Thin-film chemical sensors based on electron tunneling

The physical mechanisms underlying a novel chemical sensor based on electron tunneling in metal-insulator-metal (MIM) tunnel junctions were studied. Chemical sensors based on electron tunneling were shown to be sensitive to a variety of substances that include iodine, mercury, bismuth, ethylenedibromide, and ethylenedichloride. A sensitivity of 13 parts per billion of iodine dissolved in hexane was demonstrated. The physical mechanisms involved in the chemical sensitivity of these devices were determined to be the chemical alteration of the surface electronic structure of the top metal electrode in the MIM structure. In addition, electroreflectance spectroscopy (ERS) was studied as a complementary surface-sensitive technique. ERS was shown to be sensitive to both iodine and mercury. Electrolyte electroreflectance and solid-state MIM electroreflectance revealed qualitatively the same chemical response. A modified thin-film structure was also studied in which a chemically active layer was introduced at the top Metal-Insulator interface of the MIM devices. Cobalt phthalocyanine was used for the chemically active layer in this study. Devices modified in this way were shown to be sensitive to iodine and nitrogen dioxide. The chemical sensitivity of the modified structure was due to conductance changes in the active layer

Strain-stiffening in random packings of entangled granular chains

Random packings of granular chains are presented as a model polymer system to investigate the contribution of entanglements to strain-stiffening in the absence of Brownian motion. The chain packings are sheared in triaxial compression experiments. For short chain lengths, these packings yield when the shear stress exceeds a the scale of the confining pressure, similar to packings of spherical particles. In contrast, packings of chains which are long enough to form loops exhibit strain-stiffening, in which the effective stiffness of the material increases with strain, similar to many polymer materials. The latter packings can sustain stresses orders-of-magnitude greater than the confining pressure, and do not yield until the chain links break. X-ray tomography measurements reveal that the strain-stiffening packings contain system-spanning clusters of entangled chains.Comment: 4 pages, 4 figures. submitted to Physical Review Letter

New types of bialgebras arising from the Hopf equation

New types of bialgebras arising from the Hopf equation (pentagonal equation) are introduced and studied. They will play from the Hopf equation the same role as the co-quasitriangular do from the quantum Yang Baxter equation.Comment: Latex2e, Comm Algebra, in pres

Genome-wide association studies of parasite resistance, productivity and immunology traits in meat in Scottish Blackface sheep

Gastrointestinal parasitism represents a global problem for grazing ruminants, which can be addressed sustainably by breeding animals to be more resistant against infection by parasites. The aim of this study was to assess the genetic architecture underlying traits associated with gastrointestinal parasite resistance, immunological profile and production in meat sheep, and identify and characterise candidate genes affecting these traits. Data on gastrointestinal parasite infection (faecal egg counts for Strongyles (FECS) and Nematodirus (FECN) and faecal oocyst counts for Coccidia, along with faecal soiling scores (DAG), characterised by the accumulation of faeces around the perineum) and production (live weight (LWT)) were gathered from a flock Scottish Blackface lambs at three and four months of age. Data on the immune profile were also collected from a subset of these lambs at two and five months of age. Immune traits included the production of Interferon-γ (IFN-γ), Interleukin (IL)-4 and IL-10 following stimulation of whole blood with pokeweed mitogen (PWM) or antigen from the gastric parasite Teladorsagia circumcincta (T-ci), and serum levels of T. circumcincta-specific immunoglobulin A (IgA). Animals were genotyped with genome-wide DNA arrays, and a total of 1 766 animals and 45 827 Single Nucleotide Polymorphisms (SNPs) were retained following quality control and imputation. Genome-wide association studies were performed for 24 traits. The effects of individual markers with significant effects were estimated, and the genotypic effect solutions were used to estimate additive and dominance effects, and the proportion of additive genetic variance attributed to each SNP locus. A total of 15 SNPs were associated at least at a suggestive level with FECS, FECN, DAG, IgA, PWM-induced IFN-γ and IL-4, and T-ci-induced IL-10. This study uncovered 52 genes closely related to immune function in proximity to these SNPs. A number of genes encoding C-type lectins and killer cell lectin-like family members were close to a SNP associated with FECN while several genes encoding IL-1 cytokine family members were found to be associated with IgA. Potential candidate genes belonging to or in close proximity with the Major Histocompatibility Complex (MHC) were revealed, including Homeostatic Iron Regulator and butyrophilin coding genes associated with IFN-γ(PWM), and IL-17 coding genes associated with IgA. Due to the importance of the MHC in the control of immune responses, these genes may play an important role in resistance to parasitic infections. Our results reveal a largely complex and polygenic genetic profile of the studied traits in this Scottish Blackface sheep population.</p

The Hopf modules category and the Hopf equation

We study the Hopf equation which is equivalent to the pentagonal equation, from operator algebras. A FRT type theorem is given and new types of quantum groups are constructed. The key role is played now by the classical Hopf modules category. As an application, a five dimensional noncommutative noncocommutative bialgebra is given.Comment: 30 pages, Letax2e, Comm. Algebra in pres

Vaccination with viral vectors expressing NP, M1 and chimeric hemagglutinin induces broad protection against influenza virus challenge in mice

Seasonal influenza virus infections cause up to half a million deaths each year, the majority of which are older adults. Annual influenza virus vaccination protects against disease, but in the event of a mismatch between the circulating strain and vaccine strain, vaccine effectiveness is severely impacted. Therefore, there is an urgent need for a vaccine that induces broad protection against drifted seasonal and emerging pandemic influenza viruses. One approach in designing such a universal influenza virus vaccine is based on targeting conserved regions of the influenza virus hemagglutinin (HA), the major glycoprotein on the surface of the virus. Using chimeric hemagglutinin constructs (cHA), the immune system can be primed to produce antibody responses against the conserved immunosubdominant stalk region rather than the variable immunodominant head region. Furthermore, replication deficient viral vectors based on Chimpanzee Adenovirus (ChAdOx1) and Modified Vaccinia Ankara (MVA) virus expressing the influenza virus internal antigens, such as the nucleoprotein (NP) and the matrix protein 1 (M1), are capable of inducing strong influenza specific T cell responses in vaccinated individuals. This is another approach towards a broadly cross-protective influenza vaccine given the degree of conservation of NP and M1 across different influenza virus strains. Here, we combine these two platforms to evaluate the efficacy of a viral vector-based group 2 cHA intramuscular vaccination regime in mice to confer protection against influenza virus challenge of matched and mismatched group 2 strains. We show that vectored vaccines expressing both cHA and an NP-M1 fusion protein, in a prime-boost regimen (with different cHAs given at each vaccination), provide enhanced protection against H3N2 and H10N8 virus challenge when compared to vaccination with cHA alone or NP-M1 alone. The vaccine induced antibody responses against divergent HAs, NP, M1, and whole virus correlated with nature of administered vaccine and extent of protection seen across vaccinated groups. Influenza specific T cell responses were also increased in the vectored vaccines expressing both the cHA and the NP-M1 fusion protein. For further characterization, we are interested in looking at an optimal vaccination regimen, the possibility of an additional boost to induce cross-reactive antibodies, and the nature of the induced antibodies. Overall, these results improve our understanding of vaccination platforms capable of harnessing cellular and humoral immunity with the ultimate goal of designing a universal influenza vaccine

Machine learning algorithms for the prediction of EUROP classification grade and carcass weight, using 3-dimensional measurements of beef carcasses

Introduction: Mechanical grading can be used to objectively classify beef carcasses. Despite its many benefits, it is scarcely used within the beef industry, often due to infrastructure and equipment costs. As technology progresses, systems become more physically compact, and data storage and processing methods are becoming more advanced. Purpose-built imaging systems can calculate 3-dimensional measurements of beef carcasses, which can be used for objective grading.Methods: This study explored the use of machine learning techniques (random forests and artificial neural networks) and their ability to predict carcass conformation class, fat class and cold carcass weight, using both 3-dimensional measurements (widths, lengths, and volumes) of beef carcasses, extracted using imaging technology, and fixed effects (kill date, breed type and sex). Cold carcass weight was also included as a fixed effect for prediction of conformation and fat classes.Results: Including the dimensional measurements improved prediction accuracies across traits and techniques compared to that of results from models built excluding the 3D measurements. Model validation of random forests resulted in moderate-high accuracies for cold carcass weight (R2 = 0.72), conformation class (71% correctly classified), and fat class (55% correctly classified). Similar accuracies were seen for the validation of the artificial neural networks, which resulted in high accuracies for cold carcass weight (R2 = 0.68) and conformation class (71%), and moderate for fat class (57%).Discussion: This study demonstrates the potential for 3D imaging technology requiring limited infrastructure, along with machine learning techniques, to predict key carcass traits in the beef industry