Objective To compare the differences between two statistical methods for evaluating non-sensitivity of pathogenic bacteria to antimicrobial agents,and explore effect of non-consideration of clinical background on evalua-ting extent of bacterial resistance.Methods Data of Staphylococcus aureus and Acinetobacter spp .in a hospital in the first half year of 2008,2010 and 2013 were collected and conducted statistical analysis with two methods (me-thod 1 :based on all clinically isolated bacteria;method 2 :based on infection-related non-repetitive bacteria),two methods for evaluating bacterial non-sensitive rates to antimicrobial agents were compared.Results The non-sensi-tive rates of Acinetobacter spp .to various antimicrobial agents :statistical results by using method 1 were generally higher than those using method 2,absolute difference between two statistical methods was 10.46%-33.77%;the non-sensitive rates of Staphylococcus aureus to various antimicrobial agents :except compound sulfamethoxazole in 2010 and 2013(difference were 6.17% and 10.21 % respectively),penicillin G (difference was 3.86%),erythromy-cin (difference was 2.71 %),and azithromycin in 2013 (difference was 2.43%),statistical results by using method 1 were generally higher than those using method 2,absolute difference between two statistical methods was 0-18.04%.Conclusion There are deviation in the non-sensitive rates of bacterial strains to antimicrobial agents by using two different statistical methods,deviation is larger in Acinetobacter spp ..The resistance level might be incorrectly higher when evaluating the resistance status without considering clinical background of bacteria.

Objective To investigate the prevalence of skin diseases in pre-school children aged 0-7 years in cities of China.Methods From November 2014 to April 2015,12 cities were chosen as survey spots,and pre-school children aged 0-7 years served as respondents.A population-based study was conducted,and 40 vaccination clinics and 80 kindergartens were selected by stratified random sampling.A questionnaire survey and dermatological examination were performed by trained dermatologists.Results A total of 20 033 pre-school children received questionnaires,whose age was 2.41 ± 1.82 years (range,0.08-6.83 years).Among these respondents,7 823 children were found to have skin diseases,with the total prevalence of skin diseases being 39.05％ (7 823/20 033).Additionally,the prevalence of skin diseases reported in the 12 cities from high to low was as follows:66.96％ (612/914,Dalian),56.73％ (2 310/4 072,Shanghai),55.49％ (556/1 002,Wuhan),49.18％ (390/793,Taiyuan),47.16％ (316/670,Chengdu),41.93 ％ (566/1 350,Nanjing),41.03％ (318/775,Chongqing),35.98％ (240/667,Hefei),33.87％ (677/1999,Shenzhen),31.37％ (554/1 766,Changsha),23.52％ (1 107/4 706,Beijing),13.42％ (177/1 319,Shenyang).Totally,40 kinds of skin diseases were investigated,and the 10 most common skin diseases were eczema/infantile eczema/atopic dermatitis (18.71％,3 749/20 033),ichthyosis vulgaris(6.25％,1 253/20 033),lichen pilaris (5.73％,1 148/20 033),diaper dermatitis (5.29％,1 059/20 033),papular urticaria(5.25％,1 052/20 033),hemangioma/vascular malformation (3.86％,774/20 033),pityriasis alba (3.45％,691/20 033),infectious skin diseases (2.59％,519/20 033),urticaria(1.71％,344/20 033)and contact dermatitis (0.5％,100/20 033).Conclusion The total prevalence of skin diseases among pre-school children in cities of China is 39.05％,and eczema/atopic dermatitis is the most common skin disease.

Objective:To evaluate the association between long-term exposure to ambient ozone (O 3) and sperm quality. Methods:From January 1, 2014, to December 31, 2019, healthy sperm donors were recruited through the Human Sperm Bank of Shandong University Affiliated Reproductive Hospital. A total of 37 977 sperm donation data from 2 971 healthy volunteers were analyzed. The average annual O 3 concentration (0.01°× 0.01°) was matched according to household address. A multivariate mixed-effect model was used to analyze the exposure-response relationship between the average O 3 exposure concentration and sperm quality in the previous year, with each donor as a random intercept. All results were presented as % changes with 95% confidence intervals ( CIs) for all sperm parameters associated with 10 μg/m 3 increases in O 3. The effects of individual characteristics on the association between O 3 and sperm quality were evaluated by stratified analysis. Results:The average O 3 concentration in the year before semen collection was (107.09±7.50) μg/m 3. Each 10 μg/m 3 increase in O 3 was associated with declined sperm concentration (-3.12%, 95% CI:-4.55%, -1.67%), total sperm count (-5.21%, 95% CI:-7.28%, -3.09%), total sperm motility (-1.49%, 95% CI:-2.37%, -0.61%), progressive motility (-2.53%, 95% CI:-3.78%, -1.26%), total motile sperm count (-5.82%, 95% CI:-8.17%, -3.41%), and progressively motile sperm count (-6.22%, 95% CI:-8.73%, -3.64%). Men aged 30 and above, obese, and with lower education levels might be more susceptible to the influence of O 3 on sperm quality, but the difference was not statistically significant ( P>0.05). Conclusion:Long-term exposure to O 3 in Shandong Province is associated with a decrease in sperm quality.

Objective:To evaluate the association between long-term exposure to ambient ozone (O 3) and sperm quality. Methods:From January 1, 2014, to December 31, 2019, healthy sperm donors were recruited through the Human Sperm Bank of Shandong University Affiliated Reproductive Hospital. A total of 37 977 sperm donation data from 2 971 healthy volunteers were analyzed. The average annual O 3 concentration (0.01°× 0.01°) was matched according to household address. A multivariate mixed-effect model was used to analyze the exposure-response relationship between the average O 3 exposure concentration and sperm quality in the previous year, with each donor as a random intercept. All results were presented as % changes with 95% confidence intervals ( CIs) for all sperm parameters associated with 10 μg/m 3 increases in O 3. The effects of individual characteristics on the association between O 3 and sperm quality were evaluated by stratified analysis. Results:The average O 3 concentration in the year before semen collection was (107.09±7.50) μg/m 3. Each 10 μg/m 3 increase in O 3 was associated with declined sperm concentration (-3.12%, 95% CI:-4.55%, -1.67%), total sperm count (-5.21%, 95% CI:-7.28%, -3.09%), total sperm motility (-1.49%, 95% CI:-2.37%, -0.61%), progressive motility (-2.53%, 95% CI:-3.78%, -1.26%), total motile sperm count (-5.82%, 95% CI:-8.17%, -3.41%), and progressively motile sperm count (-6.22%, 95% CI:-8.73%, -3.64%). Men aged 30 and above, obese, and with lower education levels might be more susceptible to the influence of O 3 on sperm quality, but the difference was not statistically significant ( P>0.05). Conclusion:Long-term exposure to O 3 in Shandong Province is associated with a decrease in sperm quality.

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological contexts.To fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico pre-diction of TSSs solely based on genomic sequences.Most of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome scale.Here,we present DeeReCT-TSS,a deep learning-based method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing data.We show that by effectively incorporating these two sources of information,DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types.Furthermore,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific TSSs.Finally,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin states.The source code for DeeReCT-TSS is available at https://github.-com/JoshuaChou2018/DeeReCT-TSS_release and https://ngdc.cncb.ac.cn/biocode/tools/BT007316.