Self-growing neural network architecture using crisp and fuzzy entropy

The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed

SOTXTSTREAM: Density-based self-organizing clustering of text streams

A streaming data clustering algorithm is presented building upon the density-based selforganizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clustering algorithms use a two-phase clustering approach. In the first phase, a micro-clustering solution is maintained online, while in the second phase, the micro-clustering solution is clustered offline to produce a macro solution. By performing self-organization techniques on micro-clusters in the online phase, SOSTREAM is able to maintain a macro clustering solution in a single phase. Leveraging concepts from SOSTREAM, a new density-based self-organizing text stream clustering algorithm, SOTXTSTREAM, is presented that addresses several shortcomings of SOSTREAM. Gains in clustering performance of this new algorithm are demonstrated on several real-world text stream datasets

Fuzzy sets predict flexural strength and density of silicon nitride ceramics

In this work, we utilize fuzzy sets theory to evaluate and make predictions of flexural strength and density of NASA 6Y silicon nitride ceramic. Processing variables of milling time, sintering time, and sintering nitrogen pressure are used as an input to the fuzzy system. Flexural strength and density are the output parameters of the system. Data from 273 Si3N4 modulus of rupture bars tested at room temperature and 135 bars tested at 1370 C are used in this study. Generalized mean operator and Hamming distance are utilized to build the fuzzy predictive model. The maximum test error for density does not exceed 3.3 percent, and for flexural strength 7.1 percent, as compared with the errors of 1.72 percent and 11.34 percent obtained by using neural networks, respectively. These results demonstrate that fuzzy sets theory can be incorporated into the process of designing materials, such as ceramics, especially for assessing more complex relationships between the processing variables and parameters, like strength, which are governed by randomness of manufacturing processes

Radial basis function network learns ceramic processing and predicts related strength and density

Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction.</p> <p>Results</p> <p>SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors.</p> <p>Conclusion</p> <p>The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.</p

Fast and reliable HPLC method for determination of cefuroxime in human serum : application to optimization of dosing regimen in patients with lower respiratory tract infection

A rapid and inexpensive high-performance liquid chromatography method with UV detection for determination of cefuroxime (CFU) in small human serum samples was developed and validated. In this method, serum samples were spiked with an internal standard and proteins were precipitated by 0.4 M perchloric acid. Separation was carried out on an RP-18 column with a mobile phase composed of 20 mM potassium dihydrogen phosphate buffer and methanol (85 : 15; v/v), pH 4.5. In order to assess the usefulness of newly developed method in CFU dosage design, CFU concentrations in serum from 6 patients with lower respiratory tract infections ranging in age from 43 to 91 years were determined. The antibiotic was administered intravenously at a dose of 1500 mg every 8 hours for 10-14 days. Pharmacokinetic analysis and simulations were performed using Phoenix WinNonlin. Dosage optimization was based on pharmacokinetic pharmacodynamic (PK/PD) indices. The lower limit of quantification of the assay was 0.25 μg/mL and the calibration curve was linear at the concentration range from 0.25 to 300 μg/mL. The method was characterized by an excellent precision (≤ 6.4%) and accuracy (≤ 9.0%). Recoveries ranged from 92% to 96%. CFU in serum samples was stable when stored at -30OC for at least 10 days, at room temperature (+22OC) for up to 6 h, and during three freezeñthaw cycles, when stored at -30OC and thawed to room temperature. Pharmacokinetic analysis showed significant differences in pharmacokinetic parameters of CFU in the studied patients: volume of distribution was from 8.9 to 20.6 L, terminal elimination half-life from 1.3 to 5.3 h, and total body clearance from 31 to 232 mL/min. In the elderly patients studied dosage optimization was required. These results suggest that our simple and rapid HPLC method may be useful to monitor serum CFU concentrations in patients on standard dosages and to support determination of CFU dosage regimens based on the PK/PD indices

Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome

Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets

Determination of linezolid in human serum by reversed-phase high performance liquid chromatography with ultrafiolet and diode array detection

A high-performance liquid chromatographic (HPLC) method with UV and DAD detection for the quantitative determination of linezolid in human serum was developed in present work. Chromatography was carried out by reversed-phase technique on a RP-18 column with a mobile phase composed of 50 mM phosphate buffer and acetonitrile (76 : 26, v/v), adjusted to pH 3.5 with orthophosphoric acid. Serum samples were deproteinized with methanol centrifuged and then, the supernatant was analyzed using HPLC procedure. No interference was observed at the retention times of linezolid from blank serum or ten commonly used antibiotics. A concentration range from 0.50 to 30.0 g/mL was utilized to construct calibration curves. The lower limit of detection was determined to be 0.1 μg/mL of serum for both detectors. The lower limit of quantification of 0.25 μg/mL (CV = 2.6%) was established for determination using HPLC-UV and 0.5 μg/mL (CV = 5.42%) for HPLC-DAD. The recovery of linezolid was approximately 100%. Intra-day accuracy ranged from 0.97 to 12.63% and 0.74 to 10.85% for HPLC-UV and HPLC-DAD method, respectively. Intra-day precision was less than 4.69% for HPLC-UV and less than 5.42% for HPLC-DAD method. Tests confirmed the stability of linezolid in serum during three freeze-thaw cycles and during long-term storage of frozen serum for up to 6 weeks; in extracts it was stable in the HPLC autosampler over 24 h. Statistical analysis by Student's t-test showed no significant difference between the results obtained by these two methods. In summary, these methods will be used and adapted for infected patients in intensive care unit, to determine linezolid serum concentrations in order to know the pharmacokinetic profiles of linezolid