Effects of delta ferrite content on the mechanical properties of E308-16 stainless steel weld metal

The effects of ferrite content on the properties of type 308 stainless steel shielded metal-arc (SMA) welds were investigated. Welds were made at four levels of ferrite content ranging from 2 to 15 FN (Ferrite Number). Creep and tensile tests were performed. Specimens were aged at 1100/sup 0/C (593/sup 0/C) for times up to 10,000 h (36 Ms) and Charpy V-notch impact tests were performed. Chemical analysis of the original deposits, Magne-gage evaluations, and metallographic evaluation of tested specimens were made. The E308-16 stainless steel electrodes were formulated to produce SMA welds with 2, 5, 9, and 15 FN. The ferrite number was made to vary by varying the nickel and chromium concentrations. Magne-gage determinations revealed that as-welded structures contained an average of 1.8, 4.2, 9.6, and 14.5 FN, respectively. Chemical anslysis of these deposits revealed no unusually high concentrations of tramp elements that would significantly affect mechanical properties. The extra low-ferrite electrodes were made with a different core wire, which produced deposits with slightly higher molybdenum concentrations. This variation in molybdenum should affect properties only minimally. From these chemical analyses and a constitutional diagram, ferrite concentrations were calculated, and the results correlated with the Magne-gage value

The PANTHER database of protein families, subfamilies, functions and pathways

PANTHER is a large collection of protein families that have been subdivided into functionally related subfamilies, using human expertise. These subfamilies model the divergence of specific functions within protein families, allowing more accurate association with function (ontology terms and pathways), as well as inference of amino acids important for functional specificity. Hidden Markov models (HMMs) are built for each family and subfamily for classifying additional protein sequences. The latest version, 5.0, contains 6683 protein families, divided into 31 705 subfamilies, covering ∼90% of mammalian protein-coding genes. PANTHER 5.0 includes a number of significant improvements over previous versions, most notably (i) representation of pathways (primarily signaling pathways) and association with subfamilies and individual protein sequences; (ii) an improved methodology for defining the PANTHER families and subfamilies, and for building the HMMs; (iii) resources for scoring sequences against PANTHER HMMs both over the web and locally; and (iv) a number of new web resources to facilitate analysis of large gene lists, including data generated from high-throughput expression experiments. Efforts are underway to add PANTHER to the InterPro suite of databases, and to make PANTHER consistent with the PIRSF database. PANTHER is now publicly available without restriction at http://panther.appliedbiosystems.com

On Online Collaboration and Construction of Shared Knowledge: Assessing Mediation Capability in Computer Supported Argument Visualization Tools

Collaborative Computer-Supported Argument Visualization (CCSAV) has often been proposed as an alternative over more conventional, mainstream platforms for online discussion (e.g., online forums and wikis). CCSAV tools require users to contribute to the creation of a joint artifact (argument map) instead of contributing to a conversation. In this paper we assess empirically the effects of this fundamental design choice and show that the absence of conversational affordances and socially salient information in representation-centric tools is detrimental to the users' collaboration experience. We report empirical findings from a study in which subjects using different collaborative platforms (a forum, an argumentation platform, and a socially augmented argumentation tool) were asked to discuss and predict the price of a commodity. By comparing users' experience across several metrics we found evidence that the collaborative performance decreases gradually when we remove conversational interaction and other types of socially salient information. We interpret these findings through theories developed in conversational analysis (common ground theory) and communities of practice and discuss design implications. In particular, we propose balancing the trade-off between knowledge reification and participation in representation-centric tools with the provision of social feedback and functionalities supporting meaning negotiation

Human polyomavirus 6 and 7 are associated with pruritic and dyskeratotic dermatoses

ABSTRACT Background: Human Polyomavirus 6 (HPyV6) and Human Polyomavirus 7 (HPyV7) are shed chronically from human skin. HPyV7, but not HPyV6, has been linked to a pruritic skin eruption of immunosuppression. Objective: We determined whether biopsies showing a characteristic pattern of dyskeratosis and parakeratosis might be associated with polyomavirus infection. Methods: We screened biopsies showing "peacock plumage" histology by PCR for human polyomaviruses. Cases positive for HPyV 6 or 7 were then analyzed by immunohistochemistry, electron microscopy (EM), immunofluorescence, quantitative PCR, and complete sequencing, including unbiased, next generation sequencing (NGS). Results: We identified three additional cases of HPyV6 or 7 skin infections. Expression of T antigen and viral capsid was abundant in lesional skin. Dual immunofluorescence staining experiments confirmed that HPyV7 primarily infects keratinocytes. High viral loads in lesional skin compared to normal skin and the identification of intact virions by both EM and NGS support a role for active viral infections in these skin diseases. Limitation: This was a small case-series of archived materials. Conclusion: We have found that HPyV6 and HPyV7 are associated with rare, pruritic skin eruptions with a unique histologic pattern and describe this entity as "HPyV6- and HPyV7-associated pruritic and dyskeratotic dermatosis (H6PD and H7PD).

Comprehensive survey of energetic electron events in Mercury\u27s magnetosphere with data from the MESSENGER Gamma-Ray and Neutron Spectrometer

Data from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) Gamma-Ray and Neutron Spectrometer have been used to detect and characterize energetic electron (EE) events in Mercury\u27s magnetosphere. This instrument detects EE events indirectly via bremsstrahlung photons that are emitted when instrument and spacecraft materials stop electrons having energies of tens to hundreds of keV. From Neutron Spectrometer data taken between 18 March 2011 and 31 December 2013 we have identified 2711 EE events. EE event amplitudes versus energy are distributed as a power law and have a dynamic range of a factor of 400. The duration of the EE events ranges from tens of seconds to nearly 20 min. EE events may be classified as bursty (large variation with time over an event) or smooth (small variation). Almost all EE events are detected inside Mercury\u27s magnetosphere on closed field lines. The precise occurrence times of EE events are stochastic, but the events are located in well-defined regions with clear boundaries that persist in time and form what we call “quasi-permanent structures.” Bursty events occur closer to dawn and at higher latitudes than smooth events, which are seen near noon-to-dusk local times at lower latitudes. A subset of EE events shows strong periodicities that range from hundreds of seconds to tens of milliseconds. The few-minute periodicities are consistent with the Dungey cycle timescale for the magnetosphere and the occurrence of substorm events in Mercury\u27s magnetotail region. Shorter periods may be related to phenomena such as north-south bounce processes for the energetic electrons

Assessment of Genome-Wide Protein Function Classification for Drosophila melanogaster

The functional classification of genes on a genome-wide scale is now in its infancy, and we make a first attempt to assess existing methods and identify sources of error. To this end, we compared two independent efforts for associating proteins with functions, one implemented by FlyBase and the other by PANTHER at Celera Genomics. Both methods make inferences based on sequence similarity and the available experimental evidence. However, they differ considerably in methodology and process. Overall, assuming that the systematic error across the two methods is relatively small, we find the protein-to-function association error rate of both the FlyBase and PANTHER methods to be <2%. The primary source of error for both methods appears to be simple human error. Although homology-based inference can certainly cause errors in annotation, our analysis indicates that the frequency of such errors is relatively small compared with the number of correct inferences. Moreover, these homology errors can be minimized by careful tree-based inference, such as that implemented in PANTHER. Often, functional associations are made by one method and not the other, indicating that one of the greatest challenges lies in improving the completeness of available ontology associations