Effect of bending rigidity on the knotting of a polymer under tension

A coarse-grained computational model is used to investigate how the bending rigidity of a polymer under tension affects the formation of a trefoil knot. Thermodynamic integration techniques are applied to demonstrate that the free-energy cost of forming a knot has a minimum at non-zero bending rigidity. The position of the minimum exhibits a power-law dependence on the applied tension. For knotted polymers with non-uniform bending rigidity, the knots preferentially localize in the region with a bending rigidity that minimizes the free-energy.Comment: 15 pages, 6 figures. Corrected problem with references to equation

A set of ontologies to drive tools for the control of vector-borne diseases

We are developing a set of ontologies that deal with vector-borne diseases and the arthropod vectors that transmit them. For practical reasons (application priorities), we initiated this project with an ontology of insecticide resistance followed by a series of ontologies that describe malaria as well as physiological processes of mosquitoes that are relevant to, and involved in, disease transmission. These will be expanded to encompass other vector-borne diseases as well as non-mosquito vectors. The aim of the whole undertaking, which is worked out in the frame of the international IDO (Infectious Disease Ontology) project, is to provide the community with a set of ontological tools that can be used both in the development of specific databases and, most importantly, in the construction of decision support systems to control these diseases

Approximate Micromechanics Treatise of Composite Impact

A formalism is described for micromechanic impact of composites. The formalism consists of numerous equations which describe all aspects of impact from impactor and composite conditions to impact contact, damage progression, and penetration or containment. The formalism is based on through-the-thickness displacement increments simulation which makes it convenient to track local damage in terms of microfailure modes and their respective characteristics. A flow chart is provided to cast the formalism (numerous equations) into a computer code for embedment in composite mechanic codes and/or finite element composite structural analysis

MIRO and IRbase: IT Tools for the Epidemiological Monitoring of Insecticide Resistance in Mosquito Disease Vectors

It is a historical fact that a successful campaign against vector populations is one of the prerequisites for effectively fighting and eventually eradicating arthropod-borne diseases, be that in an epidemic or, even more so, in endemic cases. Based mostly on the use of insecticides and environmental management, vector control is now increasingly hampered by the occurrence of insecticide resistance that manifests itself, and spreads rapidly, briefly after the introduction of a (novel) chemical substance. We make use here of a specially built ontology, MIRO, to drive a new database, IRbase, dedicated to storing data on the occurrence of insecticide resistance in mosquito populations worldwide. The ontological approach to the design of databases offers the great advantage that these can be searched in an efficient way. Moreover, it also provides for an increased interoperability of present and future epidemiological tools. IRbase is now being populated by both older data from the literature and data recently collected from field

Predicting Properties of Unidirectional-Nanofiber Composites

A theory for predicting mechanical, thermal, electrical, and other properties of unidirectional-nanofiber/matrix composite materials is based on the prior theory of micromechanics of composite materials. In the development of the present theory, the prior theory of micromechanics was extended, through progressive substructuring, to the level of detail of a nanoscale slice of a nanofiber. All the governing equations were then formulated at this level. The substructuring and the equations have been programmed in the ICAN/JAVA computer code, which was reported in "ICAN/JAVA: Integrated Composite Analyzer Recoded in Java" (LEW-17247), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 36. In a demonstration, the theory as embodied in the computer code was applied to a graphite-nanofiber/epoxy laminate and used to predict 25 properties. Most of the properties were found to be distributed along the through-the-thickness direction. Matrix-dependent properties were found to have bimodal through-the-thickness distributions with discontinuous changes from mode to mode

Composite Nanomechanics: A Mechanistic Properties Prediction

A unique mechanistic theory is described to predict the properties of nanocomposites. The theory is based on composite micromechanics with progressive substructuring down to a nanoscale slice of a nanofiber where all the governing equations are formulated. These equations have been programmed in a computer code. That computer code is used to predict 25 properties of a mononanofiber laminate. The results are presented graphically and discussed with respect to their practical significance. Most of the results show smooth distributions. Results for matrix-dependent properties show bimodal through-the-thickness distribution with discontinuous changes from mode to mode

Printable microscale interfaces for long-term peripheral nerve mapping and precision control

The nascent field of bioelectronic medicine seeks to decode and modulate peripheral nervous system signals to obtain therapeutic control of targeted end organs and effectors. Current approaches rely heavily on electrode-based devices, but size scalability, material and microfabrication challenges, limited surgical accessibility, and the biomechanically dynamic implantation environment are significant impediments to developing and deploying advanced peripheral interfacing technologies. Here, we present a microscale implantable device – the nanoclip – for chronic interfacing with fine peripheral nerves in small animal models that begins to meet these constraints. We demonstrate the capability to make stable, high-resolution recordings of behaviorally-linked nerve activity over multi-week timescales. In addition, we show that multi-channel, current-steering-based stimulation can achieve a high degree of functionally-relevant modulatory specificity within the small scale of the device. These results highlight the potential of new microscale design and fabrication techniques for the realization of viable implantable devices for long-term peripheral interfacing.https://www.biorxiv.org/node/801468.fullFirst author draf

VectorBase: improvements to a bioinformatics resource for invertebrate vector genomics.

VectorBase (http://www.vectorbase.org) is a NIAID-supported bioinformatics resource for invertebrate vectors of human pathogens. It hosts data for nine genomes: mosquitoes (three Anopheles gambiae genomes, Aedes aegypti and Culex quinquefasciatus), tick (Ixodes scapularis), body louse (Pediculus humanus), kissing bug (Rhodnius prolixus) and tsetse fly (Glossina morsitans). Hosted data range from genomic features and expression data to population genetics and ontologies. We describe improvements and integration of new data that expand our taxonomic coverage. Releases are bi-monthly and include the delivery of preliminary data for emerging genomes. Frequent updates of the genome browser provide VectorBase users with increasing options for visualizing their own high-throughput data. One major development is a new population biology resource for storing genomic variations, insecticide resistance data and their associated metadata. It takes advantage of improved ontologies and controlled vocabularies. Combined, these new features ensure timely release of multiple types of data in the public domain while helping overcome the bottlenecks of bioinformatics and annotation by engaging with our user community

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) network is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using an IoT Data set, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks