Changes in the Material Characteristics of Maize Straw during the Pretreatment Process of Methanation

Pretreatment technology is important to the direct methanation of straw. This study used fresh water, four bacterium agents (stem rot agent, “result” microbe decomposition agent, straw pretreatment composite bacterium agent, and complex microorganism agent), biogas slurry, and two chemical reagents (sodium hydroxide and urea) as pretreatment promoters. Different treatments were performed, and the changes in the straw pH value, temperature, total solid (TS), volatile solid (VS), and carbon-nitrogen ratio (C/N ratio) under different pretreatment conditions were analyzed. The results showed that chemical promoters were more efficient than biological promoters in straw maturity. Pretreatment using sodium hydroxide induced the highest degree of straw maturity. However, its C/N ratio had to be reduced during fermentation. In contrast, the C/N ratio of the urea-pretreated straw was low and was easy to regulate when used as anaerobic digestion material. The biogas slurry pretreatment was followed by pretreatments using four different bacterium agents, among which the effect of the complex microorganism agent (BA4) was more efficient than the others. The current study is significant to the direct and efficient methanation of straw

Novel Reassortant Highly Pathogenic Avian Influenza (H5N5) Viruses in Domestic Ducks, China

In China, domestic ducks and wild birds often share the same water, in which influenza viruses replicate preferentially. Isolation of 2 novel reassortant highly pathogenic avian influenza (H5N5) viruses from apparently healthy domestic ducks highlights the role of these ducks as reassortment vessels. Such new subtypes of influenza viruses may pose a pandemic threat

A network medicine approach to build a comprehensive atlas for the prognosis of human cancer

The Cancer Genome Atlas project has generated multi-dimensional and highly integrated genomic data from a large number of patient samples with detailed clinical records across many cancer types, but it remains unclear how to best integrate the massive amount of genomic data into clinical practice. We report here our methodology to build a multi-dimensional subnetwork atlas for cancer prognosis to better investigate the potential impact of multiple genetic and epigenetic (gene expression, copy number variation, microRNA expression and DNA methylation) changes on the molecular states of networks that in turn affects complex cancer survivorship. We uncover an average of 38 novel subnetworks in the protein-protein interaction network that correlate with prognosis across four prominent cancer types. The clinical utility of these subnetwork biomarkers was further evaluated by prognostic impact evaluation, functional enrichment analysis, drug target annotation, tumor stratification and independent validation. Some pathways including the dynactin, cohesion and pyruvate dehydrogenase-related subnetworks are identified as promising new targets for therapy in specific cancer types. In conclusion, this integrative analysis of existing protein interactome and cancer genomics data allows us to systematically dissect the molecular mechanisms that underlie unexpected outcomes for cancer, which could be used to better understand and predict clinical outcomes, optimize treatment and to provide new opportunities for developing therapeutics related to the subnetworks identified

The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection

Genomic selection is a promising research area due to its practical application in breeding. In this study, impact of realized genetic relationship and linkage disequilibrium (LD) on marker density and training population size required was investigated and their impact on practical application was further discussed. This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17). Two populations were genotyped with different marker sets at different density: IBM Syn4 and IBM Syn10. A high-density marker set in Syn10 was imputed into the Syn4 population with low marker density. Seven different prediction scenarios were carried out with a random regression best linear unbiased prediction (RR-BLUP) model. The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy. Taken the short-term cost for consideration, relationship information is more valuable than LD information. Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications

Development of multiphysics coupling system for nuclear fuel rod with COMSOL and RMC

Coupling fuel performance and neutronics can help improve the prediction accuracy of fuel rod behavior, which is important for fuel design and performance evaluation. A fuel rod multiphysics coupled system was developed with multiphysics software COMSOL and 3D Monte Carlo neutron transport code RMC. The fuel performance analysis module was built on top of COMSOL with the ability to simulate the fuel behavior in two-dimensional axisymmetric (2D-RZ) or three-dimensional (3D) mode. RMC was innovatively wrapped as a component of COMSOL to communicate with the fuel performance analysis module. The data transferring and the coupling process was maintained using COMSOL’s functionality. Two-way coupling was achieved by mapping power distribution and fast neutron flux fields from RMC to COMSOL and the temperature and coolant density fields from COMSOL to RMC. A fuel rod pin lattice was modeled to demonstrate the coupling. Results show that the calculated power and temperature distributions are reasonable. Considering the flexibility of the coupled system, it can be applied to the performance evaluation of new fuel design

Genome-wide identification and analysis of heterotic loci in three maize hybrids

Heterosis, or hybrid vigour, is a predominant phenomenon in plant genetics, serving as the basis of crop hybrid breeding, but the causative loci and genes underlying heterosis remain unclear in many crops. Here, we present a large-scale genetic analysis using 5360 offsprings from three elite maize hybrids, which identifies 628 loci underlying 19 yield-related traits with relatively high mapping resolutions. Heterotic pattern investigations of the 628 loci show that numerous loci, mostly with complete–incomplete dominance (the major one) or overdominance effects (the secondary one) for heterozygous genotypes and nearly equal proportion of advantageous alleles from both parental lines, are the major causes of strong heterosis in these hybrids. Follow-up studies for 17 heterotic loci in an independent experiment using 2225 F2 individuals suggest most heterotic effects are roughly stable between environments with a small variation. Candidate gene analysis for one major heterotic locus (ub3) in maize implies that there may exist some common genes contributing to crop heterosis. These results provide a community resource for genetics studies in maize and new implications for heterosis in plants

Evolution and Association Analysis of Ghd7 in Rice

Plant height, heading date, and yield are the main targets for rice genetic improvement. Ghd7 is a pleiotropic gene that controls the aforementioned traits simultaneously. In this study, a rice germplasm collection of 104 accessions (Oryza sativa) and 3 wild rice varieties (O.rufipogon) was used to analyze the evolution and association of Ghd7 with plant height, heading date, and yield. Among the 104 accessions, 76 single nucleotide polymorphisms (SNPs) and six insertions and deletions were found within a 3932-bp DNA fragment of Ghd7. A higher pairwise π and θ in the promoter indicated a highly diversified promoter of Ghd7. Sixteen haplotypes and 8 types of Ghd7 protein were detected. SNP changes between haplotypes indicated that Ghd7 evolved from two distinct ancestral gene pools, and independent domestication processes were detected in indica and japonica varietals respectively. In addition to the previously reported premature stop mutation in the first exon of Ghd7, which caused phenotypic changes of multiple traits, we found another functional C/T mutation (SNP S_555) by structure-based association analysis. SNP S_555 is located in the promoter and was related to plant height probably by altering gene expression. Moreover, another seven SNP mutations in complete linkage were found to be associated with the number of spikelets per panicle, regardless of the photoperiod. These associations provide the potential for flexibility of Ghd7 application in rice breeding programs

Genome-Wide Association Studies Reveal the Genetic Basis of Ionomic Variation in Rice

Rice (Oryza sativa) is an important dietary source of both essential micronutrients and toxic trace elements for humans. The genetic basis underlying the variations in the mineral composition, the ionome, in rice remains largely unknown. Here, we describe a comprehensive study of the genetic architecture of the variation in the rice ionome performed using genome-wide association studies (GWAS) of the concentrations of 17 mineral elements in rice grain from a diverse panel of 529 accessions, each genotyped at ∼6.4 million single nucleotide polymorphism loci. We identified 72 loci associated with natural ionomic variations, 32 that are common across locations and 40 that are common within a single location. We identified candidate genes for 42 loci and provide evidence for the causal nature of three genes, the sodium transporter gene Os-HKT1;5 for sodium, Os-MOLYBDATE TRANSPORTER1;1 for molybdenum, and Grain number, plant height, and heading date7 for nitrogen. Comparison of GWAS data from rice versus Arabidopsis (Arabidopsis thaliana) also identified well-known as well as new candidates with potential for further characterization. Our study provides crucial insights into the genetic basis of ionomic variations in rice and serves as an important foundation for further studies on the genetic and molecular mechanisms controlling the rice ionome

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe