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Phage therapy is recognized as a promising alternative to antibiotics in treating pulmonary bacterial infections, however, its use has not been reported for treating secondary bacterial infections during virus pandemics such as coronavirus disease 2019 (COVID-19). We enrolled 4 patients hospitalized with critical COVID-19 and pulmonary carbapenem-resistant Acinetobacter baumannii (CRAB) infections to compassionate phage therapy (at 2 successive doses of 10 9 plaque-forming unit phages). All patients in our COVID-19-specific intensive care unit (ICU) with CRAB positive in bronchoalveolar lavage fluid or sputum samples were eligible for study inclusion if antibiotic treatment failed to eradicate their CRAB infections. While phage susceptibility testing revealed an identical profile of CRAB strains from these patients, treatment with a pre-optimized 2-phage cocktail was associated with reduced CRAB burdens. Our results suggest the potential of phages on rapid responses to secondary CRAB outbreak in COVID-19 patients.

The increase in the prevalence of multidrug-resistant Acinetobacter baumannii (MDRAB) strains is a serious public health concern. Antimicrobial peptides (AMPs) are a possible solution to this problem. In this study, we examined whether AMPs could be derived from phage endolysins. We synthesized four AMPs based on an amphipathic helical region in the C-terminus of endolysin LysAB2 encoded by the A . baumannii phage ΦAB2. These peptides showed potent antibacterial activity against A . baumannii (minimum inhibitory concentration, 4–64 μM), including some MDR and colistin-resistant A . baumannii . Of the four peptides, LysAB2 P3, with modifications that increased its net positive charge and decreased its hydrophobicity, showed high antibacterial activity against A . baumannii but little haemolytic and no cytotoxic activity against normal eukaryotic cells. The results of electron microscopy experiments and a fluorescein isothiocyanate staining assay indicated that this peptide killed A . baumannii through membrane permeabilization. Moreover, in a mouse intraperitoneal infection model, at 4 h after the bacterial injection, LysAB2 P3 decreased the bacterial load by 13-fold in ascites and 27-fold in blood. Additionally, LysAB2 P3 rescued sixty percent of mice heavily infected with A . baumannii from lethal bacteremia. Our results confirmed that bacteriophage endolysins are a promising resource for developing effective AMPs.

Background: In 2015, intrinsic capacity (IC) was proposed by the WHO as a new measure for healthy aging. Evidence has shown that physical activity (PA) benefits the physical and mental health of older adults. However, the association between PA and IC among older adults was not well evaluated or reported. This study aims to investigate the association between PA and general and specific IC among Chinese older adults. Method: The study included individuals aged 60 and above from the China Health and Retirement Longitudinal Study in 2015. The IC scores were constructed based on the WHO concept of five domains: psychological capacity, cognition, locomotion, vitality, and sensory abilities. Total PA and leisure PA were measured based on different activity purposes. Linear mixed-effects models and generalized linear mixed-effects models were developed to assess the associations between PA and IC. Results: A total of 3359 participants were included in this study. Older adults who reported some PA were associated with a higher composite IC score, with a mean difference of 0.14 (95% CI: 0.09–0.18, p < 0.001) compared to those who reported no PA. In terms of leisure PA, physically active adults had a higher composite IC score with a mean difference of 0.06 (95% CI: 0.03–0.09, p < 0.001). Older adults with a high level of leisure PA also had a significantly higher composite IC score (diff. in mean = 0.07, 95% CI: 0.01–0.13, p < 0.05) compared to those with low-level leisure PA. In addition, PA was positively and significantly associated with three specific IC domains: locomotion, cognition, and vitality. Conclusions: Improving both general and leisure PA can be an effective way to prevent the decline in IC among older adults, thus reducing the personal and public load of primary healthcare for aging countries such as China.

Taxi demand can be divided into pick-up demand and drop-off demand, which are firmly related to human’s travel habits. Accurately predicting taxi demand is of great significance to passengers, drivers, ride-hailing platforms and urban managers. Most of the existing studies only forecast the taxi demand for pick-up and separate the interaction between spatial correlation and temporal correlation. In this paper, we first analyze the historical data and select three highly relevant parts for each time interval, namely closeness, period and trend. We then construct a multi-task learning component and extract the common spatiotemporal feature by treating the taxi pick-up prediction task and drop-off prediction task as two related tasks. With the aim of fusing spatiotemporal features of historical data, we conduct feature embedding by attention-based long short-term memory (LSTM) and capture the correlation between taxi pick-up and drop-off with 3D ResNet. Finally, we combine external factors to simultaneously predict the taxi demand for pick-up and drop-off in the next time interval. Experiments conducted on real datasets in Chengdu present the effectiveness of the proposed method and show better performance in comparison with state-of-the-art models.

Recently, more and more mobile apps are employed in the marketing field with technical advances. Mobile marketing apps have become a prevalent way for enterprise marketing. Therefore, it has been an important and urgent problem to provide personalized and accurate recommendation in mobile marketing, with a large number of items and limited capability of mobile devices. Recommendation have been investigated widely, however, most existing approaches fail to consider the stability or change of users’ behaviors over time. In this paper, we first propose to mine the periodic trends of users’ consuming behavior from historical records by KNN(K-nearest neighbor) and SVR (support vector regression) based time series prediction, and predict the next time when a user re-purchases the item, so that we can recommend the items which users have purchased before at proper time. Second, we aim to find the regularity of users’ purchasing behavior during different life stages and recommend the new items that are needed and proper for their current life stage. In order to solve this, we mine the mapping model from items to user’s life stage first. Based on the model, users’ current life stage can be estimated from their recent behaviors. Finally, users will be recommended with new items which are proper to their estimated life stage. Experimental results show that it has improved the effectiveness of recommendation obviously by mining users’ consuming behaviors with temporal evolution.

Recently, object trackers based on Siamese networks have attracted considerable attentions due to their remarkable tracking performance and widespread application. Especially, the anchor-based methods exploit the region proposal subnetwork to get accurate prediction of a target and make great performance improvement. However, those trackers cannot capture the spatial information very well and the pre-defined anchors will hinder robustness. To solve these problems, we propose a Siamese-based anchor-free object tracking algorithm with multiscale spatial attentions in this paper. Firstly, we take ResNet-50 as the backbone network to generate multiscale features of both template patch and search regions. Secondly, we propose the spatial attention extraction (SAE) block to capture the spatial information among all positions in the template and search region feature maps. Thirdly, we put these features into the SAE block to get the multiscale spatial attentions. Finally, an anchor-free classification and regression subnetwork is used for predicting the location of the target. Unlike anchor-based methods, our tracker directly predicts the target position without predefined parameters. Extensive experiments with state-of-the-art trackers are carried out on four challenging visual object tracking benchmarks: OTB100, UAV123, VOT2016 and GOT-10k. Those experimental results confirm the effectiveness of our proposed tracker.

Background Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and customize early intervention programs for the patients in need. With the development of the electronic medical records, deep learning methods are introduced to deal with the prediction task. In the electronic medical records, clinical notes always contain rich and diverse medical information, including the clinical histories and reports during admission. Mortality prediction methods mostly rely on the temporal events such as medical examinations and ignore the related reports and history information in the clinical notes. We hope that we can utilize both temporal events and clinical notes information to get better mortality prediction results. Results We propose a multimodal temporal-clinical note network to model both temporal and clinical notes. Specifically, the clinical text are further processed for differentiating the chronic illness patients in the historical information of clinical notes from non-chronic illness patients. In order to further mine the information related to the mortality in the text, we learn the time series embedding with Long Short Term Memory networks and the clinical notes embedding with a label aware convolutional neural network. We also propose a scoring function to measure the importance of clinical note sections. Our approach achieved a better AUCPR and AUCROC than competing methods and visual explanations for word importance showed the interpretability improvement of the model. Conclusions We have tested our methodology on the MIMIC-III dataset. Contributions of different clinical note sections were uncovered by visualization methods. Our work demonstrates that the introduction of the medical history related information can improve the performance of the mortality prediction. Using label aware convolutional neural networks can further improve the results.

The efficient Siamese anchor-free tracker has fewer parameters, but it produces a large number of low-quality bounding boxes which are located far away from the center of the object. Moreover, a plenty of background information or distractors also interfere with the tracking process, resulting in the inaccurate results of classification and regression. As such, we propose a novel Siamese anchor-free network based on criss-cross attention and an improved head network. We apply ResNet-50 to extract the features of the template image and search region, then feed the feature maps into a recurrent criss-cross attention module to make it more discriminative. The enhanced feature maps are inputted into our improved head network, which include the center-ness branch based on the original classification and regression branches to filter out low-quality bounding boxes. Our proposed tracker reduces the impact of background information or distractors and can obtain high-quality bounding boxes, generating more accurate and robust tracking results. Extensive experiments and comparisons with state-of-the-art trackers are conducted on many challenging benchmarks such as VOT2016, VOT2018, GOT-10k, UAV123 and OTB2015. Our tracker achieves excellent performance with a considerable real-time speed.

Periodontitis is a chronic inflammatory and destructive disease caused by periodontal microbial infection and mediated by host immune response. As the main cause of loosening and loss of teeth in adults, it is considered to be one of the most common and serious oral diseases in the world. The co-existence of periodontitis and systemic chronic inflammatory diseases such as rheumatoid arthritis, psoriasis, inflammatory bowel disease, diabetes and so on is very common. It has been found that interleukin-17A (IL-17A) secreted by various innate and adaptive immune cells can activate a series of inflammatory cascade reactions, which mediates the occurrence and development of periodontitis and related systemic chronic inflammatory diseases. In this work, we review the role of IL-17A in the pathomechanisms of periodontitis and related systemic chronic inflammatory diseases, and briefly discuss the therapeutic potential of cytokine targeted agents that modulate the IL-17A signaling. A deep understanding of the possible molecular mechanisms in the relationship between periodontitis and systemic diseases will help dentists and physicians update their clinical diagnosis and treatment ideas.