Biosorption of Heavy Metals onto the Surface of Bacteriophage T4

Biosorption of heavy metals by bacterial and eukaryotic cell surfaces and the subsequent transport in aqueous environments is well recognized. However, very little is known about the roles viruses play in biosorption. Viruses outnumber prokaryotes and eukaryotes in environmental systems. These organisms represent abundant nanoparticulate organic colloids with reactive surfaces. Here we conducted a series of experiments to assess the biosorption potential of Escherichia coli bacteriophage T4. Adsorption of a heavy metal, Zn2+, to the surface of phage T4 was tested in a series of purified phage/metal solutions (0 µM – 1000 µM at 23°C). The Langmuir isotherm reasonably describes the sorption data, with an R-square of 0.8116. The Langmuir constant was determined to be 0.01265 which demonstrates that the adsorption of zinc onto the surface of phage T4 does occur, but not at a rapid rate. Studies have shown that the phage T4 capsid proteins possess negatively charged binding sites, which are the C-terminus for Soc and the N-terminus for Hoc. These two sites were proven to be biologically active and are able to bind certain proteins and antibodies. Thus, it is likely that these sites adsorb cations. Zeta potential analysis demonstrated phage T4 (1010 VLPs mL-1) not exposed to zinc at pH 7.0 to be approximately -11.48 ± 1.16 mV. These results demonstrate the surface of phage T4 is naturally electronegative, which supports the capability of the surface of phage T4 to adsorb metal cations. This was subsequently demonstrated when the zeta potential shifted to -2.96 ± 1.60mV at pH 7.0 and exposure of 1010 VLPs mL-1 to 150µM Zn2+, which suggests that adsorption of Zn2+ ions onto the phage resulted in the neutralization of negative charges on the phage surface. The effects of pH have been determined to have an effect on the adsorption of Zn2+ onto the surface of phage T4. Zn2+ adsorption is at a minimum when exposed to acidic pH and the amount of Zn2+ adsorbed increases with the rise of pH until a pH of 7.5, where precipitation of zinc hydroxide begins to occur and interferes with the adsorption process. Phage decay can alter the available surface area for metal adsorption. Interestingly, the presence of 150 µM Zn2+ significantly increased infectivity relative to unamended controls (ANOVA p2+ enhances phage T4 infectivity. Together, the results suggest that the sorption of metals to the surface of viruses could not only contribute to nanoparticulate metal transport but also enhance infectivity that contributes to cell lysis in environmental systems. Advisor: Karrie A. Webe

An Optimization Scheduling Model for Wind Power and Thermal Power with Energy Storage System considering Carbon Emission Trading

Wind power has the characteristics of randomness and intermittence, which influences power system safety and stable operation. To alleviate the effect of wind power grid connection and improve power system’s wind power consumptive capability, this paper took emission trading and energy storage system into consideration and built an optimization model for thermal-wind power system and energy storage systems collaborative scheduling. A simulation based on 10 thermal units and wind farms with 2800 MW installed capacity verified the correctness of the models put forward by this paper. According to the simulation results, the introduction of carbon emission trading can improve wind power consumptive capability and cut down the average coal consumption per unit of power. The introduction of energy storage system can smooth wind power output curve and suppress power fluctuations. The optimization effects achieve the best when both of carbon emission trading and energy storage system work at the same time

Layered Path Planning with Human Motion Detection for Autonomous Robots

Reactively planning a path in a dynamic and unstructured environment is a key challenge for mobile robots and autonomous systems. Planning should consider factors including the long-term and short-term prediction, current environmental situation, and human context. In this chapter, we present a novel robotic path-planning method with human activity information in a large-scale three-dimensional (3D) environment. In the learning stage, this method uses human motion detection results and preliminary environmental information to build a long-term context model with a hidden Markov model (HMM) to describe and predict human activities in the environment. In the application stage, when a robot detects humans in the environment, it first uses the long-term context model to generate impedance areas in the environment. Then, the robot searches each area of the environment to find paths between key locations, such as escalators, to generate a Reactive Key Cost Map (RKCM), whose vertexes are those key locations and edges are generated paths. The graphs of all areas are connected using the key nodes in the subgraphs to build a global graph of the whole environment. Finally, the robot can reactively plan a path based on the current environmental situation and predicted human activities. This method enables robots to navigate robustly in a large-scale 3D environment with regular human activities, and it significantly reduces computing workload with proposed RKCM

A case study : using choice experiment in an open distance learning

There are approximately 62 private higher education providers in Malaysia as on May 2014. Among them, there are several institutions offer the open and distance (ODL) mode of teaching and learning pedagogy. Due to the ODL flexibility mode, there has been quite a stir of competition in the education industry. Learners of ODL tend to be more challenging to fulfill their needs as they have other commitments in life, hence the ODL mode to be chosen. Therefore, the ODL education institution need to able to read and provide the necessary needs to these learners. The aim of this study is to investigate the attributes that contributes to choosing an ODL private higher education institution in Malaysia and to explore the consumer behavior in the area of student choice, and consumers’ willingness-to-pay price. Although there are studies on the attributes that influence student choice of a university, but has failed to use the choice experiment theory to examine the attributes that influence choice of course particularly an ODL mode. The sample population was 320 using face-to-face interview. The results would be able to introduce the right marketing strategy for the institution in Malaysia. (Abstract by author

Monolayer Molybdenum Disulfide Nanoribbons with High Optical Anisotropy

Two-dimensional Molybdenum Disulfide (MoS2) has shown promising prospects for the next generation electronics and optoelectronics devices. The monolayer MoS2 can be patterned into quasi-one-dimensional anisotropic MoS2 nanoribbons (MNRs), in which theoretical calculations have predicted novel properties. However, little work has been carried out in the experimental exploration of MNRs with a width of less than 20 nm where the geometrical confinement can lead to interesting phenomenon. Here, we prepared MNRs with width between 5 nm to 15 nm by direct helium ion beam milling. High optical anisotropy of these MNRs is revealed by the systematic study of optical contrast and Raman spectroscopy. The Raman modes in MNRs show strong polarization dependence. Besides that the E' and A'1 peaks are broadened by the phonon-confinement effect, the modes corresponding to singularities of vibrational density of states are activated by edges. The peculiar polarization behavior of Raman modes can be explained by the anisotropy of light absorption in MNRs, which is evidenced by the polarized optical contrast. The study opens the possibility to explore quasione-dimensional materials with high optical anisotropy from isotropic 2D family of transition metal dichalcogenides

Virtual Node Tuning for Few-shot Node Classification

Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer structural knowledge from base classes with abundant labels to target novel classes. However, existing solutions become ineffective or inapplicable when base classes have no or limited labeled nodes. To address this challenge, we propose an innovative method dubbed Virtual Node Tuning (VNT). Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task. A unique feature of VNT is that, by incorporating a Graph-based Pseudo Prompt Evolution (GPPE) module, VNT-GPPE can handle scenarios with sparse labels in base classes. Experimental results on four datasets demonstrate the superiority of the proposed approach in addressing FSNC with unlabeled or sparsely labeled base classes, outperforming existing state-of-the-art methods and even fully supervised baselines.Comment: Accepted to KDD 202

Concession Period Decision Models for Public Infrastructure Projects Based on Option Games

Concession period is an important decision-making variable for the investment and construction of public infrastructure projects. However, we currently have few scientific methods to exactly determine the concession period. This paper managed to seek out concession period decision models for public infrastructure with option game theory, studied the influence of minimum government income guarantee and government investment on concession period, and demonstrated those models in the formulas mentioned in the paper. The research results showed that the increase of minimum government income guarantee value would shorten the concession period, while the increase of income volatility, that is, the uncertainty, would lengthen the concession period. In terms of government investment, optimal concession period would lengthen to some extent with the increase of government investment ratio and the income and the decrease of its guarantee value. Yet, optimal concession period would shorten in case of extreme highness of the government investment ratio due to its high guarantee value. And the government would accordingly shorten the concession period in case of the unchanged government investment ratio with the increased income volatility and risks. Still, the paper put forward the argument that the government would apply various guarantee methods and implement flexible concession period in accordance with the specific circumstances of public infrastructure projects

Contrastive Meta-Learning for Few-shot Node Classification

Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. Particularly, in this paper, we refer to the task of classifying nodes in classes with a few labeled nodes as the few-shot node classification problem. To tackle such a label shortage issue, existing works generally leverage the meta-learning framework, which utilizes a number of episodes to extract transferable knowledge from classes with abundant labeled nodes and generalizes the knowledge to other classes with limited labeled nodes. In essence, the primary aim of few-shot node classification is to learn node embeddings that are generalizable across different classes. To accomplish this, the GNN encoder must be able to distinguish node embeddings between different classes, while also aligning embeddings for nodes in the same class. Thus, in this work, we propose to consider both the intra-class and inter-class generalizability of the model. We create a novel contrastive meta-learning framework on graphs, named COSMIC, with two key designs. First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes. Second, we strengthen the inter-class generalizability by generating hard node classes via a novel similarity-sensitive mix-up strategy. Extensive experiments on few-shot node classification datasets verify the superiority of our framework over state-of-the-art baselines. Our code is provided at https://github.com/SongW-SW/COSMIC.Comment: SIGKDD 202