BIOCHEMICAL STUDY OF ENDONUCLEASE V AND ITS APPLICATION IN MUTATION SCANNING

The integrity of the genetic information encoded by DNA is essential to all living organisms, yet the reactive bases of DNA are constantly attacked by endogenous and exogenous agents resulting in as many as one million individual molecular lesions per cell per day. Excessive DNA damage or deficiency in DNA repair enzymes may cause cancer, premature aging, and neurodegenerative diseases. Endonuclease V (Endo V) is a DNA repair enzyme which can recognize all four types of DNA deamination products, specifically, uracil, hypoxanthine, xanthine and oxanine. It was also shown that endo V can recognize mismatches. We screened about 60 mutants of endo V from Thermotoga maritima and found some mutants had altered base preferences for mismatches. Tma endo V Y80A was shown to become a C-specific mismatch endonuclease. G13D mutation in K-ras oncogene which was not recognized by wild type Tma endo V was successfully cleaved by Tma endo V Y80A. This study provides valuable information on base recognition and active site organization of Tma endo V. Tma endo V mutants can be used for cancer mutation scanning and mutation recognition. In order to further understand the role of Y80 of endo V in base recognition, we substituted the Y80 with sixteen amino acids. Together with three Y80 mutants isolated before, we characterized all nineteen mutants of Tma endo V Y80 using deaminated base-containing DNA substrates and mismatch-containing DNA substrates. This comprehensive amino acid substitution at a single site (Y80) underlines the importance of aromatic ring and hydrogen bond donor capacity in base recognition by endo V, reveals additional Y80 mutants with altered base preferences in mismatch cleavage, and offers new insight on the role of Y80 in base recognition. Though endo V was shown to be important for repair of deaminated lesions in vivo, its DNA repair pathway remains unknown. In order to understand the DNA repair pathway mediated by endo V, we have developed a cell-free system from Escherichia coli. The preliminary results indicated that the repair patch of endo V mediated DNA repair pathways may consist of a long patch and a short patch repair pathway

Chemical Fractionation Of Cu And Zn And Their Impacts On Microbial Properties In Slightly Contaminated Soils

Chemical fractionation of Cu and Zn in bulk soil and its effects on soil microbial properties were determined in Cu and Zn contaminated soils (Cu: 35.57~46.37 mg·kg-1, Zn: 74.33~127.20 mg·kg-1) sampled from an agricultural field in outskirts of Zibo, China during the month of September, 2011. A sequential extraction technique (SET) was used for metals chemical fractionation analysis in soils and a correlation analysis was applied to determinate the effects of metal on soil microbial properties. Chemical speciation showed that Cu and Zn were mostly present in the residual fraction and their concentrations in the most labile fraction (acid soluble fraction) were the lowest in the investigated soils. However, the correlation analysis indicated that the labile forms of Cu/Zn, such as its acid soluble, reducible or oxidizable fractions, were usually significantly negatively correlated with the tested microbial activities at 0.05 or 0.01 probability levels. These results indicate that the metal labile fractions could exert an inhibitory effect on the soil microbial parameters even in the minor contaminated soils. Int. J. Agril. Res. Innov. & Tech. 3 (1): 20-25, June, 2013 DOI: http://dx.doi.org/10.3329/ijarit.v3i1.1604

Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelong learning approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to lifelong learning scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: i) developing a model capable of addressing the depth scale variation through scale-aware depth learning, ii) devising an effective learning strategy to handle significant domain gaps, and iii) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this paper, we present i) a lightweight multi-head framework that effectively tackles the depth scale imbalance, ii) an uncertainty-aware lifelong learning solution that adeptly handles significant domain gaps, and iii) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8% to 15%