Skeletal muscle accounts for about 40% of mammalian body weight, the development of which is a dynamic, complex and precisely regulated process that is critical for meat production. We here described the transcriptome expression profile in 21 goat samples collected at 7 growth stages from fetus to kid, including fetal 45 (F45), 65 (F65), 90 (F90), 120 (F120), and 135 (F135) days, and birth 1 (B1) day and 90 (B90) days kids.  Paraffin sections combined with RNA-seq data of the 7 stages divided the transcriptomic functions of skeletal muscle into 4 states: before F90, F120, F135 and B1, and B90.  And the dynamic expression of all 4 793 differentially expressed genes (DEGs) was identified.  Furthermore, DEGs were clustered by weighted gene correlation network analysis into 4 modules (turquoise, grey, blue and brown) that corresponded to these 4 states.  Functional and pathway analysis indicated that the active genes in the stages before F90 (turquoise) were closely related to skeletal muscle proliferation.  The DEGs in the F120-related module (grey) were found to participate in the regulation of skeletal muscle structure and skeletal muscle development by regulating tRNA.  The brown module (F135 and B1) regulated fatty acid biological processes to maintain the normal development of muscle cells.  The DEGs of B90 high correlation module (blue) were involved the strengthening and power of skeletal muscle through the regulation of actin filaments and tropomyosin.  Our current data thus revealed the internal functional conversion of the goat skeletal muscle in the growth from fetus to kid.  The results provided a theoretical basis for analyzing the involvement of mRNA in skeletal muscle development.

 

To understand the effect of nitrogen (N) fertilizer on rice (Oryza sativa L.) eating and cooking quality (ECQ).  Here, we investigated the ECQ attributes, physicochemical foundation of ECQ, and amylopectin fine structure of two Waxy (Wx) alleles japonica rice cultivars Nanjing 9108 (NJ9108) and Huaidao 5 (HD5) under four N rates (0, 150, 300, and 450 kg ha^–1).  Sensory and pasting properties of the two cultivars varied depending on N rates.  Compared with the control (0 kg ha^–1), the overall eating quality and sensory value were significantly decreased under the N rates of 300 and 450 kg ha^–1.  Further, conventional descriptive analysis showed that the stickiness and retrogradation of cooked rice were significantly decreased.  These results indicated that application of N fertilizer seems to affect the texture of cooked rice, causing it to be less sticky, lowering its retrogradation, and consequently reducing its palatability.  Results from rapid visco analyzer (RVA) revealed that the peak and breakdown viscosities significantly decreased, while the setback viscosity and peak time increased under the N rates of 300 and 450 kg ha^–1.  However, no significant difference was observed when the N rate was 150 kg ha^–1, indicating that less N fertilization can maintain rice ECQ.  As the N rate increasing, protein content increased, whereas apparent amylose content, starch content, and gel consistency almost unchanged.  Interestingly, compared with the control, under N treatments, the percentage of short amylopectin branches in NJ9108 was decreased, but increased in HD5, as controlled by amylopectin synthesis-related genes.  Notably, SSI and BEIIb were down-regulated in NJ9108, whereas BEIIb was up-regulated in HD5.  Thus, the palatability of both rice cultivars was significantly decreased under excessive N fertilization as a consequence of reduced stickiness and retrogradation of the cooked rice, which might have resulted from an elevated protein content and altered amylopectin fine structure.  In addition, amylopectin synthesis appeared to be affected by N fertilizer and the genotype of the rice cultivar.

 

Mango is a commercial crop on Hainan Island, China, that is cultivated to develop the tropical rural economy.  The development of accurate and up-to-date maps of the spatial distribution of mango plantations is necessary for agricultural monitoring and decision management by the local government.  Pixel-based and object-oriented image analysis methods for mapping mango plantations were compared using two machine learning algorithms (support vector machine (SVM) and Random Forest (RF)) based on Chinese high-resolution Gaofen-1 (GF-1) imagery in parts of Hainan Island.  To assess the importance of different features on classification accuracy, a combined layer of four original bands, 32 gray-level co-occurrence (GLCM) texture indices, and 10 vegetation indices were used as input features.  Then five different sets of variables (5, 10, 20, and 30 input variables and all 46 variables) were classified with the two machine learning algorithms at object-based level.  Results of the feature optimization suggested that homogeneity and variance were very important variables for distinguishing mango plantations patches.  The object-based classifiers could significantly improve overall accuracy between 2–7% when compared to pixel-based classifiers.  When there were 5 and 10 input variables, SVM showed higher classification accuracy than RF, and when the input variables exceeded 20, RF showed better performances.  After the accuracy achieved saturation points, there were only slightly classification accuracy improvements along with the numbers of feature increases for both of SVM and RF classifiers.  The results indicated that GF-1 imagery can be successfully applied to mango plantation mapping in tropical regions, which would provide a useful framework for accurate tropical agriculture land management. 

Even today, academics continue to debate the effect of feminization of agricultural labor force on agricultural output.  By considering the dimensions of participation in decision-making and production, this study divides the various agricultural production models into three types: (i) the traditional model of decisions made either jointly by men and women or by men alone while both genders participate in production, (ii) complete feminization of agricultural decision-making and the production labor force, and (iii) feminization of the agricultural production labor force only.  This study investigates the effects of combining or separating decision-making and production in regard to agricultural development in the context of feminization of the agricultural labor force.  Using follow-up data collected from 2004–2008 by the Ministry of Agriculture of China, we built a comprehensive panel data model to test our hypotheses.  Our research shows that in comparison to traditional agricultural households and fully feminized agricultural labor forces, partially feminized production resulted in lower grain yield and technological advancement.  The feminization of agricultural labor does not necessarily have a negative impact on agricultural output, especially since heavy manual labor is being increasingly replaced by agricultural machinery and outsourcing of tasks.  The degree of feminization of the decision-making and production processes should be an important consideration when evaluating the purported negative effects of the feminization of agricultural labor.