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F1Fo-ATP synthase was originally thought to exclusively locate in the inner membrane of the mitochondria. However, recent studies prove the existence of ectopic F1Fo-ATP synthase on the outside of the cell membrane. Ectopic ATP synthase was proposed as a marker for tumor target therapy. Nevertheless, the protein transport mechanism of the ectopic ATP synthase is still unclear. The specificity of the ectopic ATP synthase, with regard to tumors, is questioned because of its widespread expression. In the current study, we constructed green fluorescent protein-ATP5B fusion protein and introduced it into HepG2 cells to study the localization of the ATP synthase. The expression of ATP5B was analyzed in six cell lines with different 'malignancies'. These cells were cultured in both normal and tumor-like acidic and hypoxic conditions. The results suggested that the ectopic expression of ATP synthase is a consequence of translocation from the mitochondria. The expression and catalytic activity of ectopic ATP synthase were similar on the surface of malignant cells as on the surface of less malignant cells. Interestingly, the expression of ectopic ATP synthase was not up-regulated in tumor-like acidic and hypoxic microenvironments. However, the catalytic activity of ectopic ATP synthase was up-regulated in tumor-like microenvironments. Therefore, the specificity of ectopic ATP synthase for tumor target therapy relies on the high level of catalytic activity that is observed in acidic and hypoxic microenvironments in tumor tissues.

Background: The pandemic of COVID-19 posed a challenge to global healthcare. The mortality rates of severe cases range from 8.1% to 31.8%, and it is particularly important to identify risk factors that aggravate the disease.Methods: We performed a systematic review of the literature with meta-analysis, using 7 databases to assess clinical characteristics, comorbidities and complications in severe and non-severe patients with COVID-19. All the observational studies were included. We performed a random or fixed effects model meta-analysis to calculate the pooled proportion and 95% CI. Measure of heterogeneity was estimated by Cochran’s Q statistic, I2 index and P value.Results: 4881 cases from 25 studies related to COVID-19 were included. The most prevalent comorbidity was hypertension (severe: 33.4%, 95% CI: 25.4% - 41.4%; non-severe 21.6%, 95% CI: 9.9% - 33.3%), followed by diabetes (severe: 14.4%, 95% CI: 11.5% - 17.3%; non-severe: 8.5%, 95% CI: 6.1% - 11.0%). The prevalence of ARDS, AKI and shock were all higher in severe cases, with 41.1% (95% CI: 14.1% - 68.2%), 16.4% (95% CI: 3.4% - 29.5%) and 19.9% (95% CI: 5.5% - 34.4%), rather than 3.0% (95% CI: 0.6% - 5.5%), 2.2% (95% CI: 0.1% - 4.2%) and 4.1% (95% CI -4.8% - 13.1%) in non-severe patients, respectively. The death rate was higher in severe cases (30.3%, 95% CI: 13.8% - 46.8%) than non-severe cases (1.5%, 95% CI: 0.1% - 2.8%).Conclusions: Hypertension, diabetes and cardiovascular diseases may be risk factors for COVID-19 patients to develop into severe cases.

Objective Guidelines from different areas on the use of non-invasive ventilation in COVID-19 have generally been inconsistent. The goals were to appraise the quality and availability of guidelines stated and whether non-invasive ventilation in the early stage of the pandemic is of importance. Design and Method Databases including PubMed, Web of Science, Cochrane Library, and websites of international organizations and gray databases were searched up to June 23, 2020. We also hand-searched the reference lists of eligible papers. Results A total of 26 guidelines met the inclusion criteria. Regarding the appraisal by the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, the guidelines’ methodological quality was low. Among six domains, Rigour. of Development and Editorial Independence were of the lowest quality. Given the lack of evidence from randomized clinical trials and the great differences between different regions, non-invasive ventilation’s recommendations generated a considerable debate at the early stage of COVID-19. Conclusions Improving the methodological quality of the guidelines should be a goal in future pandemics. Additionally, more well-designed randomized clinical trials are needed to solve the controversy on the impact of non-invasive ventilation.

ObjectiveThe present study aimed to investigate circular RNA (circRNA) expression in uveal melanoma (UM).MethodsFirst, we used microarray to compare the expression profiles of circRNA in five UM samples and five normal uvea tissues. Next, bioinformatics analyses, including gene ontology (GO) analysis and pathway analysis, were applied to study these differentially expressed circRNAs to predict pathogenic pathways that may be involved. Quantitative real-time polymerase chain reaction (qRT-PCR) in 20 UM samples and 20 normal uvea samples was used to confirm the circRNA expression profiles obtained from the microarray data. Finally, we analyzed the interaction between validated circRNAs and their potential cancer-associated miRNA targets.ResultsIn total, 50,579 circRNAs [fold change (FC) ≥2.0; P<0.05], including 20,654 up-regulated and 29,925 down-regulated circRNAs, were identified as differentially expressed between UM tissues and normal uvea tissues. We used qRT-PCR to verify seven dysregulated circRNAs indicated by the microarray data, including hsa_circ_0119873, hsa_circ_0128533, hsa_circ_0047924, hsa_circ_0103232, hsa-circRNA10628-6, hsa_circ_0032148 and hsa_circ_0133460, which may be promising candidates to study future molecular mechanisms.ConclusionsThis study explored, for the first time, the abnormal expression of circRNAs in UM and described the expression profile of circRNAs, providing a new potential target for the mechanism of UM and future treatment of UM.

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Self-supervised speech representations such as wav2vec 2.0 and HuBERT are making revolutionary progress in Automatic Speech Recognition (ASR). However, self-supervised models have not been totally proved to produce better performance on tasks other than ASR. In this work, we explore partial fine-tuning and entire finetuning on wav2vec 2.0 and HuBERT pre-trained models for three non-ASR speech tasks : Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding. We also compare pre-trained models with/without ASR fine-tuning. With simple down-stream frameworks, the best scores reach 79.58% weighted accuracy for Speech Emotion Recognition on IEMOCAP, 2.36% equal error rate for Speaker Verification on VoxCeleb1, 87.51% accuracy for Intent Classification and 75.32% F1 for Slot Filling on SLURP, thus setting a new state-of-the-art for these three benchmarks, proving that fine-tuned wav2vec 2.0 and HuBERT models can better learn prosodic, voice-print and semantic representations.

Abstract:With the rapid development of mobile data acquisition technology, the volume of available spatial data is growing at an increasingly fast pace. The real-time processing of big spatial data has become a research frontier in the field of Geographic Information Systems (GIS). To cope with these highly dynamic data, we aim to reduce the time complexity of data updating by modifying the traditional spatial index. However, existing algorithms and data structures are based on single work nodes, which are incapable of handling the required high numbers and update rates of moving objects. In this paper, we present a distributed spatial index based on Apache Storm, an open-source distributed real-time computation system. Using this approach, we compare the range and K-nearest neighbor (KNN) query efficiency of four spatial indexes on a single dataset and introduce a method of performing spatial joins between two moving datasets. In particular, we build a secondary distributed index for spatial join queries based on the grid-partition index. Finally, a series of experiments are presented to explore the factors that affect the performance of the distributed index and to demonstrate the feasibility of the proposed distributed index based on Storm. As a real-world application, this approach has been integrated into an information system that provides real-time traffic decision support.