Engineered Macrophages: A Safe-by-Design Approach for the Tumor Targeting Delivery of Sub-5 nm Gold Nanoparticles

Ultrasmall nanoparticles (NPs) are a promising platform for the diagnosis and therapy of cancer, but the particles in sizes as small as several nanometers have an ability to translocate across biological barriers, which may bring unpredictable health risks. Therefore, it is essential to develop workable cell-based tools that can deliver ultrasmall NPs to the tumor in a safer manner. Here, this work uses macrophages as a shuttle to deliver sub-5 nm PEGylated gold (Au) NPs to tumors actively or passively, while reducing the accumulation of Au NPs in the brain. This work demonstrates that sub-5 nm Au NPs can be rapidly exocytosed from live macrophages, reaching 45.6% within 24 h, resulting in a labile Au NP-macrophage system that may release free Au NPs into the blood circulation in vivo. To overcome this shortcoming, two straightforward methods are used to engineer macrophages to obtain “half-dead” and “dead” macrophages. Although the efficiency of engineered macrophages for delivering sub-5 nm Au NPs to tumors is 2.2–3.8% lower than that of free Au NPs via the passive enhanced permeability and retention effect, this safe-by-design approach can dramatically reduce the accumulation of Au NPs in the brain by more than one order of magnitude. These promising approaches offer an opportunity to expand the immune cell- or stem cell-mediated delivery of ultrasmall NPs for the diagnosis and therapy of diseases in a safer way in the future.     

Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

A data warehouse is an important decision support system with cleaned and integrated data for knowledge discovery and data mining systems. In reality, the data warehouse mining system has provided many applicable solutions in industries, yet there are still many problems causing users extra problems in discovering knowledge or even failing to obtain the real and useful knowledge they need. To improve the overall data warehouse mining process, we present an intelligent data warehouse mining approach incorporated with schema ontology, schema constraint ontology, domain ontology and user preference ontology. The structures of these ontologies are illustrated and how they benefit the mining process is also demonstrated by examples utilizing rule mining. Finally, we present a prototype multidimensional association mining system, which with intelligent assistance through the support of the ontologies, can help users build useful data mining models, prevent ineffective pattern generation, discover concept extended rules, and provide an active knowledge re-discovering mechanism.     

Reputation management algorithms for DHT-based peer-to-peer environment

This paper addresses the problem of integration of reputation management mechanisms into data look-up and routing processes to cope with some specific security problems in peer-to-peer (P2P) networks based on Distributed Hash Tables (DHT). The goal is to find a mode for “good” peers to cooperate in order to withstand malicious activity of single malevolent peers and their collusions as well.We start from consideration of individual instruments for reputation evaluation that every single peer should be provided with to autonomously define the level of its own trust towards others. We also propose a possible scenario of integration of some existing reputation management techniques and routing mechanisms in a P2P network based on the popular Kademlia algorithm. Then, we present an interaction algorithm that allows peers to obtain the global trust value for each single node through exchanging opinions in the form of local trust values independently calculated by every peer with other members of the community. Such collaboration between peers is indispensable to cope with malicious activity of inconsistent nature specific to DHT-based environment. To render the algorithm resilient to the presence of malicious participants we propose to apply solutions for Byzantine Agreement to exchange opinions among peers. We also provide mathematical evaluations concerning the complexity and computational costs of the proposed algorithm that evidence apparent improvements over the previous one.     

汾河水库上游河流水环境风险评价

研究以满足太原市集中饮用水源地水质要求为前提,采用河流一维水质模型,模拟和定量评价了汾河水库上游突发性水环境污染事故风险。具体包括：识别污染事故风险危害,表征水环境危害大小,估算事故危害区范围以及危害期长短;最终定量评价了汾河上游水环境风险,为制定汾河上游水环境风险防范与控制对策提供了技术依据。     

环保型水处理药剂应用现状及发展趋势

分析我国水处理药剂发展面临的机遇,阐述环保型水处理药剂的应用现状,认为水处理药剂在混凝理论不断创新的坚实基础上,将向绿色水处理药剂、多元复合水处理药剂和纳米材料、微生物絮凝剂等新型高效水处理药剂的方向高速发展。     

天津滨海新区湿地生态恢复需水量评估

生态需水量的核算是湿地水文生态恢复的关键步骤。通过分析计算,评估天津滨海新区湿地生态系统各类生态需水的3个特征值:最低生态需水量(即湿地生态系统丧失生态功能的临界需水量)、最适生态需水量(即维持湿地生态功能最佳状态的需水量)、最高生态需水量(即湿地生态系统所能承受的最大水量),得出如下结论:天津滨海地区湿地的生态需水主要包括蒸发蒸散需水量、湿地土壤需水量、补给地下水、湿地生物栖息地需水量4个部分;湿地的最低生态需水量为177.68×106～276.85×106 m3,最适生态需水量539.23×106～807.81×106 m3,最高生态需水量1 206.55×106～1 409.02×106 m3。     

A database framework of digital forestry towards sustainable forestry development in China

Six forestry scientific and technological projects being actualized in China were the implementations of ecological construction and security, development of forestry biological technique and fine variety cultivation, protection and utilization of forest biological germplasm resources, development of forestry bio-industry and digital forestry, and construction of forestry innovation ability. Among them, digital forestry was an effective measure to improve the ability of forestry innovation, to realize resources sharing, and to develop sustainable forestry. Spatial database of digital forestry should be constructed according to the criteria and index system of forest sustainable management. Based on analyzing the status and advances of China’s digital forestry construction, a database framework of digital forestry towards sustainable forestry development was studied and established preparatorily. The database was divided into four layers as theory layer, system layer, status layer, and parameter layer. It was concluded that during the process of digital forestry platform construction, the relative social, economic, and politic data should be considered and collected, which meant that the database should represent not only the current forestry development, but also the social, politic, and economic situations. Constructing China’s digital forestry information platform under full utilization of idea and index system for sustainable forest management was urgent and important.     

Rapid and controllable synthesis of nitrogen doped reduced graphene oxide using microwave-assisted hydrothermal reaction for high power-density supercapacitors

Nitrogen doped reduced graphene oxide (N-RGO) is synthesized using microwave-assisted hydrothermal (MAHA) reaction. The proper configurations of nitrogen atoms in graphene sheets considerably increase the intrinsic electrical properties of N-RGO resultantly improving its capacitance and other kinetic properties in supercapacitor. Here, under the controlled MAHA reaction, we adjusted the ratio of nitrogen configurations (pyridinic-N, pyrrolic-N and quaternary-N) for the most optimum supercapacitor performances of N-RGOs in the shortest time ever reported, and clarified that its enhanced electrical conductivity and supercapacitor performances are attributed to its enlarged concentration of quaternary-N. With this MAHA reaction, we present a supercapacitor based on N-RGO, which is capable of displaying the promising electrochemical properties.     

Estimation of the degree of hydration of concrete through automated machine learning based microstructure analysis – A study on effect of image magnification

The scanning electron microscopy (SEM) images are commonly used to understand the microstructure of the concrete. With the advancements in the field of computer vision, many researchers have adopted the image processing technique for the microstructure analysis. Most of the previous methods are not adaptable, non-reproducible, semi-automated, and most importantly all these methods are highly influenced by image magnification. Therefore, to overcome these challenges, this paper presents a machine learning based image segmentation method for microstructure analysis and degree of hydration measurement using SEM images. In addition, the authors looked into the impact of magnification of SEM images on the model accuracy and classifier training for the degree of hydration measurement considering two scenarios. First, the image segmentation was performed using a classifier of specific magnification, and then a common classifier is trained using the image of different magnification. The results show that the Random Forest classifier algorithm is suitable for microstructure analysis using SEM images. Through the statistical analysis, it has been proved that there is no significant effect of magnification on model training and accuracy for the degree of hydration measurement. So, a single classifier can be used to process the images of different magnification of a specimen which reduces the effort of training and computational time. The proposed method can generate highly accurate and reliable results in a shorter time and lower cost. Moreover, the findings in this research can be useful for researchers to determine the optimum magnification required for the microstructure analysis.     

Promoting effect of the addition of Ce and Fe on manganese oxide catalyst for 1,2-dichlorobenzene catalytic combustion

Mn-based mixed-oxide (MnO_x) catalysts were modified with Fe, Ce, and Ce + Fe, and its catalytic oxidation activity was tested by using 1,2-dichlorobenzene (o-DCB) as models of chlorinated volatile organic compounds. Addition of Ce or Ce + Fe into MnO_x promoted their crystals to turn into amorphous powder, enhanced their specific surface area and changed their redox property. The catalytic activity of MnO_x improved remarkably by adding Ce or Ce + Fe indicating Ce plays an important role. Both Mn-Ce and Mn-Ce-Fe catalysts exhibited good stability for catalytic oxidation of o-DCB, indicating that the introduction of promoter is an important method to improve the catalytic performance.