Crop residue return sustains global soil ecological stoichiometry balance.

Although soil ecological stoichiometry is constrained in natural ecosystems, its responses to anthropogenic perturbations are largely unknown. Inputs of inorganic fertilizer and crop residue are key cropland anthropogenic managements, with potential to alter their soil ecological stoichiometry. We conducted a global synthesis of 682 data pairs to quantify the responses of soil carbon (C), nitrogen (N), and phosphorus (P) and grain yields to combined inputs of crop residue plus inorganic fertilizer compared with only inorganic fertilizer application. Crop residue inputs enhance soil C (10.5%-12%), N (7.63%-9.2%), and P (2.62%-5.13%) contents, with an increase in C:N (2.51%-3.42%) and C:P (7.27%-8.00%) ratios, and grain yields (6.12%-8.64%), indicating that crop residue alleviated soil C limitation caused by inorganic fertilizer inputs alone and was able to sustain balanced stoichiometry. Moreover, the increase in soil C and C:N(P) ratio reached saturation in ~13-16 years after crop residue return, while grain yield increase trend discontinued. Furthermore, we identified that the increased C, N, and P contents and C:N(P) ratios were regulated by the initial pH and C content, and the increase in grain yield was not only related to soil properties, but also negatively related to the amount of inorganic N fertilizer input to a greater extent. Given that crop residual improvement varies with soil properties and N input levels, we propose a predictive model to preliminary evaluate the potential for crop residual improvement. Particularly, we suggest that part of the global budget should be used to subsidize crop residue input management strategies, achieving to a win-win situation for agricultural production, ecological protection, and climate change mitigation.

[Correlation of cyclooxygenase 2 expression with microlymphatic density and its clinical significance].

OBJECTIVE: To study the expression of cyclooxygenase-2 (COX-2) in esophageal carcinoma and its correlation with microlymphatic density (MLD), and to investigate the clinicopathological and prognostic significance of COX-2 and MLD in patients with esophageal cancer. METHODS: COX-2 expression and MLD were detected by immunohistochemistry in 100 cases of esophageal squamous cell carcinoma. Follow up was available in 76 patients. Multivariable Cox regression was used to analyze the association between the laboratory indices and overall survival of patients with esophageal carcinoma. RESULTS: COX-2 expression was present in 73% of patients. MLD in patients with high COX-2 expression (99.71±39.62) was significantly higher than that in those with low or no COX-2 expression (80.22±30.36) (P<0.05). No correlations were observed between the over expression of COX-2 and clinicopathologic parameters including tumor size and lymphatic metastasis (P<0.05). However, MLD was associated with lymphatic metastasis and the depth of invasion (P<0.05, P<0.01). In the 76 patients with followed up, the median survival was 25.5 months. Cox regression showed that the COX-2 expression, histological grade of the tumor and MLD were risk factors of overall survival of esophageal carcinoma. CONCLUSIONS: COX-2 may contribute to the lymphangiogenesis in the tumor. COX-2 may be a new target point for the treatment of esophageal carcinoma. COX-2 expression and microlymphatic vessel density are of significant prognostic value for esophageal carcinoma.

Toward automatic behavioral screen: a computational model for analyzing Caenorhabditis elegans locomotion.

Genetic screen has been facilitating molecular geneticists to analyze mutants that produce certain phenotypes. However traditional methods for behavioral phenotype screen of mutant Caenorhabditis elegans rely on human observers and therefore are subjective and imprecise. This work dedicates a model to quantify and analyze the worm behavior using automatically-tracking and time-coded images. We have delved into the following questions: (1) how to achieve simplified worm-shape representation, (2) how to describe worm locomotion, (3) how to obtain frequent locomotion patterns and then representative behavioral patterns, and (4) how to discover interesting behavioral actions within a representative behavioral pattern. Since the methodologies focus on rigorous image-based behavioral screening and phenotyping, the proposed methods should be trustworthy for behavior analysis of tiny organisms based on their microscopic video frames.