A new model to simulate climate-change impacts on forest succession for local land management

We developed a new climate-sensitive vegetation state-and-transition simulation model (CV-STSM) to simulate future vegetation at a fine spatial grain commensurate with the scales of human land-use decisions, and under the joint influences of changing climate, site productivity, and disturbance. CV-STSM integrates outputs from four different modeling systems. Successional changes in tree species composition and stand structure were represented as transition probabilities and organized into a state-and-transition simulation model. States were characterized based on assessments of both current vegetation and of projected future vegetation from a dynamic global vegetation model (DGVM). State definitions included sufficient detail to support the integration of CV-STSM with an agent-based model of land-use decisions and a mechanistic model of fire behavior and spread. Transition probabilities were parameterized using output from a stand biometric model run across a wide range of site productivities. Biogeographic and biogeochemical projections from the DGVM were used to adjust the transition probabilities to account for the impacts of climate change on site productivity and potential vegetation type. We conducted experimental simulations in the Willamette Valley, Oregon, USA. Our simulation landscape incorporated detailed new assessments of critically imperiled Oregon white oak (Quercus garryana) savanna and prairie habitats among the suite of existing and future vegetation types. The experimental design fully crossed four future climate scenarios with three disturbance scenarios. CV-STSM showed strong interactions between climate and disturbance scenarios. All disturbance scenarios increased the abundance of oak savanna habitat, but an interaction between the most intense disturbance and climate-change scenarios also increased the abundance of subtropical tree species. Even so, subtropical tree species were far less abundant at the end of simulations in CV-STSM than in the dynamic global vegetation model simulations. Our results indicate that dynamic global vegetation models may overestimate future rates of vegetation change, especially in the absence of stand-replacing disturbances. Modeling tools such as CV-STSM that simulate rates and direction of vegetation change affected by interactions and feedbacks between climate and land-use change can help policy makers, land managers, and society as a whole develop effective plans to adapt to rapidly changing climate.This is the publisher’s final pdf. The published article is copyrighted by the Ecological Society of America and can be found at: http://www.esajournals.org/loi/ecapKeywords: Envision, Agent-based model, Disturbance, Dynamic global vegetation model, MC1, State-and-transition simulation model, Oregon, Fire, Willamette Valle

µChemLab: twenty years of developing CBRNE detection systems with low false alarm rates

Gas Chromatography (GC) is routinely used in the laboratory to temporally separate chemical mixtures into their constituent components for improved chemical identification. This paper will provide a overview of more than twenty years of development of one-dimensional field-portable micro GC systems, highlighting key experimental results that illustrate how a reduction in false alarm rate (FAR) is achieved in real-world environments. Significantly, we will also present recent results on a micro two-dimensional GC (micro GCxGC) technology. This ultra-small system consists of microfabricated columns, NanoElectroMechanical System (NEMS) cantilever resonators for detection, and a valve-based stop-flow modulator. The separation of a 29-component polar mixture in less than 7 seconds is demonstrated along with peak widths in the second dimension ranging from 10-60 ms. For this system, a peak capacity of just over 300 was calculated for separation in about 6 s. This work has important implications for field detection, to drastically reduce FAR and significantly improve chemical selectivity and identification. This separation performance was demonstrated with the NEMS resonator and bench scale FID. But other detectors, suitably fast and sensitive can work as well. Recent research has shown that the identification power of GCxGC-FID can match that of GC-MS. This result indicates a path to improved size, weight, power, and performance in micro GCxGC systems outfitted with relatively non-specific, lightweight detectors. We will briefly discuss the performance of possible options, such as the pulsed discharge helium ionization detector (PDHID) and miniature correlation ion mobility spectrometer (mini-CIMS)

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Using the Light Microscopy Module (LMM) on the International Space Station (ISS), The Advanced Colloids Experiment (ACE) and MacroMolecular Biophysics (MMB)

The Light Microscopy Module (LMM) was launched to the International Space Station (ISS) in 2009 and began science operations in 2010. It continues to support Physical and Biological scientific research on ISS. During 2016, if all goes as planned, three experiments will be completed: [1] Advanced Colloids Experiments with Heated base-2 (ACE-H2) and [2] Advanced Colloids Experiments with Temperature control (ACE-T1). Preliminary results, along with an overview of present and future LMM capabilities will be presented; this includes details on the planned data imaging processing and storage system, along with the confocal upgrade to the core microscope. [1] a consortium of universities from the State of Kentucky working through the Experimental Program to Stimulate Competitive Research (EPSCoR): Stuart Williams, Gerold Willing, Hemali Rathnayake, et al. and [2] from Chungnam National University, Daejeon, S. Korea: Chang-Soo Lee, et al

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201