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This paper deals with the finite-time synchronization problem for a class of uncertain coupled switched neural networks under asynchronous switching. By constructing appropriate Lyapunov-like functionals and using the average dwell time technique, some sufficient criteria are derived to guarantee the finite-time synchronization of considered uncertain coupled switched neural networks. Meanwhile, the asynchronous switching feedback controller is designed to finite-time synchronize the concerned networks. Finally, two numerical examples are introduced to show the validity of the main results.

Background A number of meta-analyses have been carried out to evaluate the effects of minimally invasive surgery (MIS) versus open surgery (OS) for acute Achilles tendon rupture. However, discordant findings were seen in these meta-analyses. The present study, performing a systematic review of overlapping meta-analyses regarding MIS versus OS of acute Achilles tendon rupture, aimed to assist decision-makers interpret and choose among conflicting meta-analyses, as well as to offer treatment recommendations based on current best evidence. Methods The literature search was performed to identify systematic reviews comparing MIS with OS for Achilles tendon rupture. Meta-analyses only comprising randomized controlled trials (RCTs) were included. Two authors individually evaluated the quality of meta-analysis and extracted data. The Jadad decision algorithm was conducted to ascertain which meta-analysis offered the best evidence. Results A total of four meta-analyses was included. All these meta-analyses comprised RCTs or quasi-RCTs and were determined as Level-II evidence. The scores of the Assessment of Multiple Systematic Reviews (AMSTAR) ranged from 7 to 10 (median 9.5). The Jadad algorithm indicated that the best meta-analysis should be chosen according to the search strategies and application of selection. A high-quality meta-analysis with more RCTs was chosen, which suggested that there was no statistically significant difference between MIS and OS regarding rerupture rate, tissue adhesion, sural nerve injury, deep infection, and deep vein thrombosis. However, MIS could decrease superficial infection rate, and had a better patient satisfaction for good to excellent outcomes in comparison to OS. Conclusions Based on the best available evidence, MIS may be superior to OS for treating acute Achilles tendon rupture. However, due to some limitations, this should be cautiously interpreted, and further high-quality studies are needed.

As a molecular and noninvasive detection technology, Raman spectroscopy is promising for use in the early diagnosis of tumors. The SNR of spectra obtained from portable Raman spectrometers is low, which makes classification more difficult. A classification algorithm with a high recognition rate is required. In this paper, an algorithm of entropy weighted local-hyperplane k-nearest-neighbor (EWHK) is proposed for the identification of the spectra. When calculating the weighted distance between the prediction and the sample hyperplane, EWHK introduces the information entropy weighting to improve the algorithm of adaptive weighted k-local hyperplane (AWKH). It can reflect all of the sample information in the classification objectively and improve the classification accuracy. The breast cancer detection experimental results of EWHK showed a significant improvement compared with those of AWKH and k-nearest neighbor (KNN). The EWHK classifier yielded an average diagnostic accuracy of 92.33%, a sensitivity of 93.81%, a specificity of 87.77%, a positive prediction rate of 95.99% and a negative prediction rate of 83.69% during randomized grouping validation. The algorithm is effective for cancer diagnosis.

Early diagnosis and early medical treatments are the keys to save the patients' lives and improve the living quality. Fourier transform infrared (FT-IR) spectroscopy can distinguish malignant from normal tissues at the molecular level. In this paper, programs were made with pattern recognition method to classify unknown samples. Spectral data were pretreated by using smoothing and standard normal variate (SNV) methods. Leave-one-out cross validation was used to evaluate the discrimination result of support vector machine (SVM) method. A total of 54 gastric tissue samples were employed in this study, including 24 cases of normal tissue samples and 30 cases of cancerous tissue samples. The discrimination results of SVM method showed the sensitivity with 100%, specificity with 83.3%, and total discrimination accuracy with 92.2%.

A new algorithm (QN) for the (15)N /(14)N quantitation of relative protein abundances in complex proteomic samples is described. QN takes advantage of the high resolution, mass accuracy and throughput of the hybrid mass spectrometer LTQ-FT MS. Peptide quantitation is based on MS peak intensity (measured in the FT MS), while peptide identification is performed in the MS/MS mode (measured in the LTQ linear ion trap). Accuracy of the protein abundance is enhanced by a novel scoring procedure, allowing filtering of less reliable measurements of peptide abundances. The performance of QN is illustrated in the relative quantitative analysis of M. acetivorans C2A cultures grown with carbon monoxide vs methanol as substrate. Roughly 1,000 proteins were quantitated with an average CV of 9% for the protein abundance ratios. QN performs quantitation without manual intervention, does not require high processing power, and generates files compatible with the Guidelines for Proteomic Data Publication.

For achieving the development of a portable, low-cost and in vivo cancer diagnosis instrument, a laser 785 nm miniature Raman spectrometer was used to acquire the Raman spectra for breast cancer detection in this paper. However, because of the low spectral signal-to-noise ratio, it is difficult to achieve high discrimination accuracy by using the miniature Raman spectrometer. Therefore, a pattern recognition method of the adaptive net analyte signal (NAS) weight k-local hyperplane (ANWKH) is proposed to increase the classification accuracy. ANWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN), and combines the advantages of the adaptive weight k-local hyperplane (AWKH) and the net analyte signal (NAS). In this algorithm, NAS was first used to eliminate the influence caused by other non-target factors. Then, the distance between the test set samples and hyperplane was calculated with consideration of the feature weights. The HKNN only works well for small values of the nearest-neighbor. However, the accuracy decreases with increasing values of the nearest-neighbor. The method presented in this paper can resolve the basic shortcoming by using the feature weights. The original spectra are projected into the vertical subspace without the objective factors. NAS was employed to obtain the spectra without irrelevant information. NAS can improve the classification accuracy, sensitivity, and specificity of breast cancer early diagnosis. Experimental results of Raman spectra detection in vitro of breast tissues showed that the proposed algorithm can obtain high classification accuracy, sensitivity, and specificity. This paper demonstrates that the ANWKH algorithm is feasible for early clinical diagnosis of breast cancer in the future.

Background Many studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins. Results We have experimented to operate on several parameters to control a FDR, including a fold-change, a statistical test, and a minimum number of permuted significant pairings. Although none of these parameters alone gives a satisfactory control of a FDR, we find that a combination of these parameters provides a very effective means to control a FDR without compromising the sensitivity. The results suggest that it is possible to perform a significance analysis without protein sample replicates. Only duplicate LC/MS injections per sample are needed. We illustrate that differentially expressed proteins can be detected with a FDR between 0 and 15% at a positive rate of 4–16%. The method is evaluated for its sensitivity and specificity by a ROC analysis, and is further validated with a [15N]-labeled internal-standard protein sample and additional unlabeled protein sample replicates. Conclusion We demonstrate that a statistical significance can be inferred without protein sample replicates in label-free quantitative proteomics. The approach described in this study would be useful in many exploratory experiments where a sample amount or instrument time is limited. Naturally, this method is also suitable for proteomics experiments where multiple sample replicates are available. It is simple, and is complementary to other more sophisticated algorithms that are not designed for dealing with a small number of sample replicates.

Background Although fold change is a commonly used criterion in quantitative proteomics for differentiating regulated proteins, it does not provide an estimation of false positive and false negative rates that is often desirable in a large-scale quantitative proteomic analysis. We explore the possibility of applying the Significance Analysis of Microarray (SAM) method (PNAS 98:5116-5121) to a differential proteomics problem of two samples with replicates. The quantitative proteomic analysis was carried out with nanoliquid chromatography/linear iron trap-Fourier transform mass spectrometry. The biological sample model included two Mycobacterium smegmatis unlabeled cell cultures grown at pH 5 and pH 7. The objective was to compare the protein relative abundance between the two unlabeled cell cultures, with an emphasis on significance analysis of protein differential expression using the SAM method. Results using the SAM method are compared with those obtained by fold change and the conventional t-test. Results We have applied the SAM method to solve the two-sample significance analysis problem in liquid chromatography/mass spectrometry (LC/MS) based quantitative proteomics. We grew the pH5 and pH7 unlabelled cell cultures in triplicate resulting in 6 biological replicates. Each biological replicate was mixed with a common 15N-labeled reference culture cells for normalization prior to SDS/PAGE fractionation and LC/MS analysis. For each biological replicate, one center SDS/PAGE gel fraction was selected for triplicate LC/MS analysis. There were 121 proteins quantified in at least 5 of the 6 biological replicates. Of these 121 proteins, 106 were significant in differential expression by the t-test (p < 0.05) based on peptide-level replicates, 54 were significant in differential expression by SAM with Δ = 0.68 cutoff and false positive rate at 5%, and 29 were significant in differential expression by the t-test (p < 0.05) based on protein-level replicates. The results indicate that SAM appears to overcome the false positives one encounters using the peptide-based t-test while allowing for identification of a greater number of differentially expressed proteins than the protein-based t-test. Conclusion We demonstrate that the SAM method can be adapted for effective significance analysis of proteomic data. It provides much richer information about the protein differential expression profiles and is particularly useful in the estimation of false discovery rates and miss rates.