Realization of a self-recognition algorithm based on the biological immune system

As the use of the computer is popularized, the damage from computer viruses and hacking by malicious users is increasing rapidly. To block the hacking that is an intrusion into a person's computer, and the viruses that destroy data, a study into an intrusion detection and virus detection system based on the biological immune system is in progress. In this article, we describe a model of positive and negative selection for self-recognition, which has a similar function to the cytotoxic T cells that play an important role in the biological immune system. We propose a self/nonself discrimination algorithm for a computer system, which will the important when we detect data infected by a computer virus, of data modified by an intrusion from outside. We also show the validity and effectiveness of the proposed self-recognition algorithm by a computer simulation of some infected data obtained from cell changes and string changes in the self-file. This work was presented, in part, at the Seventh International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Robust reweighted MAP motion estimation

This paper proposes a motion estimation algorithm that is robust to motion discontinuity and noise. The proposed algorithm is constructed by embedding the least median squares (LMedS) of robust statistics into the maximum a posteriori (MAP) estimator. Difficulties in accurate estimation of the motion field arise from the smoothness constraint and the sensitivity to noise. To cope robustly with these problems, a median operator and the concept of reweighted least squares (RLS) are applied to the MAP motion estimator, resulting in the reweighted robust MAP (RRMAP). The proposed RRMAP motion estimation algorithm is also generalized for multiple image frame cases. Computer simulation with various synthetic image sequences shows that the proposed algorithm reduces errors, compared to three existing robust motion estimation algorithms that are based on M-estimation, total least squares (TLS), and Hough transform. It is also observed that the proposed algorithm is statistically efficient and robust to additive Gaussian noise and impulse noise. Furthermore, the proposed algorithm yields reasonable performance for real image sequences     

Negotiation Agents that Make Prudent Compromises and are Slightly Flexible in Reaching Consensus

The contribution of this work is designing and developing enhanced market-driven agents with the flexibility to (1) respond to changing market conditions, and (2) raise and relax trade aspirations. Previous theoretical analyses have shown that market-driven agents ( MDA s) make prudent compromises by reacting to changing market situations by taking into account factors such as competition, deadlines, and trading options. This work augments the design of an MDA with three fuzzy decision controllers that guide the agent in (i) relaxing trade aspiration in face of intense negotiation pressure, and (ii) raising trade aspiration in extremely favorable markets. Results from extensive simulations conducted using an implemented testbed suggest that when compared to MDA s, agents in this work achieved (1) higher success rates in reaching deals, (2) higher average utilities, and (3) higher expected utility.     

Effect of shot noise and secondary emission noise in scanning electron microscope images

The effect of shot noise and emission noise due to materials that have different emission properties was simulated. Local variations in emission properties affect the overall signal‐to‐noise ratio (SNR) value of the scanning electron microscope image. In the case in which emission noise is assumed to be absent, the image SNRs for silicon and gold on a black background are identical. This is because only shot noise in the primary beam affects the SNRs, irrespective of the assumed noiseless secondary electron emission or backscattered electron emission processes. The addition of secondary emission noise degrades the SNR. Materials with higher secondary electron yield and backscattering electron yield give rise to higher SNR. For images formed from two types of material, the contrast of the image is lower. The reduction in image signal reduces the overall image SNR. As expected, large differences in δ or η give rise to higher SNR images.     

Autoregressive Wiener filtering in a scanning electron microscopy imaging system

In this paper, we propose to use the autoregressive (AR)-based interpolator with Wiener filter and apply the idea to scanning electron microscope (SEM) images. The concept for combining the AR-based interpolator with Wiener filtering comes from the essential requirement of Wiener filtering for accurate and consistent estimation of the power of the noise in images prior to filter implementation. The resultant filter is called AR-Wiener filter. The proposed filter is embedded onto the frame grabber card of the scanning electron microscope (SEM) for real-time image processing. Different images are captured using SEM and used to compare the performances of the conventional Wiener and the proposed AR-Wiener technique.     

Image signal-to-noise ratio estimation using Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average model

We propose to cascade the Shape-Preserving Piecewise Cubic Hermite model with the Autoregressive Moving Average (ARMA) interpolator; we call this technique the Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average (SP2CHARMA) model. In a few test cases involving different images, this model is found to deliver an optimum solution for signal to noise ratio (SNR) estimation problems under different noise environments. The performance of the proposed estimator is compared with two existing methods: the autoregressive-based and autoregressive moving average estimators. Being more robust with noise, the SP2CHARMA estimator has efficiency that is significantly greater than those of the two methods.     

Evaluation of Four Permeable Pavement Sites in Eastern North Carolina for Runoff Reduction and Water Quality Impacts

Four permeable pavement applications in North Carolina’s Coastal Plain were constructed and monitored to determine their effectiveness of reducing runoff quantity and improving water quality. Sites were either constructed of permeable interlocking concrete pavers (2), porous concrete (1), or concrete grid pavers (1). One site of each pavement type was monitored for runoff reduction for periods ranging from 10 to 26 months. Measured runoff depths from rainfall events over 50?mm were used to determine permeable pavement equivalent curve numbers for the sites, which ranged from 45 to 85. Only the two permeable interlocking concrete pavement (PICP) sites were monitored for water quality. Runoff and exfiltrate samples were intended to be collected, in addition to runoff monitoring, from the Swansboro PICP site. However, no runoff was produced during this study from the Swansboro PICP site for rainfall events up to 88?mm. From exfiltrate concentrations, nutrient retention was estimated to be 3.4 and 0.4?kg/ha/year for total nitrogen and total phosphorus, respectively. For the Goldsboro PICP site, water quality of asphalt runoff and PICP exfiltrate were compared. Analysis of water quality samples from the second site determined that concentrations of total Kjeldahl nitrogen, ammonia, total phosphorus, and zinc were significantly (p ? 0.05) lower in permeable pavement exfiltrate than asphalt runoff.     

Benzoylpaeoniflorin Activates Anti-Inflammatory Mechanisms to Mitigate Sepsis in Cell-Culture and Mouse Sepsis Models

Xuebijing injection (XBJI) (comprising of five herbs) is a widely used traditional Chinese medicine for sepsis treatment. However, the bioactive components of XBJI and the mechanisms responsible for its sepsis-mitigating action have not been experimentally determined. One of the main bioactive compounds in XBJI—benzoylpaeoniflorin (BPF)—inhibits the expressions of key mediators of inflammation such as nuclear factor kappa B (NF-κB), cyclooxygenase-1 (COX-1), and COX-2. However, its effects on sepsis have not been determined yet. Therefore, here, we investigated the immunomodulatory effect of BPF on severely inflamed endothelial cells, THP-1 macrophages, peritoneal macrophages, and mice. Human umbilical vein endothelial cells (HUVECs) and THP-1-macrophages were activated using lipopolysaccharide (LPS) after pretreatment with BPF. Subsequently, changes in the expression profiles of pro-inflammatory molecules including inducible nitric oxide synthase (iNOS), tumor necrosis factor (TNF)-α, and interleukin (IL)-6 were determined using quantitative real-time polymerase chain reaction (qPCR) and Western blot analysis. Furthermore, we monitored the phosphorylation of NF-kB and mitogen-activated protein kinases (MAPKs) to determine their activation levels. Using the LPS-induced mouse model of sepsis, we studied the effects of BPF on inflammatory cytokine production, pulmonary histopathology, and survival rates. Finally, we evaluated whether BPF protects against cecal ligation and puncture (CLP)-induced sepsis, as it closely mimics human sepsis. BPF pretreatment inhibited LPS-induced increase in mRNA and protein levels of iNOS, TNF-α, and IL-6 in HUVECs and THP-1-macrophages. It also suppressed LPS-mediated phosphorylation of p65, p38, JNK, and ERK. Mice with LPS-induced-sepsis who were treated with BPF had lower serum levels of IL-6, TNF-α, IL-1β, CXCL1, and CXCL2 than the control mice treated with BPF. Histopathology revealed that BPF treatment alleviated LPS-induced lung damage. In addition, in mice given a lethal dose of LPS, BPF treatment showed a dose-dependent improvement in survival rates. BPF treatment dose-dependently inhibited the LPS-induced IL-6, TNF-α, and CXCL1 production in peritoneal macrophages. BPF treatment also dose-dependently improved the survival rates in mice with CLP-induced sepsis. These results show that BPF alleviates LPS-stimulated septic conditions and protects mice from CLP-induced sepsis. Our research marks BPF as a potential drug in the treatment of sepsis and various inflammatory diseases.     

A Novel Antibody-Drug Conjugate Targeting Nectin-2 Suppresses Ovarian Cancer Progression in Mouse Xenograft Models

Ovarian cancer is the fifth leading cause of cancer, followed by front line is mostly platinum agents and PARP inhibitors, and very limited option in later lines. Therefore, there is a need for alternative therapeutic options. Nectin-2, which is overexpressed in ovarian cancer, is a known immune checkpoint that deregulates immune cell function. In this study, we generated a novel anti-nectin-2 antibody (chimeric 12G1, c12G1), and further characterized it using epitope mapping, enzyme-linked immunosorbent assay, surface plasmon resonance, fluorescence-activated cell sorting, and internalization assays. The c12G1 antibody specifically bound to the C2 domain of human nectin-2 with high affinity (K_D = 2.90 × 10⁻¹⁰ M), but not to mouse nectin-2. We then generated an antibody-drug conjugate comprising the c12G1 antibody conjugated to DM1 and investigated its cytotoxic effects against cancer cells in vitro and in vivo. c12G1-DM1 induced cell cycle arrest at the mitotic phase in nectin-2-positive ovarian cancer cells, but not in nectin-2-negative cancer cells. c12G1-DM1 induced ~100-fold cytotoxicity in ovarian cancer cells, with an IC₅₀ in the range of 0.1 nM~7.4 nM, compared to normal IgG-DM1. In addition, c12G1-DM1 showed ~91% tumor growth inhibition in mouse xenograft models transplanted with OV-90 cells. These results suggest that c12G1-DM1 could be used as a potential therapeutic agent against nectin-2-positive ovarian cancers.     

Autonomous clustering using rough set theory

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.