Texts of Kolima dialect of Yukaghir

<p>Clinical chemistry data of monkeys fed on diets containing GM rice or non-GM rice.</p

Quinoline-Based Fluorescent Probe for Ratiometric Detection of Lysosomal pH

A new pH-responsive fluorescent probe has been reported based on protonation-activable resonance charge transfer. In aqueous solution, probe PQ-Lyso exhibits ratiometric detection of pH changes with a large hypsochromic shift of 76 nm and remarkable changes in the fluorescence intensity ratio (<i>R</i> = <i>F</i>_494 nm/<i>F</i>_570 nm, <i>R</i>/<i>R</i>₀ = 105). Furthermore, PQ-Lyso, which is localized to lysosomes in living cells, can calibrate lysosomal pH using fluorescence ratiometry

Collaborative representation-based classification of microarray gene expression data

<div><p>Microarray technology is important to simultaneously express multiple genes over a number of time points. Multiple classifier models, such as sparse representation (SR)-based method, have been developed to classify microarray gene expression data. These methods allocate the gene data points to different clusters. In this paper, we propose a novel collaborative representation (CR)-based classification with regularized least square to classify gene data. First, the CR codes a testing sample as a sparse linear combination of all training samples and then classifies the testing sample by evaluating which class leads to the minimum representation error. This CR-based classification approach is remarkably less complex than traditional classification methods but leads to very competitive classification results. In addition, compressive sensing approach is adopted to project the high-dimensional gene expression dataset to a lower-dimensional space which nearly contains the whole information. This compression without loss is beneficial to reduce the computational load. Experiments to detect subtypes of diseases, such as leukemia and autism spectrum disorders, are performed by analyzing the gene expression. The results show that the proposed CR-based algorithm exhibits significantly higher stability and accuracy than the traditional classifiers, such as support vector machine algorithm.</p></div

Gap Arthroplasty versus Interpositional Arthroplasty for Temporomandibular Joint Ankylosis: A Meta-Analysis

<div><p>Gap arthroplasty (GA) and interpositional arthroplasty (IA) are widely used for the treatment of temporomandibular joint ankylosis (TMJA). However, controversy remains as to whether IA is superior to GA. PubMed, EMBASE, the Cochrane Library, the Web of science and the China National Knowledge Infrastructure were searched for literature regarding these procedures (published from 1946 to July 28, 2014). A study was included in this analysis if it was: (1) a randomized controlled trial or non-randomized observational cohort study; (2) comparing the clinical outcomes between GA and IA with respect to the maximal incisal opening (MIO) and reankylosis; (3) with a follow-up period of at least 12 months. The methodological quality of the included studies was evaluated according to the Newcastle-Ottawa Scale Eight non-randomized observational cohort studies with 272 patients were included. All the statistical analyses were performed using the RevMan 5.3 and Stat 12. The pooled analysis showed no significant difference in the incidence of reankylosis between the IA group (13/120) and the GA group (29/163) (RR= 0.67, 95% CI=0.38 to 1.16; Z=1.43, p=0.15). The IA group showed a significantly larger MIO than the GA group (MD=1.96, 95% CI=0.21 to 3.72, Z=2.19, p=0.03, I²=0%). In conclusion, patients with TMJA could benefit more from IA than GA, with a larger MIO and a similar incidence of reankylosis. IA shows to be an adequate option in the treatment of TMJA based on the results of maximal incisal opening.</p></div

Forest plot of the occurrences of reankylosis—GA vs. IA (TMF).

<p>Forest plot of the occurrences of reankylosis—GA vs. IA (TMF).</p

Funnel plot of the occurrences of reankylosis in the GA and IA groups, Egger’s test results: t = -0.12, p = 0.912.

<p>Funnel plot of the occurrences of reankylosis in the GA and IA groups, Egger’s test results: t = -0.12, p = 0.912.</p

The average accuracy of CRC for leukemia dataset with reduced dimensionality.

<p>The average accuracy of CRC for leukemia dataset with reduced dimensionality.</p

Flow diagram of the study selection.

<p>Flow diagram of the study selection.</p

Funnel plot of the occurrences of reankylosis in GA and IA (TMF) groups, Egger’s test results: t = 0.91, p = 0.458.

<p>Funnel plot of the occurrences of reankylosis in GA and IA (TMF) groups, Egger’s test results: t = 0.91, p = 0.458.</p

Gene expression data.

<p>Gene expression data.</p