Notes

One-year follow-up-case report of supplementary pressure pneumothorax inside a COVID-19 pneumonia individual.
Many patients suffer from declined motor abilities after a brain injury. To provide appropriate rehabilitation programs and encourage motor-impaired patients to participate further in rehabilitation, sufficient and easy evaluation methodologies are necessary. This study is focused on the sit-to-stand motion of post-stroke patients because it is an important daily activity. Our previous study utilized muscle synergies (synchronized muscle activation) to classify the degree of motor impairment in patients and proposed appropriate rehabilitation methodologies. However, in our previous study, the patient was required to attach electromyography sensors to his/her body; thus, it was difficult to evaluate motor ability in daily circumstances. Here, we developed a handrail-type sensor that can measure the force applied to it. Using temporal features of the force data, the relationship between the degree of motor impairment and temporal features was clarified, and a classification model was developed using a random forest model to determine the degree of motor impairment in hemiplegic patients. The results show that hemiplegic patients with severe motor impairments tend to apply greater force to the handrail and use the handrail for a longer period. It was also determined that patients with severe motor impairments did not move forward while standing up, but relied more on the handrail to pull their upper body upward as compared to patients with moderate impairments. Furthermore, based on the developed classification model, patients were successfully classified as having severe or moderate impairments. The developed classification model can also detect long-term patient recovery. The handrail-type sensor does not require additional sensors on the patient's body and provides an easy evaluation methodology.Recent image-to-image translation models have shown great success in mapping local textures between two domains. Existing approaches rely on a cycle-consistency constraint that supervises the generators to learn an inverse mapping. However, learning the inverse mapping introduces extra trainable parameters and it is unable to learn the inverse mapping for some domains. As a result, they are ineffective in the scenarios where (i) multiple visual image domains are involved; (ii) both structure and texture transformations are required; and (iii) semantic consistency is preserved. To solve these challenges, the paper proposes a unified model to translate images across multiple domains with significant domain gaps. Unlike previous models that constrain the generators with the ubiquitous cycle-consistency constraint to achieve the content similarity, the proposed model employs a perceptual self-regularization constraint. With a single unified generator, the model can maintain consistency over the global shapes as well as the local texture information across multiple domains. Extensive qualitative and quantitative evaluations demonstrate the effectiveness and superior performance over state-of-the-art models. It is more effective in representing shape deformation in challenging mappings with significant dataset variation across multiple domains.The number of online news articles available nowadays is rapidly increasing. When exploring articles on online news portals, navigation is mostly limited to the most recent ones. The spatial context and the history of topics are not immediately accessible. To support readers in the exploration or research of articles in large datasets, we developed an interactive 3D globe visualization. We worked with datasets from multiple online news portals containing up to 45000 articles. Using agglomerative hierarchical clustering, we represent the referenced locations of news articles on a globe with different levels of detail. We employ two interaction schemes for navigating the viewpoint on the visualization, including support for hand-held devices and desktop PCs, and provide search functionality and interactive filtering. Based on this framework, we explore additional modules for jointly exploring the spatial and temporal domain of the dataset and incorporating live news into the visualization.In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from high memory costs, which restrict their applicability to mobile devices with tight memory budgets. To address this issue, we propose a distilled Siamese tracking framework to learn small, fast and accurate trackers (students, which capture critical knowledge from large Siamese trackers (teachers by a teacher-students knowledge distillation model. This model is intuitively inspired by the one teacher vs. multiple students learning method typically employed in schools. In particular, our model contains a single teacher-student distillation module and a student-student knowledge sharing mechanism. The former is designed using a tracking-specific distillation strategy to transfer knowledge from a teacher to students. The latter is utilized for mutual learning between students to enable in-depth knowledge understanding. Extensive empirical evaluations on several popular Siamese trackers demonstrate the generality and effectiveness of our framework. Moreover, the results on five tracking benchmarks show that the proposed distilled trackers achieve compression rates of up to 18 times and frame-rates of 265 FPS, while obtaining comparable tracking accuracy compared to base models.In recent years, remarkable progress in zero-shot learning (ZSL has been achieved by generative adversarial networks (GAN . To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios. Mevastatin molecular weight Accordingly, in this paper, we turn to neural architecture search (NAS and make the first attempt to bring NAS techniques into the ZSL realm. Specifically, we propose a differentiable GAN architecture search method over a specifically designed search space for zero-shot learning, referred to as ZeroNAS. Considering the relevance and balance of the generator and discriminator, ZeroNAS jointly searches their architectures in a min-max player game via adversarial training. Extensive experiments conducted on four widely used benchmark datasets demonstrate that ZeroNAS is capable of discovering desirable architectures that perform favorably against state-of-the-art ZSL and generalized zero-shot learning (GZSL approaches.ObjectiveThis study evaluated whether a consumer codesigned leaflet about the common skin infection cellulitis would improve patient satisfaction.MethodsA patient information leaflet was codesigned with consumers incorporating health literacy principles and attached to a new adult lower limb cellulitis management plan launched in three regional Victorian health services. Health service staff were educated to provide the leaflet during hospital care. Patients discharged with a diagnosis of cellulitis in an 8-month period were followed-up via telephone between 31 and 60 days after their discharge. Each patient was asked to provide feedback on the utility of the leaflet (if received) and their overall satisfaction with the information provided to them using a five-point scale (with scores of 4 or 5 considered to indicate satisfaction).ResultsIn all, 81 of 127 (64%) patients (or carers) were contactable, consented to the study and answered the questions. Of these, 27% (n = 22) reported receiving, accepting and re provided to them.What are the implications for practitioners?These findings are a timely reminder for practitioners that even a simple intervention, such a providing a hard copy information leaflet, can improve patient satisfaction. A national repository of similar consumer codesigned materials would be valuable and could minimise existing duplication of effort in resource development across health sectors. Real-world strategies to embed the delivery of such resources is required to ensure that more patients receive the benefit.A novel marine bacterium, designated strain CHFG3-1-5T, was isolated from mangrove sediment sampled at Jiulong River estuary, Fujian, PR China. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain CHFG3-1-5T belonged to the genus Marinobacter, with the highest sequence similarity to Marinobacter segnicrescens SS011B1-4T (97.6%), followed by Marinobacter nanhaiticus D15-8WT (97.5%), Marinobacter bohaiensis T17T (97.1%) and Marinobacter hydrocarbonoclasticus SP.17T (90.6%). The bacterium was Gram-stain-negative, facultative anaerobic, oxidase- and catalase-positive, rod-shaped and motile with a polar flagellum. Strain CHFG3-1-5T grew optimally at 32-37 °C, pH 6.0-8.0 and in the presence of 2.0-3.0% (w/v) NaCl. The G+C content of the chromosomal DNA was 61.1 mol%. The major respiratory quinone was determined to be Q-9. The principal fatty acids were C16 0, summed feature 3 (C16 1 ω7c/ω6c), C12 0, summed feature 9 (C17 1 iso ω9c and/or C16 0 10-methyl), C12 0 3-OH and summed feature 8 (C18 1 ω7c and/or C18 1 ω6c). The polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, three phospholipids, one glycolipid and two aminolipids. The average nucleotide identity and digital DNA-DNA hybridization values among the genomes of strain CHFG3-1-5T and the reference strains were 73.4-79.4 and 19.6-22.4%, respectively. Like many other species reported in the genus Marinobacter, strain CHFG3-1-5T was able to oxidise iron. The combined genotypic and phenotypic data showed that strain CHFG3-1-5T represents a novel species within the genus Marinobacter, for which the name Marinobacter mangrovi sp. nov. is proposed, with the type strain CHFG3-1-5T (=MCCC 1A18306T=KCTC 82398T).A novel growth-promoting and indole acetic acid-producing strain, designated NEAU-LLBT, was isolated from cow dung collected from Shangzhi, Heilongjiang Province, PR China. Cells of strain NEAU-LLBT were Gram-stain-positive, non-motile, aerobic and non-spore-forming. Phylogenetic analysis based on 16S rRNA gene sequence indicated that strain NEAU-LLBT belonged to the genus Microbacterium. Strain NEAU-LLBT had high 16S rRNA sequence similarities of 98.81 and 98.41 % to Microbacterium paludicola DSM 16915T and Microbacterium marinilacus DSM 18904T, and less than 98 % to other members of the genus Microbacterium. link2 Chemotaxonomic characteristics showed that MK-11 and MK-12 were detected as the predominant menaquinones. The peptidoglycan contained glutamic acid, aspartic acid, glycine, ornithine and a small amount of alanine, with ornithine as the diagnostic diamino acid. The major polar lipids were diphosphatidylglycerol, phosphatidylglycerol and an unidentified glycolipid. The major fatty acids were identified as anteiso-C15  0, iso-C16  0 and iso-C17  0. The genomic DNA G+C content of strain NEAU-LLBT was 70.2 mol%. In addition, the average nucleotide identity values between strain NEAU-LLBT and its reference strains, M. paludicola DSM 16915T, M. marinilacus DSM 18904T and M. album SYSU D8007T, were found to be 81.1, 79.4 and 78.7 %, respectively, and the level of digital DNA-DNA hybridization between them were 23.8, 22.6 and 21.8 %, respectively. link3 Based on the phenotypic, phylogenetic and genotypic data, strain NEAU-LLBT is considered to represent a novel species of the genus Microbacterium, for which the name Microbacterium stercoris sp. nov is proposed, with NEAU-LLBT (=CCTCC AA 2018028T=JCM 32660T) as the type strain.
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