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The head–discharge relationship of an overflow weir is a prerequisite for flow measurement. Conventionally, it is determined by regression methods. With machine learning techniques, data-driven modelling becomes an alternative. However, a standalone model may be inadequate to generate satisfactory results, particularly for a complex system. With the intention of improving the performance of standard gene expression programming (GEP), a hybrid evolutionary scheme is proposed, which is coupled with grey system theory and probabilistic technique. As a gene filter, grey relational analysis (GRA) eliminates noise and simulated annealing (SA) reduces overfitting by optimising the gene weights. The proposed GEP-based model was developed and validated using experimental data of a submerged pivot weir. Compared with standalone GEP, the GRA–GEP–SA model was found to generate more accurate results. Its coefficients of determination and correlation were improved by 3.6% and 1.7%, respectively. The root mean square error was lowered by 24.8%, which is significant. The number of datasets with an error of less than 10% and 20% was increased by 15% and 12%, respectively. The proposed approach outperforms classic genetic programming and shows a comparative error level with the empirical formula. The hybrid procedure also provides a reference for applications in other hydraulic issues.

Orientador: Lilian Maria de Castilho Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas Resumo: Introdução: Os genes híbridos são responsáveis pela formação de variantes Rh comuns em pacientes portadores de anemia falciforme (AF). No entanto, sua detecção muitas vezes não é possível por protocolos moleculares convencionais. No presente estudo, genes RH híbridos foram investigados usando o método Quantitative Multiplex Polymerase Chain Reaction of Short Fluorescent Fragments (QMPSF), um protocolo molecular que quantifica o número de cópias de exons dos genes RHD e RHCE. Além disso, exploramos informações relevantes adicionais obtidas com a utilização de QMPSF, como o reconhecimento de variantes RHCE e a determinação da zigosidade RHD. Método: Três grupos foram selecionados para o estudo: pacientes com AF, doadores autodeclarados afrodescendentes (ADA) e doadores fenotipados como RhD-negativo (DN). Os genes híbridos RHD e RHCE foram investigados pelo método QMPSF, além disso foi utilizado o ensaio de PCR multiplex em tempo real para confirmar o número de cópias do gene RHD. A clonagem foi realizada para caracterizar um novo alelo encontrado, no qual um paciente com genótipo RHCE*cE/RHCE*ce apresentou a variação de um único nucleotídeo (SNV) c.916A>G. A densidade antigênica do antígeno RhD foi investigada por citometria de fluxo e a fenotipagem RhCE foi realizada em microplaca e gel teste. Resultados: Foram analisados um total de 507 amostras, a frequência dos alelos RH híbridos foi de 20.08% em pacientes com AF, 18,22% nos doadores ADA e 3.67% em doadores D-negativos. Os grupos AF e ADA apresentaram maior frequência de alelos híbridos, mais comumente envolvendo o exon 8, onde encontramos associação com o SNV c.733C>G, polimorfismo comum observado em indivíduos afrodescendentes com variantes RHCE. Curiosamente, dois pacientes com AF apresentaram três cópias do gene RHD, confirmado por PCR em tempo real, mas não foi observado aumento na expressão do antígeno D nesses pacientes. Além disso, o método QMPSF proporcionou a investigação de 144 variantes RHCE, a determinação da zigosidade RHD e a identificação de dois novos alelos. Conclusão: QMPSF mostrou-se relevante para identificar alelos híbridos envolvidos em fenótipos Rh alterados em doadores de sangue e pacientes com AF. A associação encontrada do alelo híbrido RHCE-D(8)-CE com c.733C>G indica que este alelo híbrido pode ser utilizado como marcador para detecção das variantes RHCE mais frequentes encontradas em pacientes com AF Abstract: Background: Hybrid genes are responsible for Rh variants formation and common in patients with sickle cell disease (SCD). However, their detection is often not possible by conventional molecular protocols. In the present study, hybrid genes were investigated by using the Quantitative Multiplex Polymerase chain reaction of Short Fluorescent Fragments (QMPSF), a molecular protocol that quantifies the copy number of RHD and RHCE exons. In addition, we explored additional relevant information obtained with QMPSF, such as recognition of variant RHCE and RHD zygosity. Method: Three groups were selected for the study: patients with SCD, self-declared African descent donors (SDA) and D-negative donors. RHD and RHCE hybrids genes were investigated by the QMPSF method. Real-time multiplex PCR assay was used to confirm the copy number of the RHD. Cloning was performed for investigating of allele. Relative RhD antigen density was investigated by flow cytometry and RhCE phenotyping was performed in microplate and gel test. Results: In a total of 507 samples analyzed, hybrid allele frequencies were 20.08% in patients with SCD, 18,22% in individuals from SDA group and 3.67% of D-negative donors. The SCD and SDA groups had a higher frequency of hybrid alleles, most commonly involving exon 8, which we found an association with c.733C>G, a common polymorphism observed in Afro-descendent individuals. Of note, two patients with SCD were shown to carry three gene copies, as confirmed by quantitative PCR. No increase in D expression was observed in these patients. Additionally, the QMPSF guided the investigation of 144 RHCE variants and RHD zygosity and two novel alleles were identified. Conclusion: The QMPSF was shown to be relevant to identify hybrid alleles involved in altered Rh phenotypes in African Brazilian donors and patients with SCD. The association of the hybrid RHCE-D(8)-CE allele with c.733C>G allow this hybrid allele may be used as a marker to detection of the most frequent variants found in patients with SCD Doutorado Clínica Médica Doutora em Ciências

The simultaneous impact of excavation-induced stress concentration and mining disturbances on deep underground mines/tunnels can result in severe and catastrophic failure like strain bursting. In this regard, the proper measurement of proneness to different rock failure mechanisms has great importance in terms of safety and economics. This study proposes a practical hybrid gene expression programming-based logistic regression (GEP-LR) model, as a multi-class classifier, to detect the failure mechanism (i.e. squeezing, slabbing and strain burst) in hard rock based on four intact rock properties. Three non-linear binary models are developed to predict the occurrence/non-occurrence of each failure mechanism. The logistic regression technique is linked to the developed GEP models to measure the occurrence probability of each failure mechanism. Finally, the failure mechanism that has the maximum probability of occurrence is selected as the predicted output. The performance analysis of the developed model shows that it is efficiently capable of detecting failure mechanisms with high accuracy. The failure mechanism detection models are presented in MATLAB codes to be easily used in practice by engineers/researchers as an initial guide for failure/stability analysis of underground openings. Finally, the validity of the proposed model is further evaluated by new datasets compiled from different studies.

Advances in bacterial and fungal genome mining uncover a plethora of cryptic secondary metabolite biosynthetic gene clusters. Guided by the genome information, targeted transcriptional derepression could be employed to determine the product of a cryptic gene cluster and to explore its biological rol...

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine (IG-SVM) in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYL9, and GUCA2B.

The gene expression dataset generated by DNA microarray technology contains expression profiles of huge quantities of genes for very small samples. Among these genes, a very small number of genes are informative for cancer sample identification and classification. Informative genes finding is an essential task of microarray gene expression data analysis. Here, a new hybrid gene selection-sample classification model (NHGSSC) is proposed for selection of relevant genes and classification of cancer samples. The NHGSSC performs two tasks-gene selection and sample classification. For gene selection, a new hybrid single filter and α-depth limited best first search based single wrapper method (SFα-BFSSW) is proposed. From these subsets, highly informative genes are selected by counting frequency of occurrence (FO) of every gene. Then SFα-BFSSW method-based ensemble classifier (SFα-BFSSWEC) is built by combining the classifiers created for the selected gene subsets. Experimental results demonstrate the superiority of the NHGSSC to other existing models.

Motivation Metagenomic and metatranscriptomic analyses can provide an abundance of information related to microbial communities. However, straightforward analysis of this data does not provide optimal results, with a required integration of data types being needed to thoroughly investigate these microbiomes and their environmental interactions. Results Here, we present MetaQUBIC, an integrated biclustering-based computational pipeline for gene module detection that integrates both metagenomic and metatranscriptomic data. Additionally, we used this pipeline to investigate 735 paired DNA and RNA human gut microbiome samples, resulting in a comprehensive hybrid gene expression matrix of 2.3 million cross-species genes in the 735 human fecal samples and 155 functional enriched gene modules. We believe both the MetaQUBIC pipeline and the generated comprehensive human gut hybrid expression matrix will facilitate further investigations into multiple levels of microbiome studies. Availability and implementation The package is freely available at https://github.com/OSU-BMBL/metaqubic. Supplementary information Supplementary data are available at Bioinformatics online.

Effective prediction of pavement performance is essential for transportation agencies to appropriately strategize maintenance, rehabilitation, and reconstruction of roads. One of the primary performance indicators is the international roughness index (IRI) which represents the pavement roughness. Correlating the pavement roughness to other performance measures has been under continuous development in the past decade. However, the drawback of existing correlations is that most of them are not practical yet reliable for prediction of roughness. In this study a novel approach was developed to predict the IRI, utilizing two data sets extracted from long term pavement performance (LTPP) database. The proposed methodology included the application of a hybrid technique which combines the gene expression programming (GEP) and artificial neural network (ANN). The developed algorithm showed reasonable performance for prediction of IRI using traffic parameters and structural properties of pavement. Furthermore, estimation of present IRI from historical data was evaluated through another set of LTPP data. The second prediction model also depicted a reasonable performance power. Further extension of the proposed models including different pavement types, traffic and environmental conditions would be desirable in future studies.

In cancer research, it is important to classify tissue samples in different classes (normal, tumour, tumour type, etc.). Gene selection purpose is to find the minimum number of genes that can predict sample classes with efficacy. This work is focused on the gene selection problem by introducing a new hybrid method. This new method combines a first step of gene filtering with an optimization algorithm in a second step to find the best subset of genes for the classification task. The first step uses the Analytic Hierarchy Process, in which five ranking methods are used to select the most relevant genes in the dataset. In this way, this gene filtering reduces the number of genes to manage. Regarding the second step, the gene selection can be divided into two objectives: minimizing the number of selected genes and maximizing the classification accuracy. Therefore, we have used a multi-objective optimization approach. More exactly, an Artificial Bee Colony based on Dominance (ABCD) algorithm has been proposed for this second step. Our approach has been tested with eleven real cancer datasets and the results have been compared with several multi-objective methods proposed in the scientific literature. Our results show a high accuracy in the classification task with a small subset of genes. Also, to prove the relevance of our proposal, a biological analysis has been developed on the genes selected. The conclusions of this biological analysis are positive, because the selected genes are closely linked to the cancer dataset they belong to.

In this review, we summarize the rational design and versatile application of organic/inorganic hybrid gene carriers as multifunctional delivery systems. Organic/inorganic nanohybrids with both organic and inorganic components in one nanoparticle have attracted intense attention because of their favorable properties. Particularly, nanohybrids comprising cationic polymers and inorganic nanoparticles are considered to be promising candidates as multifunctional gene delivery systems. In this review, we begin with an introduction of gene delivery and gene carriers to demonstrate the incentive for fabricating nanohybrids as multifunctional carriers. Next, the construction strategies and morphology effects of organic/inorganic hybrid gene carriers are summarized and discussed. Both sections provide valuable information for the design and synthesis of hybrid gene carriers with superior properties. Finally, an overview is provided of the application of nanohybrids as multifunctional gene carriers. Diverse therapies and versatile imaging-guided therapies have been achieved via the rational design of nanohybrids. In addition to a simple combination of the functions of organic and inorganic components, the performances arising from the synergistic effects of both components are considered to be more intriguing. In summary, this review might offer guidance for the understanding of organic/inorganic nanohybrids as multifunctional gene delivery systems.

A protein–polymer hybrid gene carrier HEP was successfully constructed based on thermophilic histone and polyethylenimine. The carrier exhibited low cytotoxicity and a high transfection efficiency due to the synergistic effects between the two components, which makes it a potential vehicle in tumor gene therapy.

During the period from June to September 2009, the effect of the thermal stress of 16 suckling sows of the Segher hybrid gene line in Tabasco Mexico was evaluated using indirect indicators of respiratory rate and rectal temperature variations. Four periods of the day (nocturnal, daytime and dusk) were evaluated during four stages of the lactation period (1, 7, 14, 21 day). There was a significant increase (P≤0.5) in the respiratory rate during the hottest periods of the day (day and night) and no significant difference was observed between the days evaluated in the lactation period (P≥0.5). It was concluded that the sows of the segher hybrid gene line are adapted to the tropical conditions of Mexico.

This paper present intrusion detection on hybrid gene expression programming (ID-HGEP), which combined theory of attribution reduction in rough set to improve precision and efficiency on intrusion detection. Through extensive experiments on high dimension data sets, it is shown that ID-HGEP is apparently more advantageous in terms of convergence speed, intrusion detection efficiency and average consumptive time in contrast with traditional intrusion detection algorithm on GEP.

tRNAs are mostly transcribed from un-fragmented genes, but occasionally also from split genes, with separated 5' and 3' halves. A reanalysis of the existing data on Staphylothermus marinus and Staphylothermus hellenicus hints of a novel hybrid gene that encodes both an un-fragmented and a 5'-split-half together in one. The corresponding 3' complement-gene is located elsewhere on the genome. As un-fragmented, the hybrid gene transcribes to tRNA(lys)(TTT). But as 5'-half, it trans-splices with its 3'-complement-half to tRNA(lys)(CTT), the tRNA missed so far. This hybrid of the split and the un-fragmented in one suggests a deeper synergy between the two, and hints of co-evolution. Furthermore, in a subtle contrast to the widely held idea of conservation of 3'-half, it is precisely the 3'-half that varies in these two tRNAs; the 5'-half remains conserved.