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AimsMany governments and companies have committed to moving to net-zero emissions by 2030 or 2050 to tackle climate change, which require the development of new carbon capture and sequestration/storage (CCS) techniques. A proposed method of sequestration is to deposit carbon in soils as plant matter including root mass and root exudates. Adding perennial traits such as rhizomes to crops as part of a sequestration strategy would result in annual crop regrowth from rhizome meristems rather than requiring replanting from seeds which would in turn encourage no-till agricultural practices. Integrating these traits into productive agriculture requires a belowground phenotyping method compatible with high throughput breeding and selection methods (i.e., is rapid, inexpensive, reliable, and non-invasive), however none currently exist. MethodsGround penetrating radar (GPR) is a non-invasive subsurface sensing technology that shows potential as a phenotyping technique. In this study, a prototype GPR antenna array was used to scan roots of the perennial sorghum hybrid, PSH09TX15. A-scan level time-domain analyses and B-scan level time/frequency analyses using the continuous wavelet transform were utilized to extract features of interest from the acquired radargrams. ResultsOf six A-scan diagnostic indices examined, the standard deviation of signal amplitude correlated most significantly with belowground biomass. Time frequency analysis using the continuous wavelet transform yielded high correlations of B-scan features with belowground biomass. ConclusionThese results demonstrate that continued refinement of GPR data analysis workflows should yield a highly applicable phenotyping tool for breeding efforts in environments where selection is otherwise impractical on a large scale.

Background: Root phenotyping methods are of increasing importance as researchers seek to understand belowground productivity and breeders work to select for root traits. Effective non-destructive root phenotyping methods do not exist for bulked-root and tuber crops such as potato and cassava. Cassava is a tropical crop widely grown by subsistence farmers throughout the tropics and is the fourth most important staple food crop in the world, yet lags in research. It has an extensive growth period sometimes exceeding 12 months. Early maturity is a major goal for breeders, but the ability to select for it is hampered by the lack of non-destructive yield estimation methods. GPR is a tool with potential to aid in bulked root selection, but standard methods have yet to be developed. In this study, we demonstrate good practice in GPR estimation of root mass, which was used as a proxy for cassava root mass, and investigate the effect of soil water content on measurement.Results: Significant correlation between GPR data and daikon root mass was found for three of the five irrigation treatments. Correlation strength improved with increased soil water content and decreased variation of soil water content between plots. Pearson correlation coefficient varied from 0.53 – 0.79.Conclusions: GPR can be used to estimate bulked root mass. Wet soil can improve the predictive quality of GPR data, but water content needs to be homogeneous throughout the study site and period. Determining the optimal soil water content will require further research.

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project’s outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases—Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)—as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2., Author summary The goal of this study and paper has been to develop an international resource to generate an informed and current understanding of the impact of genetic variation on cancer risk across the cancer predisposition genes, BRCA1 and BRCA2. Reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org, to provide a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype.

Previous studies have associated the overexpression of histone deacetylase 2 (HDAC2) and the presence of TP53 mutations with the progression to advanced stage drug resistant colorectal cancer (CRC). However, the mechanistic link between HDAC2 expression and the TP53 mutational status has remained unexplored. Here, we investigated the function of HDAC2 in drug resistance by assessing the synergistic effects of DNA-targeted chemotherapeutic agents and HDAC inhibitors (HDACis) on two TP53-mutated colorectal adenocarcinoma CRC cell lines (SW480 and HT-29) and on the TP53-wild type carcinoma cell line (HCT116 p53+/+) and its TP53 deficient sub-line (HCT116 p53-/-). We showed that in the untreated SW480 and HT-29 cells the steadystate level of HDAC2 was low compared to a TP53-wild type carcinoma cell line (HCT116 p53+/+). Increased expression of HDAC2 correlated with drug resistance, and depletion by shRNA sensitised the multi-drug resistance cell line HT-29 to CRC chemotherapeutic drugs such as 5-fluorouracil (5-FU) and oxaliplatin (Oxa). Combined treatment with the HDACi suberoylanilide hydroxamic acid plus 5-FU or Oxa reduced the level of HDAC2 expression, modified chromatin structure and induced mitotic cell death in HT-29 cells. Non-invasive bioluminescence imaging revealed significant reductions in xenograft tumour growth with HDAC2 expression level reduced to < 50% in treated animals. Elevated levels of histone acetylation on residues H3K9, H4K12 and H4K16 were also found to be associated with resistance to VPA/Dox or SAHA/Dox treatment. Our results suggest that HDAC2 expression rather than the p53 mutation status influences the outcome of combined treatment with a HDACi and DNA-damaging agents in CRC.

Lists of variations in genomic DNA and their effects have been kept for some time and have been used in diagnostics and research. Although these lists have been carefully gathered and curated, there has been little standardization and coordination, complicating their use. Given the myriad possible variations in the estimated 24,000 genes in the human genome, it would be useful to have standard criteria for databases of variation. Incomplete collection and ascertainment of variants demonstrates a need for a universally accessible system. These and other problems led to the World Heath Organization-cosponsored meeting on June 20-23, 2006 in Melbourne, Australia, which launched the Human Variome Project. This meeting addressed all areas of human genetics relevant to collection of information on variation and its effects. Members of each of eight sessions (the clinic and phenotype, the diagnostic laboratory, the research laboratory, curation and collection, informatics, relevance to the emerging world, integration and federation and funding and sustainability) developed a number of recommendations that were then organized into a total of 96 recommendations to act as a foundation for future work worldwide. Here we summarize the background of the project, the meeting and its recommendations. © 2007 Nature Publishing Group.

Background/Objectives: Short-chain fatty acids, produced by microbiome fermentation of carbohydrates, have been linked to a reduction in appetite, body weight and adiposity. However, determining the contribution of central and peripheral mechanisms to these effects has not been possible. Subjects/Methods:C57BL/6 mice fed with either normal or high-fat diet were treated with nanoparticle-delivered acetate, and the effects on metabolism were investigated. Results:In the liver, acetate decreased lipid accumulation and improved hepatic function, as well as increasing mitochondrial efficiency. In white adipose tissue, it inhibited lipolysis and induced 'browning', increasing thermogenic capacity that led to a reduction in body adiposity. Conclusions:This study provides novel insights into the peripheral mechanism of action of acetate, independent of central action, including ‘browning’ and enhancement of hepatic mitochondrial function.

Traffic count data are useful for many purposes, but often not available for significant portions of road networks. It would be prohibitive to cover all roads with traditional sensor-based traffic monitoring system, particularly for rural, low-class roads. In cases where traffic volumes are needed but unavailable, travel demand models (TDMs) can be used to estimate such information. A literature review indicates that research work for estimating traffic volumes for low-class roads using TDM is scarce. The majority of previous research used traffic count data-based regressions. The problem of such an approach is that it relies on available traffic counts to develop, calibrate, and validate regression models. Nevertheless, few or no traffic counts are collected on low-class roads, and therefore make it inapplicable. This study implements TDMs for two regions in the province of New Brunswick, Canada to estimate traffic volumes for low-class roads. Geographical Information System-based TDMs using census data and Institute of Transportation Engineers (ITE) Quick Response Method produce forecasted traffic for a significant portion of road network previously without any traffic information and limit the average estimation errors for low-class roads to less than 40%. Available traffic data were increased by 45% in York County and 144% in the Beresford area. The traffic estimation errors are comparable to or better than those reported in the literature, and the forecast traffic volumes provide a solid foundation for identifying high-volume road segments and prioritizing funding. Study results clearly show TDM is a practical, useful, cost-effective way for estimating traffic parameters on low-class roads.