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Forest degradation has been considered as one of the main causes of climate change in recent years. The knowledge of estimating degraded forest areas without the application of remote sensing tools can be useful in finding solutions to resolve degradation problems through appropriate restoration methods. Using the existing knowledge through literature review and field-based primary information, we generated new knowledge by combining the information obtained from multi-criteria decision analyses with an analytic hierarchy process, and this was then used to estimate degraded forest area. Estimation involves determining forest degradation index (FDI) and degradation threshold. Continuous inventory data of permanent sample plots collected from degraded forests, consisting of various forest types divided by dominant tree species in the Guangdong province and Tibet autonomous region of China, were used for the purposes. We identified four different forest degradation levels through the determination and comprehensive evaluation of FDI. The degraded forest area with broad-leaved species as dominant tree species in the Guangdong province was estimated to be 83.3% of a total forest area of 24,037 km2. In the same province, the degraded forest area with eucalyptus as a dominant tree species was 59.5% of a total forest area of 18,665 km2. In the Tibet autonomous region, the degraded forest area with spruce as a dominant tree species was 99.1% of a total forest area of 17,614 km2, and with fir as a dominant tree species, the degraded area was 98.4% of a forest area of 12,103 km2. A sampling accuracy of forest areas with national forest inventory was about 95% in both provinces. Our study concludes that the FDI method used has a certain scientific rationality in estimating degraded forest area. The forest provides a variety of tangible and intangible goods and services for humans. Therefore, forest management should focus on the improvement of its overall productivity, which is only possible with improving forest site quality. One of the important steps to improve the quality of a forest site is to resolve its degradation issues. The presented method in this article will be useful in finding the solutions to forest degradation problems. This method, which does not need any remote sensing tool, is simple and can be easily applied for estimating any degraded forest area and developing effective forest restoration plans.

The Empoasca onukii (Hemiptera: Cicadellidae) female lays its eggs inside the epidermis of the tea plant shoots. This has led to speculation that shoot harvesting could represent a method of egg removal. To verify the validity of this hypothesis, we sought to determine which part of the shoot was used for the oviposition and how the value of the harvested shoot affects the cost of the egg removal. In this study, four tea cultivars were chosen to examine the preferences for the site of oviposition. In addition, a mathematical model was used to describe the correlation between the economic value of the selected shoot and eggs laid within the shoot. Our study revealed that the pest preferred the 3rd and 4th leaf order intervals of the shoot as the oviposition sites, and the oviposition preferences was dependent on the leaf order interval class across all tea cultivars. In addition, a significant negative exponential relationship was found between the economic value of the selected shoot and the percentage of the eggs laid within the shoot, indicating that egg removal through shoot harvesting was limited. The findings of this study could be used to better understand the role of shoot harvesting in egg removal and would provide new insights into the understanding of the incidence of this pest.

Relationship of total height and diameter at breast height (hereafter diameter) of the trees is generally nonlinear, and therefore has complex characteristics, which can be accurately described by the height-diameter model developed using the back propagation (BP) neural network approach. The multiple hidden layered-BP neural network has several hidden layers and neurons, and is therefore considered more appropriate modeling approach compared to the single hidden layered-BP neural network approach. However, the former approach is not widely applied for tree height prediction due to absence of the effective optimization method, but it can be done using the BP neural network modeling approach. The poplar (Populus spp. L.) plantation data acquired from Guangdong province of China were used for evaluating the BP neural network modeling approach and compared its results with those obtained from the traditional regression modeling and mixed-effects modeling approaches. We determined the best BP neural network structure with two hidden layers and five neurons in each layer, and logistic sigmoid transfer functions. Relative to the Mitscherlich height-diameter model that had the highest fitting precision among the six traditional height-diameter models evaluated, coefficient of determination (R2) of the neural network height-diameter model increased by 10.3%, root mean squares error (RMSE) and mean absolute error (MAE) decreased by 12% and 13.5%, respectively. The BP neural network height-diameter model also appeared more accurate than the mixed-effects height-diameter model. Our study proposes the method of determining the optimal numbers of hidden layers, neurons of each layer, and transfer functions in the BP neural network structure. This method can be useful for other modeling studies of similar or different types, such as tree crown modeling, height, and diameter increments modeling, and so on.

Rapidly advancing airborne laser scanning technology has become greatly useful to estimate tree- and stand-level variables at a large scale using high spatial resolution data. Compared with that of ground measurements, the accuracy of the inferred information of diameter at breast height (DBH) from a remotely sensed database and the models developed with traditional regression approaches (e.g., ordinary least square regression) may not be sufficient. Thus, this regression approach is no longer appropriate to develop accurate models and predict DBH from remotely sensed-related variables because DBH is subject to the random effects of forest stands. This study developed a generalized nonlinear mixed-effects DBH estimation model from remotely sensed imagery data. The light detection and ranging (LiDAR)-derived stand canopy density, crown projection area, and tree height were used as predictors in the DBH estimation model. These variables can be more readily measured over an extensive forest area with higher accuracy compared to the conventional field-based methods. The airborne LiDAR data for a total of 402 Picea crassifolia Kom trees on a sample plot that were divided into 16 sub-sample plots and located in the most important distribution region of western China were used. The leave-one sub-sample plot-out cross-validation method was applied to evaluate the model’s prediction accuracy. The results indicated that the random effects of the sub-sample plot on the prediction of DBH were large and their inclusion into the DBH model significantly improved the prediction accuracy. The prediction accuracy of the proposed model at the mean (M) response was also substantially improved relative to the accuracy obtained from the base model. Among several tree selection alternatives evaluated, a sample size of the two largest trees per sub-sample plot used in estimating the random effects showed a significantly higher accuracy compared to other sampling alternatives. This sample size would balance both the measurement cost and potential prediction errors. The nonlinear mixed-effects DBH estimation model at the M response can also be applied if obtaining the estimates of individual tree DBH with a relatively lower cost, and a lower prediction accuracy was the purpose of the study.

Prostate cancer (PCa) is the second most frequently diagnosed cancer and the fifth leading cause of death from cancer in men worldwide. Increased understanding of the prostate cancer metastasis mechanisms will help identify more efficient intervention strategies to prevent or treat this deadly disease in the future. To identify the candidate proteins that contribute to metastasis of PCa, isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis was performed to explore differentially expressed proteins between two homologous human prostate cancer cell lines including highly-metastatic PC-3M-1E8 cell line and poorly-metastatic PC-3M-2B4 cell line. Here, a total of 58 proteins were identified to be significantly differentially expressed between PC-3M-1E8 and PC-3M-2B4 cells, which were further verified using real-time quantitative PCR and western blot analysis. The bioinformatic analysis suggested that the differentially expressed proteins, like MMP1 and FHL1, may contribute to the higher metastatic ability of PC-3M-1E8 cells than PC-3M-2B4 cells. In addition, functional analyses proved MMP1's positive effect on the higher metastatic ability of PC-3M-1E8 cells than PC-3M-2B4 cells. These findings provided a unique resource to specifically reveal the complex molecular regulatory mechanisms underlying the progression of prostate cancer from poorly-metastatic to highly-metastatic stage.

Background Litchi stink-bug, Tessaratoma papillosa Drury (Hemiptera: Tessaratomidae), is one of the most widespread and destructive pest species on Litchi chinensis Sonn and Dimocarpus longan Lour in Southern China. Inappropriate use of chemical pesticides has resulted in serious environmental problems and food pollution. Generating an improved Integrated Pest Management (IPM) strategy for litchi stink-bug in orchard farming requires development of an effective biological control agent. Entomopathogenic fungi are regarded as a vital ecological factor in the suppression of pest populations under field conditions. With few effective fungi and pathogenic strains available to control litchi stink-bug, exploration of natural resources for promising entomopathogenic fungi is warranted. Methods & Results In this study, two pathogenic fungi were isolated from cadavers of adult T. papillosa. They were identified as Paecilomyces lilacinus and Beauveria bassiana by morphological identification and rDNA-ITS homogeneous analysis. Infection of T. papillosa with B. bassiana and P. lilacinus occurred initially from the antennae, metameres, and inter-segmental membranes. Biological tests showed that the two entomopathogenic fungi induced high mortality in 2nd and 5th instar nymphs of T. papillosa. B. bassiana was highly virulent on 2nd instar nymphs of T. papillosa, with values for cadaver rate, LC50 and LT50 of 88.89%, 1.92 × 107 conidia/mL and 4.34 days respectively. Discussion This study provides two valuable entomopathogenic fungi from T. papillosa. This finding suggests that the highly virulent P. lilacinus and B. bassiana play an important role in the biocontrol of T. papillosa in China. These pathogenic fungi had no pollution or residue risk, and could provide an alternative option for IPM of litchi stink-bug.

The rational design of high-performance flexible pressure sensors with both high sensitivity and wide linear range attracts great attention because of their potential applications in wearable electronics and human-machine interfaces. Here, polyaniline nanofiber wrapped nonwoven fabric was used as the active material to construct high performance, flexible, all fabric pressure sensors with a bottom interdigitated textile electrode. Due to the unique hierarchical structures, large surface roughness of the polyaniline coated fabric and high conductivity of the interdigitated textile electrodes, the obtained pressure sensor shows superior performance, including ultrahigh sensitivity of 46.48 kPa−1 in a wide linear range (

An isocratic, sensitive and stability-indicating high performance liquid chromatographic (HPLC) method for separation and determination of the related substances of micafungin sodium was developed. The chromatographic separation was achieved on Agilent Zorbax SB-C18 column (250 × 4.6 mm, 5 μm). Forced degradation study confirmed that the newly developed method was specific and selective to the degradation products. The performance of the method was validated according to the present ICH guidelines for specificity, linearity, accuracy, precision and robustness. Regression analysis showed correlation coefficient value greater than 0.999 for micafungin sodium and its six impurities. Limit of detection of impurities was in the range of 0.006%–0.013% indicating the high sensitivity of the newly developed method. Accuracy of the method was established based on the recovery obtained between 98.2% and 102.0% for all impurities. RSD obtained for the repeatability and intermediate precision experiments, was less than 1.0%. The method was successfully applied to quantify related substances of micafungin sodium in bulk drugs.

BACKGROUND: Destruxin A (DA) is a microbial insecticide with potent bioactivity against Spodoptera litura larvae. A previous proteomic analysis of S. litura (SL-1) cells exposed to DA showed the abnormal expression of wing disc-like protein of S. litura (SLAWD). To further understand the effect of DA on SLAWD expression, a functional study was carried out. PRINCIPAL FINDINGS: The SLAWD gene (SLAWD) was cloned and an open reading frame of 537 bp encoding a polypeptide of 178 amino acids was detected. Real-time fluorescence quantitative PCR (qRT-PCR) suggested that SLAWD is expressed in all developmental stages of S. litura, but expression was highest during the pupal and adult stages. RNAi knockdown of SLAWD expression in 6th-stage larvae was achieved by the microinjection of a specific double-stranded RNA (dsRNA). The results showed a significant decrease in SLAWD mRNA expression levels between the prepupal and adult stages. Interestingly, SLAWD expression was similarly down-regulated by treating 6th-stage larvae with DA. Growth- and development-related statistics confirmed the observed abnormalities in S. litura development after either RNAi or DA treatment. CONCLUSIONS: SLAWD appears to have a biosynthetic function in the pupal and adult stages of S. litura. The toxic effect of DA on S. litura development is due the negative effect of DA on SLAWD gene expression.