Machine Learning-Enabled Regional Multi-Hazards Risk Assessment Considering Social Vulnerability

The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribution in different areas, specifically a major priority in the areas of high hazards level and social vulnerability. We propose a multi-hazards risk assessment method which considers social vulnerability into the analyzing and utilize machine learning-enabled models to solve this issue. The proposed methodology integrates three aspects as follows: (1) characterization and mapping of multi-hazards (Flooding, Wildfires, and Seismic) using five machine learning methods including Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), and K-Means (KM); (2) evaluation of social vulnerability with a composite index tailored for the case-study area and using machine learning models for classification; (3) risk-based quantification of spatial interaction mechanisms between multi-hazards and social vulnerability. The results indicate that RF model performs best in both hazard-related and social vulnerability datasets. The most cities at multi-hazards risk account for 34.12% of total studied cities (covering 20.80% land). Additionally, high multi-hazards level and socially vulnerable cities account for 15.88% (covering 4.92% land). This study generates a multi-hazards risk map which show a wide variety of spatial patterns and a corresponding understanding of where regional high hazards potential and vulnerable areas are. It emphasizes an urgent need to implement information-based prioritization when natural hazards coming, and effective policy measures for reducing natural-hazards risks in future

Long Noncoding RNA BCYRN1 Promotes the Proliferation of Colorectal Cancer Cells via Up-Regulating NPR3 Expression

Background/Aims: Long noncoding RNAs (lncRNAs) constitute a large proportion of noncoding transcripts that have recently emerged as a new class of important regulators in cancers. LncRNA BCYRN1, also known as BC200, has a potential function in tumorigenesis. However, the clinical significance of BCYRN1 and its effect on colorectal cancer (CRC) progression remains unclear. Methods: Quantitative reverse transcriptase PCR (qRT-PCR) was performed to investigate the expression of BCYRN1 in CRC tissues and cell lines. The biological function of BCYRN1 was also investigated through knockdown and overexpression of BCYRN1 in vitro. Microarray bioinformatics analysis was performed to analyze the putative targets of BCYRN1. Results: The results showed that BCYRN1 expression was significantly upregulated in 96 CRC tumor tissues compared with para-carcinoma control tissues. Additionally, BCYRN1 overexpression was associated with larger tumor size and advanced pathological stages in CRC patients. In vitro BCYRN1 knockdown significantly inhibited the proliferation and apoptosis of CRC cells. Furthermore, NPR3 was identified to be a target of BCYRN1 and was downregulated by BCYRN1 knockdown. Conclusion: Together, we provide the first evidence that BCYRN1 plays an oncogenic role in CRC cells. BCYRN1 may be a promising prognostic biomarker and a potential therapeutic target for CRC

Analysis on epidemiological characteristics of nontyphoid Salmonella infection in Shanghai, 2018

Objective To identify the epidemiological characteristics of nontyphoid Salmonella infection in Shanghai, and provide scientific basis for the prevention and control. Methods The descriptive epidemiological method was used to analyze the epidemiological information of the collected diarrhea cases and the nontyphoid Salmonella test result from 26 foodborne disease surveillance sentinel hospitals in Shanghai. Results A total of 5 580 infectious diarrhea cases and specimens were enrolled. The positive detection rate of nontyphoid Salmonella was 4.19% (234/5 580). The serotypes were mainly Salmonella Enteritidis and Salmonella Typhimurium. There was no difference in the positive detection rate of nontyphoid Salmonella among different genders, ages and occupations. Spring and summer were the seasons when nontyphoid Salmonella positive detection rate was high. The positive rate of nontyphoid Salmonella in patients with fever or watery stool was significantly higher than in patients without fever or waterless stool. The positive rate of nontyphoid Salmonella was relatively high among the patients from small and medium-sized catering service and canteen. Conclusion Salmonella Enteritidis and Salmonella Typhimurium were the main serotypes of nontyphoid Salmonella in Shanghai, and the peak of nontyphoid Salmonella infection occured in spring and summer. The authorities should strengthen the supervision and management of catering service and canteen

Targeted Delivery of Chlorin e6 via Redox Sensitive Diselenide-Containing Micelles for Improved Photodynamic Therapy in Cluster of Differentiation 44-Overexpressing Breast Cancer

The off-target activation of photosensitizers is one of the most well-known obstacles to effective photodynamic therapy (PDT). The selected activation of photosensitizers in cancer cells is highly desired to overcome this problem. We developed a strategy that enabled diselenide bonds to link hyaluronic acid (HA) and photosensitizer chlorin e6 (Ce6) to assemble the micelles (HA-sese-Ce6 NPs) that can target cancer and achieve a redox responsive release of drugs to enhance the PDT efficiency in breast cancer. The HA was used to form a hydrophilic shell that can target cluster of differentiation 44 (CD44) on the cancer cells. The selenium-containing core is easily dissembled in a redox environment to release Ce6. The triggered release of Ce6 in a redox condition and the positive feedback release by activated Ce6 were observed in vitro. In cytotoxicity assays and in vitro cellular uptake assays, the increased PDT efficiency and targeted internalization of HA-sese-Ce6 NPs in the cells were verified, compared to a free Ce6 treated group. Similar results were showed in the therapeutic study and in vivo fluorescence imaging in an orthotopic mammary fat pad tumor model. In addition, a significant inhibition of metastasis was found after the HA-sese-Ce6 NPs treatment. In general, this study promises an ingenious and easy strategy for improved PDT efficiency

Development and Preclinical Studies of Broad-Spectrum Anti-HIV Agent (3′ R ,4′ R )-3-Cyanomethyl-4-methyl-3′,4′-di- O -( S )-camphanoyl-(+)- cis -khellactone (3-Cyanomethyl-4-methyl-DCK)

In prior investigation, we discovered that (3'R,4'R)-3-cyanomethyl-4-methyl-3',4'-di-O-(S)-camphanoyl-(+)-cis-khellactone (4, 3-cyanomethyl-4-methyl-DCK) showed promising anti-HIV activity. In these current studies, we developed and optimized successfully a practical ten-step synthesis for scale-up preparation to increase the overall yield of 4 from 7.8% to 32%. Furthermore, compound 4 exhibited broad-spectrum anti-HIV activity against wild-type and drug-resistant viral infection of CD4+ T cell lines as well as peripheral blood mononuclear cells by both laboratory-adapted and primary HIV-1 isolates with distinct subtypes and tropisms. Compound 4 was further subjected to in vitro and in vivo pharmacokinetic studies. These studies indicated that 4 has moderate cell permeability, moderate oral bioavailability and low systemic clearance. These results suggest that 4 should be developed as a promising anti-HIV agent for development as a clinical trial candidate

Weight Least Squares Algorithm for Rational Models with Outliers

A weight least squares algorithm is developed for rational models with outliers in this paper. Different weights are assigned for each cost function, and by calculating the derivatives of these cost functions, the parameter estimates can be estimated. Compared with the traditional least squares algorithm, the proposed algorithm can remove the bad effect caused by the outliers, thus has more accurate parameter estimates. A simulation example is proposed to validate the effectiveness of the proposed algorithm

RheologyNet: A Physics-Informed Neural Network Solution to Evaluate the Thixotropic Properties of Cementitious Materials

Thixotropic behaviors can be predicted by rheological partial differential equations (PDEs) of cementitious materials. The ability to solve the rheological PDEs of viscous fluids accurately and efficiently has become an emerging interest in research. However, due to the growing number of parameters in rheological constitutive equations and the non-ideal behavior of materials from experiments, solving the rheological PDEs becomes computationally costive and error-prone. We propose a physics-informed neural network (PINN)-based framework, RheologyNet, as a surrogate solution to predict the general thixotropic behavior of cementitious materials. The complex PDEs are embedded in the well-designed RheologyNet architecture to link macroscopic viscous flow behaviors and microstructural changes. Numerical experiments suggested that RheologyNet can accurately and efficiently predict the rheological properties of cementitious materials compared to the traditional Fully-connected Neural Network (FNN) and mechanistic Finite Element Analysis (FEA). Particularly, RheologyNet demonstrated great promise for simulating history-dependent thixotropic behaviors

An efficient image encryption algorithm based on S-box and DNA code

In order to ensure the security of image information transmission, a new image encryption algorithm is proposed based on S-box and DNA coding theory. Through the construction of the dynamic S-box, the key space of the encryption algorithm is enlarged, and the encryption process is more flexible. The flexibility of DNA encoding allows for a larger key volume and higher security in image encryption. Experimental results show that the encrypted image gray level distribution is uniform, the correlation between adjacent pixels is lower, the encryption effect is good, and it has good security