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Teleost fishes, which are the largest and most diverse group of living vertebrates, have a rich history of ancient and recent polyploidy. Previous studies of allotetraploid common carp and goldfish (cyprinids) reported a dominant subgenome, which is more expressed and exhibits biased gene retention. However, the underlying mechanisms contributing to observed subgenome dominance remains poorly understood. Here we report high-quality genomes of twenty-one cyprinids to investigate the origin and subsequent subgenome evolution patterns following three independent allopolyploidy events. We identify the closest extant relatives of the diploid progenitor species, investigate genetic and epigenetic differences among subgenomes, and conclude that observed subgenome dominance patterns are likely due to a combination of maternal dominance and transposable element densities in each polyploid. These findings provide an important foundation to understanding subgenome dominance patterns observed in teleost fishes, and ultimately the role of polyploidy in contributing to evolutionary innovations.

At present,concept drift in the nonstationary data stream presents a trend of different speeds and and different space distribution,which has brought great challenges to many fields such as data mining and machine learning.In the past two de-cades,many methods dedicated to handling concept drift in nonstationary data streamsemerged.A novel perspective is proposed to classify these methods.The current concept drift handling methods are comprehensively explained from the explicit method of actively detection and the implicit method of passively adaption.In particular,active detection methods are analyzed from the per-spective of handling one specific type of concept drift and handling multiple types of concept drift,and passive adaptive methods are analyzed from the perspectives of single learner and ensemble learning.Many concept drift handling methods are analyzed and summarized in terms of the comparison algorithm,learning model,applicable drift type,advantages and disadvantages of algorithms.Finally,further research directions are given,including the concept drift handling methods in class-imbalanced data streams,the concept drift handling methods in data stream with the existence of novel classes,and the concept drift handling methods in the data stream with noise.

Most of the traditional multi-label classification algorithms use supervised learning,but in real life,there are many unlabeled data.Manual tagging of all required data is costly.Semi-supervised learning algorithms can work with a large amount of unlabeled data and labeled data,so they have received more attention from people.For the first time,multi-label classification algorithms are explained from the perspective of supervised learning and semi-supervised learning,and application fields of multi-label classification algorithms are comprehensively summarized.Among them,supervised learning algorithms of label non-correlation and label correlation are described in terms of decision trees,Bayesian,support vector machines,neural networks,and ensemble,semi-supervised learning algorithms are summarized from the perspectives of batch and online learning.The real-world application areas are introduced from the perspectives of image classification,text classification and other fields.Secondly,this paper briefly introduces evaluation metrics of multi-label.Finally,research directions of complex concept drift under semi-supervised learning,feature selection,complex correlation of labels and class imbalance are given.

Volatile organic compounds (VOCs) are important precursors of ground-level ozone and secondary organic aerosol, and its monitoring and emission control are very necessary for national environmental protection. Under the guide of policy and demand, on-board monitoring technology for VOCs becomes popular. The on-board monitoring technology has high spatial and temporal resolution and high efficiency for VOCs monitoring, which has become the hotspot technology in VOCs rapid monitoring filed. However, when there are many target compounds with the same quality, there is only one peak in the mass spectrum for these compounds obtained by the traditional on-board monitoring technology with the method of single mass spectrometer, which reduces the detection precision. Therefore, dual mode VOCs on-board monitoring system including SPI-TOF MS model and fast GC-SPI-TOF MS model were established by developing fast gas chromatography (GC)-single photon ionization (SPI)-time of flight mass spectrometry (TOF MS) devices combing with mobile car. When VOCs were monitored by the on-board monitoring system, the SPI-TOF MS mode was firstly used to analyze the VOCs in air by the process of driving, and the results of real-time qualitative quantitative analysis for each point of VOCs were obtained. When high value point of VOCs pollution was found, the car was stopped, and the analysis mode was switched to GC-SPI-TOF MS to determine whether there were compounds with the same m/z, which aimed at ensuring the speed of onboard monitoring and improving the detection accuracy at the same time. During the research, the performance of the system was evaluated by PAMs and TO-15 standard gas. The results showed that the system had a good detection ability for isomers and co-effluent components from GC. The reproducibility error of signal intensities were lower than 5.00% for 92% compounds by both SPI-TOF MS mode and GC-SPI-TOF MS mode, the reproducibility error of retention time was lower than 0.30% for 100% compounds, and the detection limits for typical VOCs species were between 0.28×10-9 and 8.50×10-9, which were better than the national or local testing standard. The system was applied to VOCs monitoring at an industrial park. With high time resolution (in seconds) monitoring ability, m/z 120 was identified for normal propyl benzene which came from suspicious enterprises B. Rapid GC-SPI-TOF MS is a quick and accurate monitoring equipment for VOCs, which not only improves the ability of equipment for VOCs monitoring, but also provides data support for VOCs emission management and control in China.

To evaluate the prognostic value of the mean platelet volume (MPV) and red blood cell distribution width (RDW) in sepsis among premature neonates.

A meta‐analysis was conducted comprehensively to investigate the impact of evidence‐based nursing (EBN) interventions on pressure injury (PI) in the intensive care unit (ICU) patients. Computer searches were performed, from databases inception to November 2023, in Wanfang, PubMed, China National Knowledge Infrastructure, Google Scholar, Embase, and Cochrane Library for randomized controlled trials (RCTs) on the application of EBN interventions in ICU patients. Two independent researchers conducted screenings of the literature, extracted data, and carried out quality evaluations. Stata 17.0 software was employed for data analysis. Overall, 25 RCTs, involving 2494 ICU patients, were included. It was found that compared to conventional care methods, the implementation of EBN interventions in ICU patients markedly decreased the occurrence of PI (odds ratio [OR]: 0.22, 95% confidence interval [CI]: 0.17–0.30, p < 0.001), delayed the onset time of pressure ulcers (standardized mean difference [SMD]: −1.61, 95% CI: −2.00 to −1.22, p < 0.001), and also improved nursing satisfaction (OR: 1.18, 95% CI: 1.14–1.23, p < 0.001). Our findings suggest the implementation of EBN interventions in the care of PI in ICU patients is highly valuable, can reduce the occurrence of PI, can delay the time of appearance, and is associated with relatively higher nursing satisfaction, making it worthy of promotion. [ABSTRACT FROM AUTHOR]