Simulations of nonlinear harmonic generation by an internal wave beam incident on a pycnocline

Internal wave beams generated by oceanic tidal flows propagate upward and interact with the increasing stratification found at the pycnocline. The nonlinear generation of harmonic modes by internal wave beams incident on a pycnocline has recently been demonstrated by laboratory experiments and numerical simulations. In these previous studies, the harmonic modes were trapped within the pycnocline because their frequencies exceeded that of the stratified fluid below. Here, two-dimensional numerical simulations are used to explore the effect of incidence angle on harmonic generation at a thin pycnocline. At incidence angles less than 30 degrees (typical of oceanic beams), the lowest harmonic mode freely radiates in the form of an internal wave beam rather than being trapped within the pycnocline. The results indicate that nonlinear refraction is the primary mechanism for harmonic generation at incidence angles exceeding 30 degrees, but that interaction of the incident and reflected beams is more important at smaller incidence angles. The simulations are compared to weakly nonlinear theory based on refraction at the pycnocline. The results yield good agreement for trapped harmonics, but weakly nonlinear theory substantially underpredicts the amplitude of the radiated harmonics

EarthN: A new Earth System Nitrogen Model

The amount of nitrogen in the atmosphere, oceans, crust, and mantle have important ramifications for Earth's biologic and geologic history. Despite this importance, the history and cycling of nitrogen in the Earth system is poorly constrained over time. For example, various models and proxies contrastingly support atmospheric mass stasis, net outgassing, or net ingassing over time. In addition, the amount available to and processing of nitrogen by organisms is intricately linked with and provides feedbacks on oxygen and nutrient cycles. To investigate the Earth system nitrogen cycle over geologic history, we have constructed a new nitrogen cycle model: EarthN. This model is driven by mantle cooling, links biologic nitrogen cycling to phosphate and oxygen, and incorporates geologic and biologic fluxes. Model output is consistent with large (2-4x) changes in atmospheric mass over time, typically indicating atmospheric drawdown and nitrogen sequestration into the mantle and continental crust. Critical controls on nitrogen distribution include mantle cooling history, weathering, and the total Bulk Silicate Earth+atmosphere nitrogen budget. Linking the nitrogen cycle to phosphorous and oxygen levels, instead of carbon as has been previously done, provides new and more dynamic insight into the history of nitrogen on the planet.Comment: 36 pages, 12 figure

Estimating percentages of fusarium-damaged kernels in hard wheat by near-infrared hyperspectral imaging

Fusarium head blight (FHB) is among the most common fungal diseases affecting wheat, resulting in decreased yield, low-density kernels, and production of the mycotoxin deoxynivalenol, a compound toxic to humans and livestock. Human visual analysis of representative wheat samples has been the traditional method for FHB assessment in both official inspection and plant breeding operations. While not requiring specialized equipment, visual analysis is dependent on a trained and consistent workforce, such that in the absence of these aspects, biases may arise among inspectors and evaluation dates. This research was intended to avoid such pitfalls by using longer wavelength radiation than the visible using hyperspectral imaging (HSI) on individual kernels. Linear discriminant analysis models to differentiate between sound and scab-damaged kernels were developed based on mean of reflectance values of the interior pixels of each kernel at four wavelengths (1100, 1197, 1308, and 1394 nm). Other input variables were examined, including kernel morphological properties and histogram features from the pixel responses of selected wavelengths of each kernel. The results indicate the strong potential of HSI in estimating fusarium damage. However, improvement in aligning this procedure to visual analysis is hampered by the inherent level of subjectivity in visual analysis

Near infrared hyperspectral imaging of blends of conventional and waxy hard wheats

Recent development of hard winter waxy (amylose-free) wheat adapted to the North American climate has prompted the quest to find a rapid method that will determine mixture levels of conventional wheat in lots of identity preserved waxy wheat. Previous work documented the use of conventional near infrared (NIR) reflectance spectroscopy to determine the mixture level of conventional wheat in waxy wheat, with an examined range, through binary sample mixture preparation, of 0–100% (weight conventional / weight total). The current study examines the ability of NIR hyperspectral imaging of intact kernels to determine mixture levels. Twenty-nine mixtures (0, 1, 2, 3, 4, 5, 10, 15, …, 95, 96, 97, 98, 99, 100%) were formed from known genotypes of waxy and conventional wheat. Two-class partial least squares discriminant analysis (PLSDA) and statistical pattern recognition classifier models were developed for identifying each kernel in the images as conventional or waxy. Along with these approaches, conventional PLS1 regression modelling was performed on means of kernel spectra within each mixture test sample. Results indicated close agreement between all three approaches, with standard errors of prediction for the better preprocess transformations (PLSDA models) or better classifiers (pattern recognition models) of approximately 9 percentage units. Although such error rates were slightly greater than ones previously published using non-imaging NIR analysis of bulk whole kernel wheat and wheat meal, the HSI technique offers an advantage of its potential use in sorting operations

Near infrared hyperspectral imaging of blends of conventional and waxy hard wheats

Recent development of hard winter waxy (amylose-free) wheat adapted to the North American climate has prompted the quest to find a rapid method that will determine mixture levels of conventional wheat in lots of identity preserved waxy wheat. Previous work documented the use of conventional near infrared (NIR) reflectance spectroscopy to determine the mixture level of conventional wheat in waxy wheat, with an examined range, through binary sample mixture preparation, of 0–100% (weight conventional / weight total). The current study examines the ability of NIR hyperspectral imaging of intact kernels to determine mixture levels. Twenty-nine mixtures (0, 1, 2, 3, 4, 5, 10, 15, …, 95, 96, 97, 98, 99, 100%) were formed from known genotypes of waxy and conventional wheat. Two-class partial least squares discriminant analysis (PLSDA) and statistical pattern recognition classifier models were developed for identifying each kernel in the images as conventional or waxy. Along with these approaches, conventional PLS1 regression modelling was performed on means of kernel spectra within each mixture test sample. Results indicated close agreement between all three approaches, with standard errors of prediction for the better preprocess transformations (PLSDA models) or better classifiers (pattern recognition models) of approximately 9 percentage units. Although such error rates were slightly greater than ones previously published using non-imaging NIR analysis of bulk whole kernel wheat and wheat meal, the HSI technique offers an advantage of its potential use in sorting operations

Enabling the Rational Design of Low-Fat Snack Foods: Insights from In Vitro Oral Processing

Texture perception can be conceptualized as an emergent cognitive response to several of the physical and chemical properties of a food. Contemporary oral processing research is focused on revealing the relationships between the sensory perceptions and the food properties, with the goal of enabling rational product design. One major challenge is the complexity of molecular and biocolloid interactions, underpinning even simple texture properties. Here, we will introduce the in vitro oral processing approach, which divides oral processing into discrete units of operation (first bite, comminution, granulation, bolus formation, and tribology) and then systematically investigates the material properties that govern each specific oral processing unit operation without the added complexity inherent to biological systems. We will describe how we used the approach to rationally design a low-fat potato chip by investigating the impact from adding back, to a low fat potato chip, a small amount of oil mixed with the surface active agent polyglycerol polyricinoleate (PGPR). The relevance of instrumental measures was validated by sensory assessment wherein panelists ranked the perceived oiliness of three different types of potato chips. The sensory results indicated that perceived oiliness was higher when the low- fat potato chip was supplemented with a 0.5% by weight topical coating (0.5% by weight 15% by weight PGPR in oil mixture) compared to the unaltered low-fat potato chip. The perceived difference in oiliness was found to correspond to in vitro transient friction of saliva in the presence and absence of PGPR. These results illustrate how dividing oral processing into distinct phases allows one to more readily align sensory and in vitro measures, allowing for integration of the two disciplines and more rational design when modifying macronutrients

Evaluation of BLAST-based edge-weighting metrics used for homology inference with the Markov Clustering algorithm

Clustering protein sequences according to inferred homology is a fundamental step in the analysis of many large data sets. Since the publication of the Markov Clustering (MCL) algorithm in 2002, it has been the centerpiece of several popular applications. Each of these approaches generates an undirected graph that represents sequences as nodes connected to each other by edges weighted with a BLAST-based metric. MCL is then used to infer clusters of homologous proteins by analyzing these graphs. The various approaches differ only by how they weight the edges, yet there has been very little direct examination of the relative performance of alternative edge-weighting metrics. This study compares the performance of four BLAST-based edge-weighting metrics: the bit score, bit score ratio (BSR), bit score over anchored length (BAL), and negative common log of the expectation value (NLE). Performance is tested using the Extended CEGMA KOGs (ECK) database, which we introduce here. All metrics performed similarly when analyzing full-length sequences, but dramatic differences emerged as progressively larger fractions of the test sequences were split into fragments. The BSR and BAL successfully rescued subsets of clusters by strengthening certain types of alignments between fragmented sequences, but also shifted the largest correct scores down near the range of scores generated from spurious alignments. This penalty outweighed the benefits in most test cases, and was greatly exacerbated by increasing the MCL inflation parameter, making these metrics less robust than the bit score or the more popular NLE. Notably, the bit score performed as well or better than the other three metrics in all scenarios. The results provide a strong case for use of the bit score, which appears to offer equivalent or superior performance to the more popular NLE. The insight that MCL-based clustering methods can be improved using a more tractable edge-weighting metric will greatly simplify future implementations. We demonstrate this with our own minimalist Python implementation: Porthos, which uses only standard libraries and can process a graph with 25 m + edges connecting the 60 k + KOG sequences in half a minute using less than half a gigabyte of memory.https://doi.org/10.1186/s12859-015-0625-xhttps://doi.org/10.1186/s12859-015-0690-

Veillonella rogosae sp. nov., an anaerobic, Gram-negative coccus isolated from dental plaque

Strains of a novel anaerobic, Gram-negative coccus were isolated from the supra-gingival plaque of children. Independent strains from each of six subjects were shown, at a phenotypic level and based on 16S rRNA gene sequencing, to be members of the genus Veillonella. Analysis revealed that the six strains shared 99.7 % similarity in their 16S rRNA gene sequences and 99.0 % similarity in their rpoB gene sequences. The six novel strains formed a distinct group and could be clearly separated from recognized species of the genus Veillonella of human or animal origin. The novel strains exhibited 98 and 91 % similarity to partial 16S rRNA and rpoB gene sequences of Veillonella parvula ATCC 10790T, the most closely related member of the genus. The six novel strains could be differentiated from recognized species of the genus Veillonella based on partial 16S rRNA and rpoB gene sequencing. The six novel strains are thus considered to represent a single novel species of the genus Veillonella, for which the name Veillonella rogosae sp. nov. is proposed. The type strain is CF100T (=CCUG 54233T=DSM 18960T)