Selection Mapping Identifies Loci Underpinning Autumn Dormancy in Alfalfa (Medicago sativa).

Autumn dormancy in alfalfa (Medicago sativa) is associated with agronomically important traits including regrowth rate, maturity, and winter survival. Historical recurrent selection experiments have been able to manipulate the dormancy response. We hypothesized that artificial selection for dormancy phenotypes in these experiments had altered allele frequencies of dormancy-related genes. Here, we follow this hypothesis and analyze allele frequency changes using genome-wide polymorphisms in the pre- and postselection populations from one historical selection experiment. We screened the nondormant cultivar CUF 101 and populations developed by three cycles of recurrent phenotypic selection for taller and shorter plants in autumn with markers derived from genotyping-by-sequencing (GBS). We validated the robustness of our GBS-derived allele frequency estimates using an empirical approach. Our results suggest that selection mapping is a powerful means of identifying genomic regions associated with traits, and that it can be exploited to provide regions on which to focus further mapping and cloning projects

Genomic studies of abiotic stresses in grasses

In this dissertation studies, BdCBF genes that are involved in cold acclimation in Brachypodium distachyon are identified and differentially expressed cold-responsive genes in cbf3 mutants are analyze by RNA-seq analysis. In addition, a high throughput RNAi library by the Phi29-amplified RNAi construct (PARC) method from a creeping bentgrass cDNA library with mRNA from salt-treated tissue is constructed. Generation of AsBri1 loss-of-function mutants using the PARC method in creeping bentgrass has validated the PARC system. And disruption of BR reception results in reduced growth along with increased drought tolerance in creeping bentgrass. Furthermore, the brassinosteroid signaling network including implications of brassinosteroid on yield and stress tolerance is reviewed. These genomic studies provide insights into the pathways involved in cold acclimation, BR signaling and plant stress responses

Benchmarking Inverse Optimization Algorithms for Molecular Materials Discovery

Machine learning-based molecular materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, metaheuristic algorithms are not as widely applied to materials discovery applications. We comprehensively compare 11 different optimization algorithms for molecular materials design with targeted properties. These algorithms include Bayesian Optimization (BO) and multiple metaheuristic algorithms. We performed 5000 material evaluations repeated 5 times with different randomized initialization to optimize defined target properties. By maximizing the bulk modulus and minimizing the Fermi energy through perturbing parameterized molecular representations, we estimated the unique counts of molecular materials, mean density scan of the objectives space, mean objectives, and frequency distributed over the materials' representations and objectives. GA, GWO, and BWO exhibit higher variances for materials count, density scan, and mean objectives; and BO and Runge Kutta optimization (RUN) display generally lower variances. These results unveil that nature-inspired algorithms contain more uncertainties in the defined molecular design tasks, which correspond to their dependency on multiple hyperparameters. RUN exhibits higher mean objectives whereas BO displayed low mean objectives compared with other benchmarked methods. Combined with materials count and density scan, we propose that BO strives to approximate a more accurate surrogate of the design space by sampling more molecular materials and hence have lower mean objectives, yet RUN will repeatedly sample the targeted molecules with higher objective values. Our work shed light on automated digital molecular materials design and is expected to elicit future studies on materials optimization such as composite and alloy design based on specific desired properties.Comment: 15 pages, 5 figures, for the main manuscrip

A Multimodal Learning Framework for Comprehensive 3D Mineral Prospectivity Modeling with Jointly Learned Structure-Fluid Relationships

This study presents a novel multimodal fusion model for three-dimensional mineral prospectivity mapping (3D MPM), effectively integrating structural and fluid information through a deep network architecture. Leveraging Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP), the model employs canonical correlation analysis (CCA) to align and fuse multimodal features. Rigorous evaluation on the Jiaojia gold deposit dataset demonstrates the model's superior performance in distinguishing ore-bearing instances and predicting mineral prospectivity, outperforming other models in result analyses. Ablation studies further reveal the benefits of joint feature utilization and CCA incorporation. This research not only advances mineral prospectivity modeling but also highlights the pivotal role of data integration and feature alignment for enhanced exploration decision-making

Fabrication and Antibacterial Performance of Pea Protein Isolate/Pullulan/Allicin Composite Electrospun Nanofibers

Using pea protein isolate (PPI) and pullulan (PUL) as raw materials and allicin (AC) as an antibacterial substance, nanofiber materials were prepared by electrospinning technology. The influence of allicin concentration on the structural and morphological characteristics, diameter distribution and antibacterial effects of nanofibers were investigated. Fourier transform infrared spectroscopy (FTIR) indicated that allicin was wrapped in the composite nanofibers. Scanning electron microscopy (SEM) showed that spherical structures of different sizes appeared around the nanofibers due to the addition of allicin. The size of the spherical structures increased (P < 0.05) with an increase in allicin concentration, while the nanofiber diameter gradually decreased (P < 0.05). With increasing allicin concentration up to 15%, the elastic modulus and tensile strength of the composite nanofibers gradually increased (P < 0.05), and the elongation at break gradually decreased (P < 0.05). Additionally, the composite nanofibers with more than 10% allicin exhibited an obvious bacteriostatic effect, and it was strongest at allicin concentrations of 15% and 20%, with inhibition zone diameters of 16.5 and 12.8 mm against E. coli and S. aureus, respectively. This research will provide data support and a theoretical basis for the development and application of new green food packaging nanomaterials

Effect on impact properties of adding tantalum to V-4Cr-4Ti ternary vanadium alloy

Four V-Ta-4Cr-4Ti quaternary alloys containing different quantities of Ta were investigated to determine the effect of Ta content on the Charpy impact properties. Five button-shaped ingots of the V-4Cr-4Ti ternary alloy and V-xTa-4Cr-4Ti quaternary alloys (x = 3, 9, 15, and 22 wt.%) were fabricated on a laboratory scale by using non-consumable arc-melting in an argon atmosphere. Charpy impact tests were conducted at temperatures ranging from 77 K to 293 K using an instrumented impact tester. Both the upper shelf energy and the ductile–brittle transition temperature increased with increasing Ta content. The addition of 3 wt.% Ta resulted in solid solution strengthening without any degradation of the Charpy impact properties. Thus, the addition of 3 wt.% Ta (V-3Ta-4Cr-4Ti) is an appropriate amount to use in blanket structural materials for nuclear fusion reactors. The spectra of TEM-EDS for V-3Ta-4Cr-4Ti quaternary alloy indicate that there is no significant enrichment of Ta in the matrix as compared with that in the precipitate. However, thermal aging may result in the formation of the Laves phase, causing the degradation of Charpy impact properties. The characterization of precipitates, thermal aging, and creep tests of the V-3Ta-4Cr-4Ti quaternary alloy need to be investigated to determine the optimum Ta content

High-efficiency 100-W Kerr-lens mode-locked Yb:YAG thin-disk oscillator

We demonstrate a Kerr-lens mode-locked femtosecond Yb:YAG thin-disk oscillator and investigate the approach to increase the optical-to-optical efficiency based on the scheme of direct multiple passes of the laser beam through the thin-disk medium. With twelve passes through the thin disk, 266-fs pulses were delivered from the oscillator with an average power of 105.6 W at a repetition rate of 20 MHz. The corresponding optical-to-optical efficiency is 31.1%, which is, to the best of our knowledge, the highest efficiency of any mode-locked thin-disk oscillator with pulse duration below 300 fs. This demonstration paves the way to even more efficient mode-locked femtosecond thin-disk oscillators, and provides an excellent laser source for the applications such as non-linear frequency conversion and high-precision industrial processing

Genomic studies of abiotic stresses in grasses

In this dissertation studies, BdCBF genes that are involved in cold acclimation in Brachypodium distachyon are identified and differentially expressed cold-responsive genes in cbf3 mutants are analyze by RNA-seq analysis. In addition, a high throughput RNAi library by the Phi29-amplified RNAi construct (PARC) method from a creeping bentgrass cDNA library with mRNA from salt-treated tissue is constructed. Generation of AsBri1 loss-of-function mutants using the PARC method in creeping bentgrass has validated the PARC system. And disruption of BR reception results in reduced growth along with increased drought tolerance in creeping bentgrass. Furthermore, the brassinosteroid signaling network including implications of brassinosteroid on yield and stress tolerance is reviewed. These genomic studies provide insights into the pathways involved in cold acclimation, BR signaling and plant stress responses.</p

Benchmarking inverse optimization algorithms for materials design

Machine learning-based inverse materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, many metaheuristic algorithms are not as widely applied to materials discovery applications as machine learning methods. There are ongoing challenges in applying different optimization algorithms to discover materials with single- or multi-elemental compositions and how these algorithms differ in mining the ideal materials. We comprehensively compare 11 different optimization algorithms for the design of single- and multi-elemental crystals with targeted properties. By maximizing the bulk modulus and minimizing the Fermi energy through perturbing the parameterized elemental composition representations, we estimated the unique counts of elemental compositions, mean density scan of the objectives space, mean objectives, and frequency distributed over the materials’ representations and objectives. We found that nature-inspired algorithms contain more uncertainties in the defined elemental composition design tasks, which correspond to their dependency on multiple hyperparameters. Runge–Kutta optimization (RUN) exhibits higher mean objectives, whereas Bayesian optimization (BO) displayed low mean objectives compared with other methods. Combined with materials count and density scan, we propose that BO strives to approximate a more accurate surrogate of the design space by sampling more elemental compositions and hence have lower mean objectives, yet RUN will repeatedly sample the targeted elemental compositions with higher objective values. Our work sheds light on the automated digital design of materials with single- and multi-elemental compositions and is expected to elicit future studies on materials optimization, such as composite and alloy design based on specific desired properties

The intraday patterns of liquidity and volatility in Chinese stock markets: A comparison

Conference Name:WIT Transactions on Information and Communication Technologies. Conference Address: Wuhan, China. Time:May 7, 2013 - May 8, 2013.WIT Transactions on Information and Communication TechnologiesAccording to microstructure theory, the intraday patterns of liquidity and volatility are different among different stock exchanges because of the market structure and trading mechanism. The paper examines the liquidity and volatility of limit order markets in Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE) and compares the differences between both stock markets. We find that their intraday patterns of liquidity and volatility both are L-shaped while SHSE is more effective than SZSE on the whole. ? 2014 WIT Press