The overexpression of TDP-43 in astrocytes causes neurodegeneration via a PTP1B-mediated inflammatory response

Background: Cytoplasmic inclusions of transactive response DNA binding protein of 43 kDa (TDP-43) in neurons and astrocytes are a feature of some neurodegenerative diseases, such as frontotemporal lobar degeneration with TDP-43 (FTLD-TDP) and amyotrophic lateral sclerosis (ALS). However, the role of TDP-43 in astrocyte pathology remains largely unknown. Methods: To investigate whether TDP-43 overexpression in primary astrocytes could induce inflammation, we transfected primary astrocytes with plasmids encoding Gfp or TDP-43-Gfp. The inflammatory response and upregulation of PTP1B in transfected cells were examined using quantitative RT-PCR and immunoblot analysis. Neurotoxicity was analysed in a transwell coculture system of primary cortical neurons with astrocytes and cultured neurons treated with astrocyte-conditioned medium (ACM). We also examined the lifespan, performed climbing assays and analysed immunohistochemical data in pan-glial TDP-43-expressing flies in the presence or absence of a Ptp61f RNAi transgene. Results: PTP1B inhibition suppressed TDP-43-induced secretion of inflammatory cytokines (interleukin 1 beta (IL-1β), interleukin 6 (IL-6) and tumour necrosis factor alpha (TNF-α)) in primary astrocytes. Using a neuron-astrocyte coculture system and astrocyte-conditioned media treatment, we demonstrated that PTP1B inhibition attenuated neuronal death and mitochondrial dysfunction caused by overexpression of TDP-43 in astrocytes. In addition, neuromuscular junction (NMJ) defects, a shortened lifespan, inflammation and climbing defects caused by pan-glial overexpression of TDP-43 were significantly rescued by downregulation of ptp61f (the Drosophila homologue of PTP1B) in flies. Conclusions: These results indicate that PTP1B inhibition mitigates the neuronal toxicity caused by TDP-43-induced inflammation in mammalian astrocytes and Drosophila glial cells. © 2020, The Author(s).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

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

Design and Fabrication of Millimeter-Wave Frequency-Tunable Metamaterial Absorber Using MEMS Cantilever Actuators

In this paper, a MEMS (Micro Electro Mechanical Systems)-based frequency-tunable metamaterial absorber for millimeter-wave application was demonstrated. To achieve the resonant-frequency tunability of the absorber, the unit cell of the proposed metamaterial was designed to be a symmetric split-ring resonator with a stress-induced MEMS cantilever array having initial out-of-plane deflections, and the cantilevers were electrostatically actuated to generate a capacitance change. The dimensional parameters of the absorber were determined via impedance matching using a full electromagnetic simulation. The designed absorber was fabricated on a glass wafer with surface micromachining processes using a photoresist sacrificial layer and the oxygen-plasma-ashing process to release the cantilevers. The performance of the fabricated absorber was experimentally validated using a waveguide measurement setup. The absorption frequency shifted down according to the applied DC (direct current) bias voltage from 28 GHz in the initial off state to 25.5 GHz in the pull-down state with the applied voltage of 15 V. The measured reflection coefficients at those frequencies were -5.68 dB and -33.60 dB, corresponding to the peak absorptivity rates of 72.9 and 99.9%, respectively.N

Design and Fabrication of Millimeter-Wave Frequency-Tunable Metamaterial Absorber Using MEMS Cantilever Actuators

In this paper, a MEMS (Micro Electro Mechanical Systems)-based frequency-tunable metamaterial absorber for millimeter-wave application was demonstrated. To achieve the resonant-frequency tunability of the absorber, the unit cell of the proposed metamaterial was designed to be a symmetric split-ring resonator with a stress-induced MEMS cantilever array having initial out-of-plane deflections, and the cantilevers were electrostatically actuated to generate a capacitance change. The dimensional parameters of the absorber were determined via impedance matching using a full electromagnetic simulation. The designed absorber was fabricated on a glass wafer with surface micromachining processes using a photoresist sacrificial layer and the oxygen-plasma-ashing process to release the cantilevers. The performance of the fabricated absorber was experimentally validated using a waveguide measurement setup. The absorption frequency shifted down according to the applied DC (direct current) bias voltage from 28 GHz in the initial off state to 25.5 GHz in the pull-down state with the applied voltage of 15 V. The measured reflection coefficients at those frequencies were −5.68 dB and −33.60 dB, corresponding to the peak absorptivity rates of 72.9 and 99.9%, respectively

RING-Type E3 Ubiquitin Ligases AtRDUF1 and AtRDUF2 Positively Regulate the Expression of PR1 Gene and Pattern-Triggered Immunity

The importance of E3 ubiquitin ligases from different families for plant immune signaling has been confirmed. Plant RING-type E3 ubiquitin ligases are members of the E3 ligase superfamily and have been shown to play positive or negative roles during the regulation of various steps of plant immunity. Here, we present Arabidopsis RING-type E3 ubiquitin ligases AtRDUF1 and AtRDUF2 which act as positive regulators of flg22- and SA-mediated defense signaling. Expression of AtRDUF1 and AtRDUF2 is induced by pathogen-associated molecular patterns (PAMPs) and pathogens. The atrduf1 and atrduf2 mutants displayed weakened responses when triggered by PAMPs. Immune responses, including oxidative burst, mitogen-activated protein kinase (MAPK) activity, and transcriptional activation of marker genes, were attenuated in the atrduf1 and atrduf2 mutants. The suppressed activation of PTI responses also resulted in enhanced susceptibility to bacterial pathogens. Interestingly, atrduf1 and atrduf2 mutants showed defects in SA-mediated or pathogen-mediated PR1 expression; however, avirulent Pseudomonas syringae pv. tomato DC3000-induced cell death was unaffected. Our findings suggest that AtRDUF1 and AtRDUF2 are not just PTI-positive regulators but are also involved in SA-mediated PR1 gene expression, which is important for resistance to P. syringae

EzTaxon: a web-based tool for the identification of prokaryotes based on 16S ribosomal RNA gene sequences

16S rRNA gene sequences have been widely used for the identification of prokaryotes. However, the flood of sequences of non-type strains and the lack of a peer-reviewed database for 16S rRNA gene sequences of type strains have made routine identification of isolates difficult and labour-intensive. In the present study, we generated a database containing 16S rRNA gene sequences of all prokaryotic type strains. In addition, a web-based tool, named EzTaxon, for analysis of 16S rRNA gene sequences was constructed to achieve identification of isolates based on pairwise nucleoticle similarity values and phylogenetic inference methods. The system developed provides users with a similarity-based search, multiple sequence alignment and various phylogenetic analyses. All of these functions together with the 16S rRNA gene sequence database of type strains can be successfully used for automated and reliable identification of prokaryotic isolates.

Comparative Transcriptome-Based Mining of Senescence-Related MADS, NAC, and WRKY Transcription Factors in the Rapid-Senescence Line DLS-91 of Brassica rapa

Leaf senescence is a developmental process induced by various molecular and environmental stimuli that may affect crop yield. The dark-induced leaf senescence-91 (DLS-91) plants displayed rapid leaf senescence, dramatically decreased chlorophyll contents, low photochemical efficiencies, and upregulation of the senescence-associated marker gene BrSAG12-1. To understand DLS molecular mechanism, we examined transcriptomic changes in DLS-91 and control line DLS-42 following 0, 1, and 4 days of dark treatment (DDT) stages. We identified 501, 446, and 456 DEGs, of which 16.7%, 17.2%, and 14.4% encoded TFs, in samples from the three stages. qRT-PCR validation of 16 genes, namely, 7 MADS, 6 NAC, and 3 WRKY, suggested that BrAGL8-1, BrAGL15-1, and BrWRKY70-1 contribute to the rapid leaf senescence of DLS-91 before (0 DDT) and after (1 and 4 DDT) dark treatment, whereas BrNAC046-2, BrNAC029-2/BrNAP, and BrNAC092-1/ORE1 TFs may regulate this process at a later stage (4 DDT). In-silico analysis of cis-acting regulatory elements of BrAGL8-1, BrAGL42-1, BrNAC029-2, BrNAC092-1, and BrWRKY70-3 of B. rapa provides insight into the regulation of these genes. Our study has uncovered several AGL-MADS, WRKY, and NAC TFs potentially worthy of further study to understand the underlying mechanism of rapid DLS in DLS-91