Rev Proteins of Human and Simian Immunodeficiency Virus Enhance RNA Encapsidation

The main function attributed to the Rev proteins of immunodeficiency viruses is the shuttling of viral RNAs containing the Rev responsive element (RRE) via the CRM-1 export pathway from the nucleus to the cytoplasm. This restricts expression of structural proteins to the late phase of the lentiviral replication cycle. Using Rev-independent gag-pol expression plasmids of HIV-1 and simian immunodeficiency virus and lentiviral vector constructs, we have observed that HIV-1 and simian immunodeficiency virus Rev enhanced RNA encapsidation 20- to 70-fold, correlating well with the effect of Rev on vector titers. In contrast, cytoplasmic vector RNA levels were only marginally affected by Rev. Binding of Rev to the RRE or to a heterologous RNA element was required for Rev-mediated enhancement of RNA encapsidation. In addition to specific interactions of nucleocapsid with the packaging signal at the 5′ end of the genome, the Rev/RRE system provides a second mechanism contributing to preferential encapsidation of genomic lentiviral RNA

Unintended spread of a biosafety level 2 recombinant retrovirus

<p>Abstract</p> <p>Background</p> <p>Contamination of vertebrate cell lines with animal retroviruses has been documented repeatedly before. Although such viral contaminants can be easily identified with high sensitivity by PCR, it is impossible to screen for all potential contaminants. Therefore, we explored two novel methods to identify viral contaminations in cell lines without prior knowledge of the kind of contaminant.</p> <p>Results</p> <p>The first hint for the presence of contaminating retroviruses in one of our cell lines was obtained by electron microscopy of exosome-like vesicles released from the supernatants of transfected 293T cells. Random amplification of particle associated RNAs (PAN-PCR) from supernatant of contaminated 293T cells and sequencing of the amplicons revealed several nucleotide sequences showing highest similarity to either murine leukemia virus (MuLV) or squirrel monkey retrovirus (SMRV). Subsequent mass spectrometry analysis confirmed our findings, since we could identify several peptide sequences originating from monkey and murine retroviral proteins. Quantitative PCRs were established for both viruses to test currently cultured cell lines as well as liquid nitrogen frozen cell stocks. Gene fragments for both viruses could be detected in a broad range of permissive cell lines from multiple species. Furthermore, experimental infections of cells negative for these viruses showed that both viruses replicate rapidly to high loads. We decided to further analyze the genomic sequence of the MuLV-like contaminant virus. Surprisingly it was neither identical to MuLV nor to the novel xenotropic MuLV related retrovirus (XMRV) but showed 99% identity to a synthetic retrovirus which was engineered in the 1980s.</p> <p>Conclusion</p> <p>The high degree of nucleotide identity suggests unintended spread of a biosafety level 2 recombinant virus, which could also affect the risk assessment of gene-modified organisms released from contaminated cell cultures. The study further indicates that both mass spectrometry and PAN-PCR are powerful methods to identify viral contaminations in cell lines without prior knowledge of the kind of contaminant. Both methods might be useful tools for testing cell lines before using them for critical purposes.</p

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Midinfrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique

Linking complex forest fuel structure and fire behaviour at fine scales

Abstract. Improved fire management of savannas and open woodlands requires better understanding of the fundamental connection between fuel heterogeneity, variation in fire behaviour and the influence of fire variation on vegetation feedbacks. In this study, we introduce a novel approach to predicting fire behaviour at the submetre scale, including measurements of forest understorey fuels using ground-based LIDAR (light detection and ranging) coupled with infrared thermography for recording precise fire temperatures. We used ensemble classification and regression trees to examine the relationships between fuel characteristics and fire temperature dynamics. Fire behaviour was best predicted by characterising fuelbed heterogeneity and continuity across multiple plots of similar fire intensity, where impacts from plot-to-plot variation in fuel, fire and weather did not overwhelm the effects of fuels. The individual plot-level results revealed the significance of specific fuel types (e.g. bare soil, pine leaf litter) as well as the spatial configuration of fire. This was the first known study to link the importance of fuelbed continuity and the heterogeneity associated with fuel types to fire behaviour at metre to submetre scales and provides the next step in understanding the complex responses of vegetation to fire behaviour

Climate change : strategies for mitigation and adaptation

The sustainability of life on Earth is under increasing threat due to humaninduced climate change. This perilous change in the Earth's climate is caused by increases in carbon dioxide and other greenhouse gases in the atmosphere, primarily due to emissions associated with burning fossil fuels. Over the next two to three decades, the effects of climate change, such as heatwaves, wildfires, droughts, storms, and floods, are expected to worsen, posing greater risks to human health and global stability. These trends call for the implementation of mitigation and adaptation strategies. Pollution and environmental degradation exacerbate existing problems and make people and nature more susceptible to the effects of climate change. In this review, we examine the current state of global climate change from different perspectives. We summarize evidence of climate change in Earth’s spheres, discuss emission pathways and drivers of climate change, and analyze the impact of climate change on environmental and human health. We also explore strategies for climate change mitigation and adaptation and highlight key challenges for reversing and adapting to global climate change

University of Florida: Sabine Grunwald

This website contains information and research concerning 2D and 3D modeling of soil architecture using GIS software, including current research and related University of Florida courses. Educational levels: Graduate or professional, Undergraduate lower division, Undergraduate upper division

Artificial intelligence and soil carbon modeling demystified: power, potentials, and perils

The global soil carbon pool has been estimated to exceed the amount of carbon stored in the atmosphere and vegetation, though uncertainties to quantify below-ground carbon and soil carbon fluxes accurately still exist. Modeling soil carbon using artificial intelligence (AI) - machine learning (ML) and deep learning (DL) algorithms - has emerged as a powerful force in the carbon science community. These AI soil carbon models have shown improved performance to predict soil organic carbon (SOC) storage, soil respiration (Rs), and other properties of the global carbon cycle when compared to other modeling approaches. AI systems have advanced abilities to optimize fits between inputs (geospatial environmental covariates) and outputs (e.g., SOC or Rs) through advanced pattern recognition, learning algorithms, latent variables, hyperparameters, hyperplanes, weighting factors, or multiple stacked processing (e.g., convolution and pooling). These machine-oriented applications have shifted focus from knowledge discovery and understanding of ecosystem processes, carbon pools and cycling toward data-driven applications that compute digital soil carbon outputs. The purpose of this review paper is to explore the emergence, applications, and progress of AI-ML and AI-DL algorithms to model soil carbon storage and Rs at regional and global scales. A critical discussion of the power, potentials, and perils of AI soil carbon modeling is provided. The paradigm shift toward AI modeling raises questions how we study soil carbon dynamics and what conclusions we draw which impacts carbon science research and education, carbon management, carbon policies, carbon markets and economies, and soil health

Reshaping How We Think about Soil Security

The soil security framework has been conceptualized and views soil as a resource that needs to be secured to avoid or minimize adverse environmental/anthropogenic impacts and undesirable consequences for people. Our critical literature review suggests that measurements, estimations, simulations, or digital mapping of soil properties fall short in assessing soil security and health. Instead, soil security that considers soil ecosystem functionality based on regionalized and optimized relationships between targeted functions and site-specific soil environmental conditions allows for the discernment of actual and attainable efficiency levels for observation sites. We discuss the pros and cons that undergird the paradigm shift toward a pedo-econometric modeling approach. Such a multiperspectival approach to soil security allows for simultaneous interpretations from economic, pedogenic, agronomic, environmental, biotic/habitat, and other perspectives. This approach is demonstrated by modeling total nutrient efficiencies in complex multi-use soilscapes with diverging soil environmental interests and concerns