Factors influencing herpetofauna abundance and diversity in a tropical agricultural landscape mosaic

Agricultural intensification and the associated factors, including land transformation, are among the major global threats affecting biodiversity especially herpetofauna. However, little information is available about how different factors shape herpetofauna species assemblages in agricultural landscape at different spatial scales from patch (125 – 250m) to the landscape (500 – 1000m). We assessed the diversity of amphibians and reptiles in areas under low and high degrees of agricultural intensification and explored different factors regulating diversity at different spatial scales using four sampling methods. Diversity and abundance of amphibians varied significantly between the two zones, but not for reptiles. Agricultural intensification index (AII), calculated based on agrochemical use and area under agriculture at 250m scale, seemed to affect amphibians both at patch as well as at 500m and 1000m landscape scales. The AII influenced reptilian diversity only at patch and 500m scales. Vicinity of natural forest had a stronger influence on reptilian abundance. Seminatural vegetation impacted herpetofauna diversities at larger spatial scales. The extent of water bodies influenced the reptilian abundance at 250m patch scale and amphibian abundance both at 250m and 1000m scale. Fallow lands affected only reptilian diversity at all spatial scales. Plantation affected amphibian at all scales but reptiles only at the landscape scale. Habitat heterogeneity regulated only amphibian diversity. These results highlight the fact that different patch and landscape-scale factors regulate the diversity of reptiles and amphibians differentially. Such scale-specific information will crucially inform future conservation action for the herpetofauna in the agricultural landscape.     

Sexual maturity increases mobility and heterogeneity in individual space use in Atlantic salmon (Salmo salar) parr

We assessed the effects of sexual maturity on space use in Atlantic salmon (Salmo salar) parr as facultative early maturation enables us to work on individuals belonging to the same cohort. We monitored the space use of 40 1-year-old males in natura throughout a breeding season. First, mature individuals covered longer distances (absolute and upstream) and located within broader home ranges than immature parr. Second, sexual maturity also generated a higher interindividual variability in space use. Finally, mature individuals exhibited a higher probability of association with likely breeding sites on average. However, some mature individuals experienced a lower probability than immature individuals, suggesting that the space use of some mature individuals may not be optimal. Moreover, mature parr exploiting a broader home range or covering longer upstream distances had a higher probability of association with likely breeding sites. Covering longer upstream distances may therefore increase the reproductive success of mature parr, while involving higher energetic costs and a greater risk of predation.     

蚂蚁扰动对土壤有机碳循环过程的影响研究进展

蚂蚁作为生态系统的消费者和分解者,其对土壤有机碳库的影响一直是学术界研究的热点。目前研究主要从蚂蚁对土壤宏量元素储量、理化性质、微生物群落活动等方面探究蚂蚁对土壤有机碳库的影响。本文综述了蚂蚁扰动对土壤有机碳循环过程特征的影响。蚂蚁筑巢改变了蚁穴土壤的微生境、微气候与土壤理化性质,并通过重构土壤微生物群落结构特征、调控地表植被演替过程与格局等方式,直接或间接的影响蚁巢中土壤有机碳来源、碳库分配过程、有机碳库稳定性、有机质微观分子特征等,在微域、局地乃至景观尺度上影响土壤有机碳的循环过程。未来研究应着重从量化蚂蚁扰动及其导致的环境因子波动对土壤碳通量变化的贡献、建立定量模型联系并统一蚂蚁影响下土壤碳循环过程、厘清蚂蚁影响土壤有机碳库稳定性的机制等方面开展深入研究,揭示蚂蚁作为“生态工程师”在调控土壤碳循环过程中的作用机制。     

基于细胞亚群调控提升生物合成效率的研究进展

采用合成生物学与代谢工程技术设计与构建微生物细胞工厂是实现化学品绿色生物制造的重要方法。微生物发酵过程中低产细胞亚群和非生产细胞亚群由于代谢负担轻,更加具有生长优势,会降低产物合成的综合效率。目前,基于响应产物浓度的生物传感器,偶联产物合成与生长的细胞亚群调控系统有助于解决这个问题。综述了细胞亚群调控系统设计和构建的常用方法,重点讨论了目前细胞亚群调控系统存在的问题及其解决策略。     

Spatial variability characteristics and environmental effects of heavy metals in surface riparian soils and surface sediments of Qinggeda Lake

Abstract

One hundred and twenty topsoil (0–20?cm layer) samples and 45 surface sediment samples were collected from the riparian area and bottom of Qinggeda Lake. The content of Pb, Cu, Cr, Ni, Zn, Mn, V, and As were determined to reveal the distribution characteristics and explore the spatial dependency relationship between environmental factors (pH, conductivity, and organic matter) and heavy metals using Kriging interpolation and the geographical weighted regression model. Results showed as follows: (1) The average contents of Pb, Cu, Cr, Ni, Zn, V, and As did not exceed the Standard of National Soil Environmental Quality. However, Cu, Cr, and V were higher than the Xinjiang soil background. (2) The target elements in the surface sediments of the lake showed a decreasing trend from the center of the lake to the shore. Also, the heavy metal concentrations in the eastern part of the lake shore were significantly higher than those in other areas, and the highest concentrations appeared in the paddy field. (3) The effects of the eight heavy metals on pH, conductivity, and organic matter displayed significant spatial nonstationary characteristics. Cr, As, Cu, and Zn are the primary factors that affect environmental characteristics such as pH, conductivity, and organic matter content.     

Multi-parametric MRI lesion heterogeneity biomarkers for breast cancer diagnosis

PurposeTo identify intra-lesion imaging heterogeneity biomarkers in multi-parametric Magnetic Resonance Imaging (mpMRI) for breast lesion diagnosis.MethodsDynamic Contrast Enhanced (DCE) and Diffusion Weighted Imaging (DWI) of 73 female patients, with 85 histologically verified breast lesions were acquired. Non-rigid multi-resolution registration was utilized to spatially align sequences. Four (4) DCE (2^nd post-contrast frame, Initial-Enhancement, Post-Initial-Enhancement and Signal-Enhancement-Ratio) and one (1) DWI (Apparent-Diffusion-Coefficient) representations were analyzed, considering a representative lesion slice. 11 1^st-order-statistics and 16 texture features (Gray-Level-Co-occurrence-Matrix (GLCM) and Gray-Level-Run-Length-Matrix (GLRLM) based) were derived from lesion segments, provided by Fuzzy C-Means segmentation, across the 5 representations, resulting in 135 features. Least-Absolute-Shrinkage and Selection-Operator (LASSO) regression was utilized to select optimal feature subsets, subsequently fed into 3 classification schemes: Logistic-Regression (LR), Random-Forest (RF), Support-Vector-Machine-Sequential-Minimal-Optimization (SVM-SMO), assessed with Receiver-Operating-Characteristic (ROC) analysis.ResultsLASSO regression resulted in 7, 6 and 7 features subsets from DCE, DWI and mpMRI, respectively. Best classification performance was obtained by the RF multi-parametric scheme (Area-Under-ROC-Curve, (AUC) ± Standard-Error (SE), AUC ± SE = 0.984 ± 0.025), as compared to DCE (AUC ± SE = 0.961 ± 0.030) and DWI (AUC ± SE = 0.938 ± 0.032) and statistically significantly higher as compared to DWI. The selected mpMRI feature subset highlights the significance of entropy (1^st-order-statistics and 2^nd-order-statistics (GLCM)) and percentile features extracted from 2^nd post-contrast frame, PIE, SER maps and ADC map.ConclusionCapturing breast intra-lesion heterogeneity, across mpMRI lesion segments with 1^st-order-statistics and texture features (GLCM and GLRLM based), offers a valuable diagnostic tool for breast cancer.     

Partitioning plant spectral diversity into alpha and beta components

Plant spectral diversity – how plants differentially interact with solar radiation – is an integrator of plant chemical, structural, and taxonomic diversity that can be remotely sensed. We propose to measure spectral diversity as spectral variance, which allows the partitioning of the spectral diversity of a region, called spectral gamma (γ) diversity, into additive alpha (α; within communities) and beta (β; among communities) components. Our method calculates the contributions of individual bands or spectral features to spectral γ‐, β‐, and α‐diversity, as well as the contributions of individual plant communities to spectral diversity. We present two case studies illustrating how our approach can identify 'hotspots’ of spectral α‐diversity within a region, and discover spectrally unique areas that contribute strongly to β‐diversity. Partitioning spectral diversity and mapping its spatial components has many applications for conservation since high local diversity and distinctiveness in composition are two key criteria used to determine the ecological value of ecosystems.