Alleviating aluminium toxicity on an acid sulphate soils in Peninsular Malaysia with application of calcium silicate

In response to human population increase, the utilization of acid sulfate soils for rice cultivation is one option for increasing production. The main problems associated with such soils are their low pH values and their associated high content of exchangeable Al, which could be detrimental to crop growth. The application of soil amendments is one approach for mitigating this problem, and calcium silicate is an alternative soil amendment that could be used. Therefore, the main objective of this study was to ameliorate soil acidity in rice-cropped soil. The secondary objective was to study the effects of calcium silicate amendment on soil acidity, exchangeable Al, exchangeable Ca, and Si content. The soil was treated with 0, 1, 2, and 3 Mg ha−1 of calcium silicate under submerged conditions and the soil treatments were sampled every 30 days throughout an incubation period of 120 days. Application of calcium silicate induced a positive effect on soil pH and exchangeable Al; soil pH increased from 2.9 (initial) to 3.5, while exchangeable Al was reduced from 4.26 (initial) to 0.82 cmolc kg−1. Furthermore, the exchangeable Ca and Si contents increased from 1.68 (initial) to 4.94 cmolc kg−1 and from 21.21 (initial) to 81.71 mg kg−1, respectively. Therefore, it was noted that calcium silicate was effective at alleviating Al toxicity in acid sulfate, rice-cropped soil, yielding values below the critical level of 2 cmolc kg−1. In addition, application of calcium silicate showed an ameliorative effect as it increased soil pH and supplied substantial amounts of Ca and Si

Allelotypes of lung adenocarcinomas featuring ALK fusion demonstrate fewer onco- and suppressor gene changes

BACKGROUND: A subset of lung adenocarcinomas harboring an EML4-ALK fusion gene resulting in dominant oncogenic activity has emerged as a target for specific therapy. EML4-ALK fusion confers a characteristic histology and is detected more frequently in never or light smokers and younger patients. METHODS: To gain insights into etiology and carcinogenic mechanisms we conducted analyses to compare allelotypes of 35 ALK fusion-positive and 95 -negative tumours using single nucleotide polymorphism (SNP) arrays and especially designed software which enabled precise global genomic profiling. RESULTS: Overall aberration numbers (gains + losses) of chromosomal alterations were 8.42 and 9.56 in tumours with and without ALK fusion, respectively, the difference not being statistically significant, although patterns of gain and loss were distinct. Interestingly, among selected genomic regions, oncogene-related examples such as 1p34.3(MYCL1), 7q11.2(EGFR), 7p21.1, 8q24.21(MYC), 16p13.3, 17q12(ERBB2) and 17q25.1 showed significantly less gain. Also, changes in tumour suppressor gene-related regions, such as 9p21.3 (CDKN2A) 9p23-24.1 (PTPRD), 13q14.2 (RB1), were significantly fewer in tumours with ALK fusion. CONCLUSION: Global genomic comparison with SNP arrays showed tumours with ALK fusion to have fewer alterations in oncogenes and suppressor genes despite a similar overall aberration frequency, suggesting very strong oncogenic potency of ALK activation by gene fusion

Agricultural IT Strategy and R&D Projects in Japan

The e-Japan strateg

Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine Learning

Seedling vigor in tomatoes determines the quality and growth of fruits and total plant productivity. It is well known that the salient effects of environmental stresses appear on the internode length; the length between adjoining main stem node (henceforth called node). In this study, we develop a method for internode length estimation using image processing technology. The proposed method consists of three steps: node detection, node order estimation, and internode length estimation. This method has two main advantages: (i) as it uses machine learning approaches for node detection, it does not require adjustment of threshold values even though seedlings are imaged under varying timings and lighting conditions with complex backgrounds; and (ii) as it uses affinity propagation for node order estimation, it can be applied to seedlings with different numbers of nodes without prior provision of the node number as a parameter. Our node detection results show that the proposed method can detect 72% of the 358 nodes in time-series imaging of three seedlings (recall = 0.72, precision = 0.78). In particular, the application of a general object recognition approach, Bag of Visual Words (BoVWs), enabled the elimination of many false positives on leaves occurring in the image segmentation based on pixel color, significantly improving the precision. The internode length estimation results had a relative error of below 15.4%. These results demonstrate that our method has the ability to evaluate the vigor of tomato seedlings quickly and accurately