Deep Causal Learning: Representation, Discovery and Inference

Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses. However, there are many problems and bottlenecks in traditional causal learning methods, such as high-dimensional unstructured variables, combinatorial optimization problems, unknown intervention, unobserved confounders, selection bias and estimation bias. Deep causal learning, that is, causal learning based on deep neural networks, brings new insights for addressing these problems. While many deep learning-based causal discovery and causal inference methods have been proposed, there is a lack of reviews exploring the internal mechanism of deep learning to improve causal learning. In this article, we comprehensively review how deep learning can contribute to causal learning by addressing conventional challenges from three aspects: representation, discovery, and inference. We point out that deep causal learning is important for the theoretical extension and application expansion of causal science and is also an indispensable part of general artificial intelligence. We conclude the article with a summary of open issues and potential directions for future work

Upregulation of Endogenous HMOX1 Expression by a Computer-Designed Artificial Transcription Factor

Heme oxygenase-1 (HO-1) is well known as a cytoprotective factor. Research has revealed that it is a promising therapeutic target for cardiovascular diseases. In the current study, an HMOX1 (HO-1 gene) enhancer-specific artificial zinc-finger protein (AZP) was designed using bioinformatical methods. Then, an artificial transcription factor (ATF) was constructed based on the AZP. In the ATF, the p65 functional domain was used as the effector domain (ED), and a nuclear localization sequence (NLS) was also included. We next analyzed the affinity of the ATF to the HMOX1 enhancer and the effect of the ATF on endogenous HMOX1 expression. The results suggest that the ATF could effectively upregulate endogenous HMOX1 expression in ECV304 cells. With further research, the ATF could be developed as a potential drug for cardiovascular diseases

Water pollutant fingerprinting tracks recent industrial transfer from coastal to inland China: a case study

In recent years, China’s developed regions have transferred industries to undeveloped regions. Large numbers of unlicensed or unregistered enterprises are widespread in these undeveloped regions and they are subject to minimal regulation. Current methods for tracing industrial transfers in these areas, based on enterprise registration information or economic surveys, do not work. The authors have developed an analytical framework combining water fingerprinting and evolutionary analysis to trace the pollution transfer features between water sources. We collected samples in Eastern China (industrial export) and Central China (industrial acceptance) separately from two water systems. Based on the water pollutant fingerprints and evolutionary trees, we traced the pollution transfer associated with industrial transfer between the two areas. The results are consistent with four episodes of industrial transfers over the past decade. The results also show likely types of the transferred industries - electronics, plastics, and biomedicines - that contribute to the water pollution transfer

Upregulation of Endogenous HMOX1 Expression by a Computer-Designed Artificial Transcription Factor

Heme oxygenase-1 (HO-1) is well known as a cytoprotective factor. Research has revealed that it is a promising therapeutic target for cardiovascular diseases. In the current study, an HMOX1 (HO-1 gene) enhancer-specific artificial zinc-finger protein (AZP) was designed using bioinformatical methods. Then, an artificial transcription factor (ATF) was constructed based on the AZP. In the ATF, the p65 functional domain was used as the effector domain (ED), and a nuclear localization sequence (NLS) was also included. We next analyzed the affinity of the ATF to the HMOX1 enhancer and the effect of the ATF on endogenous HMOX1 expression. The results suggest that the ATF could effectively upregulate endogenous HMOX1 expression in ECV304 cells. With further research, the ATF could be developed as a potential drug for cardiovascular diseases

Occurrence of Regulated and Emerging Iodinated DBPs in the Shanghai Drinking Water

10.1371/journal.pone.0059677PLoS ONE83

A secure and robust object-based video authentication system

An object-based video authentication system, which combines watermarking, error correction coding (ECC), and digital signature techniques, is presented for protecting the authenticity between video objects and their associated backgrounds. In this system, a set of angular radial transformation (ART) coefficients is selected as the feature to represent the video object and the background, respectively. ECC and cryptographic hashing are applied to those selected coefficients to generate the robust authentication watermark. This content-based, semifragile watermark is then embedded into the objects frame by frame before MPEG4 coding. In watermark embedding and extraction, groups of discrete Fourier transform (DFT) coefficients are randomly selected, and their energy relationships are employed to hide and extract the watermark. The experimental results demonstrate that our system is robust to MPEG4 compression, object segmentation errors, and some common object-based video processing such as object translation, rotation, and scaling while securely preventing malicious object modifications. The proposed solution can be further incorporated into public key infrastructure (PKI)

Event-triggered robust H∞ control for uncertain networked control systems with time delay

H∞ control for networked control systems with exogenous disturbances and norm-bounded parameter uncertainties has been extensively investigated. However, how to better use the limited network capacity and computation resources while reducing the conservativeness of the H∞ control is still not fully understood. This paper presents a new dynamic discrete event-triggered scheme with improved modelling and control design to tackle this problem. The event-triggering is designed with periodic data sampling, and consequently the closed-loop system is formulated as a unified time-delayed model with the sampled data. From this model, an augmented Lyapunov–Krasovskii functional is constructed with triple-integral terms. A new free-weight matrix technique and the Wirtinger-based inequality are utilized over the Lyapunov–Krasovskii functional to derive a less conservative controller. This leads to two delay-range-dependent stability criteria in terms of linear matrix inequalities. Integrating all these components forms a co-design method for the minimum H∞ performance index and our event-triggered scheme. Simulation experiments are conducted to demonstrate the approach presented in this paper

Laminar dependent orientation selectivity and its dynamics in macaque V1

PURPOSE: Orientation selective is an emergent property of primary visual cortex (V1). Previous studies have proposed that broadly-tuned suppression or response nonlinearity (power law) may play important roles for sharpening of cortical selectivity. We ask how the two mechanisms collaborate to sharpen the orientation selectivity in V1. METHODS: we used a linear array electrode to record neural response simultaneously from multiple cortical depths in V1 of awake macaque monkeys. Stimuli with different orientations flashed 20ms each frame. Dynamic responses was calculated with the reverse correlation method. RESULTS &amp; CONCLUSIONS:We found that dynamic responses to stimulus orientations are different cross cortical layers. Based on the response dynamics, we estimate the broadly-tuned suppression and response nonlinearity at different layers, we found that suppression is stronger in input layers, while output layers have largest nonlinearity and highest selectivity. Our results suggest that orientation selectivity is sharpened through multiple stages in V1.</p