504 research outputs found
Cilia Transport for Micropolar Fluid Through a Uniform Cylindrical Tube
The aim of this work is to study the role that the cilia motion plays in the transport of a micropolar fluid in a uniform cylindrical tube. The transport due to systems of beating cilia is responsible for the transport of bio-fluids in numerous physiological processes. Cylindrical coordinates are used to formulate the system of equations with the suitable boundary conditions governing the flow. Such system is then simplified through considering the long wavelength and low Reynolds number assumptions. Solutions are found for the velocity components, the pressure gradient, and the stream function which include many parameters such as the cilia length parameter, the eccentricity parameter of the elliptical motion and the wave number. The results shows that the pumping machinery functions more efficiently to push ahead micropolar fluid than Newtonian fluid. The obtained results may help to understand more the processes of the transport of bio-fluids in human body
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
Effects of post-anthesis heat stress and nitrogen levels on grain yield and grain growth of wheat ( T. durum and T. aestivum) genotypes
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
Low scale B-L extension of the Standard Model at the LHC
The fact that neutrinos are massive indicates that the Standard Model (SM)
requires extension. We propose a low energy (<TeV) B-L extension of the SM,
which is based on the gauge group SU(3)_C x SU(2)_L x U(1)_Y x U(1)_{B-L}. We
show that this model provides a natural explanation for the presence of three
right-handed neutrinos in addition to an extra gauge boson and a new scalar
Higgs. Therefore, it can lead to very interesting phenomenological implications
different from the SM results which can be tested at the LHC. Also we analyze
the muon anomalous magnetic moment in this class of models. We show that
one-loop with exchange Z' may give dominant new contribution ~ few x 10^{-11}.Comment: 12 page
The potential of the synthetic strigolactone analogue GR24 for the maintenance of photosynthesis and yield in winter wheat under drought: Investigations on the mechanisms of action and delivery modes
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
Evaluation of Software Product Quality Metrics
Computing devices and associated software govern everyday life, and form the
backbone of safety critical systems in banking, healthcare, automotive and
other fields. Increasing system complexity, quickly evolving technologies and
paradigm shifts have kept software quality research at the forefront. Standards
such as ISO's 25010 express it in terms of sub-characteristics such as
maintainability, reliability and security. A significant body of literature
attempts to link these subcharacteristics with software metric values, with the
end goal of creating a metric-based model of software product quality. However,
research also identifies the most important existing barriers. Among them we
mention the diversity of software application types, development platforms and
languages. Additionally, unified definitions to make software metrics truly
language-agnostic do not exist, and would be difficult to implement given
programming language levels of variety. This is compounded by the fact that
many existing studies do not detail their methodology and tooling, which
precludes researchers from creating surveys to enable data analysis on a larger
scale. In our paper, we propose a comprehensive study of metric values in the
context of three complex, open-source applications. We align our methodology
and tooling with that of existing research, and present it in detail in order
to facilitate comparative evaluation. We study metric values during the entire
18-year development history of our target applications, in order to capture the
longitudinal view that we found lacking in existing literature. We identify
metric dependencies and check their consistency across applications and their
versions. At each step, we carry out comparative evaluation with existing
research and present our results.Comment: Published in: Molnar AJ., Neam\c{t}u A., Motogna S. (2020) Evaluation
of Software Product Quality Metrics. In: Damiani E., Spanoudakis G.,
Maciaszek L. (eds) Evaluation of Novel Approaches to Software Engineering.
ENASE 2019. Communications in Computer and Information Science, vol 1172.
Springer, Cham. https://doi.org/10.1007/978-3-030-40223-5_
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
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