Pathogen hijacks programmed cell death signaling by arginine ADPR-deacylization of caspases.

Caspases are evolutionarily conserved cysteine proteases that are essential for regulating cell death and are involved in multiple development and disease processes, including immunity. Here, we show that the bacterial type III secretion system (T3SS) effector CopC (Chromobacterium outer protein C) from the environmental pathogen Chromobacterium violaceum attacks caspase-3/-7/-8/-9 by ADPR-deacylization to dysregulate programmed cell death, including apoptosis, necroptosis, and pyroptosis. This modification involves ADP-ribosylation- and deamination-mediated cyclization on Arg207 of caspase-3 by a mechanism that requires the eukaryote-specific protein calmodulin (CaM), leading to inhibition of caspase activity. The manipulation of cell death signaling by CopC is essential for the virulence of C. violaceum in a mouse infection model. CopC represents a family of enzymes existing in taxonomically diverse bacteria associated with a wide spectrum of eukaryotes ranging from humans to plants. The unique activity of CopC establishes a mechanism by which bacteria counteract host defenses through a previously unrecognized post-translational modification.

Structural insights into caspase ADPR deacylization catalyzed by a bacterial effector and host calmodulin.

Programmed cell death and caspase proteins play a pivotal role in host innate immune response combating pathogen infections. Blocking cell death is employed by many bacterial pathogens as a universal virulence strategy. CopC family type III effectors, including CopC from an environmental pathogen Chromobacterium violaceum, utilize calmodulin (CaM) as a co-factor to inactivate caspases by arginine ADPR deacylization. However, the molecular basis of the catalytic and substrate/co-factor binding mechanism is unknown. Here, we determine successive cryo-EM structures of CaM-CopC-caspase-3 ternary complex in pre-reaction, transition, and post-reaction states, which elucidate a multistep enzymatic mechanism of CopC-catalyzed ADPR deacylization. Moreover, we capture a snapshot of the detachment of modified caspase-3 from CopC. These structural insights are validated by mutagenesis analyses of CopC-mediated ADPR deacylization in vitro and animal infection in vivo. Our study offers a structural framework for understanding the molecular basis of arginine ADPR deacylization catalyzed by the CopC family.

Attention based dynamic graph neural network for asset pricing.

Recent studies suggest that networks among firms (sectors) play a vital role in asset pricing. This paper investigates these implications and develops a novel end-to-end graph neural network model for asset pricing by combining and modifying two state-of-the-art machine learning techniques. First, we apply the graph attention mechanism to learn dynamic network structures of the equity market over time and then use a recurrent convolutional neural network to diffuse and propagate firms' information into the learned networks. This novel approach allows us to model the implications of networks along with the characteristics of the dynamic comovement of asset prices. The results demonstrate the effectiveness of our proposed model in both predicting returns and improving portfolio performance. Our approach demonstrates persistent performance in different sensitivity tests and simulated data. We also show that the dynamic network learned from our proposed model captures major market events over time. Our model is highly effective in recognizing the network structure in the market and predicting equity returns and provides valuable market information to regulators and investors.

Are missing values important for earnings forecast? a machine learning perspective.

Analysts' forecast is one of the most common and important estimators for firms' future earnings. However, it is challenging to fully utilize because of the missing values. This study applies machine learning techniques to impute missing values in individual analysts' forecasts and subsequently to predict firms' future earnings based on both imputed and observed forecasts. After imputing missing values, the forecast error is reduced by 41% compared to the mean forecast, suggesting that missing values after imputation indeed useful for earnings forecast. We analyze multiple imputation methods and show that the out-performance of matrix factorization (MF) is consistent using different evaluation measures and across firms. Finally, we propose a stochastic gradient descent based coupled matrix factorization (CMF) to augment the imputation quality of missing values with multiple datasets. CMF further reduces the error of earnings forecast by 19% compared to the MF with a single dataset.

Modifications on Promoting the Proton Conductivity of Polybenzimidazole-Based Polymer Electrolyte Membranes in Fuel Cells.

Hydrogen-air proton exchange membrane fuel cells (PEMFCs) and direct methanol fuel cells (DMFCs) are excellent fuel cells with high limits of energy density. However, the low carbon monoxide (CO) tolerance of the Pt electrode catalyst in hydrogen-air PEMFCs and methanol permanent in DMFCs greatly hindered their extensive use. Applying polybenzimidazole (PBI) membranes can avoid these problems. The high thermal stability allows PBI membranes to work at elevated temperatures when the CO tolerance can be significantly improved; the excellent methanol resistance also makes it suitable for DMFCs. However, the poor proton conductivity of pristine PBI makes it hard to be directly applied in fuel cells. In the past decades, researchers have made great efforts to promote the proton conductivity of PBI membranes, and various effective modification methods have been proposed. To provide engineers and researchers with a basis to further promote the properties of fuel cells with PBI membranes, this paper reviews critical researches on the modification of PBI membranes in both hydrogen-air PEMFCs and DMFCs aiming at promoting the proton conductivity. The modification methods have been classified and the obtained properties have been included. A guide for designing modifications on PBI membranes for high-performance fuel cells is provided.