Multidimensional poverty index decomposition based on different dimensions (%).

Multidimensional poverty index decomposition based on different dimensions (%).</p

Multidimensional loss of sustainable poverty alleviation capacity of poor households.

Multidimensional loss of sustainable poverty alleviation capacity of poor households.</p

Multidimensional poverty evaluation indicators.

Multidimensional poverty evaluation indicators.</p

Sample questionnaire.

This is a sample of the questionnaire. (DOC)</p

Questionnaire statistics.

This is the statistics from the questionnaire. (XLSX)</p

Survey statistics.

Survey statistics.</p

Variable design and explanation.

Variable design and explanation.</p

Data_Sheet_1_Seasonal changes in N-cycling functional genes in sediments and their influencing factors in a typical eutrophic shallow lake, China.doc

N-cycling processes mediated by microorganisms are directly linked to the eutrophication of lakes and ecosystem health. Exploring the variation and influencing factors of N-cycling-related genes is of great significance for controlling the eutrophication of lakes. However, seasonal dynamics of genomic information encoding nitrogen (N) cycling in sediments of eutrophic lakes have not yet been clearly addressed. We collected sediments in the Baiyangdian (BYD) Lake in four seasons to explore the dynamic variation of N-cycling functional genes based on a shotgun metagenome sequencing approach and to reveal their key influencing factors. Our results showed that dissimilatory nitrate reduction (DNRA), assimilatory nitrate reduction (ANRA), and denitrification were the dominant N-cycling processes, and the abundance of nirS and amoC were higher than other functional genes by at least one order of magnitude. Functional genes, such as nirS, nirK and amoC, generally showed a consistent decreasing trend from the warming season (i.e., spring, summer, fall) to the cold season (i.e., winter). Furthermore, a significantly higher abundance of nitrification functional genes (e.g., amoB, amoC and hao) in spring and denitrification functional genes (e.g., nirS, norC and nosZ) in fall were observed. N-cycling processes in four seasons were influenced by different dominant environmental factors. Generally, dissolved organic carbon (DOC) or sediment organic matter (SOM), water temperature (T) and antibiotics (e.g., Norfloxacin and ofloxacin) were significantly correlated with N-cycling processes. The findings imply that sediment organic carbon and antibiotics may be potentially key factors influencing N-cycling processes in lake ecosystems, which will provide a reference for nitrogen management in eutrophic lakes.</p

Logistic regression models fitting results of the association between tea categories and cognitive impairment ¹.

<p>¹Binary logistic regression analysis was used to calculate ORs and 95% CIs for tea categories related to cognitive impairment which assessed with CCM, with non-consumption group treated as reference.</p><p>² P value were tested by logistic regressions in which tea category was treated as categorical variable.</p><p>³ Crude model.</p><p>⁴ Adjusted for age, sex, race, education, marriage, tea consumption volume and tea concentration.</p><p>⁵Adjusted for variables in model 2 plus physical examinations (BMI, WHR, SBP, DBP), family status (family income, have children or not) and disease situation (history of present illness and family history of hypertension, diabetes, CHD, AD, PD).</p><p>⁶ Adjusted for variables in model 3 plus behavioral risk factors (cigarette smoking, alcohol consumption, and physical activities), dietary intake (vegetables, fruits, meat, fish, beans, milk).</p><p>⁷Adjusted for variables in model 4 plus nutrition supplement, depression and ADL.</p><p>Logistic regression models fitting results of the association between tea categories and cognitive impairment <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137781#t004fn001" target="_blank">¹</a>.</p

Logistic regression models fitting results of the association between tea concentration and cognitive impairment ¹.

<p>¹Binary logistic regression analysis was used to calculate ORs and 95% CIs for cognitive impairment related with tea concentration which assessed with CCM, with non-consumption group treated as reference.</p><p>² P value were determined by logistic regressions in which tea concentration was treated as non-ordinal categorical variable.</p><p>³Crude model.</p><p>⁴ Adjusted for age, sex, race, education, marriage, tea consumption volume and tea categories.</p><p>⁵ Adjusted for variables in model 2 plus physical examinations (BMI, WHR, SBP, DBP), family status (family income, have children or not) and disease situation (history of present illness and family history of hypertension, diabetes, CHD, AD, PD).</p><p>⁶ Adjusted for variables in model 3 plus behavioral risk factors (cigarette smoking, alcohol consumption, and physical activities), dietary intake (vegetables, fruits, red meat, fish, beans, milk).</p><p>⁷Adjusted for variables in model 4 plus nutrition supplement, depression and ADL.</p><p>Logistic regression models fitting results of the association between tea concentration and cognitive impairment <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137781#t005fn001" target="_blank">¹</a>.</p