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The mechanical properties of the honeybee's abdominal muscles endow its abdomen with movement flexibility to perform various activities. However, the biomechanical properties of abdominal muscles during stretch activation remain unclear. To clarify this issue, we observed the microstructures of the abdominal muscles to obtain structural information. The similarity and symmetry of abdominal muscle distribution contribute to the ability to drive abdominal movement. Combined with the segmented structure characteristics, an experimental device to measure muscle stretch measurement of honeybees was developed to investigate the mechanical properties of the abdominal muscles. During measurement, the muscles were kept in a solution to maintain a physiological environment. The mechanical properties of abdominal muscles included phases: the ascending phase with proportional increase, stable phase with slight fluctuation, and decay phase with parabolic decline. These findings indicate that the nonlinear and rate-sensitive mechanical properties of the abdominal muscles enable them to rapidly adapt to environmental changes. The stretch force and stiffness coefficient reached 0.660 ± 0.139 mN and 14.364 ± 2.961 N/m, respectively. A simplified biomechanical model of the muscle fiber considering the hierarchical microstructure was introduced, in which the mechanical properties were consistent with the experimental data. Further analysis of the effects of the activation probability and the effective range of binding sites on the mechanical properties demonstrated the critical role in force generation, revealing the mechanism of underlying muscle stretch activation in the honeybee abdomen. The findings can provide a new reference for studying the biomechanical properties of the muscles of other arthropod insects.

When traveling by car, the driver normally feels that the return trip is shorter than the outbound trip. The reason for this feeling, called return trip effect, is not clear. To explore the internal causes of this return trip effect, an indoor driving simulation experiment and a real car driving experiment were performed in this study. Questionnaires were used to obtain the estimated time of the outbound and return trips of the drivers. An eye tracker and an electroencephalograph equipment were used to record driver's eye movement and EEG data in the real-car driving experiment. The physiological indices and questionnaire results showed that the difference in the driver's cognitive loads of the outbound and return trips was the internal cause of the return trip effect. Drivers who were unfamiliar with the road had a different cognitive load between the two trips, which resulted in the return trip effect. However, drivers who were familiar with the road did not experience a return trip effect due to the close cognitive loads of the two trips.

In this paper, the effectiveness of the composite photocatalyst was studied by using manganese dioxide (MnO2)/zirconium dioxide (ZrO2) to degrade diesel pollutants in seawater under visible light.The MnO2/ZrO2 photocatalyst was prepared by co-precipitation and characterized by scanning electron microscopy, X-ray powder diffraction, energy-dispersive spectroscopy and UV-Vis diffuse reflectance spectroscopy analysis. This is the first report on a comprehensive analytical study on the effect of various physio-chemical parameters on diesel degradation using the synthesized MnO2/ZrO2 photocatalysts. The effects of doping ratio of MnO2/ZrO2, dosage, initial diesel concentration, calcination temperature, concentration of H2O2 solutions and illumination time on the diesel degradation were investigated. The degradation of diesel pollution in seawater was optimized by orthogonal experiment. According to the results, the prepared samples were monoclinic form and the MnO2 was successfully doped into the bulk ZrO2. The absorption edge of the MnO2/ZrO2 photocatalysts exhibited red shift, and this red shifts imply enhanced photon absorption under visible light compared with the pure ZrO2. The results showed that under optimum reaction conditions, the degradation rate can reach 92.92%. The result of this study will enable ZrO2 to make more effective use of sunlight and improve the actual value of photocatalytic technology in the field of contaminant treatment.

The 3D battery concept applied on silicon–graphite electrodes (Si/C) has revealed a significant improvement of battery performances, including high-rate capability, cycle stability, and cell lifetime. 3D architectures provide free spaces for volume expansion as well as additional lithium diffusion pathways into the electrodes. Therefore, the cell degradation induced by the volume change of silicon as active material can be significantly reduced, and the high-rate capability can be achieved. In order to better understand the impact of 3D electrode architectures on rate capability and degradation process of the thick film silicon–graphite electrodes, we applied laser-induced breakdown spectroscopy (LIBS). A calibration curve was established that enables the quantitative determination of the elemental concentrations in the electrodes. The structured silicon–graphite electrode, which was lithiated by 1C, revealed a homogeneous lithium distribution within the entire electrode. In contrast, a lithium concentration gradient was observed on the unstructured electrode. The lithium concentration was reduced gradually from the top to the button of the electrode, which indicated an inhibited diffusion kinetic at high C-rates. In addition, the LIBS applied on a model electrode with micropillars revealed that the lithium-ions principally diffused along the contour of laser-generated structures into the electrodes at elevated C-rates. The rate capability and electrochemical degradation observed in lithium-ion cells can be correlated to lithium concentration profiles in the electrodes measured by LIBS.

In this paper, the effectiveness of the composite photocatalyst was studied by using manganese dioxide (MnO

De novo shoot regeneration is one of the important manifestations of cell totipotency in organogenesis, which reflects a survival strategy organism evolved when facing natural selection. Compared with tissue regeneration, and somatic embryogenesis, de novo shoot regeneration denotes a shoot regeneration process directly from detatched or injured tissues of plant. Studies on plant shoot regeneration had identified key genes mediating shoot regeneration. However, knowledge was derived from Arabidopsis; the regeneration capacity is hugely distinct among species. To achieve a comprehensive understanding of the shoot regeneration mechanism from tree species, we select four genetic lines of Populus euphratica from a natural population to be sequenced at transcriptome level. On the basis of the large difference of differentiation capacity, between the highly differentiated (HD) and low differentiated (LD) groups, the analysis of differential expression identified 4920 differentially expressed genes (DEGs), which were revealed in five groups of expression patterns by clustering analysis. Enrichment showed crucial pathways involved in regulation of regeneration difference, including “plant hormone signal transduction”, “cell differentiation”, "cellular response to auxin stimulus", and “auxin-activated signaling pathway”. The expression of nine genes reported to be associated with shoot regeneration was validated using quantitative real-time PCR (qRT-PCR). For the specificity of regeneration mechanism with P. euphratica, large amount of DEGs involved in "plant-pathogen interaction", ubiquitin-26S proteosome mediated proteolysis pathway, stress-responsive DEGs, and senescence-associated DEGs were summarized to possibly account for the differentiation difference with distinct genotypes of P. euphratica. The result in this study helps screening of key regulators in mediating the shoot differentiation. The transcriptomic characteristic in P. euphratica further enhances our understanding of key processes affecting the regeneration capacity of de novo shoots among distinct species.

Many higher plants of economic and biological importance undergo apomixis in which the maternal tissue of the ovule forms a seed, without experiencing meiosis and fertilization. This feature of apomixis has made it difficult to perform linkage mapping which relies on meiotic recombination. Here, we describe a computational model for mapping quantitative trait loci (QTLs) that control complex traits in apomictic plants. The model is founded on the mixture model-based likelihood in which maternal genotypes are dissolved into two possible components generated by meiotic and apomictic processes, respectively. The EM algorithm was implemented to discern meiotic and apomictic genotypes and, therefore, allow the marker-QTL linkage relationship to be estimated. By capitalizing on reciprocal crosses, the model is renovated to estimate and test imprinting effects of QTLs, providing a better gateway to characterize the genetic architecture of complex traits. The model was validated through computer simulation and further demonstrated for its usefulness by analyzing a real data for an apomictic woody plant. The model has for the first time provided a unique tool for genetic mapping in apomictic plants.

As a group of economically important species, linkage mapping of polysomic autotetraploids, including potato, sugarcane and rose, is difficult to conduct due to their unique meiotic property of double reduction that allows sister chromatids to enter into the same gamete. We describe and assess a statistical model for mapping quantitative trait loci (QTLs) in polysomic autotetraploids. The model incorporates double reduction, built in the mixture model-based framework and implemented with the expectation-maximization algorithm. It allows the simultaneous estimation of QTL positions, QTL effects and the degree of double reduction as well as the assessment of the estimation precision of these parameters. We performed computer simulation to examine the statistical properties of the method and validate its use through analyzing real data in tetraploid switchgrass.

Knowledge about biological shape has important implications in biology and biomedicine, but the underlying genetic mechanisms for shape variation have not been well studied. Statistical models play a pivotal role in mapping specific quantitative trait loci (QTLs) that contribute to biological shape and its developmental trajectories. We describe and assess a statistical framework for shape gene identification that incorporates shape and image analysis into a mixture-model framework for QTL mapping. Statistical parameters that define genotype-specific differences in biological shape are estimated by implementing statistical and computational algorithms. A state-of-the-art procedure is described to examine the control patterns of specific QTLs on the origin, properties and functions of biological shape. The statistical framework described will help to address many integrative biological and genetic questions and challenges in shape variation faced by the life sciences community.

Cells with the same genotype growing under the same conditions can show different phenotypes, which is known as “population heterogeneity”. The heterogeneity of hematopoietic progenitor cells has an effect on their differentiation potential and lineage choices. However, the genetic mechanisms governing population heterogeneity remain unclear. Here, we present a statistical model for mapping the quantitative trait locus (QTL) that affects hematopoietic cell heterogeneity. This strategy, termed systems mapping, integrates a system of differential equations into the framework for systems mapping, allowing hypotheses regarding the interplay between genetic actions and cell heterogeneity to be tested. A simulation approach based on cell heterogeneity dynamics has been designed to test the statistical properties of the model. This model not only considers the traditional QTLs, but also indicates the methylated QTLs that can illustrate non-genetic individual differences. It has significant implications for probing the molecular, genetic and epigenetic mechanisms of hematopoietic progenitor cell heterogeneity.