We developed a real-time colorimetric method based on LAMP-integrated microfluidic chip system for diagnosing multiple respiratory viruses.

Agricultural greenhouses (AGs) are an important facility for the development of modern agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning of modern agriculture. With the advent of deep learning algorithms, various convolutional neural network (CNN)-based models have been proposed for object detection with high spatial resolution images. In this paper, we conducted a comparative assessment of the three well-established CNN-based models, which are Faster R-CNN, You Look Only Once-v3 (YOLO v3), and Single Shot Multi-Box Detector (SSD) for detecting AGs. The transfer learning and fine-tuning approaches were implemented to train models. Accuracy and efficiency evaluation results show that YOLO v3 achieved the best performance according to the average precision (mAP), frames per second (FPS) metrics and visual inspection. The SSD demonstrated an advantage in detection speed with an FPS twice higher than Faster R-CNN, although their mAP is close on the test set. The trained models were also applied to two independent test sets, which proved that these models have a certain transability and the higher resolution images are significant for accuracy improvement. Our study suggests YOLO v3 with superiorities in both accuracy and computational efficiency can be applied to detect AGs using high-resolution satellite images operationally.

Conventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.

The purpose of the study was to investigate the mental status and psychological needs of police officers during the COVID-19 outbreak in China. The Anti-Pandemic Public Mental Status Scale and self-administered Psychological Needs Scale were administered online to police officers in Y city, a significant sub-central city of Hubei Province, where was affected by the pandemic the most seriously. A total of 5,467 valid questionnaires were collected, of which female police accounted for 17.7%. Compared with the national public and Y city public data previously measured using the Anti-Pandemic Public Mental Status Scale, this study found that 24.6% of the Y city police suffered maladaptive problems. The mental status of the national public was the best, followed by the Y city police. The mental status of the Y city public was the worst. Moreover, there was a significant interaction between gender and unit type of Y city police (p = 0.02). The mental status of female police working in prisons was worse than their male counterparts (p = 0.01). Furthermore, psychological needs survey results showed that the police most wanted to learn the topics of self-adjustment and family relations. The most desired psychological assistances were relaxation and stress reduction, while the percentage of willingness to choose psychological counseling was low. During the pandemic, some police officers showed obvious psychological symptoms and the mental health services could be provided according to their psychological needs.

Pseudouridine is a ubiquitous RNA modification type present in eukaryotes and prokaryotes, which plays a vital role in various biological processes. Almost all kinds of RNAs are subject to this modification. However, it remains a great challenge to identify pseudouridine sites via experimental approaches, requiring expensive and time-consuming experimental research. Therefore, computational approaches that can be used to perform accurate in silico identification of pseudouridine sites from the large amount of RNA sequence data are highly desirable and can aid in the functional elucidation of this critical modification. Here, we propose a new computational approach, termed Porpoise, to accurately identify pseudouridine sites from RNA sequence data. Porpoise builds upon a comprehensive evaluation of 18 frequently used feature encoding schemes based on the selection of four types of features, including binary features, pseudo k-tuple composition, nucleotide chemical property and position-specific trinucleotide propensity based on single-strand (PSTNPss). The selected features are fed into the stacked ensemble learning framework to enable the construction of an effective stacked model. Both cross-validation tests on the benchmark dataset and independent tests show that Porpoise achieves superior predictive performance than several state-of-the-art approaches. The application of model interpretation tools demonstrates the importance of PSTNPs for the performance of the trained models. This new method is anticipated to facilitate community-wide efforts to identify putative pseudouridine sites and formulate novel testable biological hypothesis.

No abstract

The frequency of outbreaks of newly emerging infectious diseases has increased in recent years. The coronavirus disease 2019 (COVID-19) outbreak in late 2019 has caused a global pandemic, seriously endangering human health and social stability. Rapid detection of infectious disease pathogens is a key prerequisite for the early screening of cases and the reduction in transmission risk. Fluorescence quantitative polymerase chain reaction (qPCR) is currently the most commonly used pathogen detection method, but this method has high requirements in terms of operating staff, instrumentation, venues, and so forth. As a result, its application in the settings such as poorly conditioned communities and grassroots has been limited, and the detection needs of the first-line field cannot be met. The development of point-of-care testing (POCT) technology is of great practical significance for preventing and controlling infectious diseases. Isothermal amplification technology has advantages such as mild reaction conditions and low instrument dependence. It has a promising prospect in the development of POCT, combined with the advantages of high integration and portability of microfluidic chip technology. This study summarized the principles of several representative isothermal amplification techniques, as well as their advantages and disadvantages. Particularly, it reviewed the research progress on microfluidic chip–based recombinase polymerase isothermal amplification technology and highlighted future prospects.

Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.