Data_Sheet_1_Longitudinal Associations Between Household Solid Fuel Use and Handgrip Strength in Middle-Aged and Older Chinese Individuals: The China Health and Retirement Longitudinal Study.pdf

BackgroundHousehold solid fuel have been associated with changes of handgrip strength (HGS). However, no study has explored the longitudinal associations between household solid fuel use and HGS. Thus, the aim of our cohort study was to investigate the longitudinal associations between household fuel use and HGS.MethodsThe study was based on the China Health and Retirement Longitudinal Study. A handheld dynamometer was used to measure HGS. Household fuel use statuses were collected using questionnaires. Analyses of covariance were performed to examine the associations between household fuel use and HGS.ResultsThe study included 9,382 participants during a 4-year follow-up. The participants who used solid fuel for cooking had more decreases of HGS than those who used clean fuel (P ConclusionUsing household solid fuel for cooking but not heating was associated with more decreases in HGS. Proper ventilation and clean fuel should be promoted for public health.</p

Hypothesis testing over accuracy with different combinations of methods.

<p>MCI converter/non-converter classification comparison of different combinations of feature selection methods and classification methods in terms of accuracy. With the same input training and testing samples and the same parameters, we compare the performances based on different combinations of methods. By varying the sets of training samples and testing samples and the settings of parameters, we obtain a series of comparisons between wHLFS+RF and another combination of methods. A positive mean value means the average improvement on accuracy by using wHLFS+RF. A <i>p</i>-value less than 0.05 means wHLFS+RF achieves a significant improvement on accuracy. The standard deviations of mean values are shown in the parentheses.</p

Performance comparison of different datasets.

<p>MCI converter/non-converter classification comparison with different datasets in terms of accuracy, sensitivity and specificity. Methods applied here include the combinations of wHLFS and different classification methods. The different feature datasets are META (E), MRI (M), and META without baseline cognitive scores (META-22). Parameters are selected by five-fold cross validation on the training dataset. The number in the parenthesis indicates the number of features in the specific dataset. The bolded and underlined entry denotes the best performance for that particular method. The standard deviations are shown in the parentheses along with the accuracy.</p

Features included in the META dataset.

<p>There are 13 different types of ADAS Sub-Scores and Total Scores and 11 different types of Neuropsychological Battery features. A detailed explanation of each cognitive score and lab test can be found at <a href="http://www.public.asu.edu/~jye02/AD-Progression/" target="_blank">www.public.asu.edu/~jye02/AD-Progression/</a>.</p

Hypothesis testing over accuracy with different input datasets.

<p>MCI converter/non-converter classification comparison with different datasets in terms of accuracy. Methods applied here include the combinations of wHLFS and different classification methods. The different feature datasets are META (E), MRI (M), and META without baseline cognitive scores (META-22). With the same input training and testing samples and the same method with the same parameters, we compare the performances based on different input feature datasets. By varying the sets of training samples and testing samples and the settings of parameters, we obtain a series of comparisons. Then paired <i>t</i>-tests are performed on the performance by using E+M dataset and the performance by using another dataset. A positive mean value means the average improvement on accuracy by using E+M dataset. A <i>p</i>-value less than 0.05 means using E+M dataset can achieve a significant improvement on accuracy. The standard deviations of mean values are shown in the parentheses.</p

MCI converter/non-converter classification performance.

<p>MCI converter/non-converter classification comparison of different combinations of feature selection methods and classification methods in terms of accuracy, specificity and sensitivity. “—” in the “FS-method” column means no feature selection method is used. “—” in the “Classify” column means the final model from the corresponding feature selection methods is directly used for classification. For this experiment, we used all the META and MRI features. The bolded and underlined entry denotes the best performance for that particular setting. The standard deviations are shown in the parentheses.</p

Identification of Glycoprotein Markers for Pancreatic Cancer CD24⁺CD44⁺ Stem-like Cells Using Nano-LC–MS/MS and Tissue Microarray

Pancreatic adenocarcinoma is characterized by late diagnosis due to lack of early symptoms, extensive metastasis, and high resistance to chemo/radiation therapy. Recently, a subpopulation of cells within pancreatic cancers, termed cancer stem cells (CSCs), has been characterized and postulated to be the drivers for pancreatic cancer and responsible for metastatic spread. Further studies on pancreatic CSCs are therefore of particular importance to identify novel diagnosis markers and therapeutic targets for this dismal disease. Herein, the malignant phenotype of pancreatic cancer stem-like CD24⁺CD44⁺ cells was isolated from a human pancreatic carcinoma cell line (PANC-1) and demonstrated 4-fold increased invasion ability compared to CD24^–CD44⁺ cells. Using lectin microarray and nano LC–MS/MS, we identified a differentially expressed set of glycoproteins between these two subpopulations. Lectin microarray analysis revealed that fucose- and galactose-specific lectins, UEA-1 and DBA, respectively, exhibit distinctly strong binding to CD24⁺CD44⁺ cells. The glycoproteins extracted by multilectin affinity chromatography were consequently analyzed by LC–MS/MS. Seventeen differentially expressed glycoproteins were identified, including up-regulated Cytokeratin 8/CK8, Integrin β1/CD29, ICAM1/CD54, and Ribophorin 2/RPN2 and down-regulated Aminopeptidase N/CD13. Immunohistochemical analysis of tissue microarrays showed that CD24 was significantly associated with late-stage pancreatic adenocarcinomas, and RPN2 was exclusively coexpressed with CD24 in a small population of CD24-positive cells. However, CD13 expression was dramatically decreased along with tumor progression, preferentially present on the apical membrane of ductal cells and vessels in early stage tumors. Our findings suggest that these glycoproteins may provide potential therapeutic targets and promising prognostic markers for pancreatic cancer

Stable expectation scores of related biosignatures.

<p>We list the related biosignatures of the top 5 negative and positive stable interactions shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082450#pone-0082450-g005" target="_blank">Figure 5</a>.</p

The top 12 stable main effect features.

<p>The top 12 stable main effect features identified by wHLFS with stability selection. The average values of different stable biosignatures for the specific group are presented. The standard deviations are shown in the parenthesis.</p

The proportion of selected interaction features.

<p>The proportion of selected interaction features.</p