MXene Nanosheets Induce Efficient Iron Selenide Active Sites to Boost the Electrocatalytic Hydrogen Evolution Reaction.

Along with the widespread utilization of hydrogen energy, the rise of highly active hydrogen evolution electrocatalysts with affordable costs presently becomes a substantial crux of this emerging domain. In this work, we demonstrate a feasible and convenient in situ seed-induced growth strategy for the construction of small-sized FeSe2 nanoparticles decorated on two-dimensional (2D) superthin Ti3C2Tx MXene sheets (FeSe2/Ti3C2Tx) through a manipulated bottom-up synthetic procedure. By virtue of the distinctive 0D/2D heterostructures, abundant exposed surface area, well-distributed FeSe2 catalytic centers, strong surface electronic coupling, and high electrical conductivity, the resultant FeSe2/Ti3C2Tx nanoarchitectures are endowed with a superior electrocatalytic hydrogen evolution capacity including a competitive onset potential of 89 mV, a favorable Tafel slope of 78 mV dec-1, and a long-period stability, significantly better than that of the pristine FeSe2 and Ti3C2Tx catalysts.

An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris.

Geosynchronous orbit (GSO) is the ideal orbit for communication, navigation, meteorology and other satellites, but the space of GSO is limited, and there are still a large number of space debris threatening the safety of spacecraft. Therefore, real-time detection of GSO debris is necessary to avoid collision accidents. Because radar is limited by transmitting power and operating distance, it is difficult to detect GSO debris, so photoelectric detection becomes the mainstream way to detect GSO debris. This paper presents an adaptive real-time detection algorithm for GSO debris in the charge coupled device (CCD) images. The main work is as follows: An image adaptive fast registration algorithm and an enhanced dilation difference algorithm are proposed. Combining with mathematical morphology, threshold segmentation and global nearest neighbor (GNN) multi-target tracking algorithm, the functions of image background suppression, registration, suspected target extraction and multi-target tracking are realized. The processing results of a large number of measured data show that the algorithm can detect dim geostationary earth orbit (GEO) and non-GEO debris in GSO belt stably and efficiently, and the processing speed meets the real-time requirements, with strong adaptive ability, and has high practical application value.

Blood Cell DNA Methylation of Aging-Related Ubiquitination Gene DZIP3 Can Predict the Onset of Early Stage Colorectal Cancer.

There is a body of evidence that the aging immune system is linked to cancer. In this study, with aging- and immune-related DNA methylation data, we investigated the DNA methylation regulation changes in promoters with other regions of genes during aging and their association with the immune-cell proportion in the circulating whole blood of individuals. The analyses for aging- and CD4+ T cell proportion-derived differential genes showed that ubiquitination plays an important role in the aging immune system and tumorigenesis. Therefore, starting from a set of pre-annotated ubiquitination genes, we found that among the differentially ubiquitinated genes, DZIP3, an E3 ubiquitin ligase with no reports on its function in immune cells and tumorigenesis, was significantly associated with both aging (P-value = 3.86e-06) and CD4+ T cell proportion (P-value = 1.97e-05) in circulating blood. By collecting a cohort of 100 colon cancer patients and 50 healthy individuals, we validated that the 1st exon DNA methylation of DZIP3 could predict the onset of early stage (AUC = 0.833, OR = 8.82) and all pTNM stages of colorectal cancer (AUC = 0.782, OR = 5.70). Thus, the epigenetically regulated ubiquitination machine plays an important role in immune aging and tumorigenesis.

[Controllable synthesis and UV-Vis spectral analysis of silver nanoparticles in AOT microemulsion].

Colloidal silver nanoparticles were synthesized in water-in-oil microemulsion using silver nitrate solubilized in the water core of a microemulsion as source of silver ions, hydrazine hydrate solubilized in the water core of another one as reducing agent, cyclohexane as the continuous phase, and sodium bis(2-ethylhexyl) sulfosuccinate (AOT) as the surfactant. The main factors affecting the formation of silver nanoparticles were systematically studied. Ultraviolet-visible (UV-Vis) spectra were used for analyzing the effects of reaction parameters, including the type of reducing agents, the molar ratio of water to surfactant and the concentration of AgNO3 and AOT and so on, on the formation of silver nanoparticles. Original results for the controllable synthesis of silver nanoparticles were obtained when the synthesis proceeded in AOT-cyclohexane-AgNO3 microemulsion. The UV-Vis spectra of silver sols formed in the microemulsion with various parameters were studied systematically. The results show that the amount and average size of the obtained nanoparticles obviously depend on the above parameters. When the concentration of AgNO3 is lower, smaller silver nanoparticles are easy to form by increasing the concentration of AgNO3 appropriately. The higher W value was found to form larger numbers of silver nanoparticles with larger particle size. Compared to the solubility of NaBH4 in AOT reverse micelles, hydrazine hydrate is well soluble in these micelles, and thus it is favorable to reduce the silver ions solubilized in the water core of AOT-cyclohexane-AgNO3 microemulsion. The increase in the concentration of AOT induces an increase in the number of AOT micelles and a decrease in the molar ratio of water to surfactant. As a result, the solubilization capacity of reactants in the micelles increases and the radii of the micelles decrease. That is to say, with the increase in AOT concentration, the amount of the formed nanoparticles increases and the average size of the particles decreases.

A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize.

Complex quantitative traits of plants as measured on collections of genotypes across multiple environments are the outcome of processes that depend in intricate ways on genotype and environment simultaneously. For a better understanding of the genetic architecture of such traits as observed across environments, genotype-by-environment interaction should be modeled with statistical models that use explicit information on genotypes and environments. The modeling approach we propose explains genotype-by-environment interaction by differential quantitative trait locus (QTL) expression in relation to environmental variables. We analyzed grain yield and grain moisture for an experimental data set composed of 976 F(5) maize testcross progenies evaluated across 12 environments in the U.S. corn belt during 1994 and 1995. The strategy we used was based on mixed models and started with a phenotypic analysis of multi-environment data, modeling genotype-by-environment interactions and associated genetic correlations between environments, while taking into account intraenvironmental error structures. The phenotypic mixed models were then extended to QTL models via the incorporation of marker information as genotypic covariables. A majority of the detected QTL showed significant QTL-by-environment interactions (QEI). The QEI were further analyzed by including environmental covariates into the mixed model. Most QEI could be understood as differential QTL expression conditional on longitude or year, both consequences of temperature differences during critical stages of the growth.

Mapping quantitative trait loci using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.).

Many commercial inbred lines are available in crops. A large amount of genetic variation is preserved among these lines. The genealogical history of the inbred lines is usually well documented. However, quantitative trait loci (QTL) responsible for the genetic variances among the lines are largely unexplored due to lack of statistical methods. In this study, we show that the pedigree information of the lines along with the trait values and marker information can be used to map QTL without the need of further crossing experiments. We develop a Monte Carlo method to estimate locus-specific identity-by-descent (IBD) matrices. These IBD matrices are further incorporated into a mixed-model equation for variance component analysis. QTL variance is estimated and tested at every putative position of the genome. The actual QTL are detected by scanning the entire genome. Applying this new method to a well-documented pedigree of maize (Zea mays L.) that consists of 404 inbred lines, we mapped eight QTL for the maize male flowering trait, growing degree day heat units to pollen shedding (GDUSHD). These detected QTL contributed >80% of the variance observed among the inbred lines. The QTL were then used to evaluate all the inbred lines using the best linear unbiased prediction (BLUP) technique. Superior lines were selected according to the estimated QTL allelic values, a technique called marker-assisted selection (MAS). The MAS procedure implemented via BLUP may be routinely used by breeders to select superior lines and line combinations for development of new cultivars.

Programmed gradient descent biosorption of strontium ions by Saccaromyces cerevisiae and ashing analysis: A decrement solution for nuclide and heavy metal disposal.

One of the waste disposal principles is decrement. The programmed gradient descent biosorption of strontium ions by Saccaromyces cerevisiae regarding bioremoval and ashing process for decrement were studied in present research. The results indicated that S. cerevisiae cells showed valid biosorption for strontium ions with greater than 90% bioremoval efficiency for high concentration strontium ions under batch culture conditions. The S. cerevisiae cells bioaccumulated approximately 10% of strontium ions in the cytoplasm besides adsorbing 90% strontium ions on cell wall. The programmed gradient descent biosorption presented good performance with a nearly 100% bioremoval ratio for low concentration strontium ions after 3 cycles. The ashing process resulted in a huge volume and weight reduction ratio as well as enrichment for strontium in the ash. XRD results showed that SrSO4 existed in ash. Simulated experiments proved that sulfate could adjust the precipitation of strontium ions. Finally, we proposed a technological flow process that combined the programmed gradient descent biosorption and ashing, which could yield great decrement and allow the supernatant to meet discharge standard. This technological flow process may be beneficial for nuclides and heavy metal disposal treatment in many fields.

Correcting the bias in estimation of genetic variances contributed by individual QTL.

In addition to locating chromosomal positions of quantitative trait loci (QTL), estimating the sizes of identified QTL is also an important component in QTL mapping. The size of a QTL is usually measured by the proportion of the phenotypic variance contributed by the QTL. However, the genetic variance may be overestimated in a small line crossing experiment. In this study, we investigate this bias and develop a simple method to correct the bias. The bias correction, however, requires the error of the estimated genetic effect, which is not trivial if the genetic effect is estimated using the Expectation and Maximization (EM) algorithm. Therefore, we also develop a simple method to estimate the standard error of the estimated genetic effect, which is subsequently used to correct the bias in the variance estimate.