Search

Small-molecule carboxyl methyltransferases (CbMTs) constitute a small proportion of the reported methyltransferases, but they have received extensive attention due to their important physiological functions. Most of the small-molecule CbMTs isolated to date originate from plants and are members of the SABATH family. In this study, we identified a type of CbMT (OPCMT) from a group of Mycobacteria , which has a distinct catalytic mechanism from the SABATH methyltransferases. The enzyme contains a large hydrophobic substrate-binding pocket (~400 Å 3 ) and utilizes two conserved residues, Thr20 and Try194, to retain the substrate in a favorable orientation for catalytic transmethylation. The OPCMT_like MTs have a broad substrate scope and can accept diverse carboxylic acids enabling efficient production of methyl esters. They are widely (more than 10,000) distributed in microorganisms, including several well-known pathogens, whereas no related genes are found in humans. In vivo experiments implied that the OPCMT_like MTs was indispensable for M. neoaurum , suggesting that these proteins have important physiological functions.

Soil water sources for shrub encroachment in arid and semiarid regions have not been fully understood. This study used stable δ2H and δ18O isotope ratios of plants, soil water and precipitation, coupled with proportional similarity index of species to identify relations between shrub encroachment and water sources for four shrub and grass species at various shrub encroachment stages in a semiarid grassland of Inner Mongolia. We found that shallow rooted grasses (Agriophyllum squarrosum and Stipa bungeana) predominantly used shallow soil water (0–20 cm) during growing season. In contrast, deeply-rooted semi-shrubs (Artemisia ordosica and Artemisia sphaerocephala) used middle and deep soil water in July but shallow and middle soil depth water in May and September. Plants competition for soil water in different shrub encroachment stages were different depending on plant water use sources. The highest plant competition existed in the A. ordosica community in semi-fixed sandy land, followed by A. squarrosum community in shifting sandy land, A. ordosica community in fixed sandy land, and S. bungeana community in fixed sandy land. Competition for soil water and differences of water use sources of grass and shrub species were crucial in driving shrub encroachment in semiarid grasslands. Communities dominated by A. ordosica were not stable, with an increase in surface soil water availability, the dominance was replaced by S. bungeana. Our findings on the linkage between plant water use sources and patterns and shrub encroachment shall help future arid and semiarid grassland management under changing climate conditions.

Land use/cover and vegetation patterns are influenced by many ecological factors. However, the effect of various factors on different classification systems and different levels of the same system is unclear. We conducted a redundancy analysis with 10 landscape metrics and ecological factors in four periods (1986–2005/2007, 1991–2005/2007, 1996–2005/2007, 2001–2005/2007) to explore their effects on the land use/cover system, vegetation group and vegetation type, and formation and subformation levels of the vegetation classification system in the Jing-Jin-Ji region. Soil, temperature and precipitation from 1986–2005, 1991–2005, and 2001–2005 were the important causal factors, and anthropogenic disturbance and atmospheric factors in 1996–2005 were causal factors at the land use/cover level. The total explained variance from 1996–2005 and 2001–2005 was higher than that from 1986–2005 and 1991–2005 at the land use/cover level. Causal factors and the variance explained by causal factors at the vegetation group, vegetation type, and formation and subformation levels were similar but different in the land use/cover system. Geography, soil and anthropogenic disturbance were the most important causal factors at the three vegetation levels, and the total explained variance from 2001–2007 was higher than that from 1986–2007, 1991–2007, and 1996–2007 at the three vegetation levels. In environmental research, natural resource management and urban or rural planning, geographic factors should be considered at the vegetation group, vegetation type and formation and subformation levels while atmospheric and temperature factors should be considered at the land use/cover level.

Background Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends. The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance. Methods We used climate, topographic, and spectral data as the input variables for four machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and maximum likelihood classification (MLC)) on three vegetation classification units (vegetation group (I), vegetation type (II), and formation and subformation (III)) in Jing-Jin-Ji, one of China’s most developed regions. We used a total of 2,789 vegetation points for model training and 974 vegetation points for model assessment. Results Our results showed that the RF method was the best of the four models, as it could effectively simulate vegetation distribution in all three classification units. The DT method could only simulate vegetation distribution in units I and II, while the other two models could not simulate vegetation distribution in any of the units. Kappa coefficients indicated that the DT and RF methods had more accurate predictions for units I and II than for unit III. The three vegetation classification units were most affected by six variables: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Mid-infrared ratio of winter vegetation index and brightness index of summer vegetation index). Variables Combination 7, including annual mean temperature, annual precipitation, mean diurnal range and precipitation of driest month, produced the highest simulation accuracy. Conclusions We determined that the RF model was the most effective for simulating vegetation distribution in all classification units present in the Jing-Jin-Ji region. The RF model produced high accuracy vegetation distributions in classification units I and II, but relatively low accuracy in classification unit III. Four climate variables were sufficient for vegetation distribution simulation in such region.

Biclustering is an important technique in data mining for searching similar patterns. Geometric biclustering (GBC) method is used to reduce the complexity of the NP-complete biclustering algorithm. This paper studies three commonly used modern platforms including multi-core CPU, GPU and FPGA to accelerate this GBC algorithm. By analyzing the parallelizing property of the GBC algorithm, we design (1) a multi-threaded software running on a server grade multi-core CPU system, (2) a CUDA program for GPU to accelerate the GBC algorithm, and (3) a novel parameterizable and scalable hardware architecture implemented on an FPGA. Genes microarray pattern analysis is employed as an example to demonstrate performance comparisons on different platforms. In particular, we compare the speed and energy efficiency of the three proposed methods. We found that (a) GPU achieves the highest average speedup of 48x compared to single-threaded GBC program, (b) Our FPGA design can achieve higher speedup of 4x for the computation for large microarray, and (c) FPGA consumes the least energy, which is about 3.53x more efficient than the single-threaded GBC program.

Biclustering is one of the important techniques in neurocomputing and bioinformatics. Geometric Biclustering (GBC) algorithm is used to find the common patterns in given microarray data for neural processing. A microarray can produce a massive amount of data and require high computational power for data analysis. With intrinsic parallel architecture and appropriate mapping technique Graphical Processing Unit (GPU) has the advantage of processing large number of threads and data compared to CPU. This paper analyzes the parallelism and data reuse of the GBC algorithm, and presents three different efficient implementations using five benchmarks from real world. The proposed GPU-based GBC program achieves significant speedup over highly optimized CPU program. By comparing implementation results, the paper studies how to design a scalable architecture for mapping the GBC and other similar algorithms that deal with microarray data analysis. The paper also explores how GPU-based GBC is affected by the input data size.

The Burrows-Wheeler Transform (BWT) based methodology seems ideally suited for DNA sequence alignment due to its high speed and low space complexity. Despite being efficient in exact matching, the application of BWT in inexact matching still has problems due to the excessive backtracking process. This paper presents a hardware architecture for the BWT-based inexact sequence mapping algorithm using the Field Programmable Gate Array (FPGA). The proposed design can handle up to two errors, including mismatches and gaps. The original recursive algorithm implementation is dealt with using hierarchical tables, and is then parallelized to a large extension through a dual-base extension method. Extensive performance evaluations for the proposed architecture have been conducted using both Virtex 6 and Virtex 7 FPGAs. This design is considerably faster than a direct implementation. When compared with the popular software evaluation tool BWA, our architecture can achieve the same match quality tolerating up to two errors. In an execution speed comparison with the BWA aln process, our design outperforms a range of CPU platforms with multiple threads under the same configuration conditions.

Collision detection, which refers to the computational problem of finding the relative placement or con-figuration of two or more objects, is an essential component of many applications in computer graphics and robotics. In image-guided robotic surgery, real-time collision detection is critical for preserving healthy anatomical structures during the surgical procedure. However, the computational complexity of the problem usually results in algorithms that operate at low speed. In this paper, we present a fast and accurate algorithm for collision detection between Oriented-Bounding-Boxes (OBBs) that is suitable for real-time implementation. Our proposed Sweep and Prune algorithm can perform a preliminary filtering to reduce the number of objects that need to be tested by the classical Separating Axis Test algorithm, while the OBB pairs of interest are preserved. These OBB pairs are re-checked by the Separating Axis Test algorithm to obtain accurate overlapping status between them. To accelerate the execution, our Sweep and Prune algorithm is tailor-made for the proposed method. Meanwhile, a high performance scalable hardware architecture is proposed by analyzing the intrinsic parallelism of our algorithm, and is implemented on FPGA platform. Results show that our hardware design on the FPGA platform can achieve around 8X higher running speed than the software design on a CPU platform. As a result, the proposed algorithm can achieve a collision frame rate of 1 KHz, and fulfill the requirement for the medical surgery scenario of Robot Assisted Laparoscopy.

It is difficult to improve the performance for the traditional processor architecture. The power consumption and the temperature of the processors are the main bottlenecks for CPU with single core. Chip multiprocessor is proposed to solve such problems. In order to solve the communication problem between the on chip components, network on chip is taken as the promising diagram. The on-chip cores are connected by lines. However, the communication is now the core of the on-chip network and the power consumed is more than before. This paper aims at the energy consumption problem in communication. The tasks will be mapped to the cores according to the communication density to reduce the power consumption and improve the performance. The experimental results show that our algorithm can achieve its target.