A research regarding the relationship among intensive care nurses' self-esteem, job satisfaction and subjective well-being

Objective:The paper aims to explore the status of intensive care unit(ICU)nurses'self-esteem,job satisfaction,and subjective well-being and to investigate the relationship among the three elements.Methods:A cross-sectional survey of 224 nurses from five teaching hospitals in Tianjin,China,was conducted.Self-esteem scale,job satisfaction scale,and subjective well-being scale were used to explore the status of ICU nurses'self-esteem,job satisfaction,and subjective well-being.Correlation among the three elements was calculated by SPSS 17.0.Results:The scores of three scales(i.e.,ICU nurses'self-esteem,job satisfaction,and subjective well-being)were 27.67±3.41,43.57±9.04,and 69.73±11.60,respectively.The subjective well-being was significantly positively correlated with self-esteem and job satisfaction(r=0.454,P<0.01;r=0.584,P<0.01,respectively).Conclusion:The study shows that the prevalence of subjective well-being and self-esteem with ICU nurses are at a low level,and job satisfaction is at the median level.Associations among self-esteem,job satisfaction,and subjective well-being in ICUs have been confirmed.Self-esteem and job satisfaction positively correlated with nurse subjective well-being.The higher the self-esteem and job satisfaction levels are,the stronger the subjective well-being is.The working pressure in ICUs cannot be alleviated temporarily;hence,to promote a high subjective well-being,managers and ICU nurses increase their self-esteem and job satisfaction through taking strategies that strengthen psychological construction.

Integration of early disease-resistance phenotyping, histological characterization, and transcriptome sequencing reveals insights into downy mildew resistance in impatiens

Downy mildew(DM),caused by obligate parasitic oomycetes,is a destructive disease for a wide range of crops worldwide.Recent outbreaks of impatiens downy mildew(IDM)in many countries have caused huge economic losses.A system to reveal plant–pathogen interactions in the early stage of infection and quickly assess resistance/susceptibility of plants to DM is desired.In this study,we established an early and rapid system to achieve these goals using impatiens as a model.Thirty-two cultivars of Impatiens walleriana and I.hawkeri were evaluated for their responses to IDM at cotyledon,first/second pair of true leaf,and mature plant stages.All I.walleriana cultivars were highly susceptible to IDM.While all I.hawkeri cultivars were resistant to IDM starting at the first true leaf stage,many(14/16)were susceptible to IDM at the cotyledon stage.Two cultivars showed resistance even at the cotyledon stage.Histological characterization showed that the resistance mechanism of the I.hawkeri cultivars resembles that in grapevine and type II resistance in sunflower.By integrating full-length transcriptome sequencing(Iso-Seq)and RNA-Seq,we constructed the first reference transcriptome for Impatiens comprised of 48,758 sequences with an N50 length of 2060 bp.Comparative transcriptome and qRT-PCR analyses revealed strong candidate genes for IDM resistance,including three resistance genes orthologous to the sunflower gene RGC203,a potential candidate associated with DM resistance.Our approach of integrating early disease-resistance phenotyping,histological characterization,and transcriptome analysis lay a solid foundation to improve DM resistance in impatiens and may provide a model for other crops.

Style-conditioned music generation with Transformer-GANs

Recently,various algorithms have been developed for generating appealing music.However,the style control in the generation process has been somewhat overlooked.Music style refers to the representative and unique appearance presented by a musical work,and it is one of the most salient qualities of music.In this paper,we propose an innovative music generation algorithm capable of creating a complete musical composition from scratch based on a specified target style.A style-conditioned linear Transformer and a style-conditioned patch discriminator are introduced in the model.The style-conditioned linear Transformer models musical instrument digital interface(MIDI)event sequences and emphasizes the role of style information.Simultaneously,the style-conditioned patch discriminator applies an adversarial learning mechanism with two innovative loss functions to enhance the modeling of music sequences.Moreover,we establish a discriminative metric for the first time,enabling the evaluation of the generated music’s consistency concerning music styles.Both objective and subjective evaluations of our experimental results indicate that our method’s performance with regard to music production is better than the performances encountered in the case of music production with the use of state-of-the-art methods in available public datasets.