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Bilateration is one of the localization methods that provides the location information of the target node using two anchors’ location information and two distances between the target node and each anchor. This paper shows that bilateration with different configurations in a three-anchor positioning system gives better precision than trilateration and the same precision as the nonlinear least square (NLS) method when the target is outside the convex hull of the anchors, and also that the combinatorial use of bilaterations gives the same level of precision as the NLS method or tilateration even when the target is inside the convex hull of the anchors. For the rigorous proof of this claim, we first develop a theory that the bilateration method can be naturally modeled and understood by adopting a pair of distances as a complex variable and the mapping in the bilateration method as a complex-valued sine function with a complex variable, respectively. We explain why the proposed complexification is more intuitive and applicable to real-world problems than the real-valued bilateration method. Since the complex sine function has many nice properties and one-to-one correspondence, it can be regarded as a fundamental transformation from the measurement space to position space in a two-dimensional positioning system. Next, based on the complex-valued function theory for bilateration, we describe the error propagation and show that a nonlinear least square method can be replaced with the closed formula in the bilateration localization system. The error propagation of the bilateration has a unique property: the error in position space propagates only in the horizontal direction with anchors and is unchanged in the other direction regardless of the target’s position.

A substantial body of work has been done to identify network anomalies using supervised and unsupervised learning techniques with their unique strengths and weaknesses. In this work, we propose a new approach that takes advantage of both worlds of unsupervised and supervised learnings. The main objective of the proposed approach is to enable supervised anomaly detection without the provision of the associated labels by users. To this end, we estimate the labels of each connection in the training phase using clustering. The “estimated” labels are then utilized to establish a supervised learning model for the subsequent classification of connections in the testing stage. We set up a new property that defines anomalies in the context of network anomaly detection to improve the quality of estimated labels. Through our extensive experiments with a public dataset (NSL-KDD), we will prove that the proposed method can achieve performance comparable to one with the “original” labels provided in the dataset. We also introduce two heuristic functions that minimize the impact of the randomness of clustering to improve the overall quality of the estimated labels.

Network application identification is one of the core elements in network operations and management to provide enhanced network service and security. For accurate identification, an approach using common patterns called “signatures” is widely used to compensate the limitations of the traditional transport-layer port-based classification. However, our simulation results indicate that using the signatures generated from a set of well known algorithms may lead to very poor identification performance, with less than 60% of true positives even in an optimal case. To improve the quality of signatures, we present a technique in this paper, which consists of two steps: (i) pairwise merging to consider every possible combination of the initially collected signatures to reduce their specificity that causes the signatures to be less common; and (ii) signature reduction to identify effective signatures with greater importance from a large set of signatures produced in the merging step, so as to manage the space/time complexity in the identification process for greater scalability. Our experimental results show that the proposed technique can dramatically improve the performance, even with a small number of signatures (e.g., 95% true positives rate with 30 signatures per application) which is more compact than the initial signature set. Keywords: Network application identification, Application signatures, Pairwise merging, Signature reduction, Explicit string patterns

Miaomei Yu,1 Lili Pan,1 Chen Sang,2 Qinfeng Mu,1 Lu Zheng,1 Guanghua Luo,1 Ning Xu31Comprehensive Laboratory, the Third Affiliated Hospital of Soochow University, Changzhou 213003, People’s Republic of China; 2Department of Cardiothoracic Surgery, the Third Affiliated Hospital of Soochow University, Changzhou 213003, People’s Republic of China; 3Section of Clinical Chemistry and Pharmacology, Institute of Laboratory Medicine, Lunds University, Lund S‑22185, SwedenObjective: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with high mortality-to-incidence ratios. Apolipoprotein M (ApoM), a member of the apolipoprotein family, is mainly synthesized in the liver, whereas its role in HCC has not been elucidated. Here, we examined the effect of ApoM on the biological behavior of HCC cells and the possible mechanisms.Methods: We used CRISPR/Cas9 technology to knock out ApoM in SMMC7721 cells. Differentially expressed genes before and after ApoM knockout (KO) were analyzed by GeneChip microarrays and confirmed by qRT-PCR. Cell assays of proliferation, apoptosis, migration and invasion were performed in SMMC7721 cells, and the expression of epithelial–mesenchymal transition (EMT) markers was performed by western blot. And we performed functional recovery experiments by overexpressing vitamin D receptor (VDR) in SMMC7721.Results: The ApoM-KO SMMC7721 cell line was successfully constructed using the CRISPR/Cas9 technology. Our results showed that silencing ApoM suppressed apoptosis and promoted proliferation, migration, invasion and EMT of SMMC7721 cells. The microarray data revealed that a total of 1,868 differentially expressed genes were identified, including VDR. The qRT-PCR and western blot verification results demonstrated that knocking out ApoM could significantly reduce the expression of VDR. The functional recovery experiments indicated that VDR overexpression could offset the inhibition of cell apoptosis and the promotion of cell proliferation, migration, invasion and EMT caused by knocking out ApoM in SMMC7721 cells.Conclusion: ApoM could function as a tumor suppressor to inhibit the growth and metastasis of SMMC7721 cells via VDR signaling in HCC.Keywords: apolipoprotein M, CRISPR/Cas9, hepatocellular carcinoma cells, GeneChip microarrays, vitamin D receptor

Deep learning has been widely studied in many technical domains such as image analysis and speech recognition, with its benefits that effectively deal with complex and high-dimensional data. Our preliminary experiments show a high degree of non-linearity from the network connection data, which explains why it is hard to improve the performance of identifying network anomalies by using conventional learning methods (e.g., Adaboosting, SVM, and Random Forest). In this study, we design and examine deep learning models constructed based on Fully Connected Networks (FCNs), Variational AutoEncoder (VAE), and Sequence-to-Sequence (Seq2Seq) structures. For the extensive evaluation, we employ a broad range of the public datasets with unique characteristics. Our experimental results confirm the feasibility of deep learning-based network anomaly detection, with the improved performance compared to the conventional learning techniques. In particular, the detection model based on Seq2Seq with LSTM is highly promising, consistently yielding over 99% of accuracy to identify network anomalies from the entire datasets employed in the evaluation.

Vigilin, a nucleocytoplasmic shuttling protein, post-transcriptionally suppresses proto-oncogene c-fms expression (encoding CSF-1R) in breast cancer by binding to a 69 nt cis-acting 3-UTR element in CSF-1R mRNA. CSF-1R is an important mediator of breast cancer development, metastasis, and survival. We confirm that vigilin decreases in vitro reporter luciferase activity as well as the translation rate of target mRNAs. We further explore the mechanism of suppression of CSF-1R. We show that the 69 nt binding element has profound effects on translation efficiency of CSF-1R mRNA, not seen in the presence of mutation of the element. Also, mutation of the 69 nt element in the CSF-1R mRNA 3′UTR both interferes with direct vigilin binding and obviates effect of vigilin overexpression on translational repression of CSF-1R. We show that stable vigilin binding requires the full length 69 nt CSF-1R element, including the 26 nt pyrimidine-rich core. Furthermore, titration of endogenous vigilin and other proteins which bind the 69 nt element, by exogenously introduced CSF-1R mRNA 3′UTR containing the pyrimidine-rich sequence, increases the adhesion, motility, and invasion of breast cancer cells. This phenotypic effect is not seen when the 69 nt element is deleted. Lastly, we are the first to show that human breast tissues exhibit strong vigilin expression in normal breast epithelium. Our pilot data suggest decreased vigilin protein expression, along with shift from the nucleus to the cytoplasmic location, in the transition to ductal carcinoma in situ.

In this study, we concisely investigate two phases in the hybrid A-star algorithm for non-holonomic robots: the forward search phase and analytic expansion phase. The forward search phase considers the kinematics of the robot model in order to plan continuous motion of the robot in discrete grid maps. Reeds-Shepp (RS) curve in the analytic expansion phase augments the accuracy and the speed of the algorithm. However, RS curves are often produced close to obstacles, especially at corners. Consequently, the robot may collide with obstacles through the process of movement at these corners because of the measurement errors or errors of motor controllers. Therefore, we propose an improved RS method to eventually improve the hybrid A-star algorithm’s performance in terms of safety for robots to move in indoor environments. The advantage of the proposed method is that the non-holonomic robot has multiple options of curvature or turning radius to move safer on pathways. To select a safer route among multiple routes to a goal configuration, we introduce a cost function to evaluate the cost of risk of robot collision, and the cost of movement of the robot along the route. In addition, generated paths by the forward search phase always consist of unnecessary turning points. To overcome this issue, we present a fine-tuning of motion primitive in the forward search phase to make the route smoother without using complex path smoothing techniques. In the end, the effectiveness of the improved method is verified via its performance in simulations using benchmark maps where cost of risk of collision and number of turning points are reduced by up to around 20%.

A mission reliability evaluation methodology for a signal traffic controller is presented in this paper. To develop the new evaluation methodology, this paper combines the Discrete Event System Specification (DEVS) formalism which has been popular in manufacturing area for three reasons: (1) its features compatible with the object-oriented modeling; (2) its rigorous formal definition; and (3) its support for the specifications of discrete event models in a hierarchical and modular manner. By using the DEVS formalism, we construct a simulation model which takes into account not only the characteristics of a traffic signal controller but also the operating environment. Once a model is constructed, it is possible to perform simulation experiments. The proposed methodology computes the mission reliability of a traffic signal controller by using a simulation record, and this information plays a vital role in preparing optimized maintenance policies that maximize availability or minimize life cycle costs.

Proposed in this paper is a new reliability model for a wafer fabrication plant (FAB). The reliability prediction of a FAB is essential for various activities; feasibility evaluation, comparing competing designs, identifying potential reliability problems, planning maintenance and logistic support strategies, and input to other studies such as life-cycle cost analysis or order selection. The conventional reliability model, however, is not appropriate for a FAB because of inherent attributes of the manufacturing system; concurrent production of various products, reentrant material flows, and the recipe arrangement problem. To overcome this problem, this paper proposes a new reliability model of a FAB consisting of three reliability functions; reliability function of an entire FAB, reliability functions of tool groups, and reliability functions of FAB tools. To demonstrate the proposed reliability model, a simulation model is constructed based on a wafer FAB data-set. The simulation experiments are carried out with commercial software MOZART®.

In this paper, a two-stage dispatching rule for the on-time delivery of an order-driven FAB is presented. Dispatching rules have a significant impact on the performance of FABs. Consequently, many such rules have been created, such as first in first out, earliest due data, operation due date, and critical ratio. Because these conventional dispatching rules make decisions only by considering the due date of each lot, they might not be suitable for dynamic order-driven FABs, for which it is very essential to monitor not only the due date of each lot but also the production rate of each step. In the first stage of the proposed two-stage dispatching rule, the production rate of each step is taken into account, and the due date of each lot is considered in the second stage. In this paper, the production rates of steps are computed by combining forward simulation and backward pegging. A simulation model, based on a wafer FAB data-set, has been developed, and simulation is conducted using commercial software SEEPLAN®. The simulation results clearly show the advantages of the proposed dispatching rule over existing rules.

Depletion of alloreactive T-lymphocytes from allogeneic bone marrow tansplants may prevent graft-versus-host disease (GVHD) without impairing donor cell engraftment, immunity, and the graft-versus-leukemia (GVL) effect. Alloreactive T-cells may be identified by their expression, upon activation, of CD137, a costimulatory receptor and putative surrogate marker for antigen-specific effector T-cells. In this context, we tested the use of anti-CD137-saporin immunotoxin to selectively deplete mouse and human alloreactive T-cells. Anti-CD137 antibodies were internalized by cells within 4 h of binding to the cell surface CD137, and anti-CD137-saporin immunotoxin effectively killed polyclonally activated T-cells or antigen-stimulated T-cells. Transfer of donor T-cells after allodepletion with anti-CD137-saporin immunotoxin failed to induce any evident expression of GVHD; however, a significant GVL effect was observed. Targeting of CD137 with an immunotoxin was also effective in killing polyclonally activated or alloreactive human T-cells. Our results indicate that anti-CD137-saporin immunotoxin may be used to deplete alloreactive T-cells prior to bone marrow transplantation and thereby prevent GVHD and the relapse of leukemia.

4-1BB (CD137, TNFRSF9), a member of the activation-induced tumor necrosis factor receptor family, is a powerful T-cell costimulatory molecule. It generally enhances CD8(+) T responses and even breaks the tolerance of CD8(+) T cells in an antigen-specific manner. In the present study we found that it was expressed in the placentas of pregnant mice and that its expression coincided with that of the immunesuppressive enzyme indoleamine 2,3-dioxygenase (IDO). Therefore, we investigated whether 4-1BB signaling is involved in fetal rejection using agonistic anti-4-1BB mAb and 4-1BB-deficient mice. Treatment with agonistic anti-4-1BB mAb markedly increased the rate of rejection of allogeneic but not syngeneic fetuses, and this was primarily dependent on CD8(+) T cells. Complement component 3 (C3) seemed to be the effector molecule because 4-1BB triggering resulting in accumulation of C3 in the placenta, and this accumulation was also reversed by anti-CD8 mAb treatment. These findings demonstrate that 4-1BB triggering breaks the tolerance of CD8(+) T cells to alloantigens in the placenta. Moreover, triggering 4-1BB protected the pregnant mice from Listeria monocytogenes (LM) infection, but led to rejection of semi-allogeneic fetuses. Therefore, given the cross-recognition of alloantigen by pathogen-reactive CD8(+) T cells, the true function of 4-1BB may be to reverse the hypo-responsiveness of pathogen-reactive CD8(+) T cells in the placenta in cases of infection, even if that risks losing the fetus.

Efficient handling of high heat flux on the plasma‐facing components, particularly the divertor targets, poses a significant challenge for the Chinese Fusion Engineering Testing Reactor (CFETR) with fusion power of Gigawatt. This work investigates the divertor plasma detachment of CFETR with a standard ITER‐like divertor geometry by neon (Ne) or argon (Ar) impurity seeding using UEDGE code. The cross‐field drifts terms are switched off, and fluid neutral models and a "fixed‐fraction" impurity model are applied to enable efficient simulations for the study of CFETR detachment. In order to reduce the heat load on the divertor targets below the acceptable level (<10 MW/m2), the impurity fraction (f), pumping speed (S), and upstream density are varied to identify the suitable operations window during Ne seeding. The effects of Ne and Ar impurities on the plasma detachment are compared. It is found that with the power across the core‐edge interface PSOL = 200 MW and separatrix density of 2.8 ×$$ \times $$ 1019m−3$$ {\mathrm{m}}^{-3} $$, Ne impurity fraction ≥1.7%, and Ar impurity fraction ≥0.24% can achieve the partial detachment. Achieving similar total radiation power (˜148 MW), the Ne fraction is 2.3% and the Ar fraction is 0.24%. Moreover, the simulation results indicate that Ar exhibits better power radiation efficiency and core compatibility compared with Ne. [ABSTRACT FROM AUTHOR]

Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this paper, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. We demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.

While the network anomaly detection is essential in network operations and management, it becomes further challenging to perform the first line of detection against the exponentially increasing volume of network traffic. In this work, we develop a technique for the first line of online anomaly detection with two important considerations: (i) availability of traffic attributes during the monitoring time, and (ii) computational scalability for streaming data. The presented learning technique is lightweight and highly scalable with the beauty of approximation based on the grid partitioning of the given dimensional space. With the public traffic traces of KDD Cup 1999 and NSL-KDD, we show that our technique yields 98.5% and 83% of detection accuracy, respectively, only with a couple of readily available traffic attributes that can be obtained without the help of post-processing. The results are at least comparable with the classical learning methods including decision tree and random forest, with approximately two orders of magnitude faster learning performance.