Adversarially Robust Neural Architectures

Deep Neural Network (DNN) are vulnerable to adversarial attack. Existing methods are devoted to developing various robust training strategies or regularizations to update the weights of the neural network. But beyond the weights, the overall structure and information flow in the network are explicitly determined by the neural architecture, which remains unexplored. This paper thus aims to improve the adversarial robustness of the network from the architecture perspective with NAS framework. We explore the relationship among adversarial robustness, Lipschitz constant, and architecture parameters and show that an appropriate constraint on architecture parameters could reduce the Lipschitz constant to further improve the robustness. For NAS framework, all the architecture parameters are equally treated when the discrete architecture is sampled from supernet. However, the importance of architecture parameters could vary from operation to operation or connection to connection, which is not explored and might reduce the confidence of robust architecture sampling. Thus, we propose to sample architecture parameters from trainable multivariate log-normal distributions, with which the Lipschitz constant of entire network can be approximated using a univariate log-normal distribution with mean and variance related to architecture parameters. Compared with adversarially trained neural architectures searched by various NAS algorithms as well as efficient human-designed models, our algorithm empirically achieves the best performance among all the models under various attacks on different datasets.Comment: 9 pages, 3 figures, 5 table

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community’s traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community’s traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators

Topographic evolution of tidal flats based on remote sensing: an example in Jiangsu coast, Southern Yellow Sea

The topographic evolution of tidal flats is critical for local ecological conservation, coastal zone management, and physical oceanographic studies. However, obtaining this knowledge is often challenging due to the lack of frequently updated topographic data over large areas. With the explosion of remotely sensed data, the waterline method has become the most operational method for tidal flat topography acquisition. In this study, digital elevation models (DEMs) of the tidal flats around Tongzhou Bay on the Jiangsu coast were constructed using the waterline method for three periods (2013, 2015, and 2017) before and after the construction of phase I of the reclamation project. Furthermore, the topographic evolution characteristics were analyzed from four aspects: contours, area changes, erosion–deposition distribution, and typical cross-sections. The results showed that: 1) During the 5 years from 2013 to 2017, the overall tidal flat area (500 km2) of Tongzhou Bay on the Jiangsu coast had been in a state of deposition, with a total siltation thickness of 0.19 m. 2) The reclamation activities affected the topography of the tidal flats quickly, but the recovery was also rapid. During the implementation of the project (in 2015), the area of the tidal flats above the −2-m contour was rapidly reduced by 20 km2 but rapidly recovered to the pre-project level after the completion of the project (in 2017). 3) The reclamation project directly affected the distribution of erosion and siltation. Outside the seawall on the east side of the Yaosha sand ridge, the 0-m contour expanded rapidly to the outer sea, reaching more than 250 m/year. 4) The sandbars in Tongzhou Bay on the Jiangsu coast generally had a southward-moving trend. Over the past 40 years, the Yaosha sand ridge had shifted southward by 2,500 m and the Lengjiasha sand ridge by more than 5,000 m. This study provides a remote sensing solution for the topographic evolution of large tidal flats under the influence of human reclamation activities

Global Infectious Diseases in December 2022: Monthly Analysis

The emergence and reoccurrence of infectious diseases constitute a significant threat to human health. Data for this paper were mainly obtained from official websites, such as the WHO and national CDC websites. The report summarizes and analyzes information on infectious diseases for early outbreak monitoring from 24 November to 23 December 2022. Monkeypox cases declined in December 2022 with few deaths, while cholera infections have increased in African regions and war-torn countries. Most sub-Saharan countries are affected by insect-borne diseases, such as dengue, Lassa, and chikungunya fever

Sinomenine Suppresses Development of Hepatocellular Carcinoma Cells via Inhibiting MARCH1 and AMPK/STAT3 Signaling Pathway

Promotion of apoptosis and suppression of proliferation in tumor cells are popular strategies for developing anticancer drugs. Sinomenine (SIN), a plant-derived alkaloid, displays antitumor activity. However, the mechanism of action of SIN against hepatocellular carcinoma (HCC) is unclear. Herein, several molecular technologies, such as Western Blotting, qRT-PCR, flow cytometry, and gene knockdown were applied to explore the role and mechanism of action of SIN in the treatment of HCC. It was found that SIN arrests HCC cell cycle at G0/G1 phase, induces apoptosis, and suppresses proliferation of HCC cells via down-regulating the expression of membrane-associated RING-CH finger protein 1 (MARCH1). Moreover, SIN induces cell death and growth inhibition through AMPK/STAT3 signaling pathway. MARCH1 expression was silenced by siRNA to explore its involvement in the regulation of AMPK/STAT3 signaling pathway. Silencing MARCH1 caused down-regulation of phosphorylation of AMPK, STAT3 and decreased cell viability and function. Our results suggested that SIN inhibits proliferation and promotes apoptosis of HCC cells by MARCH1-mediated AMPK/STAT3 signaling pathway. This study provides new support for SIN as a clinical anticancer drug and illustrates that targeting MARCH1 could be a novel treatment strategy in developing anticancer therapeutics

Global Infectious Diseases in March 2023: Monthly Analysis

Infectious diseases pose a major burden on public health and economic stability among societies worldwide. For centuries, they have been among the leading causes of death and disability, and are currently presenting growing challenges to health security and human progress. This report focuses on global outbreaks of infectious diseases, relying on Shusi Tech’s Global Epidemic Information Monitoring System to systematically summarize outbreak timing and location in infected populations from February 24, 2023, to March 23, 2023. Therefore, surveillance of infectious diseases on a continental scale is important to assess, recognize and protect against the risks that these diseases may pose to animal, domestic animal and human health on a global scale

Global Infectious Diseases in June 2023: Monthly Analysis

Infectious diseases are a class of diseases caused by various pathogens that can be transmitted between humans and animals or between humans and animals, thus seriously affecting the development of human society. To control the spread of infectious diseases worldwide and ensure the safety of people’s lives, it is essential to regularly analyze global infectious disease cases. This review is based on data from the World Health Organization, the Centers for Disease Control in countries around the world, Outbreak News Today and many other epidemiological websites to predict the global infectious disease outbreak trend. In addition, using the Shuci Technology global epidemic information monitoring system, we analyzed the distribution of infectious diseases that occurred around the world from 24 May 2023 to 23 June 2023

Global Infectious Diseases in January 2023: Monthly Analysis

Infectious diseases are a major threat to global health and the economic stability of societies worldwide. To prevent outbreaks, monitoring the growth trends of infectious diseases appears to be particularly important and necessary. Herein, data from epidemiological websites, such as the World Health Organization and National Health Council are used to illustrate the outbreak trends for infectious diseases worldwide. In the context of the COVID-19 pandemic, a global resurgence in other infectious diseases has been observed, particularly influenza in the United States. Proper surveillance and effective strategies are urgently required to keep emerging infectious diseases under control

Global Infectious Diseases in November 2022: Monthly Analysis

Infectious diseases, such as COVID-19 and monkeypox, pose a severe threat to economic development in all countries, as well as to the health of people everywhere. The World Health Organization and National Health Council epidemiological websites were used herein as data sources. Shusi Tech’s Global Epidemic Information Monitoring System was used to analyze the data for infectious diseases, determine changes in global epidemics, determine the distribution and quantity of infectious disease cases from October 24, 2022 to November 23, 2022, and analyze their changing trends. Furthermore, the analysis of these data can be used to predict prevalence rates, and assess epidemic prevention and control measures