Terricoxanthones A-E, unprecedented dihydropyran-containing dimeric xanthones from the endophytic fungus Neurospora terricola HDF-Br-2 associated with the vulnerable conifer Pseudotsuga gaussenii.

An investigation on the secondary metabolites from a rice culture broth of the endophytic fungus Neurospora terricola HDF-Br-2 derived from the vulnerable conifer Pseudotsuga gaussenii led to the isolation and characterization of 34 structurally diverse polyketides (1-34). Seven of them are previously undescribed, including five unprecedented dihydropyran-containing (terricoxanthones A-E, 1-5, resp.) and one rare tetrahydrofuran-containing (terricoxanthone F, 6) dimeric xanthones. The structures were elucidated by spectroscopic methods and single-crystal X-ray diffraction analyses. Terricoxanthones each were obtained as a racemic mixture. Their plausible biosynthetic relationships were briefly proposed. Compounds 6, aspergillusone A (8), and alatinone (27) displayed considerable inhibition against Candida albicans with MIC values of 8-16 µg/mL. 4-Hydroxyvertixanthone (12) and 27 exhibited significant inhibitory activities against Staphylococcus aureus, with MIC values of 4-8 µg/mL. Furthermore, compounds 8 and 27 could disrupt biofilm of S. aureus and C. albicans at 128 µg/mL. The findings not only extend the skeletons of xanthone dimers and contribute to the diversity of metabolites of endophytes associated with the endangered Chinese conifer P. gaussenii, but could further reveal the important role of protecting plant species diversity in support of chemical diversity and potential sources of new therapeutics.

Aberrant TDP-43 phosphorylation: a key wind gap from TDP-43 to TDP-43 proteinopathy.

TDP-43 proteinopathy is a kind of neurodegenerative diseases related to the TAR DNA-binding protein of 43-kDa molecular weight (TDP-43). The typical neurodegenerative diseases include amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD), Alzheimer's disease (AD), Parkinson's disease (PD) and so on. As the disease process cannot be blocked or slowed down, these patients have poor quality of life and poor prognosis, and bring a huge burden to the family and society. So far, the specific pathogenesis of TDP-43 proteinopathy is not clear, and there is no effective preventive measure and treatment program for this kind of disease. TDP-43 plays an important role in triggering or promoting the occurrence and progression of TDP-43 proteinopathy. The hyperphosphorylation of TDP-43 is undoubtedly an important factor in triggering or promoting the process of TDP-43 proteinopathy. Hyperphosphorylation of TDP-43 can inhibit the degradation of TDP-43, aggravate the aggregation of TDP-43 protein, increase the wrong localization of TDP-43 in cells, and enhance the cytotoxicity of TDP-43. More and more evidences show that the hyperphosphorylation of TDP-43 plays an important role in the pathogenesis of TDP-43 proteinopathy. Inhibition of TDP-43 hyperphosphorylation may be one of the important strategies for the treatment of TDP-43 proteinopathy. Therefore, this article reviews the role of TDP-43 phosphorylation in TDP-43 proteinopathy and the related mechanisms.

Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning.

Accurate weather data are important for planning our day-to-day activities. In order to monitor and predict weather information, a two-phase weather management system is proposed, which combines information processing, bus mobility, sensors, and deep learning technologies to provide real-time weather monitoring in buses and stations and achieve weather forecasts through predictive models. Based on the sensing measurements from buses, this work incorporates the strengths of local information processing and moving buses for increasing the measurement coverage and supplying new sensing data. In Phase I, given the weather sensing data, the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model are trained and verified using the data of temperature, humidity, and air pressure of the test environment. In Phase II, the trained learning model is applied to predict the time series of weather information. In order to assess the system performance, we compare the predicted weather data with the actual sensing measurements from the Environment Protection Administration (EPA) and Central Weather Bureau (CWB) of Taichung observation station to evaluate the prediction accuracy. The results show that the proposed system has reliable performance at weather monitoring and a good forecast for one-day weather prediction via the trained models.