What Users Want and May Need

An increasing number of software tools support designers and other decision makers in making design, production, and purchasing decisions. Some of these tools provide quantitative information on environmental impacts such as climate change, human toxicity, or resource use during the life cycle of these products. Very little is known, however, about how these tools are actually used, what kind of modeling and presentation approaches users really want, or whether the information provided is likely to be used the way the developers intended. A survey of users of one such software tool revealed that although users want more transparency, about half also want an easy-to-use tool and would accept built-in assumptions; that most users prefer modeling of environmental impacts beyond the stressor level, and the largest group of respondents wants results simultaneously on the stressor, impact potential, and damage level; and that although many users look for aggregated information on impacts and costs, a majority do not trust that such an aggregation is valid or believe that there are tradeoffs among impacts. Further, our results show that the temporal and spatial scales of single impact categories explain only about 6% of the variation in the weights between impact categories set by respondents if the weights are set first. If the weights are set after respondents specify temporal and spatial scales, however, these scales explain about 24% of the variation. These results not only help method and tool developers to reconsider some previous assumptions, but also suggest a number of research questions that may need to be addressed in a more focused investigation.     

A Human Health Risk Assessment Software for Facilitating Management of Urban Contaminated Sites: A Case Study: The Massa Site,Tuscany, Italy

The goal of this article is to present the Human Health Risk Assessment (HRA) software developed as one of the NORISC ^{1 1}NORISC is the acronym of the project “Network Oriented Risk assessment by In-situ Screening of Contaminated sites” realized under under the 5th European Union Community Framework Programme for Research, Technological Development and Demonstration Activities. View all notes decision support software system components that could be used as a tool for facilitating management of urban contaminated sites. The NORISC-HRA software provides sufficient technical and procedural support to conduct a simple site-specific risk assessment. The employed HRA methodology is generally based on U.S. Environmental Protection Agency (USEPA) procedures. The software determines the level and spatial distribution of human health risks at a given site and sets up site-specific preliminary Health-Based Remedial Goals (HBRGs)/Risk-Based Concentrations (RBCs) for soil and groundwater. The NORISC-HRA software is recommended for use when national soil and groundwater limit values are exceeded. Exposure pathways considered in this software are associated with three land use patterns—residential, industrial/commercial, and recreational. The aricle also presents the software testing results obtained at one of the NORISC test sites—the Massa site (Avenza-Carrara, Tuscany, Italy). Findings of the HRA indicated that the contaminated soil at the Massa test site might pose potential cancer and non-cancer risks to industrial workers in its present condition. Arsenic was the dominant substance responsible for most of the baseline risk and at the RBC of 1.77 mg/kg it was the primary driver of remedial decisions at the Massa site.     

Structural Characterization of Protein-Protein Complexes by Integrating Computational Docking with Small-angle Scattering Data

X-ray crystallography and NMR can provide detailed structural information of protein-protein complexes, but technical problems make their application challenging in the high-throughput regime. Other methods such as small-angle X-ray scattering (SAXS) are more promising for large-scale application, but at the cost of lower resolution, which is a problem that can be solved by complementing SAXS data with theoretical simulations. Here, we propose a novel strategy that combines SAXS data and accurate protein-protein docking simulations. The approach has been benchmarked on a large pool of known structures with synthetic SAXS data, and on three experimental examples. The combined approach (pyDockSAXS) provided a significantly better success rate (43% for the top 10 predictions) than either of the two methods alone. Further analysis of the influence of different docking parameters made it possible to increase the success rates for specific cases, and to define guidelines for improving the data-driven protein-protein docking protocols.     

ggtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Data

We present the ggtreeExtra package for visualizing heterogeneous data with a phylogenetic tree in a circular or rectangular layout (https://www.bioconductor.org/packages/ggtreeExtra). The package supports more data types and visualization methods than other tools. It supports using the grammar of graphics syntax to present data on a tree with richly annotated layers and allows evolutionary statistics inferred by commonly used software to be integrated and visualized with external data. GgtreeExtra is a universal tool for tree data visualization. It extends the applications of the phylogenetic tree in different disciplines by making more domain-specific data to be available to visualize and interpret in the evolutionary context.     

利用SPSS软件计算杀虫剂的LC_(50)

利用实例和SPSS 130软件上的Probit过程,介绍如何进行LC50计算,并对主要输出结果进行了解释。并通过与其它软件比较,表明应用SPSS软件进行LC50等计算简便、快速、准确。     

Event-driven time-optimal control for a class of discontinuous bioreactors

Discontinuous bioreactors may be further optimized for processing inhibitory substrates using a convenient fed-batch mode. To do so the filling rate must be controlled in such a way as to push the reaction rate to its maximum value, by increasing the substrate concentration just up to the point where inhibition begins. However, an exact optimal controller requires measuring several variables (e.g., substrate concentrations in the feed and in the tank) and also good model knowledge (e.g., yield and kinetic parameters), requirements rarely satisfied in real applications. An environmentally important case, that exemplifies all these handicaps, is toxicant wastewater treatment. There the lack of online practical pollutant sensors may allow unforeseen high shock loads to be fed to the bioreactor, causing biomass inhibition that slows down the treatment process and, in extreme cases, even renders the biological process useless. In this work an event-driven time-optimal control (ED-TOC) is proposed to circumvent these limitations. We show how to detect a "there is inhibition" event by using some computable function of the available measurements. This event drives the ED-TOC to stop the filling. Later, by detecting the symmetric event, "there is no inhibition," the ED-TOC may restart the filling. A fill-react cycling then maintains the process safely hovering near its maximum reaction rate, allowing a robust and practically time-optimal operation of the bioreactor. An experimental study case of a wastewater treatment process application is presented. There the dissolved oxygen concentration was used to detect the events needed to drive the controller.     

Mapping QTLs and candidate genes for iron and zinc concentrations in unpolished rice of Madhukar×Swarna RILs

Identifying QTLs/genes for iron and zinc in rice grains can help in biofortification programs. 168 F(7) RILs derived from Madhukar×Swarna were used to map QTLs for iron and zinc concentrations in unpolished rice grains. Iron ranged from 0.2 to 224ppm and zinc ranged from 0.4 to 104ppm. Genome wide mapping using 101 SSRs and 9 gene specific markers showed 5 QTLs on chromosomes 1, 3, 5, 7 and 12 significantly linked to iron, zinc or both. In all, 14 QTLs were identified for these two traits. QTLs for iron were co-located with QTLs for zinc on chromosomes 7 and 12. In all, ten candidate genes known for iron and zinc homeostasis underlie 12 of the 14 QTLs. Another 6 candidate genes were close to QTLs on chromosomes 3, 5 and 7. Thus the high priority candidate genes for high Fe and Zn in seeds are OsYSL1 and OsMTP1 for iron, OsARD2, OsIRT1, OsNAS1, OsNAS2 for zinc and OsNAS3, OsNRAMP1, Heavy metal ion transport and APRT for both iron and zinc together based on our genetic mapping studies as these genes strictly underlie QTLs. Several elite lines with high Fe, high Zn and both were identified.