利用近红外彩色成像技术监测松萎蔫病

有效监测患病株方法的缺失是导致对松萎蔫病难以成功控制的主要原因之一。利用航空手段调查该病导致的针叶颜色变化已被证明是一种可靠的方法。为了克服该方法固有的缺点,笔者引用了近红外彩色胶片航拍方法。该项技术在获得正摄航拍图像时,通过图像处理可以增强松针颜色变化,获得较直接拍摄图像更明显的色差。因为图像可以在室内仔细处理,因此能解决飞行时间缺乏等问题。但是,该方法也存在对被压木无法获得其影像而漏检的缺陷。笔者研发的计算机软件可以将目标树标记在计算机图像文件上,并且标记树的地理位置数据和背景航拍图像并传输到装有内置GPS接收器的掌上电脑中,借助软件产生的图像导航定位系统,有利于地面接近标记树,从而可以现场检查和校正标记树的数据,并将修正数据传回至主机。此方法利用最新航拍图像技术建立对每株树处理和管理的连续资料,大大改善了防控松萎蔫病的措施。

Gene expression of CCL8 and CXCL10 in peripheral blood leukocytes during early pregnancy in cows

Background: The aim of the present study was to evaluate CCL8 and CXCL10 expression and its regulatory mechanism in peripheral blood leukocytes(PBLs) at the time of maternal recognition in cows. Blood samples were collected on 14, 15, 16, 17 and 18 d after artificial insemination(AI). Based on the day of return of estrus, cows were divided into three groups, pregnant(n = 5), early embryonic mortality(EEM; n = 5) and late embryonic mortality(LEM; n = 5). The gene expression levels in PBLs were assessed with quantitative real-time reverse transcription PCR.Results: The expression of CCL8 and CXCL10 mRNA in PBLs gradually increased from 14 to 18 d of pregnant cows and significant differences were observed on 18 d(P < 0.05), whereas no significant changes were observed both in EEM and LEM cows. Interferon-stimulated protein 15 k Da(ISG15), myxovirus-resistance gene(MX) 1 and MX2 mRNA expression in PBLs increased from 14 to 18 d which was significant on 18 d of pregnant cows as well as in LEM cows(P < 0.05), but no changes were observed in EEM cows. To determine whether the expression of CCL8 and CXCL10 in PBLs was regulated by pregnancy-related substances or not, expression level was assessed after exposure to interferon-τ(IFNT) and CCL16. Monocytes, granulocytes and lymphocytes were obtained using density-gradient centrifugation and flow cytometry. The addition of IFNT(100 ng/mL) and CCL16(100 ng/mL) to cultured PBLs increased the expression of CCL8 and CXCL10 mRNA(P < 0.05). The expression of ISG15, MX1 and MX2 mRNA in PBLs was also stimulated by IFNT and CCL16(P < 0.05).Conclusions: The expression of CCL8 and CXCL10 genes increased in PBLs during early pregnancy. Since IFNT stimulated CCL8 and CXCL10 expression in cultured PBLs, the increase of CCL8 and CXCL10 might be pregnancy-dependent events.The expression of both CCL8 and CXCL10 in PBLs was stimulated by CCL16 as wel as IFNT, suggesting a chemokine interaction that at least includes CCL8, CXCL10 and CCL16, and may play a role in regulating maternal recognition in cows.

利用环介导恒温扩增法简易诊断松萎蔫病

诊断松萎蔫病的传统方法是从木质组织中分离松材线虫,然后用显微镜进行形态学鉴定。基于DNA分子检测的方法尽管很灵敏,不需要固定发育阶段的松材线虫,但是仍然需要用Baer-mann漏斗法分离线虫,并且需要昂贵的仪器。笔者研发了利用环介导恒温扩增法检测松材线虫的存在,该方法包括:(1)从木块中提取DNA;(2)DNA扩增;(3)通过反应溶液的颜色做出诊断。该方法仅使用培养箱且整个过程只需要90 min,比以往方法更加简便和快速。

Spatial Distribution of Surface Soil Moisture in a Small Forested Catchment

Predicting the spatial distribution of soil moisture is an important hydrological question. We measured the spatial distribution of surface soil moisture (upper 6 cm) using an Amplitude Domain Reflectometry sensor at the plot scale (2 × 2 m) and small catchment scale (0.84 ha) in a temperate forest. The spatial variation of soil water content was higher during dry conditions than that during wet conditions. Results indicated 3.1 samples at the plot scale were sufficient to estimate mean soil water content when the precision was 0.1. Soil water content increased with increasing topographic index (TI) and soil-topographic index (STI) at the small catchment scale. The correlation between soil water content and TI was higher than that between soil water content and STI. This suggests that topography is more important for estimating surface soil moisture than soil depth as formation of surface soil moisture occurs at ≤6 cm.

Combining post-disturbance land cover and tree canopy cover from Landsat time series data for mapping deforestation,forest degradation,and recovery across Cambodia

Mapping of deforestation,forest degradation,and recovery is essential to characterize country-level forest change and formulate mitigation actions.Previous studies have mainly used a simple forest/non-forest classification after forest disturbance to identify deforestation and forest degradation.However,a more flexible approach that is applicable to different forest conditions is desirable.In this study,we examined an approach for mapping deforestation,forest degradation,and recovery using disturbance types and tree canopy cover estimates from annual Landsat time-series data from 1988 to 2020 across Cambodia.We developed models to estimate both disturbance types and tree canopy cover based on a random forest algorithm using predictor variables derived from a trajectory-based temporal segmentation approach.The estimated disturbance types and canopy cover in each year were then used in a rule-based classification of deforestation,forest degradation,and recovery.The producer’s and user’s accuracies ranged from 59.1%to 72.9%and 60.8%to 91.6%,respectively,for the forest change classes of mapping deforestation,forest degradation,and recovery.The approach developed here can be adjusted for different definitions of deforestation,forest degradation,and recovery according to research objectives and thus has the potential to be applied to other study areas.

Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning:From prediction to explainability

The ability to characterize rapidly and repeatedly exchangeable potassium(Kex)content in the soil is essential for optimizing remediation of radiocaesium contamination in agriculture.In this paper,we show how this can be now achieved using a Convolutional Neural Network(CNN)model trained on a large Mid-Infrared(MIR)soil spectral library(40,000 samples with Kex determined with 1 M NH4OAc,pH 7),compiled by the National Soil Survey Center of the United States Department of Agriculture.Using Partial Least Squares Regression as a base-line,we found that our implemented CNN leads to a significantly higher prediction performance of Kex when a large amount of data is available(10000),increasing the coefficient of determination from 0.64 to 0.79,and reducing the Mean Absolute Percentage Error from 135%to 31%.Furthermore,in order to provide end-users with required interpretive keys,we implemented the GradientShap algorithm to identify the spectral regions considered important by the model for predicting Kex.Used in the context of the implemented CNN on various Soil Taxonomy Orders,it allowed(i)to relate the important spectral features to domain knowledge and(ii)to demonstrate that including all Soil Taxonomy Orders in CNN-based modeling is beneficial as spectral features learned can be reused across different,sometimes underrepresented orders.