A Kosloff/Basal method, 3D migration program implemented on the CYBER 205 supercomputer

Conventional finite difference migration has relied on approximations to the acoustic wave equation which allow energy to propagate only downwards. Although generally reliable, such approaches usually do not yield an accurate migration for geological structures with strong lateral velocity variations or with steeply dipping reflectors. An earlier study by D. Kosloff and E. Baysal (Migration with the Full Acoustic Wave Equation) examined an alternative approach based on the full acoustic wave equation. The 2D, Fourier type algorithm which was developed was tested by Kosloff and Baysal against synthetic data and against physical model data. The results indicated that such a scheme gives accurate migration for complicated structures. This paper describes the development and testing of a vectorized, 3D migration program for the CYBER 205 using the Kosloff/Baysal method. The program can accept as many as 65,536 zero offset (stacked) traces

Elemental, isotopic, and geochronological variability in Mogollon-Datil volcanic province archaeological obsidian, southwestern USA: Solving issues of intersource discrimination

Solving issues of intersource discrimination in archaeological obsidian is a recurring problem in geoarchaeological investigation, particularly since the number of known sources of archaeological obsidian worldwide has grown nearly exponentially in the last few decades, and the complexity of archaeological questions asked has grown equally so. These two parallel aspects of archaeological investigation have required more exacting understanding of the geological relationship between sources and the more accurate analysis of these sources of archaeological obsidian. This is particularly the case in the North American Southwest where the frequency of archaeological investigation is some of the highest in the world, and the theory and method used to interpret that record has become increasingly nuanced. Here, we attempt to unravel the elemental similarity of archaeological obsidian in the Mogollon-Datil volcanic province of southwestern New Mexico where some of the most important and extensively distributed sources are located and the elemental similarity between the sources is great even though the distance between the sources is large. Uniting elemental, isotopic, and geochronological analyses as an intensive pilot study, we unpack this complexity to provide greater understanding of these important sources of archaeological obsidian

A +1 ribosomal frameshifting motif prevalent among plant amalgaviruses.

Sequence accessions attributable to novel plant amalgaviruses have been found in the Transcriptome Shotgun Assembly database. Sixteen accessions, derived from 12 different plant species, appear to encompass the complete protein-coding regions of the proposed amalgaviruses, which would substantially expand the size of genus Amalgavirus from 4 current species. Other findings include evidence for UUU_CGN as a +1 ribosomal frameshifting motif prevalent among plant amalgaviruses; for a variant version of this motif found thus far in only two amalgaviruses from solanaceous plants; for a region of α-helical coiled coil propensity conserved in a central region of the ORF1 translation product of plant amalgaviruses; and for conserved sequences in a C-terminal region of the ORF2 translation product (RNA-dependent RNA polymerase) of plant amalgaviruses, seemingly beyond the region of conserved polymerase motifs. These results additionally illustrate the value of mining the TSA database and others for novel viral sequences for comparative analyses.M.L.N. was supported in part by a subcontract from NIH grant 5R01GM033050-33. J.D.P. completed his work on this project during a lab rotation for the Ph.D. Training Program in Virology at Harvard University, Cambridge, MA, USA and was supported in part by NIH grant 2T32AI007245-31. A.E.F. was supported in part by the Wellcome Trust (grant 106207).This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.virol.2016.07.00

Kinetics and Substrate Partitioning in the Polyphenol Oxidase-Catalysed Oxidation of Catechol in a Two-Phase System

ABSTRACT The kinetics of catechol oxidation catalysed by polyphenol oxidase in two-phase systems with mixtures of lauryl alcohol and hexane as the solvent phase is investigated, with particular reference to the effect of partitioning of catechol on the enzyme kinetics. Theory is developed to derive a relationship between the apparent K. and the intrinsic or \u27true\u27 Kn. The theory predicts that substrate partitioning should not change %Cue but that the relationship between the apparent and intrinsic K,, values should depend on the phase volume ratio and the partition coefficient. The theory shows good agreement with the results and gives a consistent K. value. Keywords: polyphenol oxidase, enzyme kinetics, two-phase system, partition effect

Community next steps for making globally unique identifiers work for biocollections data

Biodiversity data is being digitized and made available online at a rapidly increasing rate but current practices typically do not preserve linkages between these data, which impedes interoperation, provenance tracking, and assembly of larger datasets. For data associated with biocollections, the biodiversity community has long recognized that an essential part of establishing and preserving linkages is to apply globally unique identifiers at the point when data are generated in the field and to persist these identifiers downstream, but this is seldom implemented in practice. There has neither been coalescence towards one single identifier solution (as in some other domains), nor even a set of recommended best practices and standards to support multiple identifier schemes sharing consistent responses. In order to further progress towards a broader community consensus, a group of biocollections and informatics experts assembled in Stockholm in October 2014 to discuss community next steps to overcome current roadblocks. The workshop participants divided into four groups focusing on: identifier practice in current field biocollections; identifier application for legacy biocollections; identifiers as applied to biodiversity data records as they are published and made available in semantically marked-up publications; and cross-cutting identifier solutions that bridge across these domains. The main outcome was consensus on key issues, including recognition of differences between legacy and new biocollections processes, the need for identifier metadata profiles that can report information on identifier persistence missions, and the unambiguous indication of the type of object associated with the identifier. Current identifier characteristics are also summarized, and an overview of available schemes and practices is provided

Journal- Based Reflection in Undergraduate Service Learning and the University Therapeutic Riding Center

Principles of Therapeutic Riding, Animal Science 3309, is a service- based learning course that gives undergraduates the opportunity to participate in hippotherapy sessions. This course first offered in 1998, has been held for 12 semesters. A total of 233 students from over 15 majors have been trained in this discipline. Advanced Therapeutic Riding, Animal Science 4001, is a continuation of the Principles class that allows students to participate in the sessions and serve in leadership roles. This class has been active for 8 semesters and has included 51 students

Data mining: a tool for detecting cyclical disturbances in supply networks.

Disturbances in supply chains may be either exogenous or endogenous. The ability automatically to detect, diagnose, and distinguish between the causes of disturbances is of prime importance to decision makers in order to avoid uncertainty. The spectral principal component analysis (SPCA) technique has been utilized to distinguish between real and rogue disturbances in a steel supply network. The data set used was collected from four different business units in the network and consists of 43 variables; each is described by 72 data points. The present paper will utilize the same data set to test an alternative approach to SPCA in detecting the disturbances. The new approach employs statistical data pre-processing, clustering, and classification learning techniques to analyse the supply network data. In particular, the incremental k-means clustering and the RULES-6 classification rule-learning algorithms, developed by the present authors’ team, have been applied to identify important patterns in the data set. Results show that the proposed approach has the capability automatically to detect and characterize network-wide cyclical disturbances and generate hypotheses about their root cause

Using δ13C-CH4 and δD-CH4 to constrain Arctic methane emissions

We present a global methane modelling study assessing the sensitivity of Arctic atmospheric CH4 mole fractions, δ13C-CH4 and δD-CH4 to uncertainties in Arctic methane sources. Model simulations include methane tracers tagged by source and isotopic composition and are compared with atmospheric data at four northern high-latitude measurement sites. We find the model's ability to capture the magnitude and phase of observed seasonal cycles of CH4 mixing ratios, δ13C-CH4 and δD-CH4 at northern high latitudes is much improved using a later spring kick-off and autumn decline in northern high-latitude wetland emissions than predicted by most process models. Results from our model simulations indicate that recent predictions of large methane emissions from thawing submarine permafrost in the East Siberian Arctic Shelf region could only be reconciled with global-scale atmospheric observations by making large adjustments to high-latitude anthropogenic or wetland emission inventories

Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data

Deep learning is an effective method for ultrasonic crack characterization due to its high level of automation and accuracy. Simulating the training set has been shown to be an effective method of circumventing the lack of experimental data common to nondestructive evaluation (NDE) applications. However, a simulation can neither be completely accurate nor capture all variability present in the real inspection. This means that the experimental and simulated data will be from different (but related) distributions, leading to inaccuracy when a deep learning algorithm trained on simulated data is applied to experimental measurements. This article aims to tackle this problem through the use of domain adaptation (DA). A convolutional neural network (CNN) is used to predict the depth of surface-breaking defects, with in-line pipe inspection as the targeted application. Three DA methods across varying sizes of experimental training data are compared to two non-DA methods as a baseline. The performance of the methods tested is evaluated by sizing 15 experimental notches of length (1–5 mm) and inclined at angles of up to 20° from the vertical. Experimental training sets are formed with between 1 and 15 notches. Of the DA methods investigated, an adversarial approach is found to be the most effective way to use the limited experimental training data. With this method, and only three notches, the resulting network gives a root-mean-square error (RMSE) in sizing of 0.5 ± 0.037 mm, whereas with only experimental data the RMSE is 1.5 ± 0.13 mm and with only simulated data it is 0.64 ± 0.044 mm