Response of lettuce to Cd-enriched water and irrigation frequencies

This pot experiment was an attempt to investigate a broad response of lettuce to different cadmium (Cd) levels of irrigation water (0, 5, 10 and 20 mg l-1) under different irrigation intervals (1, 2 and 4 days). The results showed that increased level of soil Cd through irrigation eventually decreased the yield of lettuce in all cases; however, in some cases yield was increased with lower doses of Cd application. No injury symptoms were observed other than plant height and yield reduction. Shoot dry weight proved to be the most sensitive parameters to the cadmium, especially under water stress conditions. The results also showed that the concentrations of nutrient elements in lettuce shoot were suppressed by water stress. The presence of cadmium in irrigation water did not significantly affect the absorption of nutrient elements by plants except for Fe. Shoot Cd concentration and its uptake decreased with increasing irrigation frequencies and the reverse trend occurred with increasing Cd levels of irrigation water. However, the values were higher than recommended guideline in all conditions. Also, shoot Cd content showed a significant positive correlation with the final accumulated Cd concentration of soil and was expressed by a plateau model under the dry irrigation regime and linear models at other irrigation intervals. Overall, shoot Cd concentration was predicted by using a simple linear regression model regardless of evapotranspiration and transpiration rate of plant.Key words: Cadmium toxicity; chemical composition; irrigation frequency; lettuce

High dimensional biological data retrieval optimization with NoSQL technology.

Background High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data. Results In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB. Conclusions The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data

Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture

Prediction is an attempt to accurately forecast the outcome of a specific situation while using input information obtained from a set of variables that potentially describe the situation. They can be used to project physiological and agronomic processes; regarding this fact, agronomic traits such as yield can be affected by a large number of variables. In this study, we analyzed a large number of physiological and agronomic traits by screening, clustering, and decision tree models to select the most relevant factors for the prospect of accurately increasing maize grain yield. Decision tree models (with nearly the same performance evaluation) were the most useful tools in understanding the underlying relationships in physiological and agronomic features for selecting the most important and relevant traits (sowing date-location, kernel number per ear, maximum water content, kernel weight, and season duration) corresponding to the maize grain yield. In particular, decision tree generated by C&RT algorithm was the best model for yield prediction based on physiological and agronomical traits which can be extensively employed in future breeding programs. No significant differences in the decision tree models were found when feature selection filtering on data were used, but positive feature selection effect observed in clustering models. Finally, the results showed that the proposed model techniques are useful tools for crop physiologists to search through large datasets seeking patterns for the physiological and agronomic factors, and may assist the selection of the most important traits for the individual site and field. In particular, decision tree models are method of choice with the capability of illustrating different pathways of yield increase in breeding programs, governed by their hierarchy structure of feature ranking as well as pattern discovery via various combinations of features.Avat Shekoofa, Yahya Emam, Navid Shekoufa, Mansour Ebrahimi, Esmaeil Ebrahimi

A Protocol for the Secure Linking of Registries for HPV Surveillance

In order to monitor the effectiveness of HPV vaccination in Canada the linkage of multiple data registries may be required. These registries may not always be managed by the same organization and, furthermore, privacy legislation or practices may restrict any data linkages of records that can actually be done among registries. The objective of this study was to develop a secure protocol for linking data from different registries and to allow on-going monitoring of HPV vaccine effectiveness.A secure linking protocol, using commutative hash functions and secure multi-party computation techniques was developed. This protocol allows for the exact matching of records among registries and the computation of statistics on the linked data while meeting five practical requirements to ensure patient confidentiality and privacy. The statistics considered were: odds ratio and its confidence interval, chi-square test, and relative risk and its confidence interval. Additional statistics on contingency tables, such as other measures of association, can be added using the same principles presented. The computation time performance of this protocol was evaluated.The protocol has acceptable computation time and scales linearly with the size of the data set and the size of the contingency table. The worse case computation time for up to 100,000 patients returned by each query and a 16 cell contingency table is less than 4 hours for basic statistics, and the best case is under 3 hours.A computationally practical protocol for the secure linking of data from multiple registries has been demonstrated in the context of HPV vaccine initiative impact assessment. The basic protocol can be generalized to the surveillance of other conditions, diseases, or vaccination programs

Mutation of DNA and RNA sequences through the application of topological spaces

Topology is branch of modern mathematics that plays an important role in applications of biology. The aim of this paper is to study DNA sequence mutations using multisets, relations, metric functions, topology and association indices. Moreover, we use association indices to study the similarity between DNA sequences. These different ways of identifying a mutation help biologists to make a decision. A decision of mutation that depends on metrics between two sequences of genes and the topological structure produced by their relationship is presented

Mental Health Problems and Sociodemographic Correlates in Elderly Medical Inpatients in a University Hospital in Egypt

Background. Depression and cognitive impairment are two common mental and public health problems especially among elderly. In this study, we determined the prevalence of these problems and their associations with sociodemographic factors among hospitalized elderly in Egypt. To achieve this, 200 elderly medical inpatients were included in this cross-sectional study. Methods. Comprehensive geriatric assessment was done for every participant. Sociodemographic variables were assessed by interviews with patients and their family members. Depressive symptoms were screened for by the 15-item Geriatric Depression Scale (GDS), and the presence of depressive symptoms was defined as a GDS score of ≥6. Cognitive impairment was assessed by the Mini-Mental State Examination (MMSE) Scale, and cognitive impairment was defined as a MMSE score of ≤23 out of a total score of 30. Results. The prevalence of both depressive symptoms and cognitive impairment was 72% and 30%, respectively. Significant associations were noticed between each of depressive symptoms and cognitive impairment, and low income and advancing age (), respectively. Other associations were insignificant. Conclusions. The findings of this study may be an alarm for health authorities and staffs involved in elderly care to increase their awareness of social and mental health problems among the elderly