Search Results (6)

[NiFe] hydrogenases are metalloenzymes that catalyze the reversible cleavage of dihydrogen (${\mathrm{H}}_2$), a clean future fuel. Understanding the mechanism of these biocatalysts requires spectroscopic techniques that yield insights into the structure and dynamics of the [NiFe] active site. Due to the presence of CO and ${\mathrm{CN}}^{-}$ ligands at this cofactor, infrared (IR) spectroscopy represents an ideal technique for studying these aspects, but molecular information from linear IR absorption experiments is limited. More detailed insights can be obtained from ultrafast nonlinear IR techniques like IR_pump-IR_probe and two-dimensional (2D-)IR spectroscopy. However, fully exploiting these advanced techniques requires an in-depth understanding of experimental observables and the encoded molecular information. To address this challenge, we present a descriptive and predictive computational approach for the simulation and analysis of static 2D-IR spectra of [NiFe] hydrogenases and similar organometallic systems. Accurate reproduction of experimental spectra from a first-coordination-sphere model suggests a decisive role of the [NiFe] core in shaping the enzymatic potential energy surface. We also reveal spectrally encoded molecular information that is not accessible by experiments, thereby helping to understand the catalytic role of the diatomic ligands, structural differences between [NiFe] intermediates, and possible energy transfer mechanisms. Our studies demonstrate the feasibility and benefits of computational spectroscopy in the 2D-IR investigation of hydrogenases, thereby further strengthening the potential of this nonlinear IR technique as a powerful research tool for the investigation of complex bioinorganic molecules. Full article

A major challenge in ecological restoration is assessing the success of restoration plantings in producing habitats that provide the desired ecosystem functions and services. Forest structural complexity and biomass accumulation are key measures used to monitor restoration success and are important factors determining animal habitat availability and carbon sequestration. Monitoring their development through time using traditional field measurements can be costly and impractical, particularly at the landscape-scale, which is a common requirement in ecological restoration. We explored the application of proximal sensing technology as an alternative to traditional field surveys to capture the development of key forest structural traits in a restoration planting in the Midlands of Tasmania, Australia. We report the use of a hand-held laser scanner (ZEB1) to measure annual changes in structural traits at the tree-level, in a mixed species common-garden experiment from seven- to nine-years after planting. Using very dense point clouds, we derived estimates of multiple structural traits, including above ground biomass, tree height, stem diameter, crown dimensions, and crown properties. We detected annual increases in most LiDAR-derived traits, with individual crowns becoming increasingly interconnected. Time by species interaction were detected, and were associated with differences in productivity between species. We show the potential for remote sensing technology to monitor temporal changes in forest structural traits, as well as to provide base-line measures from which to assess the restoration trajectory towards a desired state. Full article

The use of unmanned aerial vehicles (UAVs) for remote sensing of natural environments has increased over the last decade. However, applications of this technology for high-throughput individual tree phenotyping in a quantitative genetic framework are rare. We here demonstrate a two-phased analytical pipeline that rapidly phenotypes and filters for genetic signals in traditional and novel tree productivity and architectural traits derived from ultra-dense light detection and ranging (LiDAR) point clouds. The goal of this study was rapidly phenotype individual trees to understand the genetic basis of ecologically and economically significant traits important for guiding the management of natural resources. Individual tree point clouds were acquired using UAV-LiDAR captured over a multi-provenance common-garden restoration field trial located in Tasmania, Australia, established using two eucalypt species (Eucalyptus pauciflora and Eucalyptus tenuiramis). Twenty-five tree productivity and architectural traits were calculated for each individual tree point cloud. The first phase of the analytical pipeline found significant species differences in 13 of the 25 derived traits, revealing key structural differences in productivity and crown architecture between species. The second phase investigated the within species variation in the same 25 structural traits. Significant provenance variation was detected for 20 structural traits in E. pauciflora and 10 in E. tenuiramis, with signals of divergent selection found for 11 and 7 traits, respectively, putatively driven by the home-site environment shaping the observed variation. Our results highlight the genetic-based diversity within and between species for traits important for forest structure, such as crown density and structural complexity. As species and provenances are being increasingly translocated across the landscape to mitigate the effects of rapid climate change, our results that were achieved through rapid phenotyping using UAV-LiDAR, raise the need to understand the functional value of productivity and architectural traits reflecting species and provenance differences in crown structure and the interplay they have on the dependent biotic communities. Full article

Next-generation sequencing (NGS) is increasingly used for HIV-1 drug resistance genotyping. NGS methods have the potential for a more sensitive detection of low-abundance variants (LAV) compared to standard Sanger sequencing (SS) methods. A standardized threshold for reporting LAV that generates data comparable to those derived from SS is needed to allow for the comparability of data from laboratories using NGS and SS. Ten HIV-1 specimens were tested in ten laboratories using Illumina MiSeq-based methods. The consensus sequences for each specimen using LAV thresholds of 5%, 10%, 15%, and 20% were compared to each other and to the consensus of the SS sequences (protease 4–99; reverse transcriptase 38–247). The concordance among laboratories’ sequences at different thresholds was evaluated by pairwise sequence comparisons. NGS sequences generated using the 20% threshold were the most similar to the SS consensus (average 99.6% identity, range 96.1–100%), compared to 15% (99.4%, 88.5–100%), 10% (99.2%, 87.4–100%), or 5% (98.5%, 86.4–100%). The average sequence identity between laboratories using thresholds of 20%, 15%, 10%, and 5% was 99.1%, 98.7%, 98.3%, and 97.3%, respectively. Using the 20% threshold, we observed an excellent agreement between NGS and SS, but significant differences at lower thresholds. Understanding how variation in NGS methods influences sequence quality is essential for NGS-based HIV-1 drug resistance genotyping. Full article

An Environmental Risk Assessment (ERA) for nanomaterials (NMs) is outlined in this paper. Contrary to other recent papers on the subject, the main data requirements, models and advancement within each of the four risk assessment domains are described, i.e., in the: (i) materials, (ii) release, fate and exposure, (iii) hazard and (iv) risk characterisation domains. The material, which is obviously the foundation for any risk assessment, should be described according to the legislatively required characterisation data. Characterisation data will also be used at various levels within the ERA, e.g., exposure modelling. The release, fate and exposure data and models cover the input for environmental distribution models in order to identify the potential (PES) and relevant exposure scenarios (RES) and, subsequently, the possible release routes, both with regard to which compartment(s) NMs are distributed in line with the factors determining the fate within environmental compartment. The initial outcome in the risk characterisation will be a generic Predicted Environmental Concentration (PEC), but a refined PEC can be obtained by applying specific exposure models for relevant media. The hazard information covers a variety of representative, relevant and reliable organisms and/or functions, relevant for the RES and enabling a hazard characterisation. The initial outcome will be hazard characterisation in test systems allowing estimating a Predicted No-Effect concentration (PNEC), either based on uncertainty factors or on a NM adapted version of the Species Sensitivity Distributions approach. The risk characterisation will either be based on a deterministic risk ratio approach (i.e., PEC/PNEC) or an overlay of probability distributions, i.e., exposure and hazard distributions, using the nano relevant models. Full article

An engineered nanomaterial (ENM) may actually consist of a population of primary particles, aggregates and agglomerates of various sizes. Furthermore, their physico-chemical characteristics may change during the various life-cycle stages. It will probably not be feasible to test all varieties of all ENMs for possible health and environmental risks. There is therefore a need to further develop the approaches for risk assessment of ENMs. Within the EU FP7 project Managing Risks of Nanoparticles (MARINA) a two-phase risk assessment strategy has been developed. In Phase 1 (Problem framing) a base set of information is considered, relevant exposure scenarios (RESs) are identified and the scope for Phase 2 (Risk assessment) is established. The relevance of an RES is indicated by information on exposure, fate/kinetics and/or hazard; these three domains are included as separate pillars that contain specific tools. Phase 2 consists of an iterative process of risk characterization, identification of data needs and integrated collection and evaluation of data on the three domains, until sufficient information is obtained to conclude on possible risks in a RES. Only data are generated that are considered to be needed for the purpose of risk assessment. A fourth pillar, risk characterization, is defined and it contains risk assessment tools. This strategy describes a flexible and efficient approach for data collection and risk assessment which is essential to ensure safety of ENMs. Further developments are needed to provide guidance and make the MARINA Risk Assessment Strategy operational. Case studies will be needed to refine the strategy. Full article