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

Plant Disease Outcome in 1967

The influence of disease on crops and trees will be decided by a large number of interacting factors. Most of the diseases responsible for loss and profit in crops can be eliminated by preventive measures you and your neighbors take

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

Secondary organic aerosol formation from in-use motor vehicle emissions using a potential aerosol mass reactor.

Secondary organic aerosol (SOA) formation from in-use vehicle emissions was investigated using a potential aerosol mass (PAM) flow reactor deployed in a highway tunnel in Pittsburgh, Pennsylvania. Experiments consisted of passing exhaust-dominated tunnel air through a PAM reactor over integrated hydroxyl radical (OH) exposures ranging from ∼ 0.3 to 9.3 days of equivalent atmospheric oxidation. Experiments were performed during heavy traffic periods when the fleet was at least 80% light-duty gasoline vehicles on a fuel-consumption basis. The peak SOA production occurred after 2-3 days of equivalent atmospheric oxidation. Additional OH exposure decreased the SOA production presumably due to a shift from functionalization to fragmentation dominated reaction mechanisms. Photo-oxidation also produced substantial ammonium nitrate, often exceeding the mass of SOA. Analysis with an SOA model highlight that unspeciated organics (i.e., unresolved complex mixture) are a very important class of precursors and that multigenerational processing of both gases and particles is important at longer time scales. The chemical evolution of the organic aerosol inside the PAM reactor appears to be similar to that observed in the atmosphere. The mass spectrum of the unoxidized primary organic aerosol closely resembles ambient hydrocarbon-like organic aerosol (HOA). After aging the exhaust equivalent to a few hours of atmospheric oxidation, the organic aerosol most closely resembles semivolatile oxygenated organic aerosol (SV-OOA) and then low-volatility organic aerosol (LV-OOA) at higher OH exposures. Scaling the data suggests that mobile sources contribute ∼ 2.9 ± 1.6 Tg SOA yr(-1) in the United States, which is a factor of 6 greater than all mobile source particulate matter emissions reported by the National Emissions Inventory. This highlights the important contribution of SOA formation from vehicle exhaust to ambient particulate matter concentrations in urban areas

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

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

Can the news tell us anything about uncertainty that the markets don’t?

This study investigates the dynamic interactions between changes in economic policy uncertainty and movement in price and trade volumes across a sample of 21 countries. Within a vector autoregressive framework, we find that an expectation of uncertainty drives market movement for 18 countries. Our analysis in terms of VAR coefficients, granger causality tests and impulse response functions show a significant market reaction to an expectation of uncertainty, implying that the markets are more sensitive to politically driven economic policy change than media commentators. In light of perceived policy change researchers are cautioned against relying solely on media generated measures of uncertainty when investigating market movements