems (CPS), where a large amount of training data may not be available. A large body of work has explored adversarial examples for CNNs, but their effectiveness on CapsNets has not yet been studied systematically. In our work, we perform an analysis to study the vulnerabilities in CapsNets to adversarial attacks. These perturbations, added to the test inputs, are small and imperceptible to humans, but can fool the network to mispredict. We propose a greedy algorithm to automatically generate imperceptible adversarial examples in a black-box attack scenario. We show that this kind of attacks, when applied to the German Traffic Sign Recognition Benchmark and CIFAR10 datasets, mislead CapsNets in making a correct classification, which can be catastrophic for smart CPS, like autonomous vehicles. Moreover, we apply the same kind of adversarial attacks to a 5-layer CNN (LeNet), to a 9-layer CNN (VGGNet), and to a 20-layer CNN (ResNet), and analyze the outcome, compared to the CapsNets, to study their different behaviors under the adversarial attacks

Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives

Crystal morphology is known to be of great importance to the end-use properties of crystal products, and to affect down-stream processing such as filtration and drying. However, it has been previously regarded as too challenging to achieve automatic closed-loop control. Previous work has focused on controlling the crystal size distribution, where the size of a crystal is often defined as the diameter of a sphere that has the same volume as the crystal. This paper reviews the new advances in morphological population balance models for modelling and simulating the crystal shape distribution (CShD), measuring and estimating crystal facet growth kinetics, and two- and three-dimensional imaging for on-line characterisation of the crystal morphology and CShD. A framework is presented that integrates the various components to achieve the ultimate objective of model-based closed-loop control of the CShD. The knowledge gaps and challenges that require further research are also identified

Gokyo Khumbu/Ama Dablam Trek 2012

In the expedition Gokyo Khumbu/Ama Dablam Trek 2012, we studied the effects of two 12-day training periods performed both at sea level and at high altitude. The main results on adult women have been published in six original articles. In women, high altitude trekking induced CD69 T cell activation and promoted anti-stress effects of the immune responses and the oxidative balance (1). Low-to-moderate exercise training at s.l. improves the regenerative capacity of skeletal muscle and depicted the epigenetic signature of satellite cells. The cell differentiation was favored by increased [Ca2+]i and fusion index (2). On the contrary, the training in hypobaric-hypoxia induced oxidative stress and impaired the regenerative capacity of satellite cells (6). Although training did not significantly modify muscle phenotype , it induced beneficial adaptations of the oxygen transport-utilization systems witnessed by faster VO2 kinetics at exercise onset (3). The two training periods did not influence the postural stability (4). In young adult women, micturition physiological parameters were affected during adaptation to hypoxia; the correlation with SpO2 strongly suggests a role of hypoxia in these changes (5

Automatic Design of Synthetic Gene Circuits through Mixed Integer Non-linear Programming

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits

Viral-bacterial co-infection in Australian Indigenous children with acute otitis media

Background: Acute otitis media with perforation (AOMwiP) affects 40% of remote Indigenous children during the first 18 months of life. Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis are the primary bacterial pathogens of otitis media and their loads predict clinical ear state. Our hypothesis is that antecedent respiratory viral infection increases bacterial density and progression to perforation

Geometry sensing by dendritic cells dictates spatial organization and PGE2-induced dissolution of podosomes

Assembly and disassembly of adhesion structures such as focal adhesions (FAs) and podosomes regulate cell adhesion and differentiation. On antigen-presenting dendritic cells (DCs), acquisition of a migratory and immunostimulatory phenotype depends on podosome dissolution by prostaglandin E2 (PGE2). Whereas the effects of physico-chemical and topographical cues have been extensively studied on FAs, little is known about how podosomes respond to these signals. Here, we show that, unlike for FAs, podosome formation is not controlled by substrate physico-chemical properties. We demonstrate that cell adhesion is the only prerequisite for podosome formation and that substrate availability dictates podosome density. Interestingly, we show that DCs sense 3-dimensional (3-D) geometry by aligning podosomes along the edges of 3-D micropatterned surfaces. Finally, whereas on a 2-dimensional (2-D) surface PGE2 causes a rapid increase in activated RhoA levels leading to fast podosome dissolution, 3-D geometric cues prevent PGE2-mediated RhoA activation resulting in impaired podosome dissolution even after prolonged stimulation. Our findings indicate that 2-D and 3-D geometric cues control the spatial organization of podosomes. More importantly, our studies demonstrate the importance of substrate dimensionality in regulating podosome dissolution and suggest that substrate dimensionality plays an important role in controlling DC activation, a key process in initiating immune responses

Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology

Synthetic biology approaches in drug discovery and pharmaceutical biotechnology

Synthetic biology is the attempt to apply the concepts of engineering to biological systems with the aim to create organisms with new emergent properties. These organisms might have desirable novel biosynthetic capabilities, act as biosensors or help us to understand the intricacies of living systems. This approach has the potential to assist the discovery and production of pharmaceutical compounds at various stages. New sources of bioactive compounds can be created in the form of genetically encoded small molecule libraries. The recombination of individual parts has been employed to design proteins that act as biosensors, which could be used to identify and quantify molecules of interest. New biosynthetic pathways may be designed by stitching together enzymes with desired activities, and genetic code expansion can be used to introduce new functionalities into peptides and proteins to increase their chemical scope and biological stability. This review aims to give an insight into recently developed individual components and modules that might serve as parts in a synthetic biology approach to pharmaceutical biotechnology

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings