HEXA-018, a Novel Inducer of Autophagy, Rescues TDP-43 Toxicity in Neuronal Cells

The autophagy-lysosomal pathway is an essential cellular mechanism that degrades aggregated proteins and damaged cellular components to maintain cellular homeostasis. Here, we identified HEXA-018, a novel compound containing a catechol derivative structure, as a novel inducer of autophagy. HEXA-018 increased the LC3-I/II ratio, which indicates activation of autophagy. Consistent with this result, HEXA-018 effectively increased the numbers of autophagosomes and autolysosomes in neuronal cells. We also found that the activation of autophagy by HEXA-018 is mediated by the AMPK-ULK1 pathway in an mTOR-independent manner. We further showed that ubiquitin proteasome system impairment- or oxidative stress-induced neurotoxicity was significantly reduced by HEXA-018 treatment. Moreover, oxidative stress-induced mitochondrial dysfunction was strongly ameliorated by HEXA-018 treatment. In addition, we investigated the efficacy of HEXA-018 in models of TDP-43 proteinopathy. HEXA-018 treatment mitigated TDP-43 toxicity in cultured neuronal cell lines and Drosophila. Our data indicate that HEXA-018 could be a new drug candidate for TDP-43-associated neurodegenerative diseases.1

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Authenticity or homogeneity? Contextualising the urban revitalisation of a post-industrial landscape through the Red Brick Landscape Preservation Project in Seoul

This study analyses the case of the Red Brick Landscape Preservation Project at Seoul Forest Park Alleys, an urban revitalisation project implemented between 2018 and 2021, based on the idea of authenticity and post-industrial landscape. The following issues on site were determined based on site analysis and data mapping. First, there is a discrepancy in preservation, use, and implementation. Second is the selective nature of the Red Brick Landscape Preservation Project, as it is adapted to the city branding strategy, which may not always prioritise the actual site-specific contexts. Third, the attempt to represent the authenticity of the Red Brick Landscape Preservation Project in the Seoul Forest Park Alleys with a red brick landscape seems unfounded. Instead, red bricks were adapted to represent an image of authenticity seen elsewhere. Finally, this projection of authenticity from elsewhere onto the site is difficult to avoid for post-industrial landscapes because of the nature of industrial cities. Considering that the post-industrial landscape remains in high demand worldwide, the criticisms in this article may extend beyond the case in point. This study concludes with recommendations for future studies of the site.N

Coverage Dependent Variation of the Adsorption Structure of 2-Thiophenecarboxaldehyde on the Ge(100)-2 × 1 Reconstructed Surface

High-resolution photoemission spectroscopy (HRPES) measurements were collected and density functional theory (DFT) calculations were performed to track the exposure-dependent variation of the adsorption structure of 2-thiophenecarboxaldehyde (C4H3SCHO: TPCA) on the Ge(100) 2 × 1 reconstructed surface at room temperature. In an effort to identify the most probable adsorption structures on the Ge(100)-2 × 1 reconstructed surface, we deposited TPCA molecules at low exposure and at high exposure and compared the differences between the electronic features measured using HRPES. The HRPES data suggested three possible adsorption structures of TPCA on the Ge(100)-2 × 1 reconstructed surface, and DFT calculations were used to determine the plausibility of these structures. HRPES analysis corroborated by DFT calculations, indicated that an S-dative bonded structure is the most probable adsorption structure at relatively low exposure levels, the [4 + 2] cycloadduct structure is the second most probable structure, and the [2 + 2]-C=O cycloadduct structure is the least probable structure on the Ge(100)-2 × 1 reconstructed surface at relatively high exposure levels

Designing a public engagement process for long-term urban park development project.

Gathering public consensus about long-term urban open space development is more difficult than ever, even though public engagement is crucial for sustainable long-term policymaking. Routine evaluation of public awareness is important for retaining project momentum and designing appropriate public engagement processes for the future. This study focuses on the Yongsan Park Development Project, which has been in progress for more than three decades. An online survey of 2,000 respondents was conducted and analyzed to evaluate the current public awareness and ask questions about respondents' expectations for public engagement. The results of this study reveal that 1) a hybrid methodology is needed to effectively approach different age groups; 2) an online survey can offer new insights for projects that repurpose U.S. army base and military sites into urban open spaces; 3) the survey results will enable us to design a better public participation process that is appropriate for post-pandemic society, in which virtual meetings and socially distanced communications are part of the new norm

Method of Profanity Detection Using Word Embedding and LSTM

With the rising number of Internet users, there has been a rapid increase in cyberbullying. Among the types of cyberbullying, verbal abuse is emerging as the most serious problem, for preventing which profanity is being identified and blocked. However, users employ words cleverly to avoid blocking. With the existing profanity discrimination methods, deliberate typos and profanity using special characters can be discriminated with high accuracy. However, as they cannot grasp the meaning of the words and the flow of sentences, standard words such as “Sibaljeom (starting point, a Korean word that sounds similar to a swear word)” and “Saekkibalgalag (little toe, a Korean word that sounds similar to another swear word)” are less accurately discriminated. Therefore, in order to solve this problem, this study proposes a method of discriminating profanity using a deep learning model that can grasp the meaning and context of words after separating Hangul into the onset, nucleus, and coda