Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes

Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies

Drugs designed for a specific target are always found to have multiple effects. Rather than hope that one bullet can be designed to hit only one target, nonlinear interactions across genomic and proteomic networks could be used to design Combinatorial Multi-Component Therapies (CMCT) that are more targeted with fewer side effects. We show here how computational approaches can be used to predict which combinations of drugs would produce the best effects. Using a nonlinear model of how the output effect depends on multiple input drugs, we show that an artificial neural network can accurately predict the effect of all 215 = 32,768 combinations of drug inputs using only the limited data of the output effect of the drugs presented one-at-a-time and pairs-at-a-time

A new interpretation of total column BrO during Arctic spring

Emission of bromine from sea-salt aerosol, frost flowers, ice leads, and snow results in the nearly complete removal of surface ozone during Arctic spring. Regions of enhanced total column BrO observed by satellites have traditionally been associated with these emissions. However, airborne measurements of BrO and O3 within the convective boundary layer (CBL) during the ARCTAS and ARCPAC field campaigns at times bear little relation to enhanced column BrO. We show that the locations of numerous satellite BrO "hotspots" during Arctic spring are consistent with observations of total column ozone and tropopause height, suggesting a stratospheric origin to these regions of elevated BrO. Tropospheric enhancements of BrO large enough to affect the column abundance are also observed, with important contributions originating from above the CBL. Closure of the budget for total column BrO, albeit with significant uncertainty, is achieved by summing observed tropospheric partial columns with calculated stratospheric partial columns provided that natural, short-lived biogenic bromocarbons supply between 5 and 10 ppt of bromine to the Arctic lowermost stratosphere. Proper understanding of bromine and its effects on atmospheric composition requires accurate treatment of geographic variations in column BrO originating from both the stratosphere and troposphere. Copyright 2010 by the American Geophysical Union

A genetic programming based fuzzy regression approach to modelling manufacturing processes

Fuzzy regression has demonstrated its ability to model manufacturing processes in which the processes have fuzziness and the number of experimental data sets for modelling them is limited. However, previous studies only yield fuzzy linear regression based process models in which variables or higher order terms are not addressed. In fact, it is widely recognised that behaviours of manufacturing processes do often carry interactions among variables or higher order terms. In this paper, a genetic programming based fuzzy regression GP-FR, is proposed for modelling manufacturing processes. The proposed method uses the general outcome of GP to construct models the structure of which is based on a tree representation, which could carry interaction and higher order terms. Then, a fuzzy linear regression algorithm is used to estimate the contributions and the fuzziness of each branch of the tree, so as to determine the fuzzy parameters of the genetic programming based fuzzy regression model.To evaluate the effectiveness of the proposed method for process modelling, it was applied to the modelling of a solder paste dispensing process. Results were compared with those based on statistical regression and fuzzy linear regression. It was found that the proposed method can achieve better goodness-of-fitness than the other two methods. Also the prediction accuracy of the model developed based on GP-FR is better than those based on the other two methods

Digital PCR provides sensitive and absolute calibration for high throughput sequencing

<p>Abstract</p> <p>Background</p> <p>Next-generation DNA sequencing on the 454, Solexa, and SOLiD platforms requires absolute calibration of the number of molecules to be sequenced. This requirement has two unfavorable consequences. First, large amounts of sample-typically micrograms-are needed for library preparation, thereby limiting the scope of samples which can be sequenced. For many applications, including metagenomics and the sequencing of ancient, forensic, and clinical samples, the quantity of input DNA can be critically limiting. Second, each library requires a titration sequencing run, thereby increasing the cost and lowering the throughput of sequencing.</p> <p>Results</p> <p>We demonstrate the use of digital PCR to accurately quantify 454 and Solexa sequencing libraries, enabling the preparation of sequencing libraries from nanogram quantities of input material while eliminating costly and time-consuming titration runs of the sequencer. We successfully sequenced low-nanogram scale bacterial and mammalian DNA samples on the 454 FLX and Solexa DNA sequencing platforms. This study is the first to definitively demonstrate the successful sequencing of picogram quantities of input DNA on the 454 platform, reducing the sample requirement more than 1000-fold without pre-amplification and the associated bias and reduction in library depth.</p> <p>Conclusion</p> <p>The digital PCR assay allows absolute quantification of sequencing libraries, eliminates uncertainties associated with the construction and application of standard curves to PCR-based quantification, and with a coefficient of variation close to 10%, is sufficiently precise to enable direct sequencing without titration runs.</p

The Role of Eif6 in Skeletal Muscle Homeostasis Revealed by Endurance Training Co-expression Networks

Regular endurance training improves muscle oxidative capacity and reduces the risk of age-related disorders. Understanding the molecular networks underlying this phenomenon is crucial. Here, by exploiting the power of computational modeling, we show that endurance training induces profound changes in gene regulatory networks linking signaling and selective control of translation to energy metabolism and tissue remodeling. We discovered that knockdown of the mTOR-independent factor Eif6, which we predicted to be a key regulator of this process, affects mitochondrial respiration efficiency, ROS production, and exercise performance. Our work demonstrates the validity of a data-driven approach to understanding muscle homeostasis

Prospective single-arm study of 72 Gy hyperfractionated radiation therapy and combination chemotherapy for anaplastic astrocytomas

<p>Abstract</p> <p>Background</p> <p>Despite intensive multimodal treatment, outcome of patients with malignant glioma remains poor, and a standard dose of radiotherapy for anaplastic astrocytoma has not been defined. In the past RTOG study (83-02), the arm of 72 Gy hyperfractionated radiotherapy (HFRT) for malignant gliomas showed better outcome than the arms of higher doses (76.8 – 81.6 Gy) and the arms of lower doses (48 – 54.4 Gy). The purpose of this study is to verify the efficacy of this protocol.</p> <p>Methods</p> <p>From July 1995, 44 consecutive eligible patients with histologically proven anaplastic astrocytoma were enrolled in this study (HFRT group). The standard regimen in this protocol was post-operative radiotherapy of 72 Gy in 60 fractions (1.2 Gy/fraction, 2 fractions/day) with concurrent chemotherapy (weekly ACNU). The primary endpoint was local control rate (LCR), and the secondary endpoints were overall survival (OS), progression-free survival (PFS) and late toxicity.</p> <p>Results</p> <p>Three-year OS of the HFRT group was 64.8% (95% confidence interval; 48.4–81.3%). Three-year PFS rate and LCR were 64.4% (95%CI: 48.4–80.3%) and 81.6% (95%CI: 69.2–94.8%), respectively.</p> <p>The number of failures at 5 years in the HFRT group were 14 (32%). The number of failures inside the irradiation field was only about half (50%) of all failures. One (2%) of the patients clinically diagnosed as brain necrosis due to radiation therapy.</p> <p>Conclusion</p> <p>The results of this study suggested that 72 Gy HFRT seemed to show favorable outcome for patients with anaplastic astrocytoma with tolerable toxicity.</p

Five mucosal transcripts of interest in ulcerative colitis identified by quantitative real-time PCR: a prospective study

<p>Abstract</p> <p>Background</p> <p>The cause and pathophysiology of ulcerative colitis are both mainly unknown. We have previously used whole-genome microarray technique on biopsies obtained from patients with ulcerative colitis to identifiy 5 changed mucosal transcripts. The aim of this study was to compare mucosal expressions of these five transcripts in ulcerative colitis patients vs. controls, along with the transcript expression in relation to the clinical ulcerative colitis status.</p> <p>Methods</p> <p>Colonic mucosal specimens from rectum and caecum were taken at ambulatory colonoscopy from ulcerative colitis patients (<it>n </it>= 49) with defined inflammatory activity and disease extension, and from controls (<it>n </it>= 67) without inflammatory bowel disease. The five mucosal transcripts aldolase B, elafin, MST-1, simNIPhom and SLC6A14 were analyzed using quantitative real-time PCR.</p> <p>Results</p> <p>Significant transcript differences in the rectal mucosa for all five transcripts were demonstrated in ulcerative colitis patients compared to controls. The grade of transcript expression was related to the clinical disease activity.</p> <p>Conclusion</p> <p>The five gene transcripts were changed in patients with ulcerative colitis, and were related to the disease activity. The known biological function of some of the transcripts may contribute to the inflammatory features and indicate a possible role of microbes in ulcerative colitis. The findings may also contribute to our pathophysiological understanding of ulcerative colitis.</p