Genetic study of hematopoiesis development by two zebrafish mutants: Ugly duckling and tc-244

Ph.DDOCTOR OF PHILOSOPH

Water Vapor Near-UV Absorption: Laboratory Spectrum, Field Evidence, and Atmospheric Impacts

Absorption of solar radiation by water vapor in the near-UV region is a poorly-understood but important issue in atmospheric science. To better understand water vapor near-UV absorption, we constructed a cavity ring-down spectrometer with bandwidth of 5 cm-1 (~0.05 nm) and obtained water vapor absorption cross-sections at 1 nm increments in the 290-350 nm region. Water vapor displays structured absorption over this range with maximum and minimum cross-sections of 8.4×10-25 and 1.6×10-25 cm2/molecule. Major water vapor absorption bands were observed at 293-295, 307-313, 319, 321-322, and 325 nm, with cross-section values higher than 4.0×10-25 cm2/molecule. To obtain further insight into major water vapor absorption bands, we measured water vapor absorption cross-sections at 0.05 nm intervals in the 292-296, 306-314, and 317-326 nm region. Field UV residual spectra not only exhibited increased attenuation at higher atmospheric water vapor loadings but also showed structures suggested by the laboratory water vapor absorption spectrum. Spaceborne UV radiance spectra have spectral structures resembling the differential cross-section spectrum constructed from the laboratory wavelength-dependent water vapor absorption cross-sections presented here. Incorporating water vapor absorption cross-section data into a radiative transfer model yielded an estimated energy budget of 0.26 W/m2 for the standard U.S. atmosphere and 0.76 W/m2 for the tropics. This shows that water vapor near-UV absorption is an important contributor for climate simulation and ozone retrievals

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/

A genetic study of the NOS3 gene for ischemic stroke in a Chinese population

We recruited 560 unrelated patients with ischemic stroke and 153 unrelated controls to undertake a genetic analysis for association between the NOS3 gene and ischemic stroke. All the subjects were Chinese of Han descent. Because the NOS3 gene spans about 21 kb of DNA and contains 26 exons, we selected a single nucleotide polymorphism (SNP) rs3918181, an A to G base change located in intron 14 of the gene, as a DNA marker. PCR-based restriction fragment length polymorphism analysis was applied to genotype rs3918181 (RsaI site). The chi-square (χ2) goodness-of-fit test showed that the genotypic distributions of the marker were not deviated from Hardy-Weinberg equilibrium in both the patient group (χ2 = 0.166, p = 0.684) and the control group (χ2 = 0.421, p = 0.517). The cocaphase analysis showed allelic association of rs3918181 with ischemic stroke in males (χ2 = 4.04, p = 0.044, OR = 1.43, 95% CI 1.01∼2.02) and frequency of allele A was significantly higher in male patients than male control subjects. The χ2 test revealed genotypic association between rs3918181 and ischemic stroke in males (χ2 = 4.26, df = 1, p = 0.039, OR = 1.61, 95% CI 1.02∼2.53) but not in females. The present work suggests that rs3918181 is associated with ischemic stroke in male patients. This finding gives further evidence in support of the eNOS association with ischemic stroke in the Chinese population

Structural bias in T4 RNA ligase-mediated 3′-adapter ligation

T4 RNA ligases are commonly used to attach adapters to RNAs, but large differences in ligation efficiency make detection and quantitation problematic. We developed a ligation selection strategy using random RNAs in combination with high-throughput sequencing to gain insight into the differences in efficiency of ligating pre-adenylated DNA adapters to RNA 3′-ends. After analyzing biases in RNA sequence, secondary structure and RNA-adapter cofold structure, we conclude that T4 RNA ligases do not show significant primary sequence preference in RNA substrates, but are biased against structural features within RNAs and adapters. Specifically, RNAs with less than three unstructured nucleotides at the 3′-end and RNAs that are predicted to cofold with an adapter in unfavorable structures are likely to be poorly ligated. The effect of RNA-adapter cofold structures on ligation is supported by experiments where the ligation efficiency of specific miRNAs was changed by designing adapters to alter cofold structure. In addition, we show that using adapters with randomized regions results in higher ligation efficiency and reduced ligation bias. We propose that using randomized adapters may improve RNA representation in experiments that include a 3′-adapter ligation step

Microplastic pollution as an environmental risk exacerbating the greenhouse effect and climate change: a review

Abstract Microplastics are polymer-based materials with carbon as their main framework. During degradation, they release greenhouse gases such as carbon dioxide and methane. Additionally, environmental microplastics can enter plant tissues, triggering oxidative stress in plant cells, adversely affecting photosynthesis, metabolism, gene expression, and other growth parameters. This reduction in plant efficiency in sequestering and utilizing atmospheric carbon dioxide indirectly impacts global carbon cycling, exacerbating the global greenhouse effect. Furthermore, environmental microplastics significantly alter soil structure and the composition of microbial communities, affecting the emissions of greenhouse gases such as carbon dioxide, methane, and nitrous oxide, thus indirectly promoting greenhouse gas emissions. Increasing research suggests a mutual reinforcement between microplastic pollution and global climate warming, where microplastic pollution exacerbates global climate warming, and the rise in global average temperature leads to the resuspension of microplastics in sediments, intensifying microplastic pollution in the environment. This article primarily focuses on the impacts of environmental microplastic pollution on different ecosystems and the relationship between microplastic pollution and global climate warming. It summarizes the effects of microplastic pollution on greenhouse gas emissions in marine, terrestrial, and atmospheric ecosystems, as well as the mechanisms by which microplastics and climate change affect ecosystem services. By delving into the intricate connection between microplastic pollution and greenhouse gas emissions, this paper aims to raise awareness of the climate change caused by microplastic pollution and calls for further research on the impacts of microplastics on ecosystems and global climate change, with the ultimate goal of protecting ecosystems and human health. Graphical Abstrac

An optimal method for data clustering

An algorithm for optimizing data clustering in feature space is studied in this work. Using graph Laplacian and extreme learning machine (ELM) mapping technique, we develop an optimal weight matrix W for feature mapping. This work explicitly performs a mapping of the original data for clustering into an optimal feature space, which can further increase the separability of original data in the feature space, and the patterns points in same cluster are still closely clustered. Our method, which can be easily implemented, gets better clustering results than some popular clustering algorithms, like k-means on the original data, kernel clustering method, spectral clustering method, and ELM k-means on data include three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, and Wine database)

Covalent Bonding of MXene/COF Heterojunction for Ultralong Cycling Li-Ion Battery Electrodes

Covalent organic frameworks (COFs) have emerged as promising renewable electrode materials for LIBs and gained significant attention, but their capacity has been limited by the densely packed 2D layer structures, low active site availability, and poor electronic conductivity. Combining COFs with high-conductivity MXenes is an effective strategy to enhance their electrochemical performance. Nevertheless, simply gluing them without conformal growth and covalent linkage restricts the number of redox-active sites and the structural stability of the composite. Therefore, in this study, a covalently assembled 3D COF on Ti3C2 MXenes (Ti3C2@COF) is synthesized and serves as an ultralong cycling electrode material for LIBs. Due to the covalent bonding between the COF and Ti3C2, the Ti3C2@COF composite exhibits excellent stability, good conductivity, and a unique 3D cavity structure that enables stable Li+ storage and rapid ion transport. As a result, the Ti3C2-supported 3D COF nanosheets deliver a high specific capacity of 490 mAh g−1 at 0.1 A g−1, along with an ultralong cyclability of 10,000 cycles at 1 A g−1. This work may inspire a wide range of 3D COF designs for high-performance electrode materials

Influence of Ambient Pressure on Performance of a Deep-sea Hydraulic Manipulator

Deep-sea hydraulic manipulator is the most commonly used operation equipment for executing subsea operations in different applications. The increasing demands on underwater operational ease require the implementation of manipulator system which can maintain similar dynamic performance in different working conditions Considering the variations of seawater properties at different ocean depths, especially high hydrostatic pressure in deep-sea environment will make the kinematic viscosity of oil obviously increased, which has a significant influence on the performance of deep-sea hydraulic manipulator. The viscosity-pressure characteristics of working medium is tested. A detailed nonlinear mathematical model and related simulations considering the significant slender pipelines between valves and actuators due to the increased oil viscosity are conducted to analyze the ambient pressures against varying depths affecting the manipulator performance. The 115MPa online pressure experimental results indicate the joint response characteristics at different ambient pressures, which provides basis for response consistency control of deep-sea hydraulic manipulator