Toxic effects of iron oxide nanoparticles on human umbilical vein endothelial cells

Iron oxide nanoparticles (IONPs) have been employed for hyperthermia treatments, stem cell therapies, cell labeling, and imaging modalities. The biocompatibility and cytotoxic effects of iron oxide nanoparticles when used in biomedical applications, however, are an ongoing concern. Endothelial cells have a critical role in this research dealing with tumors, cardiovascular disease and inflammation. However, there is little information dealing with the biologic effects of IONPs on the endothelial cell. This paper deals with the influence of dextran and citric acid coated IONPs on the behavior and function of human umbilical vein endothelial cells (HUVECs). After exposing endothelial cells to IONPs, dose-dependent effects on HUVECs viability, cytoskeleton and function were determined. Both citric acid and dextran coated particles appeared to be largely internalized by HUVECs through endocytosis and contribute to eventual cell death possibly by apoptosis. Cytoskeletal structures were greatly disrupted, as evidenced by diminished vinculin spots, and disorganized actin fiber and tubulin networks. The capacity of HUVECs to form a vascular network on Matrigel™ diminished after exposure to IONPs. Cell migration/invasion were inhibited significantly even at very low iron concentrations (0.1 mM). The results of this study indicate the great importance of thoroughly understanding nanoparticle-cell interactions, and the potential to exploit this understanding in tumor therapy applications involving IONPs as thermo/chemoembolization agents

Distributed algorithm without iterations for an integrated energy system

Existing energy management methods for integrated energy systems are mostly in distributed communication and computation now, need a large number of iterations, and each time of iteration needs lots of communication and computation. For this reason, on one hand, the iteration may cause energy-delay. On the other hand, iteration will significantly increase the communication and computation burden. The integrated energy systems contain a variety of devices and energy resources (including renewable energy resources), so the communication and computation burden is already very high. If the communication and computation cannot be solved very well, the cost functions of each device need to be much easier to ensure the operation of the system and their systematic error will be much larger. For this reason, the result of optimization will be much worse because of the accuracy of cost functions. The greatest challenge of this issue is to establish an algorithm without iteration. For handling this issue, first, we adopt the theoretical demonstration to prove that if all prices of all devices are the same, the optimization will be realized and the instantaneous price is the one-order derivative. (we assume the relationship between the operating cost and the energy flow of each device as the convex cost functions.) Second, we reshape all cost functions. Third, we change the function to the total of the foregoing functions in the directed annular path and adopt the total function of the hole system to solve the energy price. Last, we use the price to ensure their operating condition. Our theoretical demonstration has already proved the optimization, convergence, the plug and play performance, scalability, and the emergency scheduling performance of the annular partial differential algorithm (APDA)

Land use regulations, transit investment, and commuting preferences

In the U.S., various anti-sprawl land use regulations have been implemented for over two decades. Previous studies primarily investigate the impacts of local land use regulations or neighborhood-level built environment attributes on travel behaviors within a narrow time frame. Through a different lens, this paper examines how various local land use regulations and transit investment, both measured at the aggregated metropolitan level, have affected people’s long-term travel behaviors over a 15-year period, and how these impacts differ between younger and older age groups. This study combines a set of land use regulation indices measured at the metropolitan level in 2003 with 15 years of travel data (2005–2019) from a pooled representative sample of over 8 million workers in the 50 largest U.S. metropolitan areas. Results show several local anti-sprawl land use regulations (e.g., growth containment, adequate public facilities, and moratoria), when combined at the metropolitan level, effectively reduced driving notwithstanding their marginal effects. Government investment in public transit also significantly increased commuters’ likelihood of using public transit and, carpooling, as well as increased carpool group size. Moreover, the commuting mode choices of younger workers are more responsive to transit improvements and land use regulations. Urban planners should commit to regional cooperative planning to promote effective land use regulations at the metropolitan level. Regional collaborative entities, such as metropolitan planning organizations should play a larger role in coordinating local land use planning and regulations. To reduce automobile dependency, planners should commit to improving public transit through enhanced financial assistance, harnessing land use regulations in a more targeted way, and accommodating the needs of different age cohorts

The impact of mass gatherings on the local transmission of COVID-19 and the implications for social distancing policies: Evidence from Hong Kong

Mass gatherings provide conditions for the transmission of infectious diseases and pose complex challenges to public health. Faced with the COVID-19 pandemic, governments and health experts called for suspension of gatherings in order to reduce social contact via which virus is transmitted. However, few studies have investigated the contribution of mass gatherings to COVID-19 transmission in local communities. In Hong Kong, the coincidence of the relaxation of group gathering restrictions with demonstrations against the National Security Law in mid-2020 raised concerns about the safety of mass gatherings under the pandemic. Therefore, this study examines the impacts of mass gatherings on the local transmission of COVID-19 and evaluates the importance of social distancing policies. With an aggregated dataset of epidemiological, city-level meteorological and socioeconomic data, a Synthetic Control Method (SCM) is used for constructing a ‘synthetic Hong Kong’ from over 200 Chinese cities. This counterfactual control unit is used to simulate COVID-19 infection patterns (i.e., the number of total cases and daily new cases) in the absence of mass gatherings. Comparing the hypothetical trends and the actual ones, our results indicate that the infection rate observed in Hong Kong is substantially higher than that in the counterfactual control unit (2.63% vs. 0.07%). As estimated, mass gatherings increased the number of new infections by 62 cases (or 87.58% of total new cases) over the 10–day period and by 737 cases (or 97.23%) over the 30-day period. These findings suggest the necessity of tightening social distancing policies, especially the prohibition on group gathering regulation (POGGR), to prevent and control COVID-19 outbreaks

Semienzymatic cyclization of disulfide-rich peptides using sortase A

Background: Sortase A (SrtA) is a transpeptidase capable of catalyzing the formation of amide bonds. Results: SrtA was used to backbone-cyclize disulfide-rich peptides, including kalata B1, -conotoxin Vc1.1, and SFTI-1. Conclusion: SrtA-mediated cyclization is applicable to small disulfide-rich peptides. Significance: SrtA-mediated cyclization is an alternative to native chemical ligation for the cyclization of small peptides of therapeutic interest

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.0443

A protein structural study based on the centrality analysis of protein sequence feature networks.

In this paper, we use network approaches to analyze the relations between protein sequence features for the top hierarchical classes of CATH and SCOP. We use fundamental connectivity measures such as correlation (CR), normalized mutual information rate (nMIR), and transfer entropy (TE) to analyze the pairwise-relationships between the protein sequence features, and use centrality measures to analyze weighted networks constructed from the relationship matrices. In the centrality analysis, we find both commonalities and differences between the different protein 3D structural classes. Results show that all top hierarchical classes of CATH and SCOP present strong non-deterministic interactions for the composition and arrangement features of Cystine (C), Methionine (M), Tryptophan (W), and also for the arrangement features of Histidine (H). The different protein 3D structural classes present different preferences in terms of their centrality distributions and significant features

A study on separation of the protein structural types in amino acid sequence feature spaces.

Proteins are diverse with their sequences, structures and functions, it is important to study the relations between the sequences, structures and functions. In this paper, we conduct a study that surveying the relations between the protein sequences and their structures. In this study, we use the natural vector (NV) and the averaged property factor (APF) features to represent protein sequences into feature vectors, and use the multi-class MSE and the convex hull methods to separate proteins of different structural classes into different regions. We found that proteins from different structural classes are separable by hyper-planes and convex hulls in the natural vector feature space, where the feature vectors of different structural classes are separated into disjoint regions or convex hulls in the high dimensional feature spaces. The natural vector outperforms the averaged property factor method in identifying the structures, and the convex hull method outperforms the multi-class MSE in separating the feature points. These outcomes convince the strong connections between the protein sequences and their structures, and may imply that the amino acids composition and their sequence arrangements represented by the natural vectors have greater influences to the structures than the averaged physical property factors of the amino acids

Neural Architecture Search for Lightweight Neural Network in Food Recognition

Healthy eating is an essential element to prevent obesity that will lead to chronic diseases. Despite numerous efforts to promote the awareness of healthy food consumption, the obesity rate has been increased in the past few years. An automated food recognition system is needed to serve as a fundamental source of information for promoting a balanced diet and assisting users to understand their meal consumption. In this paper, we propose a novel Lightweight Neural Architecture Search (LNAS) model to self-generate a thin Convolutional Neural Network (CNN) that can be executed on mobile devices with limited processing power. LNAS has a sophisticated search space and modern search strategy to design a child model with reinforcement learning. Extensive experiments have been conducted to evaluate the model generated by LNAS, namely LNAS-NET. The experimental result shows that the proposed LNAS-NET outperformed the state-of-the-art lightweight models in terms of training speed and accuracy metric. Those experiments indicate the effectiveness of LNAS without sacrificing the model performance. It provides a good direction to move toward the era of AutoML and mobile-friendly neural model design