微晶蜡非催化氧化反应的神经网络模型

对微晶蜡进行了非催化空气氧化，并利用人工神经网络将氧化微晶蜡的酸值、酯值及微晶蜡的氧化条件（反应温度、空气流量和反应时间）进行关联，建立了微晶蜡非催化氧化的酸值、酯值的神经网络模型，并用该模型预测了反应条件对微晶蜡氧化反应过程的影响。结果表明，该模型不但具有较高的计算精度，而且具有满意的预测能力。     

Neural networks in the study of the brain

Neural networks are models of the brain and have been used within Artificial Intelligence to provide alternative explanations to the symbolic explanations of cognition in which one assumes that an intelligent system has certain explicit representations of some aspect of the world and uses these in intelligent behavior. Obviously, if neural networks are indeed good models of the brain, and give a satisfactory account of cognition, then they could be a valuable tool to neuroscientists. This article gives a brief overview of the various neural network models, and critically reviews their status as models of the brain and of cognition.     

Gas-to-liquid permeation in silicon-containing, crosslinked, glassy copotymers of methyl methacrylate

Copolymers of methyl methacrylate with disiloxane derivatives have been proposed as biomaterials for contact lens applications. Although glassy, these copolymers exhibit high oxygen permeability and adequate wettability so that they can be used for manufacture of hard, extended wear lenses. CrossHnked copolymers of poly(methyl methacrylate-co-1,3-bis(methacryloxymethyl)-1,1,3,3-tetramethyl-disiloxane), P(MMA-co-BMTDS), containing from 0.085 to 0.53 mole fraction of BMTDS were prepared and tested for oxygen permeation using a novel apparatus which simulates the atmosphere/lens/cornea conditions. The gas-to-liquid dissolved oxygen permeability, P_gd, was determined and it was found to increase with BMTDS content Permeability values for P(MMA-co-BMTDS) at 34°C were significantly higher than for pure homopolymer PMMA, although these copolymers were glassy at this temperature. The increased oxygen permeation was attributed to increased oxygen solubility in the copolymers due to the presence of the -Si-0-bonds.     

Vasculogenic mimicry has no prognostic significance in pT3 and pT4 cutaneous melanoma

The concept of vasculogenic mimicry has been introduced to define periodic acid-Schiff (PAS)-positive channels and loops lined by tumor cells, instead of endothelium, able to contribute to microcirculation in uveal melanomas. Previous studies have shown that the PAS-positive patterns are associated with a poor prognosis in uveal melanoma. The aim of the current study was to investigate whether vasculogenic mimicry has a prognostic impact in pT3 and pT4 cutaneous melanoma. Fifteen patients with pT3 and pT4 cutaneous melanoma who did not experience progression after 10 years of follow-up and 30 matched controls who underwent progression were selected. Tumor sections were stained with PAS reaction, omitting the nuclear counterstaining. For immunohistochemistry, sections were stained with CD31, CD105 (endoglin), and laminin. Differences in the distribution of the PAS-positive patterns and a series of clinicopathological variables were evaluated by the Pearson chi(2) and Mann-Whitney U tests. We observed PAS-positive linear sheets, arcs, elliptical loops, and networks encircling roundish to oval aggregates of melanoma cells. The overall distribution of the PAS-positive patterns did not match with the blood microvessels' architecture as detected by immunohistochemical analysis. No statistically significant differences in the distribution of PAS-positive patterns were found between cases and controls. The presence of a parallel pattern correlated significantly with thickness (P = 0.04), whereas an inverse correlation was found with vessel area (P = 0.05). In conclusion, our results suggest that there is a mismatch between vasculogenic mimicry and tumor angiogenesis and do not support any prognostic role of vasculogenic mimicry in thick cutaneous melanoma.     

Automated Recognition of Lateral from PA Chest Radiographs: Saving Seconds in a PACS Environment

Images acquired in a two-view digital chest examination are frequently not electronically distinguishable. As a result the lateral and posterioanterio (PA) images are often improperly positioned on a PACS work station. A series of 1998 chest radiographs (999 lateral, 999 PA or AP) were used to develop a neural network classifier. The images were down-sampled to 16 × 16 matrices, and a feed-forward neural network was trained and tested using the leave-one-out method. Using five nodes in the hidden layer, the neural network correctly identified 987 of the 999 test cases (98.8%) (average of six runs). The simple architecture and speed of this technique suggests that it would be a useful addition to PACS work station software. The accumulated time saved by correctly positioning the lateral and PA chest images on the work station monitors in accordance with each radiologists hanging protocols was estimated to be about 1 week of radiologist time per year.     

An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks

In this paper, an intelligent system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective in detecting Doppler heart sounds. The correct classification rate was about 94% for abnormal and normal subjects.     

An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions

Reverse immunogenetic approaches attempt to optimize the selection of candidate epitopes, and thus minimize the experimental effort needed to identify new epitopes. When predicting cytotoxic T cell epitopes, the main focus has been on the highly specific MHC class I binding event. Methods have also been developed for predicting the antigen-processing steps preceding MHC class I binding, including proteasomal cleavage and transporter associated with antigen processing (TAP) transport efficiency. Here, we use a dataset obtained from the SYFPEITHI database to show that a method integrating predictions of MHC class I binding affinity, TAP transport efficiency, and C-terminal proteasomal cleavage outperforms any of the individual methods. Using an independent evaluation dataset of HIV epitopes from the Los Alamos database, the validity of the integrated method is confirmed. The performance of the integrated method is found to be significantly higher than that of the two publicly available prediction methods BIMAS and SYFPEITHI. To identify 85% of the epitopes in the HIV dataset, 9% and 10% of all possible nonamers in the HIV proteins must be tested when using the BIMAS and SYFPEITHI methods, respectively, for the selection of candidate epitopes. This number is reduced to 7% when using the integrated method. In practical terms, this means that the experimental effort needed to identify an epitope in a hypothetical protein with 85% probability is reduced by 20-30% when using the integrated method.The method is available at http://www.cbs.dtu.dk/services/NetCTL. Supplementary material is available at http://www.cbs.dtu.dk/suppl/immunology/CTL.php.     

Dialysate-side urea kinetics. Neural network predicts dialysis dose during dialysis

Determination of the adequacy of dialysis is a routine but crucial procedure in patient evaluation. The total dialysis dose, expressed as Kt/V, has been widely recognised to be a major determinant of morbidity and mortality in haemodialysed patients. Many different factors influence the correct determination of Kt/V, such as urea sequestration in different body compartments, access and cardiopulmonary recirculation. These factors are responsible for urea rebound after the end of the haemodialysis session, causing poor Kt/V estimation. There are many techniques that try to overcome this problem. Some of them use analysis of blood-side urea samples, and in recent years, on-line urea monitors have become available to calculate haemodialysis dose from dialysate-side urea kinetics. All these methods require waiting until the end of the session to calculate the Kt/V dose. In this work, a neural network (NN) method is presented for early prediction of the Kt/V dose. Two different portions of the dialysate urea concentration-time profile (provided by an on-line urea minitor) were analysed: the entire curve A and the first half B, using an NN to predict the Kt/V and compare this with that provided by the monitor. The NN was able to predict Kt/V is the middle of the 4h session (B data) without a significant increase in the percentage error (B data: 6.69%±2.46%; A data: 5.58%±8.77%, mean±SD) compared with the monitor Kt/V.     

Weight Status and BMI-Related Traits in Adolescent Friendship Groups and Role of Sociodemographic Factors: The European IDEFICS/I. Family Cohort

BackgroundDuring adolescence, health behaviors and weight status are increasingly influenced by friendship and peer networks. This paper examines resemblances in weight-related characteristics and how they differ by sociodemographic factors.MethodsOver 3,000 friendships were reported by 1,603 adolescents, aged 11–16 years, who participated in the school-based I. Family study in 6 European countries. Each “source child” named 1–10 friends for whom standardized weight-related traits were available in the same survey. The mean value of the friends'' traits weighted by time spent together was calculated, and related to the source child''s trait. Country, age and sex of the source child, parental education, and immigrant background were considered for confounding and moderation.ResultsSource children''s z-scores of body fat percent and BMI were positively associated with their friends'' characteristics, in particular if they had highly educated parents. Positive associations were also found regarding the frequency of fast-food consumption, impulsivity, screen time, preference for sugar-sweetened foods, and hours spent in sports clubs, in increasing order of effect size. Additionally, correlations were observed between friends'' cognitive and school functioning and being bullied. No associations were seen for a preference for high-fat foods, weight concerns, and health-related quality of life. Finally, parental education and immigrant background were associated between friends in all countries except Sweden, where no associations were observed.ConclusionAdolescent friends shared a number of weight-related characteristics. For weight measures per se, positive associations with friends'' characteristics were only observed in adolescents with high parental education. Associations regarding energy-balance behaviors and indicators of school-related well-being did not differ by parental education. Parental education and immigrant background correlated positively in friends in most countries showing that social aggregation is already occurring in adolescence. The wide spectrum of friendship associations in weight-related traits and behaviors suggests that health promotion initiatives in adolescents should be directed towards peer groups in both school-related and leisure-time environments.ISRCTN RegistryPan-European IDEFICS/I. Family children cohort (ID ISRCTN62310987; https://doi.org/10.1186/ISRCTN62310987).     

A neural network-based similarity index for clustering DNA microarray data

A common approach to the analysis of gene expression data is to define clusters of genes that have similar expression. A critical step in cluster analysis is the determination of similarity between the expression levels of two genes. We introduce a neural network-based similarity index as a non-linear similarity index and compare the results with other proximity measures for Saccharomyces cerevisiae gene expression data. We show that the clusters obtained using Euclidean distance, correlation coefficients, and mutual information were not significantly different. The clusters formed with the neural network-based index were more in agreement with those defined by functional categories and common regulatory motifs.