Electric field modulation of topological order in thin film semiconductors

We propose a method that can consecutively modulate the topological orders or the number of helical edge states in ultrathin film semiconductors without a magnetic field. By applying a staggered periodic potential, the system undergoes a transition from a topological trivial insulating state into a non-trivial one with helical edge states emerging in the band gap. Further study demonstrates that the number of helical edge state can be modulated by the amplitude and the geometry of the electric potential in a step-wise fashion, which is analogous to tuning the integer quantum Hall conductance by a megntic field. We address the feasibility of experimental measurement of this topological transition.Comment: 4 pages, 4 figure

Temporal Segmentation of Human Motion for Rehabilitation

Current physiotherapy practice relies on visual observation of patient movement for assessment and diagnosis. Automation of motion monitoring has the potential to improve accuracy and reliability, and provide additional diagnostic insight to the clinician, improving treatment quality, and patient progress. To enable automated monitoring, assessment, and diagnosis, the movements of the patient must be temporally segmented from the continuous measurements. Temporal segmentation is the process of identifying the starting and ending locations of movement primitives in a time-series data sequence. Most segmentation algorithms require training data, but a priori knowledge of the patient's movement patterns may not be available, necessitating the use of healthy population data for training. However, healthy population movement data may not generalize well to rehabilitation patients due to large differences in motion characteristics between the two demographics. In this thesis, four key contributions will be elaborated to enable accurate segmentation of patient movement data during rehabilitation. The first key contribution is the creation of a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application specific requirements, algorithm mechanics, and validation techniques. This framework provides a structure for considering the factors that must be incorporated when constructing a segmentation and identification algorithm. The framework enables systematic comparison of different segmentation algorithms, provides the means to examine the impact of each algorithm component, and allows for a systematic approach to determine the best algorithm for a given situation. The second key contribution is the development of an online and accurate motion segmentation algorithm based on a classification framework. The proposed algorithm transforms the segmentation task into a classification problem by modelling the segment edge point directly. Given this formulation, a variety of feature transformation, dimensionality reduction and classifier techniques were investigated on several healthy and patient datasets. With proper normalization, the segmentation algorithm can be trained using healthy participant data and obtain high quality segments on patient data. Inter-participant and inter-primitive variability were assessed on a dataset of 30 healthy participants and 44 rehabilitation participants, demonstrating the generalizability and utility of the proposed approach for rehabilitation settings. The proposed approach achieves a segmentation accuracy of 83-100%. The third key contribution is the investigation of feature set generalizability of the proposed method. Nearly all segmentation techniques developed previously use a single sensor modality. The proposed method was applied to joint angles, electromyogram, motion capture, and force plate data to investigate how the choice of modality impacts segmentation performance. With proper normalization, the proposed method was shown to work with various input sensor types and achieved high accuracy on all sensor modalities examined. The proposed approach achieves a segmentation accuracy of 72-97%. The fourth key contribution is the development of a new feature set based on hypotheses about the optimality of human motion trajectory generation. A common hypothesis in human motor control is that human movement is generated by optimizing with respect to a certain criterion and is task dependent. In this thesis, a method to segment human movement by detecting changes to the optimization criterion being used via inverse trajectory optimization is proposed. The control strategy employed by the motor system is hypothesized to be a weighted sum of basis cost functions, with the basis weights changing with changes to the motion objective(s). Continuous time series data of movement is processed using a sliding fixed width window, estimating the basis weights of each cost function for each window by minimizing the Karush-Kuhn-Tucker optimality conditions. The quality of the cost function recovery is verified by evaluating the residual. The successfully estimated basis weights are averaged together to create a set of time varying basis weights that describe the changing control strategy of the motion and can be used to segment the movement with simple thresholds. The proposed algorithm is first demonstrated on simulation data and then demonstrated on a dataset of human subjects performing a series of exercise tasks. The proposed approach achieves a segmentation accuracy of 74-88%

Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM) based on a hybrid intelligent algorithms and kernel incremental extreme learning machine is proposed. At first, hybrid intelligent algorithms are proposed based on differential evolution (DE) and multiple population grey wolf optimization (MPGWO) methods which used to optimize the hidden layer neuron parameters and then to determine the effective hidden layer neurons number. The learning efficiency of the algorithm is improved by reducing the network complexity. Then, we bring in the deep network structure to the kernel incremental extreme learning machine to extract the original input data layer by layer gradually. The experiment results show that the HI-DKIELM methods proposed in this paper with more compact network structure have higher prediction accuracy and better ability of generation compared with other ELM methods

Measuring Supply Chain Performance Based on SCOR: A Case Study of a Garment Company in Taiwan

The performance of supply chain is an important factor for the success of a company since it greatly affects the ability to provide customer value. Therefore, it is very important for a company to develop independent criteria to evaluate the performance, compare with competitors, and monitor the operation of a company. In the past, many companies tried to develop criteria for measuring their performance of supply chain. However, suitable criteria are hard to develop since the supply chain is generally very complex. The purpose of this study is to develop criteria to measure the performance of supply chain by using the Supply Chain Operations Reference Model (SCOR), which was shown by several previous studies to be an effective tool to develop criteria for measuring performance in diverse industries. To investigate the effectiveness of SCOR, we use the process reference model in SCOR to analyze the current state of a famous garment company’s processes and its goals, and quantify the operational performance. Results from this study show that SCOR is a very effective tool to develop performance metrics of the supply chain. Through the use of SCOR, a company can clearly define key performance indices (KPI) to improve their performance

SEPTIN2 suppresses an IFN-γ-independent, proinflammatory macrophage activation pathway

Interferon-gamma (IFN-γ) signaling is necessary for the proinflammatory activation of macrophages but IFN-γ-independent pathways, for which the initiating stimuli and downstream mechanisms are lesser known, also contribute. Here we identify, by high-content screening, SEPTIN2 (SEPT2) as a negative regulation of IFN-γ-independent macrophage autoactivation. Mechanistically, endoplasmic reticulum (ER) stress induces the expression of SEPT2, which balances the competition between acetylation and ubiquitination of heat shock protein 5 at position Lysine 327, thereby alleviating ER stress and constraining M1-like polarization and proinflammatory cytokine release. Disruption of this negative feedback regulation leads to the accumulation of unfolded proteins, resulting in accelerated M1-like polarization, excessive inflammation and tissue damage. Our study thus uncovers an IFN-γ-independent macrophage proinflammatory autoactivation pathway and suggests that SEPT2 may play a role in the prevention or resolution of inflammation during infection

Meta-analysis of antiviral protection of white spot syndrome virus vaccine to the shrimp

Currently, white spot syndrome virus (WSSV) is one of the most serious pathogens that impacts shrimp farming around the world. A WSSV vaccine provides a significant protective benefit to the host shrimp. Although various types of vaccines against WSSV have emerged, the immune effects among them were not compared, and it remains unclear which type of vaccine has the strongest protective effect. Meanwhile, due to the lack of effective routes of administration and immunization programs, WSSV vaccines have been greatly limited in the actual shrimp farming. To answer these questions, this study conducted a comprehensive meta-analysis over dozens of studies and compared all types WSSV vaccines, which include sub-unit protein vaccines, whole virus inactivated vaccines, DNA vaccines and RNA-based vaccines. The results showed that the RNA-based vaccine had the highest protection rate over the other three types of vaccines. Among the various sub-unit protein vaccines, VP26 vaccine had the best protective effects than other sub-unit protein vaccines. Moreover, this study demonstrated that vaccines expressed in eukaryotic hosts had higher protection rates than that of prokaryotic systems. Among the three immunization modes (oral administration, immersion and injection) used in monovalent protein vaccines, oral administration had the highest protection rate. In natural conditions, shrimp are mostly infected by the virus orally. These results provide a guide for exploration of a novel WSSV vaccine and help facilitate the application of WSSV vaccines in shrimp farming

Single nucleotide polymorphisms of one-carbon metabolism and cancers of the esophagus, stomach, and liver in a Chinese population.

One-carbon metabolism (folate metabolism) is considered important in carcinogenesis because of its involvement in DNA synthesis and biological methylation reactions. We investigated the associations of single nucleotide polymorphisms (SNPs) in folate metabolic pathway and the risk of three GI cancers in a population-based case-control study in Taixing City, China, with 218 esophageal cancer cases, 206 stomach cancer cases, 204 liver cancer cases, and 415 healthy population controls. Study participants were interviewed with a standardized questionnaire, and blood samples were collected after the interviews. We genotyped SNPs of the MTHFR, MTR, MTRR, DNMT1, and ALDH2 genes, using PCR-RFLP, SNPlex, or TaqMan assays. To account for multiple comparisons and reduce the chances of false reports, we employed semi-Bayes (SB) shrinkage analysis. After shrinkage and adjusting for potential confounding factors, we found positive associations between MTHFR rs1801133 and stomach cancer (any T versus C/C, SB odds-ratio [SBOR]: 1.79, 95% posterior limits: 1.18, 2.71) and liver cancer (SBOR: 1.51, 95% posterior limits: 0.98, 2.32). There was an inverse association between DNMT1 rs2228612 and esophageal cancer (any G versus A/A, SBOR: 0.60, 95% posterior limits: 0.39, 0.94). In addition, we detected potential heterogeneity across alcohol drinking status for ORs relating MTRR rs1801394 to esophageal (posterior homogeneity P = 0.005) and stomach cancer (posterior homogeneity P = 0.004), and ORs relating MTR rs1805087 to liver cancer (posterior homogeneity P = 0.021). Among non-alcohol drinkers, the variant allele (allele G) of these two SNPs was inversely associated with the risk of these cancers; while a positive association was observed among ever-alcohol drinkers. Our results suggest that genetic polymorphisms related to one-carbon metabolism may be associated with cancers of the esophagus, stomach, and liver. Heterogeneity across alcohol consumption status of the associations between MTR/MTRR polymorphisms and these cancers indicates potential interactions between alcohol drinking and one-carbon metabolic pathway