Density alteration in non-physiological cells

In the present study an important phenomenon of cells was discovered: the change of intracellular density in cell's response to drug and environmental factors. For convenience, this phenomenon is named as "density alteration in non-physiological cells" ( DANCE). DANCE was determined by discontinuous sucrose gradient centrifugation (DSGC), in which cells were separated into several bands. The number and position of the bands in DSGC varied with the change of cell culture conditions, drugs, and physical process, indicating that cell's response to these factors was associated with alteration of intracellular density. Our results showed that the bands of cells were molecularly different from each other, such as the expression of some mRNAs. For most cells tested, intracellular density usually decreased when the cells were in bad conditions, in presence of drugs, or undergoing pathological changes. However, unlike other tissue cells, brain cells showed increased intracellular density in 24 hrs after the animal death. In addition, DANCE was found to be related to drug resistance, with higher drug-resistance in cells of lower intracellular density. Further study found that DANCE also occurred in microorganisms including bacteria and fungus, suggesting that DANCE might be a sensitive and general response of cells to drugs and environmental change. The mechanisms for DANCE are not clear. Based on our study the following causes were hypothesized: change of metabolism mode, change of cell membrane function, and pathological change. DANCE could be important in medical and biological sciences. Study of DANCE might be helpful to the understanding of drug resistance, development of new drugs, separation of new subtypes from a cell population, forensic analysis, and importantly, discovery of new physiological or pathological properties of cells

Effects of acute ammonia exposure and post-exposure recovery on nonspecific immunity in Clam Cyclina sinensis

This study aimed to assess the toxicity of ammonia on clam Cyclina sinensis and the post-exposure recovery. With increased exposure to TAN, the alkaline phosphatase (AKP) activities after exposure showed a trend of growing initially and subsequently decreasing, whereas the AKP activities after post-exposure recovery showed an increasing trend. The AKP activities after post-exposure recovery were significantly higher than those in control. The acid phosphatase (ACP) activities in T1 and T2 after post-exposure recovery were higher than those in the control, whereas the ACP activities in T3, T4, and T5 after post-exposure recovery were significantly higher than those in the control. The lysozyme (LZM) activities in T1 and T2 after exposure were significantly higher than those in control, whereas the LZM activities in T3, T4, and T5 after exposure were significantly lower than those in the control. Overall, ACP and LZM in the clams exposed to a low level of TAN (≤ 40 mg/L) can recover to the normal levels completely. However, a 48h recovery period scarcely seems adequate to compensate for AKP, ACP, and LZM activities in the clams exposed to a high level of TAN (> 40 mg/L)

Learning Personalized Risk Preferences for Recommendation

The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions. With this information, they can infer the quality of products to reduce the risk of purchase. Specifically, items with high rating scores and good reviews tend to be less risky, while items with low rating scores and bad reviews might be risky to purchase. On the other hand, the purchase behaviors will also be influenced by consumers' tolerance of risks, known as the risk attitudes. Economists have studied risk attitudes for decades. These studies reveal that people are not always rational enough when making decisions, and their risk attitudes may vary in different circumstances. Most existing works over recommendation systems do not consider users' risk attitudes in modeling, which may lead to inappropriate recommendations to users. For example, suggesting a risky item to a risk-averse person or a conservative item to a risk-seeking person may result in the reduction of user experience. In this paper, we propose a novel risk-aware recommendation framework that integrates machine learning and behavioral economics to uncover the risk mechanism behind users' purchasing behaviors. Concretely, we first develop statistical methods to estimate the risk distribution of each item and then draw the Nobel-award winning Prospect Theory into our model to learn how users choose from probabilistic alternatives that involve risks, where the probabilities of the outcomes are uncertain. Experiments on several e-commerce datasets demonstrate that our approach can achieve better performance than many classical recommendation approaches, and further analyses also verify the advantages of risk-aware recommendation beyond accuracy

A Category Classification Based Safety Risk Assessment Method for Railway Wagon Loading Status

The identification and control of safety risks in the loading state of goods wagon is one of the important tasks to ensure the safety of goods in transit. In view of the problem that the current risk assessment of transportation schemes is mainly based on manual experience and cannot be quantified, which makes it difficult to accurately determine the safety risk of transportation on the way, a risk assessment method for loading status of goods wagon based on scenario classification was proposed. Firstly, based on a detailed analysis of the safety risk points in various stages of railway freight operations, a SHEL influencing factor model based on scenario classification was constructed. Then, considering the characteristics of railway freight transportation, a fuzzy accident tree model (FTA) of goods wagon loading state risk was constructed, and the fault tree was transformed into a Bayesian network structure according to the mapping algorithm of fuzzy fault tree and Bayesian. Furthermore, a triangular fuzzy membership function was introduced to describe the fault probability of nodes, and a BN based fuzzy fault tree inference algorithm was proposed. Finally, taking a railway station and route transporting coil steel goods in China as an example, this paper explained how to integrate expert knowledge through fault tree and Bayesian network to support railway freight scheme designers in conducting risk quantification assessment of freight wagon loading status

High platelet reactivity affects the clinical outcomes of patients undergoing percutaneous coronary intervention

Receiver-operating characteristic curve predicting stent thrombosis. (TIFF 5134 kb

Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey

The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously compared. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to demonstrate the development trend of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.Comment: Submitted to IEEE Transactions on Smart Grid for Publication Consideratio

1,1′-[1,4-Phenyl­enebis(methyl­ene)]bis­(2-methyl-1H-imidazol-3-ium) 2,4-dicarb­oxy­benzene-1,5-dicarboxyl­ate monohydrate

In the dication of the title compound, C16H20N4 2+·C10H4O8 2−·H2O, the dihedral angles formed by mean planes of the imidazolium rings and the benzene ring are 69.05 (18) and 89.1 (2)°. In the crystal, the components are linked into a three-dimensional network by inter­molecular N—H⋯O and O—H⋯O hydrogen bonds

Hexa­kis­(1-benzyl-1H-imidazole-κN 3)manganese(II) bis­(perchlorate)

In the title compound, [Mn(C10H10N2)6](ClO4)2, the MnII ion, located on an inversion center, is coordinated by six N atoms from three pairs of symmetry-related 1-benzyl-1H-imidazole ligands in a distorted octa­hedral geometry. In the crystal, weak inter­molecular C—H⋯O hydrogen bonds link the complex cations and perchlorate anions