An automated diagnosis system of liver disease using artificial immune and genetic algorithms.

PubMed

Liang, Chunlin; Peng, Lingxi

2013-04-01

The rise of health care cost is one of the world's most important problems. Disease prediction is also a vibrant research area. Researchers have approached this problem using various techniques such as support vector machine, artificial neural network, etc. This study typically exploits the immune system's characteristics of learning and memory to solve the problem of liver disease diagnosis. The proposed system applies a combination of two methods of artificial immune and genetic algorithm to diagnose the liver disease. The system architecture is based on artificial immune system. The learning procedure of system adopts genetic algorithm to interfere the evolution of antibody population. The experiments use two benchmark datasets in our study, which are acquired from the famous UCI machine learning repository. The obtained diagnosis accuracies are very promising with regard to the other diagnosis system in the literatures. These results suggest that this system may be a useful automatic diagnosis tool for liver disease.

Immunity-Based Aircraft Fault Detection System

NASA Technical Reports Server (NTRS)

Dasgupta, D.; KrishnaKumar, K.; Wong, D.; Berry, M.

2004-01-01

In the study reported in this paper, we have developed and applied an Artificial Immune System (AIS) algorithm for aircraft fault detection, as an extension to a previous work on intelligent flight control (IFC). Though the prior studies had established the benefits of IFC, one area of weakness that needed to be strengthened was the control dead band induced by commanding a failed surface. Since the IFC approach uses fault accommodation with no detection, the dead band, although it reduces over time due to learning, is present and causes degradation in handling qualities. If the failure can be identified, this dead band can be further A ed to ensure rapid fault accommodation and better handling qualities. The paper describes the application of an immunity-based approach that can detect a broad spectrum of known and unforeseen failures. The approach incorporates the knowledge of the normal operational behavior of the aircraft from sensory data, and probabilistically generates a set of pattern detectors that can detect any abnormalities (including faults) in the behavior pattern indicating unsafe in-flight operation. We developed a tool called MILD (Multi-level Immune Learning Detection) based on a real-valued negative selection algorithm that can generate a small number of specialized detectors (as signatures of known failure conditions) and a larger set of generalized detectors for unknown (or possible) fault conditions. Once the fault is detected and identified, an adaptive control system would use this detection information to stabilize the aircraft by utilizing available resources (control surfaces). We experimented with data sets collected under normal and various simulated failure conditions using a piloted motion-base simulation facility. The reported results are from a collection of test cases that reflect the performance of the proposed immunity-based fault detection algorithm.

A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization

PubMed Central

Lin, Jingjing; Jing, Honglei

2016-01-01

Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive. PMID:27698662

Characteristic extraction and matching algorithms of ballistic missile in near-space by hyperspectral image analysis

NASA Astrophysics Data System (ADS)

Lu, Li; Sheng, Wen; Liu, Shihua; Zhang, Xianzhi

2014-10-01

The ballistic missile hyperspectral data of imaging spectrometer from the near-space platform are generated by numerical method. The characteristic of the ballistic missile hyperspectral data is extracted and matched based on two different kinds of algorithms, which called transverse counting and quantization coding, respectively. The simulation results show that two algorithms extract the characteristic of ballistic missile adequately and accurately. The algorithm based on the transverse counting has the low complexity and can be implemented easily compared to the algorithm based on the quantization coding does. The transverse counting algorithm also shows the good immunity to the disturbance signals and speed up the matching and recognition of subsequent targets.

A survey of artificial immune system based intrusion detection.

PubMed

Yang, Hua; Li, Tao; Hu, Xinlei; Wang, Feng; Zou, Yang

2014-01-01

In the area of computer security, Intrusion Detection (ID) is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS). The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs). This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted.

A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

PubMed Central

Zhu, Bohui; Ding, Yongsheng; Hao, Kuangrong

2013-01-01

This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. PMID:23690875

A Survey of Artificial Immune System Based Intrusion Detection

PubMed Central

Li, Tao; Hu, Xinlei; Wang, Feng; Zou, Yang

2014-01-01

In the area of computer security, Intrusion Detection (ID) is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS). The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs). This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted. PMID:24790549

Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm.

PubMed

Wu, Jianfa; Peng, Dahao; Li, Zhuping; Zhao, Li; Ling, Huanzhang

2015-01-01

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data.

A Multipopulation Coevolutionary Strategy for Multiobjective Immune Algorithm

PubMed Central

Shi, Jiao; Gong, Maoguo; Ma, Wenping; Jiao, Licheng

2014-01-01

How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems. PMID:24672330

Comparative analysis of the effectiveness of three immunization strategies in controlling disease outbreaks in realistic social networks.

PubMed

Xu, Zhijing; Zu, Zhenghu; Zheng, Tao; Zhang, Wendou; Xu, Qing; Liu, Jinjie

2014-01-01

The high incidence of emerging infectious diseases has highlighted the importance of effective immunization strategies, especially the stochastic algorithms based on local available network information. Present stochastic strategies are mainly evaluated based on classical network models, such as scale-free networks and small-world networks, and thus are insufficient. Three frequently referred stochastic immunization strategies-acquaintance immunization, community-bridge immunization, and ring vaccination-were analyzed in this work. The optimal immunization ratios for acquaintance immunization and community-bridge immunization strategies were investigated, and the effectiveness of these three strategies in controlling the spreading of epidemics were analyzed based on realistic social contact networks. The results show all the strategies have decreased the coverage of the epidemics compared to baseline scenario (no control measures). However the effectiveness of acquaintance immunization and community-bridge immunization are very limited, with acquaintance immunization slightly outperforming community-bridge immunization. Ring vaccination significantly outperforms acquaintance immunization and community-bridge immunization, and the sensitivity analysis shows it could be applied to controlling the epidemics with a wide infectivity spectrum. The effectiveness of several classical stochastic immunization strategies was evaluated based on realistic contact networks for the first time in this study. These results could have important significance for epidemic control research and practice.

Immunity-Based Optimal Estimation Approach for a New Real Time Group Elevator Dynamic Control Application for Energy and Time Saving

PubMed Central

Baygin, Mehmet; Karakose, Mehmet

2013-01-01

Nowadays, the increasing use of group elevator control systems owing to increasing building heights makes the development of high-performance algorithms necessary in terms of time and energy saving. Although there are many studies in the literature about this topic, they are still not effective enough because they are not able to evaluate all features of system. In this paper, a new approach of immune system-based optimal estimate is studied for dynamic control of group elevator systems. The method is mainly based on estimation of optimal way by optimizing all calls with genetic, immune system and DNA computing algorithms, and it is evaluated with a fuzzy system. The system has a dynamic feature in terms of the situation of calls and the option of the most appropriate algorithm, and it also adaptively works in terms of parameters such as the number of floors and cabins. This new approach which provides both time and energy saving was carried out in real time. The experimental results comparatively demonstrate the effects of method. With dynamic and adaptive control approach in this study carried out, a significant progress on group elevator control systems has been achieved in terms of time and energy efficiency according to traditional methods. PMID:23935433

An Orthogonal Evolutionary Algorithm With Learning Automata for Multiobjective Optimization.

PubMed

Dai, Cai; Wang, Yuping; Ye, Miao; Xue, Xingsi; Liu, Hailin

2016-12-01

Research on multiobjective optimization problems becomes one of the hottest topics of intelligent computation. In order to improve the search efficiency of an evolutionary algorithm and maintain the diversity of solutions, in this paper, the learning automata (LA) is first used for quantization orthogonal crossover (QOX), and a new fitness function based on decomposition is proposed to achieve these two purposes. Based on these, an orthogonal evolutionary algorithm with LA for complex multiobjective optimization problems with continuous variables is proposed. The experimental results show that in continuous states, the proposed algorithm is able to achieve accurate Pareto-optimal sets and wide Pareto-optimal fronts efficiently. Moreover, the comparison with the several existing well-known algorithms: nondominated sorting genetic algorithm II, decomposition-based multiobjective evolutionary algorithm, decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, multiobjective optimization by LA, and multiobjective immune algorithm with nondominated neighbor-based selection, on 15 multiobjective benchmark problems, shows that the proposed algorithm is able to find more accurate and evenly distributed Pareto-optimal fronts than the compared ones.

Immune allied genetic algorithm for Bayesian network structure learning

NASA Astrophysics Data System (ADS)

Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

2012-06-01

Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

Network Intrusion Detection Based on a General Regression Neural Network Optimized by an Improved Artificial Immune Algorithm

PubMed Central

Wu, Jianfa; Peng, Dahao; Li, Zhuping; Zhao, Li; Ling, Huanzhang

2015-01-01

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data. PMID:25807466

An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.

PubMed

Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun

2017-09-01

The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

NASA Astrophysics Data System (ADS)

Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue

2016-01-01

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

Spectroscopic techniques to study the immune response in human saliva

NASA Astrophysics Data System (ADS)

Nepomnyashchaya, E.; Savchenko, E.; Velichko, E.; Bogomaz, T.; Aksenov, E.

2018-01-01

Studies of the immune response dynamics by means of spectroscopic techniques, i.e., laser correlation spectroscopy and fluorescence spectroscopy, are described. The laser correlation spectroscopy is aimed at measuring sizes of particles in biological fluids. The fluorescence spectroscopy allows studying of the conformational and other structural changings in immune complex. We have developed a new scheme of a laser correlation spectrometer and an original signal processing algorithm. We have suggested a new fluorescence detection scheme based on a prism and an integrating pin diode. The developed system based on the spectroscopic techniques allows studies of complex process in human saliva and opens some prospects for an individual treatment of immune diseases.

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

DTIC Science & Technology

2007-03-01

Intelligence AIS Artificial Immune System ANN Artificial Neural Networks API Application Programming Interface BFS Breadth-First Search BIS Biological...problem domain is too large for only one algorithm’s application . It ranges from network - based sniffer systems, responsible for Enterprise-wide coverage...options to network administrators in choosing detectors to employ in future ID applications . Objectives Our hypothesis validity is based on a set

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

PubMed Central

Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

2016-01-01

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

PubMed

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy

PubMed Central

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions. PMID:27487242

AITSO: A Tool for Spatial Optimization Based on Artificial Immune Systems

PubMed Central

Zhao, Xiang; Liu, Yaolin; Liu, Dianfeng; Ma, Xiaoya

2015-01-01

A great challenge facing geocomputation and spatial analysis is spatial optimization, given that it involves various high-dimensional, nonlinear, and complicated relationships. Many efforts have been made with regard to this specific issue, and the strong ability of artificial immune system algorithms has been proven in previous studies. However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems. This paper describes a free, universal tool, named AITSO, which is capable of solving various optimization problems. It provides a series of standard application programming interfaces (APIs) which can (1) assist researchers in the development of their own problem-specific application plugins to solve practical problems and (2) allow the implementation of some advanced immune operators into the platform to improve the performance of an algorithm. As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving. It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis. PMID:25678911

Novel image encryption algorithm based on multiple-parameter discrete fractional random transform

NASA Astrophysics Data System (ADS)

Zhou, Nanrun; Dong, Taiji; Wu, Jianhua

2010-08-01

A new method of digital image encryption is presented by utilizing a new multiple-parameter discrete fractional random transform. Image encryption and decryption are performed based on the index additivity and multiple parameters of the multiple-parameter fractional random transform. The plaintext and ciphertext are respectively in the spatial domain and in the fractional domain determined by the encryption keys. The proposed algorithm can resist statistic analyses effectively. The computer simulation results show that the proposed encryption algorithm is sensitive to the multiple keys, and that it has considerable robustness, noise immunity and security.

An Artificial Immune System with Feedback Mechanisms for Effective Handling of Population Size

NASA Astrophysics Data System (ADS)

Gao, Shangce; Wang, Rong-Long; Ishii, Masahiro; Tang, Zheng

This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.

Optimization of HAART with genetic algorithms and agent-based models of HIV infection.

PubMed

Castiglione, F; Pappalardo, F; Bernaschi, M; Motta, S

2007-12-15

Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI). In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection. The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient. To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups. A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.html

A Novel Energy-Efficient Multi-Sensor Fusion Wake-Up Control Strategy Based on a Biomimetic Infectious-Immune Mechanism for Target Tracking.

PubMed

Zhou, Jie; Liang, Yan; Shen, Qiang; Feng, Xiaoxue; Pan, Quan

2018-04-18

A biomimetic distributed infection-immunity model (BDIIM), inspired by the immune mechanism of an infected organism, is proposed in order to achieve a high-efficiency wake-up control strategy based on multi-sensor fusion for target tracking. The resultant BDIIM consists of six sub-processes reflecting the infection-immunity mechanism: occurrence probabilities of direct-infection (DI) and cross-infection (CI), immunity/immune-deficiency of DI and CI, pathogen amount of DI and CI, immune cell production, immune memory, and pathogen accumulation under immunity state. Furthermore, a corresponding relationship between the BDIIM and sensor wake-up control is established to form the collaborative wake-up method. Finally, joint surveillance and target tracking are formulated in the simulation, in which we show that the energy cost and position tracking error are reduced to 50.8% and 78.9%, respectively. Effectiveness of the proposed BDIIM algorithm is shown, and this model is expected to have a significant role in guiding the performance improvement of multi-sensor networks.

Human body motion tracking based on quantum-inspired immune cloning algorithm

NASA Astrophysics Data System (ADS)

Han, Hong; Yue, Lichuan; Jiao, Licheng; Wu, Xing

2009-10-01

In a static monocular camera system, to gain a perfect 3D human body posture is a great challenge for Computer Vision technology now. This paper presented human postures recognition from video sequences using the Quantum-Inspired Immune Cloning Algorithm (QICA). The algorithm included three parts. Firstly, prior knowledge of human beings was used, the key joint points of human could be detected automatically from the human contours and skeletons which could be thinning from the contours; And due to the complexity of human movement, a forecasting mechanism of occlusion joint points was addressed to get optimum 2D key joint points of human body; And then pose estimation recovered by optimizing between the 2D projection of 3D human key joint points and 2D detection key joint points using QICA, which recovered the movement of human body perfectly, because this algorithm could acquire not only the global optimal solution, but the local optimal solution.

Optimal Solution for an Engineering Applications Using Modified Artificial Immune System

NASA Astrophysics Data System (ADS)

Padmanabhan, S.; Chandrasekaran, M.; Ganesan, S.; patan, Mahamed Naveed Khan; Navakanth, Polina

2017-03-01

An Engineering optimization leads a essential role in several engineering application areas like process design, product design, re-engineering and new product development, etc. In engineering, an awfully best answer is achieved by comparison to some completely different solutions by utilization previous downside information. An optimization algorithms provide systematic associate degreed economical ways that within which of constructing and comparison new design solutions so on understand at best vogue, thus on best solution efficiency and acquire the foremost wonderful design impact. In this paper, a new evolutionary based Modified Artificial Immune System (MAIS) algorithm used to optimize an engineering application of gear drive design. The results are compared with existing design.

Revisiting negative selection algorithms.

PubMed

Ji, Zhou; Dasgupta, Dipankar

2007-01-01

This paper reviews the progress of negative selection algorithms, an anomaly/change detection approach in Artificial Immune Systems (AIS). Following its initial model, we try to identify the fundamental characteristics of this family of algorithms and summarize their diversities. There exist various elements in this method, including data representation, coverage estimate, affinity measure, and matching rules, which are discussed for different variations. The various negative selection algorithms are categorized by different criteria as well. The relationship and possible combinations with other AIS or other machine learning methods are discussed. Prospective development and applicability of negative selection algorithms and their influence on related areas are then speculated based on the discussion.

Determination of Cole-Cole parameters using only the real part of electrical impedivity measurements.

PubMed

Miranda, David A; Rivera, S A López

2008-05-01

An algorithm is presented to determine the Cole-Cole parameters of electrical impedivity using only measurements of its real part. The algorithm is based on two multi-fold direct inversion methods for the Cole-Cole and Debye equations, respectively, and a genetic algorithm for the optimization of the mean square error between experimental and calculated data. The algorithm has been developed to obtain the Cole-Cole parameters from experimental data, which were used to screen cervical intra-epithelial neoplasia. The proposed algorithm was compared with different numerical integrations of the Kramers-Kronig relation and the result shows that this algorithm is the best. A high immunity to noise was obtained.

Cloud Model-Based Artificial Immune Network for Complex Optimization Problem

PubMed Central

Wang, Mingan; Li, Jianming; Guo, Dongliang

2017-01-01

This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators—cloning, mutation, and suppression—are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications—finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning—are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm. PMID:28630620

Cloud Model-Based Artificial Immune Network for Complex Optimization Problem.

PubMed

Wang, Mingan; Feng, Shuo; Li, Jianming; Li, Zhonghua; Xue, Yu; Guo, Dongliang

2017-01-01

This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators-cloning, mutation, and suppression-are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications-finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning-are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm.

Novel near-infrared spectrum analysis tool: Synergy adaptive moving window model based on immune clone algorithm.

PubMed

Wang, Shenghao; Zhang, Yuyan; Cao, Fuyi; Pei, Zhenying; Gao, Xuewei; Zhang, Xu; Zhao, Yong

2018-02-13

This paper presents a novel spectrum analysis tool named synergy adaptive moving window modeling based on immune clone algorithm (SA-MWM-ICA) considering the tedious and inconvenient labor involved in the selection of pre-processing methods and spectral variables by prior experience. In this work, immune clone algorithm is first introduced into the spectrum analysis field as a new optimization strategy, covering the shortage of the relative traditional methods. Based on the working principle of the human immune system, the performance of the quantitative model is regarded as antigen, and a special vector corresponding to the above mentioned antigen is regarded as antibody. The antibody contains a pre-processing method optimization region which is created by 11 decimal digits, and a spectrum variable optimization region which is formed by some moving windows with changeable width and position. A set of original antibodies are created by modeling with this algorithm. After calculating the affinity of these antibodies, those with high affinity will be selected to clone. The regulation for cloning is that the higher the affinity, the more copies will be. In the next step, another import operation named hyper-mutation is applied to the antibodies after cloning. Moreover, the regulation for hyper-mutation is that the lower the affinity, the more possibility will be. Several antibodies with high affinity will be created on the basis of these steps. Groups of simulated dataset, gasoline near-infrared spectra dataset, and soil near-infrared spectra dataset are employed to verify and illustrate the performance of SA-MWM-ICA. Analysis results show that the performance of the quantitative models adopted by SA-MWM-ICA are better especially for structures with relatively complex spectra than traditional models such as partial least squares (PLS), moving window PLS (MWPLS), genetic algorithm PLS (GAPLS), and pretreatment method classification and adjustable parameter changeable size moving window PLS (CA-CSMWPLS). The selected pre-processing methods and spectrum variables are easily explained. The proposed method will converge in few generations and can be used not only for near-infrared spectroscopy analysis but also for other similar spectral analysis, such as infrared spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System.

PubMed

Rajamani, Sripriya; Bieringer, Aaron; Wallerius, Stephanie; Jensen, Daniel; Winden, Tamara; Muscoplat, Miriam Halstead

2016-01-01

Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.

Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm

PubMed Central

Ji, Junzhong; Liu, Jinduo; Liang, Peipeng; Zhang, Aidong

2016-01-01

Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC. In the proposed algorithm, a brain effective connectivity network is mapped onto an antibody, and four immune operators are employed to perform the optimization process of antibodies, including clonal selection operator, crossover operator, mutation operator and suppression operator, and finally gets an antibody with the highest K2 score as the solution. AIAEC is then tested on Smith’s simulated datasets, and the effect of the different factors on AIAEC is evaluated, including the node number, session length, as well as the other potential confounding factors of the blood oxygen level dependent (BOLD) signal. It was revealed that, as contrast to other existing methods, AIAEC got the best performance on the majority of the datasets. It was also found that AIAEC could attain a relative better solution under the influence of many factors, although AIAEC was differently affected by the aforementioned factors. AIAEC is thus demonstrated to be an effective method for detecting the brain effective connectivity. PMID:27045295

Conditioning of Model Identification Task in Immune Inspired Optimizer SILO

NASA Astrophysics Data System (ADS)

Wojdan, K.; Swirski, K.; Warchol, M.; Maciorowski, M.

2009-10-01

Methods which provide good conditioning of model identification task in immune inspired, steady-state controller SILO (Stochastic Immune Layer Optimizer) are presented in this paper. These methods are implemented in a model based optimization algorithm. The first method uses a safe model to assure that gains of the process's model can be estimated. The second method is responsible for elimination of potential linear dependences between columns of observation matrix. Moreover new results from one of SILO implementation in polish power plant are presented. They confirm high efficiency of the presented solution in solving technical problems.

Noise-immune complex correlation for optical coherence angiography based on standard and Jones matrix optical coherence tomography

PubMed Central

Makita, Shuichi; Kurokawa, Kazuhiro; Hong, Young-Joo; Miura, Masahiro; Yasuno, Yoshiaki

2016-01-01

This paper describes a complex correlation mapping algorithm for optical coherence angiography (cmOCA). The proposed algorithm avoids the signal-to-noise ratio dependence and exhibits low noise in vasculature imaging. The complex correlation coefficient of the signals, rather than that of the measured data are estimated, and two-step averaging is introduced. Algorithms of motion artifact removal based on non perfusing tissue detection using correlation are developed. The algorithms are implemented with Jones-matrix OCT. Simultaneous imaging of pigmented tissue and vasculature is also achieved using degree of polarization uniformity imaging with cmOCA. An application of cmOCA to in vivo posterior human eyes is presented to demonstrate that high-contrast images of patients’ eyes can be obtained. PMID:27446673

A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

PubMed Central

Wang, Zhongbin; Xu, Xihua; Si, Lei; Ji, Rui; Liu, Xinhua; Tan, Chao

2016-01-01

In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others. PMID:27123002

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

2014-01-01

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Natural Inspired Intelligent Visual Computing and Its Application to Viticulture.

PubMed

Ang, Li Minn; Seng, Kah Phooi; Ge, Feng Lu

2017-05-23

This paper presents an investigation of natural inspired intelligent computing and its corresponding application towards visual information processing systems for viticulture. The paper has three contributions: (1) a review of visual information processing applications for viticulture; (2) the development of natural inspired computing algorithms based on artificial immune system (AIS) techniques for grape berry detection; and (3) the application of the developed algorithms towards real-world grape berry images captured in natural conditions from vineyards in Australia. The AIS algorithms in (2) were developed based on a nature-inspired clonal selection algorithm (CSA) which is able to detect the arcs in the berry images with precision, based on a fitness model. The arcs detected are then extended to perform the multiple arcs and ring detectors information processing for the berry detection application. The performance of the developed algorithms were compared with traditional image processing algorithms like the circular Hough transform (CHT) and other well-known circle detection methods. The proposed AIS approach gave a Fscore of 0.71 compared with Fscores of 0.28 and 0.30 for the CHT and a parameter-free circle detection technique (RPCD) respectively.

Transcriptome-derived stromal and immune scores infer clinical outcomes of patients with cancer.

PubMed

Liu, Wei; Ye, Hua; Liu, Ying-Fu; Xu, Chao-Qun; Zhong, Yue-Xian; Tian, Tian; Ma, Shi-Wei; Tao, Huan; Li, Ling; Xue, Li-Chun; He, Hua-Qin

2018-04-01

The stromal and immune cells that form the tumor microenvironment serve a key role in the aggressiveness of tumors. Current tumor-centric interpretations of cancer transcriptome data ignore the roles of stromal and immune cells. The aim of the present study was to investigate the clinical utility of stromal and immune cells in tissue-based transcriptome data. The 'Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data' (ESTIMATE) algorithm was used to probe diverse cancer datasets and the fraction of stromal and immune cells in tumor tissues was scored. The association between the ESTIMATE scores and patient survival data was asessed; it was indicated that the two scores have implications for patient survival, metastasis and recurrence. Analysis of a colorectal cancer progression dataset revealed that decreased levels immune cells could serve an important role in cancer progression. The results of the present study indicated that trasncriptome-derived stromal and immune scores may be a useful indicator of cancer prognosis.

A novel approach for the elimination of artefacts from EEG signals employing an improved Artificial Immune System algorithm

NASA Astrophysics Data System (ADS)

Suja Priyadharsini, S.; Edward Rajan, S.; Femilin Sheniha, S.

2016-03-01

Electroencephalogram (EEG) is the recording of electrical activities of the brain. It is contaminated by other biological signals, such as cardiac signal (electrocardiogram), signals generated by eye movement/eye blinks (electrooculogram) and muscular artefact signal (electromyogram), called artefacts. Optimisation is an important tool for solving many real-world problems. In the proposed work, artefact removal, based on the adaptive neuro-fuzzy inference system (ANFIS) is employed, by optimising the parameters of ANFIS. Artificial Immune System (AIS) algorithm is used to optimise the parameters of ANFIS (ANFIS-AIS). Implementation results depict that ANFIS-AIS is effective in removing artefacts from EEG signal than ANFIS. Furthermore, in the proposed work, improved AIS (IAIS) is developed by including suitable selection processes in the AIS algorithm. The performance of the proposed method IAIS is compared with AIS and with genetic algorithm (GA). Measures such as signal-to-noise ratio, mean square error (MSE) value, correlation coefficient, power spectrum density plot and convergence time are used for analysing the performance of the proposed method. From the results, it is found that the IAIS algorithm converges faster than the AIS and performs better than the AIS and GA. Hence, IAIS tuned ANFIS (ANFIS-IAIS) is effective in removing artefacts from EEG signals.

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

PubMed

Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

2018-01-01

T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95.125% and an AUC of 0.987 on the HLA-DRB1*0101 allele of the Wang benchmark dataset. The results indicate that the proposed prediction technique "GAPES" is a promising technique that will help researchers and scientists to predict the protein structure and it will assist them in the intelligent design of new epitope-based vaccines. Copyright © 2017 Elsevier B.V. All rights reserved.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Heng; Ye, Hao; Ng, Hui Wen

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithmmore » can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.« less

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE PAGES

Luo, Heng; Ye, Hao; Ng, Hui Wen; ...

2016-08-25

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithmmore » can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.« less

Rolling scheduling of electric power system with wind power based on improved NNIA algorithm

NASA Astrophysics Data System (ADS)

Xu, Q. S.; Luo, C. J.; Yang, D. J.; Fan, Y. H.; Sang, Z. X.; Lei, H.

2017-11-01

This paper puts forth a rolling modification strategy for day-ahead scheduling of electric power system with wind power, which takes the operation cost increment of unit and curtailed wind power of power grid as double modification functions. Additionally, an improved Nondominated Neighbor Immune Algorithm (NNIA) is proposed for solution. The proposed rolling scheduling model has further improved the operation cost of system in the intra-day generation process, enhanced the system’s accommodation capacity of wind power, and modified the key transmission section power flow in a rolling manner to satisfy the security constraint of power grid. The improved NNIA algorithm has defined an antibody preference relation model based on equal incremental rate, regulation deviation constraints and maximum & minimum technical outputs of units. The model can noticeably guide the direction of antibody evolution, and significantly speed up the process of algorithm convergence to final solution, and enhance the local search capability.

An immune clock of human pregnancy

PubMed Central

Aghaeepour, Nima; Ganio, Edward A.; Mcilwain, David; Tsai, Amy S.; Tingle, Martha; Van Gassen, Sofie; Gaudilliere, Dyani K.; Baca, Quentin; McNeil, Leslie; Okada, Robin; Ghaemi, Mohammad S.; Furman, David; Wong, Ronald J.; Winn, Virginia D.; Druzin, Maurice L.; El-Sayed, Yaser Y.; Quaintance, Cecele; Gibbs, Ronald; Darmstadt, Gary L.; Shaw, Gary M.; Stevenson, David K.; Tibshirani, Robert; Nolan, Garry P.; Lewis, David B.; Angst, Martin S.; Gaudilliere, Brice

2017-01-01

The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling–based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2–dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies. PMID:28864494

B-cell Ligand Processing Pathways Detected by Large-scale Comparative Analysis

PubMed Central

Towfic, Fadi; Gupta, Shakti; Honavar, Vasant; Subramaniam, Shankar

2012-01-01

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies. We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses. We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands. First, we compared the degree distributions of the generated networks. Second, we utilized a pairwise network alignment algorithm, BiNA, to align the networks based on the hubs in the networks. Third, we aligned the networks based on a set of KEGG pathways. We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition. The resulting pathways were further validated through literature for their common physiological responses. Collectively, the results based on our comparative analyses of degree distributions, alignment of hubs, and alignment based on KEGG pathways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches (e.g., gene coexpression network alignment algorithms) in elucidating biochemical pathways involved in complex signaling events in cells. PMID:22917187

Fragmenting networks by targeting collective influencers at a mesoscopic level.

PubMed

Kobayashi, Teruyoshi; Masuda, Naoki

2016-11-25

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

Fragmenting networks by targeting collective influencers at a mesoscopic level

NASA Astrophysics Data System (ADS)

Kobayashi, Teruyoshi; Masuda, Naoki

2016-11-01

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

Fragmenting networks by targeting collective influencers at a mesoscopic level

PubMed Central

Kobayashi, Teruyoshi; Masuda, Naoki

2016-01-01

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure. PMID:27886251

Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography

PubMed Central

Li, Yanqiu; Liu, Shi; Inaki, Schlaberg H.

2017-01-01

Accuracy and speed of algorithms play an important role in the reconstruction of temperature field measurements by acoustic tomography. Existing algorithms are based on static models which only consider the measurement information. A dynamic model of three-dimensional temperature reconstruction by acoustic tomography is established in this paper. A dynamic algorithm is proposed considering both acoustic measurement information and the dynamic evolution information of the temperature field. An objective function is built which fuses measurement information and the space constraint of the temperature field with its dynamic evolution information. Robust estimation is used to extend the objective function. The method combines a tunneling algorithm and a local minimization technique to solve the objective function. Numerical simulations show that the image quality and noise immunity of the dynamic reconstruction algorithm are better when compared with static algorithms such as least square method, algebraic reconstruction technique and standard Tikhonov regularization algorithms. An effective method is provided for temperature field reconstruction by acoustic tomography. PMID:28895930

ImmunemiR - A Database of Prioritized Immune miRNA Disease Associations and its Interactome.

PubMed

Prabahar, Archana; Natarajan, Jeyakumar

2017-01-01

MicroRNAs are the key regulators of gene expression and their abnormal expression in the immune system may be associated with several human diseases such as inflammation, cancer and autoimmune diseases. Elucidation of miRNA disease association through the interactome will deepen the understanding of its disease mechanisms. A specialized database for immune miRNAs is highly desirable to demonstrate the immune miRNA disease associations in the interactome. miRNAs specific to immune related diseases were retrieved from curated databases such as HMDD, miR2disease and PubMed literature based on MeSH classification of immune system diseases. The additional data such as miRNA target genes, genes coding protein-protein interaction information were compiled from related resources. Further, miRNAs were prioritized to specific immune diseases using random walk ranking algorithm. In total 245 immune miRNAs associated with 92 OMIM disease categories were identified from external databases. The resultant data were compiled as ImmunemiR, a database of prioritized immune miRNA disease associations. This database provides both text based annotation information and network visualization of its interactome. To our knowledge, ImmunemiR is the first available database to provide a comprehensive repository of human immune disease associated miRNAs with network visualization options of its target genes, protein-protein interactions (PPI) and its disease associations. It is freely available at http://www.biominingbu.org/immunemir/. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A novel artificial immune clonal selection classification and rule mining with swarm learning model

NASA Astrophysics Data System (ADS)

Al-Sheshtawi, Khaled A.; Abdul-Kader, Hatem M.; Elsisi, Ashraf B.

2013-06-01

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

Probability Distributions over Cryptographic Protocols

DTIC Science & Technology

2009-06-01

Artificial Immune Algorithm . . . . . . . . . . . . . . . . . . . 9 3 Design Decisions 11 3.1 Common Ground...creation algorithm for unbounded distribution . . . . . . . 24 4.2 Message creation algorithm for unbounded naive distribution . . . . 24 4.3 Protocol...creation algorithm for intended-run distributions . . . . . . 26 4.4 Protocol and message creation algorithm for realistic distribution . . 32 ix THIS

Collective Influence Algorithm to find influencers via optimal percolation in massively large social media

NASA Astrophysics Data System (ADS)

Morone, Flaviano; Min, Byungjoon; Bo, Lin; Mari, Romain; Makse, Hernán A.

2016-07-01

We elaborate on a linear-time implementation of Collective-Influence (CI) algorithm introduced by Morone, Makse, Nature 524, 65 (2015) to find the minimal set of influencers in networks via optimal percolation. The computational complexity of CI is O(N log N) when removing nodes one-by-one, made possible through an appropriate data structure to process CI. We introduce two Belief-Propagation (BP) variants of CI that consider global optimization via message-passing: CI propagation (CIP) and Collective-Immunization-Belief-Propagation algorithm (CIBP) based on optimal immunization. Both identify a slightly smaller fraction of influencers than CI and, remarkably, reproduce the exact analytical optimal percolation threshold obtained in Random Struct. Alg. 21, 397 (2002) for cubic random regular graphs, leaving little room for improvement for random graphs. However, the small augmented performance comes at the expense of increasing running time to O(N2), rendering BP prohibitive for modern-day big-data. For instance, for big-data social networks of 200 million users (e.g., Twitter users sending 500 million tweets/day), CI finds influencers in 2.5 hours on a single CPU, while all BP algorithms (CIP, CIBP and BDP) would take more than 3,000 years to accomplish the same task.

Collective Influence Algorithm to find influencers via optimal percolation in massively large social media.

PubMed

Morone, Flaviano; Min, Byungjoon; Bo, Lin; Mari, Romain; Makse, Hernán A

2016-07-26

We elaborate on a linear-time implementation of Collective-Influence (CI) algorithm introduced by Morone, Makse, Nature 524, 65 (2015) to find the minimal set of influencers in networks via optimal percolation. The computational complexity of CI is O(N log N) when removing nodes one-by-one, made possible through an appropriate data structure to process CI. We introduce two Belief-Propagation (BP) variants of CI that consider global optimization via message-passing: CI propagation (CIP) and Collective-Immunization-Belief-Propagation algorithm (CIBP) based on optimal immunization. Both identify a slightly smaller fraction of influencers than CI and, remarkably, reproduce the exact analytical optimal percolation threshold obtained in Random Struct. Alg. 21, 397 (2002) for cubic random regular graphs, leaving little room for improvement for random graphs. However, the small augmented performance comes at the expense of increasing running time to O(N(2)), rendering BP prohibitive for modern-day big-data. For instance, for big-data social networks of 200 million users (e.g., Twitter users sending 500 million tweets/day), CI finds influencers in 2.5 hours on a single CPU, while all BP algorithms (CIP, CIBP and BDP) would take more than 3,000 years to accomplish the same task.

Collective Influence Algorithm to find influencers via optimal percolation in massively large social media

PubMed Central

Morone, Flaviano; Min, Byungjoon; Bo, Lin; Mari, Romain; Makse, Hernán A.

2016-01-01

We elaborate on a linear-time implementation of Collective-Influence (CI) algorithm introduced by Morone, Makse, Nature 524, 65 (2015) to find the minimal set of influencers in networks via optimal percolation. The computational complexity of CI is O(N log N) when removing nodes one-by-one, made possible through an appropriate data structure to process CI. We introduce two Belief-Propagation (BP) variants of CI that consider global optimization via message-passing: CI propagation (CIP) and Collective-Immunization-Belief-Propagation algorithm (CIBP) based on optimal immunization. Both identify a slightly smaller fraction of influencers than CI and, remarkably, reproduce the exact analytical optimal percolation threshold obtained in Random Struct. Alg. 21, 397 (2002) for cubic random regular graphs, leaving little room for improvement for random graphs. However, the small augmented performance comes at the expense of increasing running time to O(N2), rendering BP prohibitive for modern-day big-data. For instance, for big-data social networks of 200 million users (e.g., Twitter users sending 500 million tweets/day), CI finds influencers in 2.5 hours on a single CPU, while all BP algorithms (CIP, CIBP and BDP) would take more than 3,000 years to accomplish the same task. PMID:27455878

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.

PubMed

Chen, Tinggui; Xiao, Renbin

2014-01-01

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

PubMed Central

Chen, Tinggui; Xiao, Renbin

2014-01-01

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

Design of cryptographically secure AES like S-Box using second-order reversible cellular automata for wireless body area network applications.

PubMed

Gangadari, Bhoopal Rao; Rafi Ahamed, Shaik

2016-09-01

In biomedical, data security is the most expensive resource for wireless body area network applications. Cryptographic algorithms are used in order to protect the information against unauthorised access. Advanced encryption standard (AES) cryptographic algorithm plays a vital role in telemedicine applications. The authors propose a novel approach for design of substitution bytes (S-Box) using second-order reversible one-dimensional cellular automata (RCA 2 ) as a replacement to the classical look-up-table (LUT) based S-Box used in AES algorithm. The performance of proposed RCA 2 based S-Box and conventional LUT based S-Box is evaluated in terms of security using the cryptographic properties such as the nonlinearity, correlation immunity bias, strict avalanche criteria and entropy. Moreover, it is also shown that RCA 2 based S-Boxes are dynamic in nature, invertible and provide high level of security. Further, it is also found that the RCA 2 based S-Box have comparatively better performance than that of conventional LUT based S-Box.

Design of cryptographically secure AES like S-Box using second-order reversible cellular automata for wireless body area network applications

PubMed Central

Rafi Ahamed, Shaik

2016-01-01

In biomedical, data security is the most expensive resource for wireless body area network applications. Cryptographic algorithms are used in order to protect the information against unauthorised access. Advanced encryption standard (AES) cryptographic algorithm plays a vital role in telemedicine applications. The authors propose a novel approach for design of substitution bytes (S-Box) using second-order reversible one-dimensional cellular automata (RCA2) as a replacement to the classical look-up-table (LUT) based S-Box used in AES algorithm. The performance of proposed RCA2 based S-Box and conventional LUT based S-Box is evaluated in terms of security using the cryptographic properties such as the nonlinearity, correlation immunity bias, strict avalanche criteria and entropy. Moreover, it is also shown that RCA2 based S-Boxes are dynamic in nature, invertible and provide high level of security. Further, it is also found that the RCA2 based S-Box have comparatively better performance than that of conventional LUT based S-Box. PMID:27733924

Establishment of HLA-DR4 Transgenic Mice for the Identification of CD4+ T Cell Epitopes of Tumor-Associated Antigens

PubMed Central

Harada, Kumiko; Michibata, Yayoi; Tsukamoto, Hirotake; Senju, Satoru; Tomita, Yusuke; Yuno, Akira; Hirayama, Masatoshi; Abu Sayem, Mohammad; Takeda, Naoki; Shibuya, Isao; Sogo, Shinji; Fujiki, Fumihiro; Sugiyama, Haruo; Eto, Masatoshi; Nishimura, Yasuharu

2013-01-01

Reports have shown that activation of tumor-specific CD4+ helper T (Th) cells is crucial for effective anti-tumor immunity and identification of Th-cell epitopes is critical for peptide vaccine-based cancer immunotherapy. Although computer algorithms are available to predict peptides with high binding affinity to a specific HLA class II molecule, the ability of those peptides to induce Th-cell responses must be evaluated. We have established HLA-DR4 (HLA-DRA*01:01/HLA-DRB1*04:05) transgenic mice (Tgm), since this HLA-DR allele is most frequent (13.6%) in Japanese population, to evaluate HLA-DR4-restricted Th-cell responses to tumor-associated antigen (TAA)-derived peptides predicted to bind to HLA-DR4. To avoid weak binding between mouse CD4 and HLA-DR4, Tgm were designed to express chimeric HLA-DR4/I-Ed, where I-Ed α1 and β1 domains were replaced with those from HLA-DR4. Th cells isolated from Tgm immunized with adjuvant and HLA-DR4-binding cytomegalovirus-derived peptide proliferated when stimulated with peptide-pulsed HLA-DR4-transduced mouse L cells, indicating chimeric HLA-DR4/I-Ed has equivalent antigen presenting capacity to HLA-DR4. Immunization with CDCA155-78 peptide, a computer algorithm-predicted HLA-DR4-binding peptide derived from TAA CDCA1, successfully induced Th-cell responses in Tgm, while immunization of HLA-DR4-binding Wilms' tumor 1 antigen-derived peptide with identical amino acid sequence to mouse ortholog failed. This was overcome by using peptide-pulsed syngeneic bone marrow-derived dendritic cells (BM-DC) followed by immunization with peptide/CFA booster. BM-DC-based immunization of KIF20A494-517 peptide from another TAA KIF20A, with an almost identical HLA-binding core amino acid sequence to mouse ortholog, successfully induced Th-cell responses in Tgm. Notably, both CDCA155-78 and KIF20A494-517 peptides induced human Th-cell responses in PBMCs from HLA-DR4-positive donors. Finally, an HLA-DR4 binding DEPDC1191-213 peptide from a new TAA DEPDC1 overexpressed in bladder cancer induced strong Th-cell responses both in Tgm and in PBMCs from an HLA-DR4-positive donor. Thus, the HLA-DR4 Tgm combined with computer algorithm was useful for preliminary screening of candidate peptides for vaccination. PMID:24386437

FOCuS: a metaheuristic algorithm for computing knockouts from genome-scale models for strain optimization.

PubMed

Mutturi, Sarma

2017-06-27

Although handful tools are available for constraint-based flux analysis to generate knockout strains, most of these are either based on bilevel-MIP or its modifications. However, metaheuristic approaches that are known for their flexibility and scalability have been less studied. Moreover, in the existing tools, sectioning of search space to find optimal knocks has not been considered. Herein, a novel computational procedure, termed as FOCuS (Flower-pOllination coupled Clonal Selection algorithm), was developed to find the optimal reaction knockouts from a metabolic network to maximize the production of specific metabolites. FOCuS derives its benefits from nature-inspired flower pollination algorithm and artificial immune system-inspired clonal selection algorithm to converge to an optimal solution. To evaluate the performance of FOCuS, reported results obtained from both MIP and other metaheuristic-based tools were compared in selected case studies. The results demonstrated the robustness of FOCuS irrespective of the size of metabolic network and number of knockouts. Moreover, sectioning of search space coupled with pooling of priority reactions based on their contribution to objective function for generating smaller search space significantly reduced the computational time.

Low Power S-Box Architecture for AES Algorithm using Programmable Second Order Reversible Cellular Automata: An Application to WBAN.

PubMed

Gangadari, Bhoopal Rao; Ahamed, Shaik Rafi

2016-12-01

In this paper, we presented a novel approach of low energy consumption architecture of S-Box used in Advanced Encryption Standard (AES) algorithm using programmable second order reversible cellular automata (RCA 2 ). The architecture entails a low power implementation with minimal delay overhead and the performance of proposed RCA 2 based S-Box in terms of security is evaluated using the cryptographic properties such as nonlinearity, correlation immunity bias, strict avalanche criteria, entropy and also found that the proposed architecture is secure enough for cryptographic applications. Moreover, the proposed AES algorithm architecture simulation studies show that energy consumption of 68.726 nJ, power dissipation of 3.856 mW for 0.18- μm at 13.69 MHz and energy consumption of 29.408 nJ, power dissipation of 1.65 mW for 0.13- μm at 13.69 MHz. The proposed AES algorithm with RCA 2 based S-Box shows a reduction power consumption by 50 % and energy consumption by 5 % compared to best classical S-Box and composite field arithmetic based AES algorithm. Apart from that, it is also shown that RCA 2 based S-Boxes are dynamic in nature, invertible, low power dissipation compared to that of LUT based S-Box and hence suitable for Wireless Body Area Network (WBAN) applications.

Novel Semi-Parametric Algorithm for Interference-Immune Tunable Absorption Spectroscopy Gas Sensing

PubMed Central

Michelucci, Umberto; Venturini, Francesca

2017-01-01

One of the most common limits to gas sensor performance is the presence of unwanted interference fringes arising, for example, from multiple reflections between surfaces in the optical path. Additionally, since the amplitude and the frequency of these interferences depend on the distance and alignment of the optical elements, they are affected by temperature changes and mechanical disturbances, giving rise to a drift of the signal. In this work, we present a novel semi-parametric algorithm that allows the extraction of a signal, like the spectroscopic absorption line of a gas molecule, from a background containing arbitrary disturbances, without having to make any assumption on the functional form of these disturbances. The algorithm is applied first to simulated data and then to oxygen absorption measurements in the presence of strong fringes.To the best of the authors’ knowledge, the algorithm enables an unprecedented accuracy particularly if the fringes have a free spectral range and amplitude comparable to those of the signal to be detected. The described method presents the advantage of being based purely on post processing, and to be of extremely straightforward implementation if the functional form of the Fourier transform of the signal is known. Therefore, it has the potential to enable interference-immune absorption spectroscopy. Finally, its relevance goes beyond absorption spectroscopy for gas sensing, since it can be applied to any kind of spectroscopic data. PMID:28991161

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

PubMed Central

Fan, Bingfei; Li, Qingguo; Wang, Chao; Liu, Tao

2017-01-01

Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm. PMID:28534858

The PROCARE consortium: toward an improved allocation strategy for kidney allografts.

PubMed

Otten, H G; Joosten, I; Allebes, W A; van der Meer, A; Hilbrands, L B; Baas, M; Spierings, E; Hack, C E; van Reekum, F; van Zuilen, A D; Verhaar, M C; Bots, M L; Seelen, M A J; Sanders, J S F; Hepkema, B G; Lambeck, A J; Bungener, L B; Roozendaal, C; Tilanus, M G J; Vanderlocht, J; Voorter, C E; Wieten, L; van Duijnhoven, E; Gelens, M; Christiaans, M; van Ittersum, F; Nurmohamed, A; Lardy, N M; Swelsen, W T; van Donselaar-van der Pant, K A M I; van der Weerd, N C; Ten Berge, I J M; Bemelman, F J; Hoitsma, A J; de Fijter, J W; Betjes, M G H; Roelen, D L; Claas, F H J

2014-10-01

Kidney transplantation is the best treatment option for patients with end-stage renal failure. At present, approximately 800 Dutch patients are registered on the active waiting list of Eurotransplant. The waiting time in the Netherlands for a kidney from a deceased donor is on average between 3 and 4 years. During this period, patients are fully dependent on dialysis, which replaces only partly the renal function, whereas the quality of life is limited. Mortality among patients on the waiting list is high. In order to increase the number of kidney donors, several initiatives have been undertaken by the Dutch Kidney Foundation including national calls for donor registration and providing information on organ donation and kidney transplantation. The aim of the national PROCARE consortium is to develop improved matching algorithms that will lead to a prolonged survival of transplanted donor kidneys and a reduced HLA immunization. The latter will positively affect the waiting time for a retransplantation. The present algorithm for allocation is among others based on matching for HLA antigens, which were originally defined by antibodies using serological typing techniques. However, several studies suggest that this algorithm needs adaptation and that other immune parameters which are currently not included may assist in improving graft survival rates. We will employ a multicenter-based evaluation on 5429 patients transplanted between 1995 and 2005 in the Netherlands. The association between key clinical endpoints and selected laboratory defined parameters will be examined, including Luminex-defined HLA antibody specificities, T and B cell epitopes recognized on the mismatched HLA antigens, non-HLA antibodies, and also polymorphisms in complement and Fc receptors functionally associated with effector functions of anti-graft antibodies. From these data, key parameters determining the success of kidney transplantation will be identified which will lead to the identification of additional parameters to be included in future matching algorithms aiming to extend survival of transplanted kidneys and to diminish HLA immunization. Computer simulation studies will reveal the number of patients having a direct benefit from improved matching, the effect on shortening of the waiting list, and the decrease in waiting time. Copyright © 2014. Published by Elsevier B.V.

Computational and Experimental Validation of B and T-Cell Epitopes of the In Vivo Immune Response to a Novel Malarial Antigen

DTIC Science & Technology

2013-08-16

approach in the context of a novel, immunologically relevant antigen. The limited accuracy of the tested algorithms to predict the in vivo immune responses...overlapping peptides spanning the entire sequence are individually tested for antibody interacting residues. Conformational B cell epitopes, in contrast...a blind assessment of this approach in the context of a novel, immunologically relevant antigen. The limited accuracy of the tested algorithms to

Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

NASA Astrophysics Data System (ADS)

Al Azzawi, Dia

Abnormal flight conditions play a major role in aircraft accidents frequently causing loss of control. To ensure aircraft operation safety in all situations, intelligent system monitoring and adaptation must rely on accurately detecting the presence of abnormal conditions as soon as they take place, identifying their root cause(s), estimating their nature and severity, and predicting their impact on the flight envelope. Due to the complexity and multidimensionality of the aircraft system under abnormal conditions, these requirements are extremely difficult to satisfy using existing analytical and/or statistical approaches. Moreover, current methodologies have addressed only isolated classes of abnormal conditions and a reduced number of aircraft dynamic parameters within a limited region of the flight envelope. This research effort aims at developing an integrated and comprehensive framework for the aircraft abnormal conditions detection, identification, and evaluation based on the artificial immune systems paradigm, which has the capability to address the complexity and multidimensionality issues related to aircraft systems. Within the proposed framework, a novel algorithm was developed for the abnormal conditions detection problem and extended to the abnormal conditions identification and evaluation. The algorithm and its extensions were inspired from the functionality of the biological dendritic cells (an important part of the innate immune system) and their interaction with the different components of the adaptive immune system. Immunity-based methodologies for re-assessing the flight envelope at post-failure and predicting the impact of the abnormal conditions on the performance and handling qualities are also proposed and investigated in this study. The generality of the approach makes it applicable to any system. Data for artificial immune system development were collected from flight tests of a supersonic research aircraft within a motion-based flight simulator. The abnormal conditions considered in this work include locked actuators (stabilator, aileron, rudder, and throttle), structural damage of the wing, horizontal tail, and vertical tail, malfunctioning sensors, and reduced engine effectiveness. The results of applying the proposed approach to this wide range of abnormal conditions show its high capability in detecting the abnormal conditions with zero false alarms and very high detection rates, correctly identifying the failed subsystem and evaluating the type and severity of the failure. The results also reveal that the post-failure flight envelope can be reasonably predicted within this framework.

Immunology-directed methods for distributed robotics: a novel immunity-based architecture for robust control and coordination

NASA Astrophysics Data System (ADS)

Singh, Surya P. N.; Thayer, Scott M.

2002-02-01

This paper presents a novel algorithmic architecture for the coordination and control of large scale distributed robot teams derived from the constructs found within the human immune system. Using this as a guide, the Immunology-derived Distributed Autonomous Robotics Architecture (IDARA) distributes tasks so that broad, all-purpose actions are refined and followed by specific and mediated responses based on each unit's utility and capability to timely address the system's perceived need(s). This method improves on initial developments in this area by including often overlooked interactions of the innate immune system resulting in a stronger first-order, general response mechanism. This allows for rapid reactions in dynamic environments, especially those lacking significant a priori information. As characterized via computer simulation of a of a self-healing mobile minefield having up to 7,500 mines and 2,750 robots, IDARA provides an efficient, communications light, and scalable architecture that yields significant operation and performance improvements for large-scale multi-robot coordination and control.

Artificial Immune System for Recognizing Patterns

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance

2005-01-01

A method of recognizing or classifying patterns is based on an artificial immune system (AIS), which includes an algorithm and a computational model of nonlinear dynamics inspired by the behavior of a biological immune system. The method has been proposed as the theoretical basis of the computational portion of a star-tracking system aboard a spacecraft. In that system, a newly acquired star image would be treated as an antigen that would be matched by an appropriate antibody (an entry in a star catalog). The method would enable rapid convergence, would afford robustness in the face of noise in the star sensors, would enable recognition of star images acquired in any sensor or spacecraft orientation, and would not make an excessive demand on the computational resources of a typical spacecraft. Going beyond the star-tracking application, the AIS-based pattern-recognition method is potentially applicable to pattern- recognition and -classification processes for diverse purposes -- for example, reconnaissance, detecting intruders, and mining data.

Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data

PubMed Central

Tong, Xuming; Chen, Jinghang; Miao, Hongyu; Li, Tingting; Zhang, Le

2015-01-01

Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data. PMID:26535589

A hybrid clustering and classification approach for predicting crash injury severity on rural roads.

PubMed

Hasheminejad, Seyed Hessam-Allah; Zahedi, Mohsen; Hasheminejad, Seyed Mohammad Hossein

2018-03-01

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011-2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.

A noise-immune cryptographic information protection method for facsimile information transmission and the realization algorithms

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Bardachenko, Vitaliy F.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Ogorodnik, Konstantin V.

2006-04-01

We analyse the existent methods of cryptographic defence for the facsimile information transfer, consider their shortcomings and prove the necessity of better information protection degree. The method of information protection that is based on presentation of input data as images is proposed. We offer a new noise-immune algorithm for realization of this method which consists in transformation of an input frame by pixels transposition according to an entered key. At decoding mode the reverse transformation of image with the use of the same key is used. Practical realization of the given method takes into account noise in the transmission channels and information distortions by scanners, faxes and others like that. We show that the given influences are reduced to the transformation of the input image coordinates. We show the algorithm in detail and consider its basic steps. We show the possibility of the offered method by the means of the developed software. The realized algorithm corrects curvature of frames: turn, scaling, fallout of pixels and others like that. At low noise level (loss of pixel information less than 10 percents) it is possible to encode, transfer and decode any types of images and texts with 12-size font character. The software filters for information restore and noise removing allow to transfer fax data with 30 percents pixels loss at 18-size font text. This percent of data loss can be considerably increased by the use of the software character recognition block that can be realized on fuzzy-neural algorithms. Examples of encoding and decryption of images and texts are shown.

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.

PubMed

Racle, Julien; de Jonge, Kaat; Baumgaertner, Petra; Speiser, Daniel E; Gfeller, David

2017-11-13

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).

Post-licensure rapid immunization safety monitoring program (PRISM) data characterization.

PubMed

Baker, Meghan A; Nguyen, Michael; Cole, David V; Lee, Grace M; Lieu, Tracy A

2013-12-30

The Post-Licensure Rapid Immunization Safety Monitoring (PRISM) program is the immunization safety monitoring component of FDA's Mini-Sentinel project, a program to actively monitor the safety of medical products using electronic health information. FDA sought to assess the surveillance capabilities of this large claims-based distributed database for vaccine safety surveillance by characterizing the underlying data. We characterized data available on vaccine exposures in PRISM, estimated how much additional data was gained by matching with select state and local immunization registries, and compared vaccination coverage estimates based on PRISM data with other available data sources. We generated rates of computerized codes representing potential health outcomes relevant to vaccine safety monitoring. Standardized algorithms including ICD-9 codes, number of codes required, exclusion criteria and location of the encounter were used to obtain the background rates. The majority of the vaccines routinely administered to infants, children, adolescents and adults were well captured by claims data. Immunization registry data in up to seven states comprised between 5% and 9% of data for all vaccine categories with the exception of 10% for hepatitis B and 3% and 4% for rotavirus and zoster respectively. Vaccination coverage estimates based on PRISM's computerized data were similar to but lower than coverage estimates from the National Immunization Survey and Healthcare Effectiveness Data and Information Set. For the 25 health outcomes of interest studied, the rates of potential outcomes based on ICD-9 codes were generally higher than rates described in the literature, which are typically clinically confirmed cases. PRISM program's data on vaccine exposures and health outcomes appear complete enough to support robust safety monitoring. Copyright © 2013 Elsevier Ltd. All rights reserved.

Distributed Immune Systems for Wireless Network Information Assurance

DTIC Science & Technology

2010-04-26

ratio test (SPRT), where the goal is to optimize a hypothesis testing problem given a trade-off between the probability of errors and the...using cumulative sum (CUSUM) and Girshik-Rubin-Shiryaev (GRSh) statistics. In sequential versions of the problem the sequential probability ratio ...the more complicated problems, in particular those where no clear mean can be established. We developed algorithms based on the sequential probability

Dynamics and control of diseases in networks with community structure.

PubMed

Salathé, Marcel; Jones, James H

2010-04-08

The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

Network immunization under limited budget using graph spectra

NASA Astrophysics Data System (ADS)

Zahedi, R.; Khansari, M.

2016-03-01

In this paper, we propose a new algorithm that minimizes the worst expected growth of an epidemic by reducing the size of the largest connected component (LCC) of the underlying contact network. The proposed algorithm is applicable to any level of available resources and, despite the greedy approaches of most immunization strategies, selects nodes simultaneously. In each iteration, the proposed method partitions the LCC into two groups. These are the best candidates for communities in that component, and the available resources are sufficient to separate them. Using Laplacian spectral partitioning, the proposed method performs community detection inference with a time complexity that rivals that of the best previous methods. Experiments show that our method outperforms targeted immunization approaches in both real and synthetic networks.

An Expanded Lateral Interactive Clonal Selection Algorithm and Its Application

NASA Astrophysics Data System (ADS)

Gao, Shangce; Dai, Hongwei; Zhang, Jianchen; Tang, Zheng

Based on the clonal selection principle proposed by Burnet, in the immune response process there is no crossover of genetic material between members of the repertoire, i. e., there is no knowledge communication during different elite pools in the previous clonal selection models. As a result, the search performance of these models is ineffective. To solve this problem, inspired by the concept of the idiotypic network theory, an expanded lateral interactive clonal selection algorithm (LICS) is put forward. In LICS, an antibody is matured not only through the somatic hypermutation and the receptor editing from the B cell, but also through the stimuli from other antibodies. The stimuli is realized by memorizing some common gene segment on the idiotypes, based on which a lateral interactive receptor editing operator is also introduced. Then, LICS is applied to several benchmark instances of the traveling salesman problem. Simulation results show the efficiency and robustness of LICS when compared to other traditional algorithms.

Optimization for Service Routes of Pallet Service Center Based on the Pallet Pool Mode

PubMed Central

He, Shiwei; Song, Rui

2016-01-01

Service routes optimization (SRO) of pallet service center should meet customers' demand firstly and then, through the reasonable method of lines organization, realize the shortest path of vehicle driving. The routes optimization of pallet service center is similar to the distribution problems of vehicle routing problem (VRP) and Chinese postman problem (CPP), but it has its own characteristics. Based on the relevant research results, the conditions of determining the number of vehicles, the one way of the route, the constraints of loading, and time windows are fully considered, and a chance constrained programming model with stochastic constraints is constructed taking the shortest path of all vehicles for a delivering (recycling) operation as an objective. For the characteristics of the model, a hybrid intelligent algorithm including stochastic simulation, neural network, and immune clonal algorithm is designed to solve the model. Finally, the validity and rationality of the optimization model and algorithm are verified by the case. PMID:27528865

Herbal compatibility of traditional Chinese medical formulas for acquired immunodeficiency syndrome.

PubMed

Cui, Meng; Li, Jinghua; Li, Haiyan; Song, Chunxin

2012-09-01

Because herbal compatibility is one of the most important reasons why Traditional Chinese Medcine (TCM) formulas are effective for acquired immunodeficiency syndrome (AIDS), our study aimed to determine the compatibility of herbs based on published AIDS clinical research in Chinese periodicals. To achieve this aim, we designed a new data-mining algorithm according to TCM data characteristics. We found 25 clinical AIDS studies, all using Chinese herbs for treatment, in the Traditional Chinese Medicine Database System, and information on diagnosis and treatment was extracted. To find out herbal compatibility, especially the formulae for herbal combinations, we proposed an improved association rule algorithm based on the frequency of combinations. In this algorithm, all the compatibility relationships were displayed in a tree structure, by which the relationship between formulas and their derivation could be clearly inferred. Data analysis showed that approximately 100 herbs have been used for treating AIDS. Based on the whole herb compatibility tree, we calculated a basic formula for AIDS: Huang Qi combined with Ren Shen, Fu Ling, Bai Zhu, Bai Zhu, Dang Gui, and Bai Shao. This formula, deriving from most of clinical prescriptions, and was chosed by most of clinicians for AIDS treatment. From data mining we found that Qi replenishment and detoxification were the main treatment principles, which coincided with the AIDS pathological mechanism in which immune function is destroyed by human immunodeficiency virus (HIV). Our data-mining results suggest that the core TCM treatment of AIDS is replenishing Qi and detoxification, by which AIDS patients' immune system may be enhanced. Compatibility of Huang Qi with some frequently-used herbs have shown real efficacy in clinical practice, which warrants pharmacological research in the future.

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

PubMed Central

Racle, Julien; de Jonge, Kaat; Baumgaertner, Petra; Speiser, Daniel E

2017-01-01

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org). PMID:29130882

Artificial immune system via Euclidean Distance Minimization for anomaly detection in bearings

NASA Astrophysics Data System (ADS)

Montechiesi, L.; Cocconcelli, M.; Rubini, R.

2016-08-01

In recent years new diagnostics methodologies have emerged, with particular interest into machinery operating in non-stationary conditions. In fact continuous speed changes and variable loads make non-trivial the spectrum analysis. A variable speed means a variable characteristic fault frequency related to the damage that is no more recognizable in the spectrum. To overcome this problem the scientific community proposed different approaches listed in two main categories: model-based approaches and expert systems. In this context the paper aims to present a simple expert system derived from the mechanisms of the immune system called Euclidean Distance Minimization, and its application in a real case of bearing faults recognition. The proposed method is a simplification of the original process, adapted by the class of Artificial Immune Systems, which proved to be useful and promising in different application fields. Comparative results are provided, with a complete explanation of the algorithm and its functioning aspects.

Integral backstepping sliding mode control for underactuated systems: swing-up and stabilization of the Cart-Pendulum System.

PubMed

Adhikary, Nabanita; Mahanta, Chitralekha

2013-11-01

In this paper an integral backstepping sliding mode controller is proposed for controlling underactuated systems. A feedback control law is designed based on backstepping algorithm and a sliding surface is introduced in the final stage of the algorithm. The backstepping algorithm makes the controller immune to matched and mismatched uncertainties and the sliding mode control provides robustness. The proposed controller ensures asymptotic stability. The effectiveness of the proposed controller is compared against a coupled sliding mode controller for swing-up and stabilization of the Cart-Pendulum System. Simulation results show that the proposed integral backstepping sliding mode controller is able to reject both matched and mismatched uncertainties with a chattering free control law, while utilizing less control effort than the sliding mode controller. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Granulocyte-colony stimulating factor in the prevention of postoperative infectious complications and sub-optimal recovery from operation in patients with colorectal cancer and increased preoperative risk (ASA 3 and 4). Protocol of a controlled clinical trial developed by consensus of an international study group. Part three: individual patient, complication algorithm and quality manage.

PubMed

Stinner, B; Bauhofer, A; Lorenz, W; Rothmund, M; Plaul, U; Torossian, A; Celik, I; Sitter, H; Koller, M; Black, A; Duda, D; Encke, A; Greger, B; van Goor, H; Hanisch, E; Hesterberg, R; Klose, K J; Lacaine, F; Lorijn, R H; Margolis, C; Neugebauer, E; Nyström, P O; Reemst, P H; Schein, M; Solovera, J

2001-05-01

Presentation of a new type of a study protocol for evaluation of the effectiveness of an immune modifier (rhG-CSF, filgrastim): prevention of postoperative infectious complications and of sub-optimal recovery from operation in patients with colorectal cancer and increased preoperative risk (ASA 3 and 4). A randomised, placebo controlled, double-blinded, single-centre study is performed at an University Hospital (n = 40 patients for each group). This part presents the course of the individual patient and a complication algorithm for the management of anastomotic leakage and quality management. In part three of the protocol, the three major sections include: The course of the individual patient using a comprehensive graphic display, including the perioperative period, hospital stay and post discharge outcome. A center based clinical practice guideline for the management of the most important postoperative complication--anastomotic leakage--including evidence based support for each step of the algorithm. Data management, ethics and organisational structure. Future studies with immune modifiers will also fail if not better structured (reduction of variance) to achieve uniform patient management in a complex clinical scenario. This new type of a single-centre trial aims to reduce the gap between animal experiments and clinical trials or--if it fails--at least demonstrates new ways for explaining the failures.

Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation.

PubMed

Cockrell, Robert Chase; An, Gary

2018-02-01

Sepsis, a manifestation of the body's inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical "sepsis," and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it from a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true "precision control" of sepsis.

Damage identification of a TLP floating wind turbine by meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.

2015-12-01

Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring (SHM). In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP (Tension-Leg Platform) floating wind turbine structure. The Genetic Algorithms (GA), Artificial Immune System (AIS), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine (TLPFWT) is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.

Immunoglobulin therapy in hematologic neoplasms and after hematopoietic cell transplantation.

PubMed

Ueda, Masumi; Berger, Melvin; Gale, Robert Peter; Lazarus, Hillard M

2018-03-01

Immunoglobulins are used to prevent or reduce infection risk in primary immune deficiencies and in settings which exploit its anti-inflammatory and immune-modulatory effects. Rigorous proof of immunoglobulin efficacy in persons with lympho-proliferative neoplasms, plasma cell myeloma, and persons receiving hematopoietic cell transplants is lacking despite many clinical trials. Further, there are few consensus guidelines or algorithms for use in these conditions. Rapid development of new therapies targeting B-cell signaling and survival pathways and increased use of chimeric antigen receptor T-cell (CAR-T) therapy will likely result in more acquired deficiencies of humoral immunity and infections in persons with cancer. We review immunoglobulin formulations and discuss efficacy and potential adverse effects in the context of preventing infections and in graft-versus-host disease. We suggest an algorithm for evaluating acquired deficiencies of humoral immunity in persons with hematologic neoplasms and recommend appropriate use of immunoglobulin therapy. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections.

PubMed

Zhang, Jingjing; Friberg, Ida M; Kift-Morgan, Ann; Parekh, Gita; Morgan, Matt P; Liuzzi, Anna Rita; Lin, Chan-Yu; Donovan, Kieron L; Colmont, Chantal S; Morgan, Peter H; Davis, Paul; Weeks, Ian; Fraser, Donald J; Topley, Nicholas; Eberl, Matthias

2017-07-01

The immune system has evolved to sense invading pathogens, control infection, and restore tissue integrity. Despite symptomatic variability in patients, unequivocal evidence that an individual's immune system distinguishes between different organisms and mounts an appropriate response is lacking. We here used a systematic approach to characterize responses to microbiologically well-defined infection in a total of 83 peritoneal dialysis patients on the day of presentation with acute peritonitis. A broad range of cellular and soluble parameters was determined in peritoneal effluents, covering the majority of local immune cells, inflammatory and regulatory cytokines and chemokines as well as tissue damage-related factors. Our analyses, utilizing machine-learning algorithms, demonstrate that different groups of bacteria induce qualitatively distinct local immune fingerprints, with specific biomarker signatures associated with Gram-negative and Gram-positive organisms, and with culture-negative episodes of unclear etiology. Even more, within the Gram-positive group, unique immune biomarker combinations identified streptococcal and non-streptococcal species including coagulase-negative Staphylococcus spp. These findings have diagnostic and prognostic implications by informing patient management and treatment choice at the point of care. Thus, our data establish the power of non-linear mathematical models to analyze complex biomedical datasets and highlight key pathways involved in pathogen-specific immune responses. Copyright © 2017 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Pooled protein immunization for identification of cell surface antigens in Streptococcus sanguinis.

PubMed

Ge, Xiuchun; Kitten, Todd; Munro, Cindy L; Conrad, Daniel H; Xu, Ping

2010-07-26

Available bacterial genomes provide opportunities for screening vaccines by reverse vaccinology. Efficient identification of surface antigens is required to reduce time and animal cost in this technology. We developed an approach to identify surface antigens rapidly in Streptococcus sanguinis, a common infective endocarditis causative species. We applied bioinformatics for antigen prediction and pooled antigens for immunization. Forty-seven surface-exposed proteins including 28 lipoproteins and 19 cell wall-anchored proteins were chosen based on computer algorithms and comparative genomic analyses. Eight proteins among these candidates and 2 other proteins were pooled together to immunize rabbits. The antiserum reacted strongly with each protein and with S. sanguinis whole cells. Affinity chromatography was used to purify the antibodies to 9 of the antigen pool components. Competitive ELISA and FACS results indicated that these 9 proteins were exposed on S. sanguinis cell surfaces. The purified antibodies had demonstrable opsonic activity. The results indicate that immunization with pooled proteins, in combination with affinity purification, and comprehensive immunological assays may facilitate cell surface antigen identification to combat infectious diseases.

Pooled Protein Immunization for Identification of Cell Surface Antigens in Streptococcus sanguinis

PubMed Central

Ge, Xiuchun; Kitten, Todd; Munro, Cindy L.; Conrad, Daniel H.; Xu, Ping

2010-01-01

Background Available bacterial genomes provide opportunities for screening vaccines by reverse vaccinology. Efficient identification of surface antigens is required to reduce time and animal cost in this technology. We developed an approach to identify surface antigens rapidly in Streptococcus sanguinis, a common infective endocarditis causative species. Methods and Findings We applied bioinformatics for antigen prediction and pooled antigens for immunization. Forty-seven surface-exposed proteins including 28 lipoproteins and 19 cell wall-anchored proteins were chosen based on computer algorithms and comparative genomic analyses. Eight proteins among these candidates and 2 other proteins were pooled together to immunize rabbits. The antiserum reacted strongly with each protein and with S. sanguinis whole cells. Affinity chromatography was used to purify the antibodies to 9 of the antigen pool components. Competitive ELISA and FACS results indicated that these 9 proteins were exposed on S. sanguinis cell surfaces. The purified antibodies had demonstrable opsonic activity. Conclusions The results indicate that immunization with pooled proteins, in combination with affinity purification, and comprehensive immunological assays may facilitate cell surface antigen identification to combat infectious diseases. PMID:20668678

Comparison of Artificial Immune System and Particle Swarm Optimization Techniques for Error Optimization of Machine Vision Based Tool Movements

NASA Astrophysics Data System (ADS)

Mahapatra, Prasant Kumar; Sethi, Spardha; Kumar, Amod

2015-10-01

In conventional tool positioning technique, sensors embedded in the motion stages provide the accurate tool position information. In this paper, a machine vision based system and image processing technique for motion measurement of lathe tool from two-dimensional sequential images captured using charge coupled device camera having a resolution of 250 microns has been described. An algorithm was developed to calculate the observed distance travelled by the tool from the captured images. As expected, error was observed in the value of the distance traversed by the tool calculated from these images. Optimization of errors due to machine vision system, calibration, environmental factors, etc. in lathe tool movement was carried out using two soft computing techniques, namely, artificial immune system (AIS) and particle swarm optimization (PSO). The results show better capability of AIS over PSO.

Algorithms For Integrating Nonlinear Differential Equations

NASA Technical Reports Server (NTRS)

Freed, A. D.; Walker, K. P.

1994-01-01

Improved algorithms developed for use in numerical integration of systems of nonhomogenous, nonlinear, first-order, ordinary differential equations. In comparison with integration algorithms, these algorithms offer greater stability and accuracy. Several asymptotically correct, thereby enabling retention of stability and accuracy when large increments of independent variable used. Accuracies attainable demonstrated by applying them to systems of nonlinear, first-order, differential equations that arise in study of viscoplastic behavior, spread of acquired immune-deficiency syndrome (AIDS) virus and predator/prey populations.

A new surface fractal dimension for displacement mode shape-based damage identification of plate-type structures

NASA Astrophysics Data System (ADS)

Shi, Binkai; Qiao, Pizhong

2018-03-01

Vibration-based nondestructive testing is an area of growing interest and worthy of exploring new and innovative approaches. The displacement mode shape is often chosen to identify damage due to its local detailed characteristic and less sensitivity to surrounding noise. Requirement for baseline mode shape in most vibration-based damage identification limits application of such a strategy. In this study, a new surface fractal dimension called edge perimeter dimension (EPD) is formulated, from which an EPD-based window dimension locus (EPD-WDL) algorithm for irregularity or damage identification of plate-type structures is established. An analytical notch-type damage model of simply-supported plates is proposed to evaluate notch effect on plate vibration performance; while a sub-domain of notch cases with less effect is selected to investigate robustness of the proposed damage identification algorithm. Then, fundamental aspects of EPD-WDL algorithm in term of notch localization, notch quantification, and noise immunity are assessed. A mathematical solution called isomorphism is implemented to remove false peaks caused by inflexions of mode shapes when applying the EPD-WDL algorithm to higher mode shapes. The effectiveness and practicability of the EPD-WDL algorithm are demonstrated by an experimental procedure on damage identification of an artificially-induced notched aluminum cantilever plate using a measurement system of piezoelectric lead-zirconate (PZT) actuator and scanning laser Doppler vibrometer (SLDV). As demonstrated in both the analytical and experimental evaluations, the new surface fractal dimension technique developed is capable of effectively identifying damage in plate-type structures.

Neural Network Based Intrusion Detection System for Critical Infrastructures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Todd Vollmer; Ondrej Linda; Milos Manic

2009-07-01

Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recordedmore » from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.« less

Bands selection and classification of hyperspectral images based on hybrid kernels SVM by evolutionary algorithm

NASA Astrophysics Data System (ADS)

Hu, Yan-Yan; Li, Dong-Sheng

2016-01-01

The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.

Sort entropy-based for the analysis of EEG during anesthesia

NASA Astrophysics Data System (ADS)

Ma, Liang; Huang, Wei-Zhi

2010-08-01

The monitoring of anesthetic depth is an absolutely necessary procedure in the process of surgical operation. To judge and control the depth of anesthesia has become a clinical issue which should be resolved urgently. EEG collected wiil be processed by sort entrop in this paper. Signal response of the surface of the cerebral cortex is determined for different stages of patients in the course of anesthesia. EEG is simulated and analyzed through the fast algorithm of sort entropy. The results show that discipline of phasic changes for EEG is very detected accurately,and it has better noise immunity in detecting the EEG anaesthetized than approximate entropy. In conclusion,the computing of Sort entropy algorithm requires shorter time. It has high efficiency and strong anti-interference.

A systems immunology approach identifies the collective impact of 5 miRs in Th2 inflammation.

PubMed

Kılıç, Ayşe; Santolini, Marc; Nakano, Taiji; Schiller, Matthias; Teranishi, Mizue; Gellert, Pascal; Ponomareva, Yuliya; Braun, Thomas; Uchida, Shizuka; Weiss, Scott T; Sharma, Amitabh; Renz, Harald

2018-06-07

Allergic asthma is a chronic inflammatory disease dominated by a CD4+ T helper 2 (Th2) cell signature. The immune response amplifies in self-enforcing loops, promoting Th2-driven cellular immunity and leaving the host unable to terminate inflammation. Posttranscriptional mechanisms, including microRNAs (miRs), are pivotal in maintaining immune homeostasis. Since an altered expression of various miRs has been associated with T cell-driven diseases, including asthma, we hypothesized that miRs control mechanisms ensuring Th2 stability and maintenance in the lung. We isolated murine CD4+ Th2 cells from allergic inflamed lungs and profiled gene and miR expression. Instead of focusing on the magnitude of miR differential expression, here we addressed the secondary consequences for the set of molecular interactions in the cell, the interactome. We developed the Impact of Differential Expression Across Layers, a network-based algorithm to prioritize disease-relevant miRs based on the central role of their targets in the molecular interactome. This method identified 5 Th2-related miRs (mir27b, mir206, mir106b, mir203, and mir23b) whose antagonization led to a sharp reduction of the Th2 phenotype. Overall, a systems biology tool was developed and validated, highlighting the role of miRs in Th2-driven immune response. This result offers potentially novel approaches for therapeutic interventions.

Low-dose interleukin-2 as a modulator of Treg homeostasis after HSCT: current understanding and future perspectives.

PubMed

Matsuoka, Ken-Ichi

2018-02-01

CD4 + CD25 + Foxp3 + Treg is a functionally distinct subset of mature T cells with broad suppressive activity and has been shown to play an important role in the establishment of immune tolerance after HSCT. Altered cytokine environment in post-HSCT lymphopenia with a relative functional deficiency of IL-2 could hamper the reconstitution of Treg, leading to refractory GVHD. Based on the theory of low-dose IL-2 in which Treg can be selectively stimulated through the high-affinity IL-2 receptor, clinical studies have been conducted and demonstrated that low-dose IL-2 administration can restore Treg homeostasis and promote the expansion of this subset on the polymorphic processes of Treg reconstitution after HSCT. The new therapeutic indication of IL-2 for immune tolerance has launched in the field of HSCT and is spreading to the other fields including the treatment for autoimmune diseases. To further extend the indication of low-dose IL-2 to more patients with various immunological problems, the optimization of the timing and dosing of IL-2 intervention and the concomitant immune suppressive therapy according to each patient-based assessment are to be desired in the near future. Further prospective studies may facilitate the development of novel therapeutic algorithms for the effective and safe induction of immune tolerance after HSCT.

Research based on the SoPC platform of feature-based image registration

NASA Astrophysics Data System (ADS)

Shi, Yue-dong; Wang, Zhi-hui

2015-12-01

This paper focuses on the study of implementing feature-based image registration by System on a Programmable Chip (SoPC) hardware platform. We solidify the image registration algorithm on the FPGA chip, in which embedded soft core processor Nios II can speed up the image processing system. In this way, we can make image registration technology get rid of the PC. And, consequently, this kind of technology will be got an extensive use. The experiment result indicates that our system shows stable performance, particularly in terms of matching processing which noise immunity is good. And feature points of images show a reasonable distribution.

International Conference on Artificial Immune Systems (1st) ICARIS 2002, held on 9, 10, and 11 September 2002

DTIC Science & Technology

2002-03-07

Michalewicz, Eds., Evolutionary Computation 1: Basic Algorithms and Operators, Institute of Physics, Bristol (UK), 2000. [3] David A. Van Veldhuizen ...2000. [4] Carlos A. Coello Coello, David A. Van Veldhuizen , and Gary B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer...Academic Publishers, 233 Spring St., New York, NY 10013, 2002. [5] David A. Van Veldhuizen , Multiobjective Evolution- ary Algorithms: Classifications

Practical homeostasis lighting control system using sensor agent robots for office space

NASA Astrophysics Data System (ADS)

Tokiwa, Momoko; Mita, Akira

2014-03-01

The comfortable space can be changed by season, age, physical condition and the like. However, the current systems are not able to resolve them absolutely. This research proposes the Homeostasis lighting control system based on the mechanism of biotic homeostasis for making the algorithms of apparatus control. Homeostasis are kept by the interaction of the three systems, endocrine system, immune system, and nervous system[1]. By the gradual reaction in the endocrine system, body's protective response in the immune system, and the electrical reaction in the nerve system, we can keep the environments against variable changes. The new lighting control system utilizes this mechanism. Firstly, we focused on legibility and comfort in the office space to construct the control model learning from the endocrine and immune systems. The mechanism of the endocrine system is used for ambient lights in the space is used considering circadian rhythm for comfort. For the legibility, the immune system is used to control considering devices near the human depending on the distance between the human. Simulations and the demonstration were conducted to show the feasibility. Finally, the nerve system was intruded to enhance the system.

Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric vehicles fuel economy

NASA Astrophysics Data System (ADS)

Mozaffari, Ahmad; Vajedi, Mahyar; Azad, Nasser L.

2015-06-01

The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.

Stable, high-order computation of impedance-impedance operators for three-dimensional layered medium simulations.

PubMed

Nicholls, David P

2018-04-01

The faithful modelling of the propagation of linear waves in a layered, periodic structure is of paramount importance in many branches of the applied sciences. In this paper, we present a novel numerical algorithm for the simulation of such problems which is free of the artificial singularities present in related approaches. We advocate for a surface integral formulation which is phrased in terms of impedance-impedance operators that are immune to the Dirichlet eigenvalues which plague the Dirichlet-Neumann operators that appear in classical formulations. We demonstrate a high-order spectral algorithm to simulate these latter operators based upon a high-order perturbation of surfaces methodology which is rapid, robust and highly accurate. We demonstrate the validity and utility of our approach with a sequence of numerical simulations.

Stable, high-order computation of impedance-impedance operators for three-dimensional layered medium simulations

NASA Astrophysics Data System (ADS)

Nicholls, David P.

2018-04-01

The faithful modelling of the propagation of linear waves in a layered, periodic structure is of paramount importance in many branches of the applied sciences. In this paper, we present a novel numerical algorithm for the simulation of such problems which is free of the artificial singularities present in related approaches. We advocate for a surface integral formulation which is phrased in terms of impedance-impedance operators that are immune to the Dirichlet eigenvalues which plague the Dirichlet-Neumann operators that appear in classical formulations. We demonstrate a high-order spectral algorithm to simulate these latter operators based upon a high-order perturbation of surfaces methodology which is rapid, robust and highly accurate. We demonstrate the validity and utility of our approach with a sequence of numerical simulations.

Speech Enhancement of Mobile Devices Based on the Integration of a Dual Microphone Array and a Background Noise Elimination Algorithm.

PubMed

Chen, Yung-Yue

2018-05-08

Mobile devices are often used in our daily lives for the purposes of speech and communication. The speech quality of mobile devices is always degraded due to the environmental noises surrounding mobile device users. Regretfully, an effective background noise reduction solution cannot easily be developed for this speech enhancement problem. Due to these depicted reasons, a methodology is systematically proposed to eliminate the effects of background noises for the speech communication of mobile devices. This methodology integrates a dual microphone array with a background noise elimination algorithm. The proposed background noise elimination algorithm includes a whitening process, a speech modelling method and an H ₂ estimator. Due to the adoption of the dual microphone array, a low-cost design can be obtained for the speech enhancement of mobile devices. Practical tests have proven that this proposed method is immune to random background noises, and noiseless speech can be obtained after executing this denoise process.

DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

PubMed

Wang, QuanQiu; Xu, Rong

2015-01-01

Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

PubMed Central

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-01-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. PMID:29194393

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection.

PubMed

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-12-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.

Reconstruction of an Immune Dynamic Model to Simulate the Contrasting Role of Auxin and Cytokinin in Plant Immunity.

PubMed

Kaltdorf, Martin; Dandekar, Thomas; Naseem, Muhammad

2017-01-01

In order to increase our understanding of biological dependencies in plant immune signaling pathways, the known interactions involved in plant immune networks are modeled. This allows computational analysis to predict the functions of growth related hormones in plant-pathogen interaction. The SQUAD (Standardized Qualitative Dynamical Systems) algorithm first determines stable system states in the network and then use them to compute continuous dynamical system states. Our reconstructed Boolean model encompassing hormone immune networks of Arabidopsis thaliana (Arabidopsis) and pathogenicity factors injected by model pathogen Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) can be exploited to determine the impact of growth hormones in plant immunity. We describe a detailed working protocol how to use the modified SQUAD-package by exemplifying the contrasting effects of auxin and cytokinins in shaping plant-pathogen interaction.

Development of a strategy and computational application to select candidate protein analogues with reduced HLA binding and immunogenicity.

PubMed

Dhanda, Sandeep Kumar; Grifoni, Alba; Pham, John; Vaughan, Kerrie; Sidney, John; Peters, Bjoern; Sette, Alessandro

2018-01-01

Unwanted immune responses against protein therapeutics can reduce efficacy or lead to adverse reactions. T-cell responses are key in the development of such responses, and are directed against immunodominant regions within the protein sequence, often associated with binding to several allelic variants of HLA class II molecules (promiscuous binders). Herein, we report a novel computational strategy to predict 'de-immunized' peptides, based on previous studies of erythropoietin protein immunogenicity. This algorithm (or method) first predicts promiscuous binding regions within the target protein sequence and then identifies residue substitutions predicted to reduce HLA binding. Further, this method anticipates the effect of any given substitution on flanking peptides, thereby circumventing the creation of nascent HLA-binding regions. As a proof-of-principle, the algorithm was applied to Vatreptacog α, an engineered Factor VII molecule associated with unintended immunogenicity. The algorithm correctly predicted the two immunogenic peptides containing the engineered residues. As a further validation, we selected and evaluated the immunogenicity of seven substitutions predicted to simultaneously reduce HLA binding for both peptides, five control substitutions with no predicted reduction in HLA-binding capacity, and additional flanking region controls. In vitro immunogenicity was detected in 21·4% of the cultures of peptides predicted to have reduced HLA binding and 11·4% of the flanking regions, compared with 46% for the cultures of the peptides predicted to be immunogenic. This method has been implemented as an interactive application, freely available online at http://tools.iedb.org/deimmunization/. © 2017 John Wiley & Sons Ltd.

An immune-inspired semi-supervised algorithm for breast cancer diagnosis.

PubMed

Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong

2016-10-01

Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Pseudo-random dynamic address configuration (PRDAC) algorithm for mobile ad hoc networks

NASA Astrophysics Data System (ADS)

Wu, Shaochuan; Tan, Xuezhi

2007-11-01

By analyzing all kinds of address configuration algorithms, this paper provides a new pseudo-random dynamic address configuration (PRDAC) algorithm for mobile ad hoc networks. Based on PRDAC, the first node that initials this network randomly chooses a nonlinear shift register that can generates an m-sequence. When another node joins this network, the initial node will act as an IP address configuration sever to compute an IP address according to this nonlinear shift register, and then allocates this address and tell the generator polynomial of this shift register to this new node. By this means, when other node joins this network, any node that has obtained an IP address can act as a server to allocate address to this new node. PRDAC can also efficiently avoid IP conflicts and deal with network partition and merge as same as prophet address (PA) allocation and dynamic configuration and distribution protocol (DCDP). Furthermore, PRDAC has less algorithm complexity, less computational complexity and more sufficient assumption than PA. In addition, PRDAC radically avoids address conflicts and maximizes the utilization rate of IP addresses. Analysis and simulation results show that PRDAC has rapid convergence, low overhead and immune from topological structures.

Bionic models for identification of biological systems

NASA Astrophysics Data System (ADS)

Gerget, O. M.

2017-01-01

This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described.

Geospatial analytics to evaluate point-of-dispensing sites for mass immunizations in Allegheny County, Pennsylvania.

PubMed

Everett, Kibri H; Potter, Margaret A; Wheaton, William D; Gleason, Sherrianne M; Brown, Shawn T; Lee, Bruce Y

2013-01-01

Public health agencies use mass immunization locations to quickly administer vaccines to protect a population against an epidemic. The selection of such locations is frequently determined by available staffing levels and in some places, not all potential sites can be opened, often because of a lack of resources. Public health agencies need assistance in determining which n sites are the prime ones to open given available staff to minimize travel time and travel distance for those in the population who need to get to a site to receive treatment. Employ geospatial analytical methods to identify the prime n locations from a predetermined set of potential locations (eg, schools) and determine which locations may not be able to achieve the throughput necessary to reach the herd immunity threshold based on varying R0 values. Spatial location-allocation algorithms were used to select the ideal n mass vaccination locations. Allegheny County, Pennsylvania, served as the study area. The most favorable sites were selected and the number of individuals required to be vaccinated to achieve the herd immunity threshold for a given R0, ranging from 1.5 to 7, was determined. Locations that did not meet the Centers for Disease Control and Prevention throughput recommendation for smallpox were identified. At R0 = 1.5, all mass immunization locations met the required throughput to achieve the herd immunity threshold within 5 days. As R0s increased from 2 to 7, an increasing number of sites were inadequate to meet throughput requirements. Identifying the top n sites and categorizing those with throughput challenges allows health departments to adjust staffing, shift length, or the number of sites. This method has the potential to be expanded to select immunization locations under a number of additional scenarios.

Highly Resolved Intravital Striped-illumination Microscopy of Germinal Centers

PubMed Central

Andresen, Volker; Sporbert, Anje

2014-01-01

Monitoring cellular communication by intravital deep-tissue multi-photon microscopy is the key for understanding the fate of immune cells within thick tissue samples and organs in health and disease. By controlling the scanning pattern in multi-photon microscopy and applying appropriate numerical algorithms, we developed a striped-illumination approach, which enabled us to achieve 3-fold better axial resolution and improved signal-to-noise ratio, i.e. contrast, in more than 100 µm tissue depth within highly scattering tissue of lymphoid organs as compared to standard multi-photon microscopy. The acquisition speed as well as photobleaching and photodamage effects were similar to standard photo-multiplier-based technique, whereas the imaging depth was slightly lower due to the use of field detectors. By using the striped-illumination approach, we are able to observe the dynamics of immune complex deposits on secondary follicular dendritic cells – on the level of a few protein molecules in germinal centers. PMID:24748007

Global efficiency of local immunization on complex networks

NASA Astrophysics Data System (ADS)

Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.

2013-07-01

Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.

Global efficiency of local immunization on complex networks.

PubMed

Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J

2013-01-01

Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.

A tale of three bio-inspired computational approaches

NASA Astrophysics Data System (ADS)

Schaffer, J. David

2014-05-01

I will provide a high level walk-through for three computational approaches derived from Nature. First, evolutionary computation implements what we may call the "mother of all adaptive processes." Some variants on the basic algorithms will be sketched and some lessons I have gleaned from three decades of working with EC will be covered. Then neural networks, computational approaches that have long been studied as possible ways to make "thinking machines", an old dream of man's, and based upon the only known existing example of intelligence. Then, a little overview of attempts to combine these two approaches that some hope will allow us to evolve machines we could never hand-craft. Finally, I will touch on artificial immune systems, Nature's highly sophisticated defense mechanism, that has emerged in two major stages, the innate and the adaptive immune systems. This technology is finding applications in the cyber security world.

A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

PubMed

Liu, Chun-Han; Liu, Lian

2017-05-08

BACKGROUND Alzheimer's disease (AD) is the most common type of dementia. The objective of this paper is to propose a novel method to identify differential pathways in hippocampus AD. MATERIAL AND METHODS We proposed a combined method by merging existed methods. Firstly, pathways were identified by four known methods (DAVID, the neaGUI package, the pathway-based co-expressed method, and the pathway network approach), and differential pathways were evaluated through setting weight thresholds. Subsequently, we combined all pathways by a rank-based algorithm and called the method the combined method. Finally, common differential pathways across two or more of five methods were selected. RESULTS Pathways obtained from different methods were also different. The combined method obtained 1639 pathways and 596 differential pathways, which included all pathways gained from the four existing methods; hence, the novel method solved the problem of inconsistent results. Besides, a total of 13 common pathways were identified, such as metabolism, immune system, and cell cycle. CONCLUSIONS We have proposed a novel method by combining four existing methods based on a rank product algorithm, and identified 13 significant differential pathways based on it. These differential pathways might provide insight into treatment and diagnosis of hippocampus AD.

Evolutionary algorithm for optimization of nonimaging Fresnel lens geometry.

PubMed

Yamada, N; Nishikawa, T

2010-06-21

In this study, an evolutionary algorithm (EA), which consists of genetic and immune algorithms, is introduced to design the optical geometry of a nonimaging Fresnel lens; this lens generates the uniform flux concentration required for a photovoltaic cell. Herein, a design procedure that incorporates a ray-tracing technique in the EA is described, and the validity of the design is demonstrated. The results show that the EA automatically generated a unique geometry of the Fresnel lens; the use of this geometry resulted in better uniform flux concentration with high optical efficiency.

EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.

PubMed

Choi, Yoonjoo; Verma, Deeptak; Griswold, Karl E; Bailey-Kellogg, Chris

2017-01-01

Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics render them subject to immune surveillance within the patient's body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity.To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure-based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new protein drugs by shortening time frames and improving hit rates.

EpiSweep: Computationally-driven Reengineering of Therapeutic Proteins to Reduce immunogenicity while Maintaining Function

PubMed Central

Choi, Yoonjoo; Verma, Deeptak; Griswold, Karl E.; Bailey-Kellogg, Chris

2016-01-01

Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics renders them subject to immune surveillance within the patient’s body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity. To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure- based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new protein drugs by shortening time frames and improving hit rates. PMID:27914063

A New Quaternion-Based Kalman Filter for Real-Time Attitude Estimation Using the Two-Step Geometrically-Intuitive Correction Algorithm.

PubMed

Feng, Kaiqiang; Li, Jie; Zhang, Xiaoming; Shen, Chong; Bi, Yu; Zheng, Tao; Liu, Jun

2017-09-19

In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions.

A New Quaternion-Based Kalman Filter for Real-Time Attitude Estimation Using the Two-Step Geometrically-Intuitive Correction Algorithm

PubMed Central

Feng, Kaiqiang; Li, Jie; Zhang, Xiaoming; Shen, Chong; Bi, Yu; Zheng, Tao; Liu, Jun

2017-01-01

In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions. PMID:28925979

How We Manage Adenosine Deaminase-Deficient Severe Combined Immune Deficiency (ADA SCID).

PubMed

Kohn, Donald B; Gaspar, H Bobby

2017-05-01

Adenosine deaminase-deficient severe combined immune deficiency (ADA SCID) accounts for 10-15% of cases of human SCID. From what was once a uniformly fatal disease, the prognosis for infants with ADA SCID has improved greatly based on the development of multiple therapeutic options, coupled with more frequent early diagnosis due to implementation of newborn screening for SCID. We review the various treatment approaches for ADA SCID including allogeneic hematopoietic stem cell transplantation (HSCT) from a human leukocyte antigen-matched sibling or family member or from a matched unrelated donor or a haplo-identical donor, autologous HSCT with gene correction of the hematopoietic stem cells (gene therapy-GT), and enzyme replacement therapy (ERT) with polyethylene glycol-conjugated adenosine deaminase. Based on growing evidence of safety and efficacy from GT, we propose a treatment algorithm for patients with ADA SCID that recommends HSCT from a matched family donor, when available, as a first choice, followed by GT as the next option, with allogeneic HSCT from an unrelated or haplo-identical donor or long-term ERT as other options.

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

Registration of medical images is an essential research topic in medical image processing and applications, and especially a preliminary and key step for multimodality image fusion. This paper offers a solution to medical image registration based on normalized multi-dimensional mutual information. Firstly, affine transformation with translational and rotational parameters is applied to the floating image. Then ordinal features are extracted by ordinal filters with different orientations to represent spatial information in medical images. Integrating ordinal features with pixel intensities, the normalized multi-dimensional mutual information is defined as similarity criterion to register multimodality images. Finally the immune algorithm is used to search registration parameters. The experimental results demonstrate the effectiveness of the proposed registration scheme.

The utility and limitations of current web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection #

PubMed Central

Chaves, Francisco A.; Lee, Alvin H.; Nayak, Jennifer; Richards, Katherine A.; Sant, Andrea J.

2012-01-01

The ability to track CD4 T cells elicited in response to pathogen infection or vaccination is critical because of the role these cells play in protective immunity. Coupled with advances in genome sequencing of pathogenic organisms, there is considerable appeal for implementation of computer-based algorithms to predict peptides that bind to the class II molecules, forming the complex recognized by CD4 T cells. Despite recent progress in this area, there is a paucity of data regarding their success in identifying actual pathogen-derived epitopes. In this study, we sought to rigorously evaluate the performance of multiple web-available algorithms by comparing their predictions and our results using purely empirical methods for epitope discovery in influenza that utilized overlapping peptides and cytokine Elispots, for three independent class II molecules. We analyzed the data in different ways, trying to anticipate how an investigator might use these computational tools for epitope discovery. We come to the conclusion that currently available algorithms can indeed facilitate epitope discovery, but all shared a high degree of false positive and false negative predictions. Therefore, efficiencies were low. We also found dramatic disparities among algorithms and between predicted IC50 values and true dissociation rates of peptide:MHC class II complexes. We suggest that improved success of predictive algorithms will depend less on changes in computational methods or increased data sets and more on changes in parameters used to “train” the algorithms that factor in elements of T cell repertoire and peptide acquisition by class II molecules. PMID:22467652

Efficient error correction for next-generation sequencing of viral amplicons

PubMed Central

2012-01-01

Background Next-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing. Results In this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones. Conclusions Both algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses. The implementations of the algorithms and data sets used for their testing are available at: http://alan.cs.gsu.edu/NGS/?q=content/pyrosequencing-error-correction-algorithm PMID:22759430

Efficient error correction for next-generation sequencing of viral amplicons.

PubMed

Skums, Pavel; Dimitrova, Zoya; Campo, David S; Vaughan, Gilberto; Rossi, Livia; Forbi, Joseph C; Yokosawa, Jonny; Zelikovsky, Alex; Khudyakov, Yury

2012-06-25

Next-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing. In this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones. Both algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses.The implementations of the algorithms and data sets used for their testing are available at: http://alan.cs.gsu.edu/NGS/?q=content/pyrosequencing-error-correction-algorithm.

Improving Allergen Prediction in Main Crops Using a Weighted Integrative Method.

PubMed

Li, Jing; Wang, Jing; Li, Jing

2017-12-01

As a public health problem, food allergy is frequently caused by food allergy proteins, which trigger a type-I hypersensitivity reaction in the immune system of atopic individuals. The food allergens in our daily lives are mainly from crops including rice, wheat, soybean and maize. However, allergens in these main crops are far from fully uncovered. Although some bioinformatics tools or methods predicting the potential allergenicity of proteins have been proposed, each method has their limitation. In this paper, we built a novel algorithm PREAL W , which integrated PREAL, FAO/WHO criteria and motif-based method by a weighted average score, to benefit the advantages of different methods. Our results illustrated PREAL W has better performance significantly in the crops' allergen prediction. This integrative allergen prediction algorithm could be useful for critical food safety matters. The PREAL W could be accessed at http://lilab.life.sjtu.edu.cn:8080/prealw .

A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis.

PubMed

Sahan, Seral; Polat, Kemal; Kodaz, Halife; Güneş, Salih

2007-03-01

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too.

A novel neural-inspired learning algorithm with application to clinical risk prediction.

PubMed

Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

2015-04-01

Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials. Copyright © 2015 Elsevier Inc. All rights reserved.

Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

NASA Technical Reports Server (NTRS)

Wong, Derek; Poll, Scott; KrishnaKumar, Kalmanje

2005-01-01

This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

A comparison between metaheuristics as strategies for minimizing cyclic instability in Ambient Intelligence.

PubMed

Romero, Leoncio A; Zamudio, Victor; Baltazar, Rosario; Mezura, Efren; Sotelo, Marco; Callaghan, Vic

2012-01-01

In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO), which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them.

A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence

PubMed Central

Romero, Leoncio A.; Zamudio, Victor; Baltazar, Rosario; Mezura, Efren; Sotelo, Marco; Callaghan, Vic

2012-01-01

In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO), which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them. PMID:23112643

Construction of an integrated gene regulatory network link to stress-related immune system in cattle.

PubMed

Behdani, Elham; Bakhtiarizadeh, Mohammad Reza

2017-10-01

The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

Fast and efficient wireless power transfer via transitionless quantum driving.

PubMed

Paul, Koushik; Sarma, Amarendra K

2018-03-07

Shortcut to adiabaticity (STA) techniques have the potential to drive a system beyond the adiabatic limits. Here, we present a robust and efficient method for wireless power transfer (WPT) between two coils based on the so-called transitionless quantum driving (TQD) algorithm. We show that it is possible to transfer power between the coils significantly fast compared to its adiabatic counterpart. The scheme is fairly robust against the variations in the coupling strength and the coupling distance between the coils. Also, the scheme is found to be reasonably immune to intrinsic losses in the coils.

Designing pediatric vaccine formularies and pricing pediatric combination vaccines using operations research models and algorithms.

PubMed

Jacobson, Sheldon H; Sewell, Edward C; Allwine, Daniel A; Medina, Enrique A; Weniger, Bruce G

2003-02-01

The National Immunization Program, housed within the Centers for Disease Control and Prevention in the USA, has identified several challenges that must be faced in childhood immunization programs to deliver and procure vaccines that immunize children from the plethora of childhood diseases. The biomedical issues cited include how drug manufacturers can combine and formulate vaccines, how such vaccines are scheduled and administered and how economically sound vaccine procurement can be achieved. This review discusses how operations research models can be used to address the economics of pediatric vaccine formulary design and pricing, as well as how such models can be used to address a new set of pediatric formulary problems that will surface with the introduction of pediatric combination vaccines into the US pediatric immunization market.

Evolution, learning, and cognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Y.C.

1988-01-01

The book comprises more than fifteen articles in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics.

Evaluation of a clinical decision support algorithm for patient-specific childhood immunization.

PubMed

Zhu, Vivienne J; Grannis, Shaun J; Tu, Wanzhu; Rosenman, Marc B; Downs, Stephen M

2012-09-01

To evaluate the effectiveness of a clinical decision support system (CDSS) implementing standard childhood immunization guidelines, using real-world patient data from the Regenstrief Medical Record System (RMRS). Study subjects were age 6-years or younger in 2008 and had visited the pediatric clinic on the campus of Wishard Memorial Hospital. Immunization records were retrieved from the RMRS for 135 randomly selected pediatric patients. We compared vaccine recommendations from the CDSS for both eligible and recommended timelines, based on the child's date of birth and vaccine history, to recommendations from registered nurses who routinely selected vaccines for administration in a busy inner city hospital, using the same date of birth and vaccine history. Aggregated and stratified agreement and Kappa statistics were reported. The reasons for disagreement between suggestions from the CDSS and nurses were also identified. For the 135 children, a total of 1215 vaccination suggestions were generated by nurses and were compared to the recommendations of the CDSS. The overall agreement rates were 81.3% and 90.6% for the eligible and recommended timelines, respectively. The overall Kappa values were 0.63 for the eligible timeline and 0.80 for the recommended timeline. Common reasons for disagreement between the CDSS and nurses were: (1) missed vaccination opportunities by nurses, (2) nurses sometimes suggested a vaccination before the minimal age and minimal waiting interval, (3) nurses usually did not validate patient immunization history, and (4) nurses sometimes gave an extra vaccine dose. Our childhood immunization CDSS can assist providers in delivering accurate childhood vaccinations. Copyright © 2012 Elsevier B.V. All rights reserved.

Damage detection based on acceleration data using artificial immune system

NASA Astrophysics Data System (ADS)

Chartier, Sandra; Mita, Akira

2009-03-01

Nowadays, Structural Health Monitoring (SHM) is essential in order to prevent damages occurrence in civil structures. This is a particularly important issue as the number of aged structures is increasing. Damage detection algorithms are often based on changes in the modal properties like natural frequencies, modal shapes and modal damping. In this paper, damage detection is completed by using Artificial Immune System (AIS) theory directly on acceleration data. Inspired from the biological immune system, AIS is composed of several models like negative selection which has a great potential for this study. The negative selection process relies on the fact that T-cells, after their maturation, are sensitive to non self cells and can not detect self cells. Acceleration data were provided by using the numerical model of a 3-story frame structure. Damages were introduced, at particular times, by reduction of story's stiffness. Based on these acceleration data, undamaged data (equivalent to self data) and damaged data (equivalent to non self data) can be obtained and represented in the Hamming shape-space with a binary representation. From the undamaged encoded data, detectors (equivalent to T-cells) are derived and are able to detect damaged encoded data really efficiently by using the rcontiguous bits matching rule. Indeed, more than 95% of detection can be reached when efficient combinations of parameters are used. According to the number of detected data, the localization of damages can even be determined by using the differences between story's relative accelerations. Thus, the difference which presents the highest detection rate, generally up to 89%, is directly linked to the location of damage.

Cough in Ambulatory Immunocompromised Adults: CHEST Expert Panel Report.

PubMed

Rosen, Mark J; Ireland, Belinda; Narasimhan, Mangala; French, Cynthia; Irwin, Richard S

2017-11-01

Cough is a common symptom prompting patients to seek medical care. Like patients in the general population, patients with compromised immune systems also seek care for cough. However, it is unclear whether the causes of cough in immunocompromised patients who are deemed unlikely to have a life-threating condition and a normal or unchanged chest radiograph are similar to those in persons with cough and normal immune systems. We conducted a systematic review to answer the question: What are the most common causes of cough in ambulatory immunodeficient adults with normal chest radiographs? Studies of patients ≥ 18 years of age with immune deficiency, cough of any duration, and normal or unchanged chest radiographs were included and assessed for relevance and quality. Based on the systematic review, suggestions were developed and voted on using the American College of Chest Physicians (CHEST) methodology framework. The results of the systematic review revealed no high-quality evidence to guide the clinician in determining the likely causes of cough specifically in immunocompromised ambulatory patients with normal chest radiographs. Based on a systematic review, we found no evidence to assess whether or not the proper initial evaluation of cough in immunocompromised patients is different from that in immunocompetent persons. A consensus of the panel suggested that the initial diagnostic algorithm should be similar to that for immunocompetent persons but that the context of the type and severity of the immune defect, geographic location, and social determinants be considered. The major modifications to the 2006 CHEST Cough Guidelines are the suggestions that TB should be part of the initial evaluation of patients with cough and HIV infection who reside in regions with a high prevalence of TB, regardless of the radiographic findings, and that specific causes and immune defects be considered in all patients in whom the initial evaluation is unrevealing. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Assessing the impact of wastage on pediatric vaccine immunization formulary costs using a vaccine selection algorithm.

PubMed

Jacobson, Sheldon H; Karnani, Tamana; Sewell, Edward C

2004-06-02

Pediatric immunization is an important factor in providing protection against numerous common preventable diseases. The success of the pharmaceutical industry in developing new pediatric vaccines has resulted in a crowded recommended immunization schedule requiring several clinic visits over the first 12 years of life. Operations research models have been developed and used to make economically sound procurement choices from among a growing number of competing vaccine products. One factor that has not been incorporated into such models is the economic impact of wastage on such decisions. This paper reports results obtained from a vaccine selection algorithm that incorporates vaccine wastage data. The lowest overall cost formularies comparing no wastage costs with wastage costs are presented. A sensitivity analysis of the vaccine formulary with respect to the wastage rates associated with each available vaccine is provided. The maximum permissible wastage rate for each vaccine is determined for which the vaccine earns a place in the lowest overall cost formulary. This research provides health maintenance organizations and healthcare providers information that can be used to gain a better understanding of wastage and its impact on pediatric formulary costs.

QML-AiNet: An immune network approach to learning qualitative differential equation models

PubMed Central

Pang, Wei; Coghill, George M.

2015-01-01

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG. PMID:25648212

QML-AiNet: An immune network approach to learning qualitative differential equation models.

PubMed

Pang, Wei; Coghill, George M

2015-02-01

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG.

[Validation of the modified algorithm for predicting host susceptibility to viruses taking into account susceptibility parameters of primary target cell cultures and natural immunity factors].

PubMed

Zhukov, V A; Shishkina, L N; Safatov, A S; Sergeev, A A; P'iankov, O V; Petrishchenko, V A; Zaĭtsev, B N; Toporkov, V S; Sergeev, A N; Nesvizhskiĭ, Iu V; Vorob'ev, A A

2010-01-01

The paper presents results of testing a modified algorithm for predicting virus ID50 values in a host of interest by extrapolation from a model host taking into account immune neutralizing factors and thermal inactivation of the virus. The method was tested for A/Aichi/2/68 influenza virus in SPF Wistar rats, SPF CD-1 mice and conventional ICR mice. Each species was used as a host of interest while the other two served as model hosts. Primary lung and trachea cells and secretory factors of the rats' airway epithelium were used to measure parameters needed for the purpose of prediction. Predicted ID50 values were not significantly different (p = 0.05) from those experimentally measured in vivo. The study was supported by ISTC/DARPA Agreement 450p.

Functional Classification of Immune Regulatory Proteins

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rubinstein, Rotem; Ramagopal, Udupi A.; Nathenson, Stanley G.

2013-05-01

Members of the immunoglobulin superfamily (IgSF) control innate and adaptive immunity and are prime targets for the treatment of autoimmune diseases, infectious diseases, and malignancies. We describe a computational method, termed the Brotherhood algorithm, which utilizes intermediate sequence information to classify proteins into functionally related families. This approach identifies functional relationships within the IgSF and predicts additional receptor-ligand interactions. As a specific example, we examine the nectin/nectin-like family of cell adhesion and signaling proteins and propose receptor-ligand interactions within this family. We were guided by the Brotherhood approach and present the high-resolution structural characterization of a homophilic interaction involving themore » class-I MHC-restricted T-cell-associated molecule, which we now classify as a nectin-like family member. The Brotherhood algorithm is likely to have a significant impact on structural immunology by identifying those proteins and complexes for which structural characterization will be particularly informative.« less

Diagnosis of diabetes diseases using an Artificial Immune Recognition System2 (AIRS2) with fuzzy K-nearest neighbor.

PubMed

Chikh, Mohamed Amine; Saidi, Meryem; Settouti, Nesma

2012-10-01

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.

Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance.

PubMed

Singh, Yashik

2017-10-01

Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is one of the major burdens of disease in developing countries, and the standard-of-care treatment includes prescribing antiretroviral drugs. However, antiretroviral drug resistance is inevitable due to selective pressure associated with the high mutation rate of HIV. Determining antiretroviral resistance can be done by phenotypic laboratory tests or by computer-based interpretation algorithms. Computer-based algorithms have been shown to have many advantages over laboratory tests. The ANRS (Agence Nationale de Recherches sur le SIDA) is regarded as a gold standard in interpreting HIV drug resistance using mutations in genomes. The aim of this study was to improve the prediction of the ANRS gold standard in predicting HIV drug resistance. A genome sequence and HIV drug resistance measures were obtained from the Stanford HIV database (http://hivdb.stanford.edu/). Feature selection was used to determine the most important mutations associated with resistance prediction. These mutations were added to the ANRS rules, and the difference in the prediction ability was measured. This study uncovered important mutations that were not associated with the original ANRS rules. On average, the ANRS algorithm was improved by 79% ± 6.6%. The positive predictive value improved by 28%, and the negative predicative value improved by 10%. The study shows that there is a significant improvement in the prediction ability of ANRS gold standard.

Association of variants in innate immune genes with asthma and eczema

PubMed Central

Sharma, Sunita; Poon, Audrey; Himes, Blanca E.; Lasky-Su, Jessica; Sordillo, Joanne E.; Belanger, Kathleen; Milton, Donald K.; Bracken, Michael B.; Triche, Elizabeth W.; Leaderer, Brian P.; Gold, Diane R.; Litonjua, Augusto A.

2012-01-01

Background The innate immune pathway is important in the pathogenesis of asthma and eczema. However, only a few variants in these genes have been associated with either disease. We investigate the association between polymorphisms of genes in the innate immune pathway with childhood asthma and eczema. In addition, we compare individual associations with those discovered using a multivariate approach. Methods Using a novel method, case control based association testing (C2BAT), 569 single nucleotide polymorphisms (SNPs) in 44 innate immune genes were tested for association with asthma and eczema in children from the Boston Home Allergens and Asthma Study and the Connecticut Childhood Asthma Study. The screening algorithm was used to identify the top SNPs associated with asthma and eczema. We next investigated the interaction of innate immune variants with asthma and eczema risk using Bayesian networks. Results After correction for multiple comparisons, 7 SNPs in 6 genes (CARD25, TGFB1, LY96, ACAA1, DEFB1, and IFNG) were associated with asthma (adjusted p-value<0.02), while 5 SNPs in 3 different genes (CD80, STAT4, and IRAKI) were significantly associated with eczema (adjusted p-value < 0.02). None of these SNPs were associated with both asthma and eczema. Bayesian network analysis identified 4 SNPs that were predictive of asthma and 10 SNPs that predicted eczema. Of the genes identified using Bayesian networks, only CD80 was associated with eczema in the single-SNP study. Using novel methodology that allows for screening and replication in the same population, we have identified associations of innate immune genes with asthma and eczema. Bayesian network analysis suggests that additional SNPs influence disease susceptibility via SNP interactions. Conclusion Our findings suggest that innate immune genes contribute to the pathogenesis of asthma and eczema, and that these diseases likely have different genetic determinants. PMID:22192168

Improvement of the fringe analysis algorithm for wavelength scanning interferometry based on filter parameter optimization.

PubMed

Zhang, Tao; Gao, Feng; Muhamedsalih, Hussam; Lou, Shan; Martin, Haydn; Jiang, Xiangqian

2018-03-20

The phase slope method which estimates height through fringe pattern frequency and the algorithm which estimates height through the fringe phase are the fringe analysis algorithms widely used in interferometry. Generally they both extract the phase information by filtering the signal in frequency domain after Fourier transform. Among the numerous papers in the literature about these algorithms, it is found that the design of the filter, which plays an important role, has never been discussed in detail. This paper focuses on the filter design in these algorithms for wavelength scanning interferometry (WSI), trying to optimize the parameters to acquire the optimal results. The spectral characteristics of the interference signal are analyzed first. The effective signal is found to be narrow-band (near single frequency), and the central frequency is calculated theoretically. Therefore, the position of the filter pass-band is determined. The width of the filter window is optimized with the simulation to balance the elimination of the noise and the ringing of the filter. Experimental validation of the approach is provided, and the results agree very well with the simulation. The experiment shows that accuracy can be improved by optimizing the filter design, especially when the signal quality, i.e., the signal noise ratio (SNR), is low. The proposed method also shows the potential of improving the immunity to the environmental noise by adapting the signal to acquire the optimal results through designing an adaptive filter once the signal SNR can be estimated accurately.

Rapid exclusion of the diagnosis of immune HIT by AcuStar HIT and heparin-induced multiple electrode aggregometry.

PubMed

Minet, V; Baudar, J; Bailly, N; Douxfils, J; Laloy, J; Lessire, S; Gourdin, M; Devalet, B; Chatelain, B; Dogné, J M; Mullier, F

2014-06-01

Accurate diagnosis of heparin-induced thrombocytopenia (HIT) is essential but remains challenging. We have previously demonstrated, in a retrospective study, the usefulness of the combination of the 4Ts score, AcuStar HIT and heparin-induced multiple electrode aggregometry (HIMEA) with optimized thresholds. We aimed at exploring prospectively the performances of our optimized diagnostic algorithm on suspected HIT patients. The secondary objective is to evaluate performances of AcuStar HIT-Ab (PF4-H) in comparison with the clinical outcome. 116 inpatients with clinically suspected immune HIT were included. Our optimized diagnostic algorithm was applied to each patient. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) of the overall diagnostic strategy as well as AcuStar HIT-Ab (at manufacturer's thresholds and at our thresholds) were calculated using clinical diagnosis as the reference. Among 116 patients, 2 patients had clinically-diagnosed HIT. These 2 patients were positive on AcuStar HIT-Ab, AcuStar HIT-IgG and HIMEA. Using our optimized algorithm, all patients were correctly diagnosed. AcuStar HIT-Ab at our cut-off (>9.41 U/mL) and at manufacturer's cut-off (>1.00 U/mL) showed both a sensitivity of 100.0% and a specificity of 99.1% and 90.4%, respectively. The combination of the 4Ts score, the HemosIL® AcuStar HIT and HIMEA with optimized thresholds may be useful for the rapid and accurate exclusion of the diagnosis of immune HIT. Copyright © 2014 Elsevier Ltd. All rights reserved.

An algorithm for treatment of patients with hypersensitivity reactions after vaccines.

PubMed

Wood, Robert A; Berger, Melvin; Dreskin, Stephen C; Setse, Rosanna; Engler, Renata J M; Dekker, Cornelia L; Halsey, Neal A

2008-09-01

Concerns about possible allergic reactions to immunizations are raised frequently by both patients/parents and primary care providers. Estimates of true allergic, or immediate hypersensitivity, reactions to routine vaccines range from 1 per 50000 doses for diphtheria-tetanus-pertussis to approximately 1 per 500000 to 1000000 doses for most other vaccines. In a large study from New Zealand, data were collected during a 5-year period on 15 marketed vaccines and revealed an estimated rate of 1 immediate hypersensitivity reaction per 450000 doses of vaccine administered. Another large study, conducted within the Vaccine Safety Datalink, described a range of reaction rates to >7.5 million doses. Depending on the study design and the time after the immunization event, reaction rates varied from 0.65 cases per million doses to 1.53 cases per million doses when additional allergy codes were included. For some vaccines, particularly when allergens such as gelatin are part of the formulation (eg, Japanese encephalitis), higher rates of serious allergic reactions may occur. Although these per-dose estimates suggest that true hypersensitivity reactions are quite rare, the large number of doses that are administered, especially for the commonly used vaccines, makes this a relatively common clinical problem. In this review, we present background information on vaccine hypersensitivity, followed by a detailed algorithm that provides a rational and organized approach for the evaluation and treatment of patients with suspected hypersensitivity. We then include 3 cases of suspected allergic reactions to vaccines that have been referred to the Clinical Immunization Safety Assessment network to demonstrate the practical application of the algorithm.

Identification of informative features for predicting proinflammatory potentials of engine exhausts.

PubMed

Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

2017-08-18

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

A Negative Selection Immune System Inspired Methodology for Fault Diagnosis of Wind Turbines.

PubMed

Alizadeh, Esmaeil; Meskin, Nader; Khorasani, Khashayar

2017-11-01

High operational and maintenance costs represent as major economic constraints in the wind turbine (WT) industry. These concerns have made investigation into fault diagnosis of WT systems an extremely important and active area of research. In this paper, an immune system (IS) inspired methodology for performing fault detection and isolation (FDI) of a WT system is proposed and developed. The proposed scheme is based on a self nonself discrimination paradigm of a biological IS. Specifically, the negative selection mechanism [negative selection algorithm (NSA)] of the human body is utilized. In this paper, a hierarchical bank of NSAs are designed to detect and isolate both individual as well as simultaneously occurring faults common to the WTs. A smoothing moving window filter is then utilized to further improve the reliability and performance of the FDI scheme. Moreover, the performance of our proposed scheme is compared with another state-of-the-art data-driven technique, namely the support vector machines (SVMs) to demonstrate and illustrate the superiority and advantages of our proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate our proposed methodology with that of the SVM under various fault severities.

Mutation drivers of immunological responses to cancer

PubMed Central

Porta-Pardo, Eduard; Godzik, Adam

2016-01-01

In cancer immunology, somatic missense mutations have been mostly studied regarding their role in the generation of neoantigens. However, growing evidence suggests that mutations in certain genes, such as CASP8 or TP53, influence the immune response against a tumor by other mechanisms. Identifying these genes and mechanisms is important because, just as the identification of cancer driver genes led to the development of personalized cancer therapies, a comprehensive catalog of such cancer immunity drivers will aid in the development of therapies aimed at restoring antitumor immunity. Here we present an algorithm, domainXplorer, that can be used to identify potential cancer immunity drivers. To demonstrate its potential, we used it to analyze a dataset of 5,164 tumor samples from TCGA and to identify protein domains whose mutation status correlates with the presence of immune cells in cancer tissue (immune infiltrate). We identified 122 such protein regions including several that belong to proteins with known roles in immune response, such as C2, CD163L1, or FCγR2A. In several cases we show that mutations within the same protein can be associated with more or less immune cell infiltration, depending on the specific domain mutated. These results expand the catalog of potential cancer immunity drivers and highlight the importance of taking into account the structural context of somatic mutations when analyzing their potential association with immune phenotypes. PMID:27401919

Seven-parameter statistical model for BRDF in the UV band.

PubMed

Bai, Lu; Wu, Zhensen; Zou, Xiren; Cao, Yunhua

2012-05-21

A new semi-empirical seven-parameter BRDF model is developed in the UV band using experimentally measured data. The model is based on the five-parameter model of Wu and the fourteen-parameter model of Renhorn and Boreman. Surface scatter, bulk scatter and retro-reflection scatter are considered. An optimizing modeling method, the artificial immune network genetic algorithm, is used to fit the BRDF measurement data over a wide range of incident angles. The calculation time and accuracy of the five- and seven-parameter models are compared. After fixing the seven parameters, the model can well describe scattering data in the UV band.

Accurate HLA type inference using a weighted similarity graph.

PubMed

Xie, Minzhu; Li, Jing; Jiang, Tao

2010-12-14

The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true. In this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test. Our algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors.

Comparison of Fiber Optic Strain Demodulation Implementations

NASA Technical Reports Server (NTRS)

Quach, Cuong C.; Vazquez, Sixto L.

2005-01-01

NASA Langley Research Center is developing instrumentation based upon principles of Optical Frequency-Domain Reflectometry (OFDR) for the provision of large-scale, dense distribution of strain sensors using fiber optics embedded with Bragg gratings. Fiber Optic Bragg Grating technology enables the distribution of thousands of sensors immune to moisture and electromagnetic interference with negligible weight penalty. At Langley, this technology provides a key component for research and development relevant to comprehensive aerospace vehicle structural health monitoring. A prototype system is under development that includes hardware and software necessary for the acquisition of data from an optical network and conversion of the data into strain measurements. This report documents the steps taken to verify the software that implements the algorithm for calculating the fiber strain. Brief descriptions of the strain measurement system and the test article are given. The scope of this report is the verification of software implementations as compared to a reference model. The algorithm will be detailed along with comparison results.

Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images.

PubMed

Spagnolo, Daniel M; Al-Kofahi, Yousef; Zhu, Peihong; Lezon, Timothy R; Gough, Albert; Stern, Andrew M; Lee, Adrian V; Ginty, Fiona; Sarachan, Brion; Taylor, D Lansing; Chennubhotla, S Chakra

2017-11-01

We introduce THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment), an open-source tool developed to assist cancer researchers in interactive hypothesis testing. The focus of this tool is to quantify spatial intratumoral heterogeneity (ITH), and the interactions between different cell phenotypes and noncellular constituents. Specifically, we foresee applications in phenotyping cells within tumor microenvironments, recognizing tumor boundaries, identifying degrees of immune infiltration and epithelial/stromal separation, and identification of heterotypic signaling networks underlying microdomains. The THRIVE platform provides an integrated workflow for analyzing whole-slide immunofluorescence images and tissue microarrays, including algorithms for segmentation, quantification, and heterogeneity analysis. THRIVE promotes flexible deployment, a maintainable code base using open-source libraries, and an extensible framework for customizing algorithms with ease. THRIVE was designed with highly multiplexed immunofluorescence images in mind, and, by providing a platform to efficiently analyze high-dimensional immunofluorescence signals, we hope to advance these data toward mainstream adoption in cancer research. Cancer Res; 77(21); e71-74. ©2017 AACR . ©2017 American Association for Cancer Research.

New Insights into the Role of the Immune Microenvironment in Breast Carcinoma

PubMed Central

de la Cruz-Merino, Luis; Barco-Sánchez, Antonio; Henao Carrasco, Fernando; Nogales Fernández, Esteban; Vallejo Benítez, Ana; Brugal Molina, Javier; Martínez Peinado, Antonio; Grueso López, Ana; Ruiz Borrego, Manuel; Codes Manuel de Villena, Manuel; Sánchez-Margalet, Víctor; Nieto-García, Adoración; Alba Conejo, Emilio; Casares Lagar, Noelia; Ibáñez Martínez, José

2013-01-01

Recently, immune edition has been recognized as a new hallmark of cancer. In this respect, some clinical trials in breast cancer have reported imppressive outcomes related to laboratory immune findings, especially in the neoadjuvant and metastatic setting. Infiltration by tumor infiltrating lymphocytes (TIL) and their subtypes, tumor-associated macrophages (TAM) and myeloid-derived suppressive cells (MDSC) seem bona fide prognostic and even predictive biomarkers, that will eventually be incorporated into diagnostic and therapeutic algorithms of breast cancer. In addition, the complex interaction of costimulatory and coinhibitory molecules on the immune synapse and the different signals that they may exert represent another exciting field to explore. In this review we try to summarize and elucidate these new concepts and knowledge from a translational perspective focusing on breast cancer, paying special attention to those aspects that might have more significance in clinical practice and could be useful to design successful therapeutic strategies in the future. PMID:23861693

A fast, robust algorithm for power line interference cancellation in neural recording.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-04-01

Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (<100 ms) and substantial interference rejection (output SNR >30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired.

A fast, robust algorithm for power line interference cancellation in neural recording

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-04-01

Objective. Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. Approach. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. Main results. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (<100 ms) and substantial interference rejection (output SNR >30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. Significance. The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired.

Identification of Immunity-Related Genes in Ostrinia furnacalis against Entomopathogenic Fungi by RNA-Seq Analysis

PubMed Central

Zhou, Fan; Wang, Guirong; An, Chunju

2014-01-01

Background The Asian corn borer (Ostrinia furnacalis (Guenée)) is one of the most serious corn pests in Asia. Control of this pest with entomopathogenic fungus Beauveria bassiana has been proposed. However, the molecular mechanisms involved in the interactions between O. furnacalis and B. bassiana are unclear, especially under the conditions that the genomic information of O. furnacalis is currently unavailable. So we sequenced and characterized the transcriptome of O. furnacalis larvae infected by B. bassiana with special emphasis on immunity-related genes. Methodology/Principal Findings Illumina Hiseq2000 was used to sequence 4.64 and 4.72 Gb of the transcriptome from water-injected and B. bassiana-injected O. furnacalis larvae, respectively. De novo assembly generated 62,382 unigenes with mean length of 729 nt. All unigenes were searched against Nt, Nr, Swiss-Prot, COG, and KEGG databases for annotations using BLASTN or BLASTX algorithm with an E-value cut-off of 10−5. A total of 35,700 (57.2%) unigenes were annotated to at least one database. Pairwise comparisons resulted in 13,890 differentially expressed genes, with 5,843 up-regulated and 8,047 down-regulated. Based on sequence similarity to homologs known to participate in immune responses, we totally identified 190 potential immunity-related unigenes. They encode 45 pattern recognition proteins, 33 modulation proteins involved in the prophenoloxidase activation cascade, 46 signal transduction molecules, and 66 immune responsive effectors, respectively. The obtained transcriptome contains putative orthologs for nearly all components of the Toll, Imd, and JAK/STAT pathways. We randomly selected 24 immunity-related unigenes and investigated their expression profiles using quantitative RT-PCR assay. The results revealed variant expression patterns in response to the infection of B. bassiana. Conclusions/Significance This study provides the comprehensive sequence resource and expression profiles of the immunity-related genes of O. furnacalis. The obtained data gives an insight into better understanding the molecular mechanisms of innate immune processes in O. furnacalis larvae against B. bassiana. PMID:24466095

Miming the cancer-immune system competition by kinetic Monte Carlo simulations

NASA Astrophysics Data System (ADS)

Bianca, Carlo; Lemarchand, Annie

2016-10-01

In order to mimic the interactions between cancer and the immune system at cell scale, we propose a minimal model of cell interactions that is similar to a chemical mechanism including autocatalytic steps. The cells are supposed to bear a quantity called activity that may increase during the interactions. The fluctuations of cell activity are controlled by a so-called thermostat. We develop a kinetic Monte Carlo algorithm to simulate the cell interactions and thermalization of cell activity. The model is able to reproduce the well-known behavior of tumors treated by immunotherapy: the first apparent elimination of the tumor by the immune system is followed by a long equilibrium period and the final escape of cancer from immunosurveillance.

The Dynamics of Germinal Centre Selection as Measured by Graph-Theoretical Analysis of Mutational Lineage Trees

PubMed Central

Dunn-Walters, Deborah K.; Belelovsky, Alex; Edelman, Hanna; Banerjee, Monica; Mehr, Ramit

2002-01-01

We have developed a rigorous graph-theoretical algorithm for quantifying the shape properties of mutational lineage trees. We show that information about the dynamics of hypermutation and antigen-driven clonal selection during the humoral immune response is contained in the shape of mutational lineage trees deduced from the responding clones. Age and tissue related differences in the selection process can be studied using this method. Thus, tree shape analysis can be used as a means of elucidating humoral immune response dynamics in various situations. PMID:15144020

Graph-based optimization of epitope coverage for vaccine antigen design

DOE PAGES

Theiler, James Patrick; Korber, Bette Tina Marie

2017-01-29

Epigraph is a recently developed algorithm that enables the computationally efficient design of single or multi-antigen vaccines to maximize the potential epitope coverage for a diverse pathogen population. Potential epitopes are defined as short contiguous stretches of proteins, comparable in length to T-cell epitopes. This optimal coverage problem can be formulated in terms of a directed graph, with candidate antigens represented as paths that traverse this graph. Epigraph protein sequences can also be used as the basis for designing peptides for experimental evaluation of immune responses in natural infections to highly variable proteins. The epigraph tool suite also enables rapidmore » characterization of populations of diverse sequences from an immunological perspective. Fundamental distance measures are based on immunologically relevant shared potential epitope frequencies, rather than simple Hamming or phylogenetic distances. Here, we provide a mathematical description of the epigraph algorithm, include a comparison of different heuristics that can be used when graphs are not acyclic, and we describe an additional tool we have added to the web-based epigraph tool suite that provides frequency summaries of all distinct potential epitopes in a population. Lastly, we also show examples of the graphical output and summary tables that can be generated using the epigraph tool suite and explain their content and applications.« less

Graph-based optimization of epitope coverage for vaccine antigen design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Theiler, James Patrick; Korber, Bette Tina Marie

Epigraph is a recently developed algorithm that enables the computationally efficient design of single or multi-antigen vaccines to maximize the potential epitope coverage for a diverse pathogen population. Potential epitopes are defined as short contiguous stretches of proteins, comparable in length to T-cell epitopes. This optimal coverage problem can be formulated in terms of a directed graph, with candidate antigens represented as paths that traverse this graph. Epigraph protein sequences can also be used as the basis for designing peptides for experimental evaluation of immune responses in natural infections to highly variable proteins. The epigraph tool suite also enables rapidmore » characterization of populations of diverse sequences from an immunological perspective. Fundamental distance measures are based on immunologically relevant shared potential epitope frequencies, rather than simple Hamming or phylogenetic distances. Here, we provide a mathematical description of the epigraph algorithm, include a comparison of different heuristics that can be used when graphs are not acyclic, and we describe an additional tool we have added to the web-based epigraph tool suite that provides frequency summaries of all distinct potential epitopes in a population. Lastly, we also show examples of the graphical output and summary tables that can be generated using the epigraph tool suite and explain their content and applications.« less

An artificial immune system approach with secondary response for misbehavior detection in mobile ad hoc networks.

PubMed

Sarafijanović, Slavisa; Le Boudec, Jean-Yves

2005-09-01

In mobile ad hoc networks, nodes act both as terminals and information relays, and they participate in a common routing protocol, such as dynamic source routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. In this paper, we investigate the use of an artificial immune system (AIS) to detect node misbehavior in a mobile ad hoc network using DSR. The system is inspired by the natural immune system (IS) of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns, and detects new misbehavior. We describe our solution for the classification task of the AIS; it employs negative selection and clonal selection, the algorithms for learning and adaptation used by the natural IS. We define how we map the natural IS concepts such as self, antigen, and antibody to a mobile ad hoc network and give the resulting algorithm for classifying nodes as misbehaving. We implemented the system in the network simulator Glomosim; we present detection results and discuss how the system parameters affect the performance of primary and secondary response. Further steps will extend the design by using an analogy to the innate system, danger signal, and memory cells.

Comparison of gating dynamics of different IP3R channels with immune algorithm searching for channel parameter distributions

NASA Astrophysics Data System (ADS)

Cai, Xiuhong; Li, Xiang; Qi, Hong; Wei, Fang; Chen, Jianyong; Shuai, Jianwei

2016-10-01

The gating properties of the inositol 1, 4, 5-trisphosphate (IP3) receptor (IP3R) are determined by the binding and unbinding capability of Ca2+ ions and IP3 messengers. With the patch clamp experiments, the stationary properties have been discussed for Xenopus oocyte type-1 IP3R (Oo-IP3R1), type-3 IP3R (Oo-IP3R3) and Spodoptera frugiperda IP3R (Sf-IP3R). In this paper, in order to provide insights about the relation between the observed gating characteristics and the gating parameters in different IP3Rs, we apply the immune algorithm to fit the parameters of a modified DeYoung-Keizer model. By comparing the fitting parameter distributions of three IP3Rs, we suggest that the three types of IP3Rs have the similar open sensitivity in responding to IP3. The Oo-IP3R3 channel is easy to open in responding to low Ca2+ concentration, while Sf-IP3R channel is easily inhibited in responding to high Ca2+ concentration. We also show that the IP3 binding rate is not a sensitive parameter for stationary gating dynamics for three IP3Rs, but the inhibitory Ca2+ binding/unbinding rates are sensitive parameters for gating dynamics for both Oo-IP3R1 and Oo-IP3R3 channels. Such differences may be important in generating the spatially and temporally complex Ca2+ oscillations in cells. Our study also demonstrates that the immune algorithm can be applied for model parameter searching in biological systems.

seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data.

PubMed

Chen, Ziyi; Quan, Lijun; Huang, Anfei; Zhao, Qiang; Yuan, Yao; Yuan, Xuye; Shen, Qin; Shang, Jingzhe; Ben, Yinyin; Qin, F Xiao-Feng; Wu, Aiping

2018-01-01

The RNA sequencing approach has been broadly used to provide gene-, pathway-, and network-centric analyses for various cell and tissue samples. However, thus far, rich cellular information carried in tissue samples has not been thoroughly characterized from RNA-Seq data. Therefore, it would expand our horizons to better understand the biological processes of the body by incorporating a cell-centric view of tissue transcriptome. Here, a computational model named seq-ImmuCC was developed to infer the relative proportions of 10 major immune cells in mouse tissues from RNA-Seq data. The performance of seq-ImmuCC was evaluated among multiple computational algorithms, transcriptional platforms, and simulated and experimental datasets. The test results showed its stable performance and superb consistency with experimental observations under different conditions. With seq-ImmuCC, we generated the comprehensive landscape of immune cell compositions in 27 normal mouse tissues and extracted the distinct signatures of immune cell proportion among various tissue types. Furthermore, we quantitatively characterized and compared 18 different types of mouse tumor tissues of distinct cell origins with their immune cell compositions, which provided a comprehensive and informative measurement for the immune microenvironment inside tumor tissues. The online server of seq-ImmuCC are freely available at http://wap-lab.org:3200/immune/.

Low Tumor Infiltrating Mast Cell Density Confers Prognostic Benefit and Reflects Immunoactivation in Colorectal Cancer.

PubMed

Mao, Yihao; Feng, Qingyang; Zheng, Peng; Yang, Liangliang; Zhu, Dexiang; Chang, Wenju; Ji, Meiling; He, Guodong; Xu, Jianmin

2018-06-06

The role of mast cells (MCs) in colorectal cancer (CRC) progression was controversial. Thus, this study was designed to evaluate the prognostic value of MCs as well as their correlation with immune microenvironment. A retrospective cohort of CRC patients of stage I-IV was enrolled in this study. 854 consecutive patients were divided into training set (427 patients) and validation set (427 patients) randomly. The findings were further validated in a GEO cohort, GSE39582 (556 patients). The mast cell density (MCD) was measured by immunohistochemical staining of tryptase or by CIBERSORT algorithm. Low MCD predicted prolonged overall survival (OS) in training and validation set. Moreover, MCD was identified as an independent prognostic indicator in both sets. Better stratification for CRC prognosis can be achieved by building a MCD based nomogram. The prognostic role of MCD was further validated in GSE39582. In addition, MCD predicted improved survival in stage II and III CRC patients receiving adjuvant chemotherapy (ACT). Multiple immune pathways were enriched in low MCD group while cytokines/chemokines promoting anti-tumor immunity were highly expressed in such group. Furthermore, MCD was negatively correlated with CD8+ T cells infiltration. In conclusion, MCD was identified as an independent prognostic factor, as well as a potential biomarker for ACT benefit in stage II and III CRC. Better stratification of CRC prognosis could be achieved by building a MCD based nomogram. Moreover, immunoactivation in low MCD tumors may contributed to improved prognosis. This article is protected by copyright. All rights reserved. © 2018 UICC.

Variability in Humoral Immunity to Measles Vaccine: New Developments

PubMed Central

Haralambieva, Iana H.; Kennedy, Richard B.; Ovsyannikova, Inna G.; Whitaker, Jennifer A.; Poland, Gregory A.

2015-01-01

Despite the existence of an effective measles vaccine, resurgence in measles cases in the United States and across Europe has occurred, including in individuals vaccinated with two doses of the vaccine. Host genetic factors result in inter-individual variation in measles vaccine-induced antibodies, and play a role in vaccine failure. Studies have identified HLA and non-HLA genetic influences that individually or jointly contribute to the observed variability in the humoral response to vaccination among healthy individuals. In this exciting era, new high-dimensional approaches and techniques including vaccinomics, systems biology, GWAS, epitope prediction and sophisticated bioinformatics/statistical algorithms, provide powerful tools to investigate immune response mechanisms to the measles vaccine. These might predict, on an individual basis, outcomes of acquired immunity post measles vaccination. PMID:26602762

Identification of transcriptional regulators in the mouse immune system

PubMed Central

Jojic, Vladimir; Shay, Tal; Sylvia, Katelyn; Zuk, Or; Sun, Xin; Kang, Joonsoo; Regev, Aviv; Koller, Daphne

2013-01-01

The differentiation of hematopoietic stem cells into immune cells has been extensively studied in mammals, but the transcriptional circuitry controlling it is still only partially understood. Here, the Immunological Genome Project gene expression profiles across mouse immune lineages allowed us to systematically analyze these circuits. Using a computational algorithm called Ontogenet, we uncovered differentiation-stage specific regulators of mouse hematopoiesis, identifying many known hematopoietic regulators, and 175 new candidate regulators, their target genes, and the cell types in which they act. Among the novel regulators, we highlight the role of ETV5 in γδT cells differntiation. Since the transcriptional program of human and mouse cells is highly conserved1, it is likely that many lessons learned from the mouse model apply to humans. PMID:23624555

Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering.

PubMed

Zhang, Jian; Zhao, Xiaowei; Sun, Pingping; Gao, Bo; Ma, Zhiqiang

2014-01-01

B-cell epitopes are regions of the antigen surface which can be recognized by certain antibodies and elicit the immune response. Identification of epitopes for a given antigen chain finds vital applications in vaccine and drug research. Experimental prediction of B-cell epitopes is time-consuming and resource intensive, which may benefit from the computational approaches to identify B-cell epitopes. In this paper, a novel cost-sensitive ensemble algorithm is proposed for predicting the antigenic determinant residues and then a spatial clustering algorithm is adopted to identify the potential epitopes. Firstly, we explore various discriminative features from primary sequences. Secondly, cost-sensitive ensemble scheme is introduced to deal with imbalanced learning problem. Thirdly, we adopt spatial algorithm to tell which residues may potentially form the epitopes. Based on the strategies mentioned above, a new predictor, called CBEP (conformational B-cell epitopes prediction), is proposed in this study. CBEP achieves good prediction performance with the mean AUC scores (AUCs) of 0.721 and 0.703 on two benchmark datasets (bound and unbound) using the leave-one-out cross-validation (LOOCV). When compared with previous prediction tools, CBEP produces higher sensitivity and comparable specificity values. A web server named CBEP which implements the proposed method is available for academic use.

[Autoantibodies in Paraneoplastic Neurological Syndrome].

PubMed

Kawachi, Izumi

2018-04-01

Paraneoplastic neurological syndromes (PNS) are caused by immune responses against neuronal antigens expressed by the tumor. Based on the immunological pathomechanisms and responsiveness of treatments, onconeuronal antibodies are divided into two categories: 1) antibodies against neural intracellular antigens and 2) antibodies against neuronal surface or synaptic antigens. The recent discovery of onconeuronal antibodies have radically changed concepts of CNS autoimmunity, including PNS. The recognition of PNS provides a foundation for the early detection of underlying tumors and initiations of prompt treatments, which can result in substantial improvement. We here review the characteristic onconeuronal antibodies, including anti-Hu, anti-Ma2, and anti-N-methyl-D-aspartate receptor, and discuss the algorithm for the diagnosis of PNS.

How I treat acute graft-versus-host disease of the gastrointestinal tract and the liver.

PubMed

McDonald, George B

2016-03-24

Treatment of acute graft-versus-host disease (GVHD) has evolved from a one-size-fits-all approach to a more nuanced strategy based on predicted outcomes. Lower and time-limited doses of immune suppression for patients predicted to have low-risk GVHD are safe and effective. In more severe GVHD, prolonged exposure to immunosuppressive therapies, failure to achieve tolerance, and inadequate clinical responses are the proximate causes of GVHD-related deaths. This article presents acute GVHD-related scenarios representing, respectively, certainty of diagnosis, multiple causes of symptoms, jaundice, an initial therapy algorithm, secondary therapy, and defining futility of treatment. © 2016 by The American Society of Hematology.

Functional Immunomics of the Squash Bug, Anasa tristis (De Geer) (Heteroptera: Coreidae)

PubMed Central

Shelby, Kent S.

2013-01-01

The Squash bug, Anasa tristis (De Geer), is a major piercing/sucking pest of cucurbits, causing extensive damage to plants and fruits, and transmitting phytopathogens. No genomic resources to facilitate field and laboratory studies of this pest were available; therefore the first de novo exome for this destructive pest was assembled. RNA was extracted from insects challenged with bacterial and fungal immunoelicitors, insects fed on different cucurbit species, and insects from all life stages from egg to adult. All treatments and replicates were separately barcoded for subsequent analyses, then pooled for sequencing in a single lane using the Illumina HiSeq2000 platform. Over 211 million 100-base tags generated in this manner were trimmed, filtered, and cleaned, then assembled into a de novo reference transcriptome using the Broad Institute Trinity assembly algorithm. The assembly was annotated using NCBIx NR, BLAST2GO, KEGG and other databases. Of the >130,000 total assemblies 37,327 were annotated identifying the sequences of candidate gene silencing targets from immune, endocrine, reproductive, cuticle, and other physiological systems. Expression profiling of the adult immune response was accomplished by aligning the 100-base tags from each biological replicate from each treatment and controls to the annotated reference assembly of the A. tristis transcriptome. PMID:26462532

Comparison of subpixel image registration algorithms

NASA Astrophysics Data System (ADS)

Boye, R. R.; Nelson, C. L.

2009-02-01

Research into the use of multiframe superresolution has led to the development of algorithms for providing images with enhanced resolution using several lower resolution copies. An integral component of these algorithms is the determination of the registration of each of the low resolution images to a reference image. Without this information, no resolution enhancement can be attained. We have endeavored to find a suitable method for registering severely undersampled images by comparing several approaches. To test the algorithms, an ideal image is input to a simulated image formation program, creating several undersampled images with known geometric transformations. The registration algorithms are then applied to the set of low resolution images and the estimated registration parameters compared to the actual values. This investigation is limited to monochromatic images (extension to color images is not difficult) and only considers global geometric transformations. Each registration approach will be reviewed and evaluated with respect to the accuracy of the estimated registration parameters as well as the computational complexity required. In addition, the effects of image content, specifically spatial frequency content, as well as the immunity of the registration algorithms to noise will be discussed.

Antibody Responses to Zika Virus Infections in Environments of Flavivirus Endemicity

PubMed Central

Keasey, Sarah L.; Pugh, Christine L.; Jensen, Stig M. R.; Smith, Jessica L.; Hontz, Robert D.; Durbin, Anna P.; Dudley, Dawn M.; O'Connor, David H.

2017-01-01

ABSTRACT Zika virus (ZIKV) infections occur in areas where dengue virus (DENV), West Nile virus (WNV), yellow fever virus (YFV), and other viruses of the genus Flavivirus cocirculate. The envelope (E) proteins of these closely related flaviviruses induce specific long-term immunity, yet subsequent infections are associated with cross-reactive antibody responses that may enhance disease susceptibility and severity. To gain a better understanding of ZIKV infections against a background of similar viral diseases, we examined serological immune responses to ZIKV, WNV, DENV, and YFV infections of humans and nonhuman primates (NHPs). Using printed microarrays, we detected very specific antibody responses to primary infections with probes of recombinant E proteins from 15 species and lineages of flaviviruses pathogenic to humans, while high cross-reactivity between ZIKV and DENV was observed with 11 printed native viruses. Notably, antibodies from human primary ZIKV or secondary DENV infections that occurred in areas where flavivirus is endemic broadly recognized E proteins from many flaviviruses, especially DENV, indicating a strong influence of infection history on immune responses. A predictive algorithm was used to tentatively identify previous encounters with specific flaviviruses based on serum antibody interactions with the multispecies panel of E proteins. These results illustrate the potential impact of exposure to related viruses on the outcome of ZIKV infection and offer considerations for development of vaccines and diagnostics. PMID:28228395

Antibody Responses to Zika Virus Infections in Environments of Flavivirus Endemicity.

PubMed

Keasey, Sarah L; Pugh, Christine L; Jensen, Stig M R; Smith, Jessica L; Hontz, Robert D; Durbin, Anna P; Dudley, Dawn M; O'Connor, David H; Ulrich, Robert G

2017-04-01

Zika virus (ZIKV) infections occur in areas where dengue virus (DENV), West Nile virus (WNV), yellow fever virus (YFV), and other viruses of the genus Flavivirus cocirculate. The envelope (E) proteins of these closely related flaviviruses induce specific long-term immunity, yet subsequent infections are associated with cross-reactive antibody responses that may enhance disease susceptibility and severity. To gain a better understanding of ZIKV infections against a background of similar viral diseases, we examined serological immune responses to ZIKV, WNV, DENV, and YFV infections of humans and nonhuman primates (NHPs). Using printed microarrays, we detected very specific antibody responses to primary infections with probes of recombinant E proteins from 15 species and lineages of flaviviruses pathogenic to humans, while high cross-reactivity between ZIKV and DENV was observed with 11 printed native viruses. Notably, antibodies from human primary ZIKV or secondary DENV infections that occurred in areas where flavivirus is endemic broadly recognized E proteins from many flaviviruses, especially DENV, indicating a strong influence of infection history on immune responses. A predictive algorithm was used to tentatively identify previous encounters with specific flaviviruses based on serum antibody interactions with the multispecies panel of E proteins. These results illustrate the potential impact of exposure to related viruses on the outcome of ZIKV infection and offer considerations for development of vaccines and diagnostics. Copyright © 2017 American Society for Microbiology.

Convergent genetic and expression data implicate immunity in Alzheimer's disease

PubMed Central

Jones, Lesley; Lambert, Jean-Charles; Wang, Li-San; Choi, Seung-Hoan; Harold, Denise; Vedernikov, Alexey; Escott-Price, Valentina; Stone, Timothy; Richards, Alexander; Bellenguez, Céline; Ibrahim-Verbaas, Carla A; Naj, Adam C; Sims, Rebecca; Gerrish, Amy; Jun, Gyungah; DeStefano, Anita L; Bis, Joshua C; Beecham, Gary W; Grenier-Boley, Benjamin; Russo, Giancarlo; Thornton-Wells, Tricia A; Jones, Nicola; Smith, Albert V; Chouraki, Vincent; Thomas, Charlene; Ikram, M Arfan; Zelenika, Diana; Vardarajan, Badri N; Kamatani, Yoichiro; Lin, Chiao-Feng; Schmidt, Helena; Kunkle, Brian; Dunstan, Melanie L; Ruiz, Agustin; Bihoreau, Marie-Thérèse; Reitz, Christiane; Pasquier, Florence; Hollingworth, Paul; Hanon, Olivier; Fitzpatrick, Annette L; Buxbaum, Joseph D; Campion, Dominique; Crane, Paul K; Becker, Tim; Gudnason, Vilmundur; Cruchaga, Carlos; Craig, David; Amin, Najaf; Berr, Claudine; Lopez, Oscar L; De Jager, Philip L; Deramecourt, Vincent; Johnston, Janet A; Evans, Denis; Lovestone, Simon; Letteneur, Luc; Kornhuber, Johanes; Tárraga, Lluís; Rubinsztein, David C; Eiriksdottir, Gudny; Sleegers, Kristel; Goate, Alison M; Fiévet, Nathalie; Huentelman, Matthew J; Gill, Michael; Emilsson, Valur; Brown, Kristelle; Kamboh, M Ilyas; Keller, Lina; Barberger-Gateau, Pascale; McGuinness, Bernadette; Larson, Eric B; Myers, Amanda J; Dufouil, Carole; Todd, Stephen; Wallon, David; Love, Seth; Kehoe, Pat; Rogaeva, Ekaterina; Gallacher, John; George-Hyslop, Peter St; Clarimon, Jordi; Lleὀ, Alberti; Bayer, Anthony; Tsuang, Debby W; Yu, Lei; Tsolaki, Magda; Bossù, Paola; Spalletta, Gianfranco; Proitsi, Petra; Collinge, John; Sorbi, Sandro; Garcia, Florentino Sanchez; Fox, Nick; Hardy, John; Naranjo, Maria Candida Deniz; Razquin, Cristina; Bosco, Paola; Clarke, Robert; Brayne, Carol; Galimberti, Daniela; Mancuso, Michelangelo; Moebus, Susanne; Mecocci, Patrizia; del Zompo, Maria; Maier, Wolfgang; Hampel, Harald; Pilotto, Alberto; Bullido, Maria; Panza, Francesco; Caffarra, Paolo; Nacmias, Benedetta; Gilbert, John R; Mayhaus, Manuel; Jessen, Frank; Dichgans, Martin; Lannfelt, Lars; Hakonarson, Hakon; Pichler, Sabrina; Carrasquillo, Minerva M; Ingelsson, Martin; Beekly, Duane; Alavarez, Victoria; Zou, Fanggeng; Valladares, Otto; Younkin, Steven G; Coto, Eliecer; Hamilton-Nelson, Kara L; Mateo, Ignacio; Owen, Michael J; Faber, Kelley M; Jonsson, Palmi V; Combarros, Onofre; O'Donovan, Michael C; Cantwell, Laura B; Soininen, Hilkka; Blacker, Deborah; Mead, Simon; Mosley, Thomas H; Bennett, David A; Harris, Tamara B; Fratiglioni, Laura; Holmes, Clive; de Bruijn, Renee FAG; Passmore, Peter; Montine, Thomas J; Bettens, Karolien; Rotter, Jerome I; Brice, Alexis; Morgan, Kevin; Foroud, Tatiana M; Kukull, Walter A; Hannequin, Didier; Powell, John F; Nalls, Michael A; Ritchie, Karen; Lunetta, Kathryn L; Kauwe, John SK; Boerwinkle, Eric; Riemenschneider, Matthias; Boada, Mercè; Hiltunen, Mikko; Martin, Eden R; Pastor, Pau; Schmidt, Reinhold; Rujescu, Dan; Dartigues, Jean-François; Mayeux, Richard; Tzourio, Christophe; Hofman, Albert; Nöthen, Markus M; Graff, Caroline; Psaty, Bruce M; Haines, Jonathan L; Lathrop, Mark; Pericak-Vance, Margaret A; Launer, Lenore J; Farrer, Lindsay A; van Duijn, Cornelia M; Van Broekhoven, Christine; Ramirez, Alfredo; Schellenberg, Gerard D; Seshadri, Sudha; Amouyel, Philippe; Holmans, Peter A

2015-01-01

Background Late–onset Alzheimer's disease (AD) is heritable with 20 genes showing genome wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease we extended these genetic data in a pathway analysis. Methods The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. Results ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (p = 3.27×10-12 after multiple testing correction for pathways), regulation of endocytosis (p = 1.31×10-11), cholesterol transport (p = 2.96 × 10-9) and proteasome-ubiquitin activity (p = 1.34×10-6). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected p 0.002 – 0.05). Conclusions The immune response, regulation of endocytosis, cholesterol transport and protein ubiquitination represent prime targets for AD therapeutics. PMID:25533204

Convergent genetic and expression data implicate immunity in Alzheimer's disease.

PubMed

2015-06-01

Late-onset Alzheimer's disease (AD) is heritable with 20 genes showing genome-wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis. The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 × 10(-12) after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 × 10(-11)), cholesterol transport (P = 2.96 × 10(-9)), and proteasome-ubiquitin activity (P = 1.34 × 10(-6)). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P = .002-.05). The immune response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics. Copyright © 2015. Published by Elsevier Inc.

Advanced end-to-end fiber optic sensing systems for demanding environments

NASA Astrophysics Data System (ADS)

Black, Richard J.; Moslehi, Behzad

2010-09-01

Optical fibers are small-in-diameter, light-in-weight, electromagnetic-interference immune, electrically passive, chemically inert, flexible, embeddable into different materials, and distributed-sensing enabling, and can be temperature and radiation tolerant. With appropriate processing and/or packaging, they can be very robust and well suited to demanding environments. In this paper, we review a range of complete end-to-end fiber optic sensor systems that IFOS has developed comprising not only (1) packaged sensors and mechanisms for integration with demanding environments, but (2) ruggedized sensor interrogators, and (3) intelligent decision aid algorithms software systems. We examine the following examples: " Fiber Bragg Grating (FBG) optical sensors systems supporting arrays of environmentally conditioned multiplexed FBG point sensors on single or multiple optical fibers: In conjunction with advanced signal processing, decision aid algorithms and reasoners, FBG sensor based structural health monitoring (SHM) systems are expected to play an increasing role in extending the life and reducing costs of new generations of aerospace systems. Further, FBG based structural state sensing systems have the potential to considerably enhance the performance of dynamic structures interacting with their environment (including jet aircraft, unmanned aerial vehicles (UAVs), and medical or extravehicular space robots). " Raman based distributed temperature sensing systems: The complete length of optical fiber acts as a very long distributed sensor which may be placed down an oil well or wrapped around a cryogenic tank.

Three dimensional indoor positioning based on visible light with Gaussian mixture sigma-point particle filter technique

NASA Astrophysics Data System (ADS)

Gu, Wenjun; Zhang, Weizhi; Wang, Jin; Amini Kashani, M. R.; Kavehrad, Mohsen

2015-01-01

Over the past decade, location based services (LBS) have found their wide applications in indoor environments, such as large shopping malls, hospitals, warehouses, airports, etc. Current technologies provide wide choices of available solutions, which include Radio-frequency identification (RFID), Ultra wideband (UWB), wireless local area network (WLAN) and Bluetooth. With the rapid development of light-emitting-diodes (LED) technology, visible light communications (VLC) also bring a practical approach to LBS. As visible light has a better immunity against multipath effect than radio waves, higher positioning accuracy is achieved. LEDs are utilized both for illumination and positioning purpose to realize relatively lower infrastructure cost. In this paper, an indoor positioning system using VLC is proposed, with LEDs as transmitters and photo diodes as receivers. The algorithm for estimation is based on received-signalstrength (RSS) information collected from photo diodes and trilateration technique. By appropriately making use of the characteristics of receiver movements and the property of trilateration, estimation on three-dimensional (3-D) coordinates is attained. Filtering technique is applied to enable tracking capability of the algorithm, and a higher accuracy is reached compare to raw estimates. Gaussian mixture Sigma-point particle filter (GM-SPPF) is proposed for this 3-D system, which introduces the notion of Gaussian Mixture Model (GMM). The number of particles in the filter is reduced by approximating the probability distribution with Gaussian components.

Identifying the Presence of Prostate Cancer in Individuals with PSA Levels <20 ng ml-1 Using Computational Data Extraction Analysis of High Dimensional Peripheral Blood Flow Cytometric Phenotyping Data.

PubMed

Cosma, Georgina; McArdle, Stéphanie E; Reeder, Stephen; Foulds, Gemma A; Hood, Simon; Khan, Masood; Pockley, A Graham

2017-01-01

Determining whether an asymptomatic individual with Prostate-Specific Antigen (PSA) levels below 20 ng ml -1 has prostate cancer in the absence of definitive, biopsy-based evidence continues to present a significant challenge to clinicians who must decide whether such individuals with low PSA values have prostate cancer. Herein, we present an advanced computational data extraction approach which can identify the presence of prostate cancer in men with PSA levels <20 ng ml -1 on the basis of peripheral blood immune cell profiles that have been generated using multi-parameter flow cytometry. Statistical analysis of immune phenotyping datasets relating to the presence and prevalence of key leukocyte populations in the peripheral blood, as generated from individuals undergoing routine tests for prostate cancer (including tissue biopsy) using multi-parametric flow cytometric analysis, was unable to identify significant relationships between leukocyte population profiles and the presence of benign disease (no prostate cancer) or prostate cancer. By contrast, a Genetic Algorithm computational approach identified a subset of five flow cytometry features ( CD 8 + CD 45 RA - CD 27 - CD 28 - ( CD 8 + Effector Memory cells); CD 4 + CD 45 RA - CD 27 - CD 28 - ( CD 4 + Terminally Differentiated Effector Memory Cells re-expressing CD45RA); CD 3 - CD 19 + (B cells); CD 3 + CD 56 + CD 8 + CD 4 + (NKT cells)) from a set of twenty features, which could potentially discriminate between benign disease and prostate cancer. These features were used to construct a prostate cancer prediction model using the k-Nearest-Neighbor classification algorithm. The proposed model, which takes as input the set of flow cytometry features, outperformed the predictive model which takes PSA values as input. Specifically, the flow cytometry-based model achieved Accuracy = 83.33%, AUC = 83.40%, and optimal ROC points of FPR = 16.13%, TPR = 82.93%, whereas the PSA-based model achieved Accuracy = 77.78%, AUC = 76.95%, and optimal ROC points of FPR = 29.03%, TPR = 82.93%. Combining PSA and flow cytometry predictors achieved Accuracy = 79.17%, AUC = 78.17% and optimal ROC points of FPR = 29.03%, TPR = 85.37%. The results demonstrate the value of computational intelligence-based approaches for interrogating immunophenotyping datasets and that combining peripheral blood phenotypic profiling with PSA levels improves diagnostic accuracy compared to using PSA test alone. These studies also demonstrate that the presence of cancer is reflected in changes in the peripheral blood immune phenotype profile which can be identified using computational analysis and interpretation of complex flow cytometry datasets.

Constructing Smart Protocells with Built-In DNA Computational Core to Eliminate Exogenous Challenge.

PubMed

Lyu, Yifan; Wu, Cuichen; Heinke, Charles; Han, Da; Cai, Ren; Teng, I-Ting; Liu, Yuan; Liu, Hui; Zhang, Xiaobing; Liu, Qiaoling; Tan, Weihong

2018-06-06

A DNA reaction network is like a biological algorithm that can respond to "molecular input signals", such as biological molecules, while the artificial cell is like a microrobot whose function is powered by the encapsulated DNA reaction network. In this work, we describe the feasibility of using a DNA reaction network as the computational core of a protocell, which will perform an artificial immune response in a concise way to eliminate a mimicked pathogenic challenge. Such a DNA reaction network (RN)-powered protocell can realize the connection of logical computation and biological recognition due to the natural programmability and biological properties of DNA. Thus, the biological input molecules can be easily involved in the molecular computation and the computation process can be spatially isolated and protected by artificial bilayer membrane. We believe the strategy proposed in the current paper, i.e., using DNA RN to power artificial cells, will lay the groundwork for understanding the basic design principles of DNA algorithm-based nanodevices which will, in turn, inspire the construction of artificial cells, or protocells, that will find a place in future biomedical research.

Discovery of novel antimicrobial peptides: A transcriptomic study of the sea anemone Cnidopus japonicus.

PubMed

Grafskaia, Ekaterina N; Polina, Nadezhda F; Babenko, Vladislav V; Kharlampieva, Daria D; Bobrovsky, Pavel A; Manuvera, Valentin A; Farafonova, Tatyana E; Anikanov, Nikolay A; Lazarev, Vassili N

2018-04-01

As essential conservative component of the innate immune systems of living organisms, antimicrobial peptides (AMPs) could complement pharmaceuticals that increasingly fail to combat various pathogens exhibiting increased resistance to microbial antibiotics. Among the properties of AMPs that suggest their potential as therapeutic agents, diverse peptides in the venoms of various predators demonstrate antimicrobial activity and kill a wide range of microorganisms. To identify potent AMPs, the study reported here involved a transcriptomic profiling of the tentacle secretion of the sea anemone Cnidopus japonicus. An in silico search algorithm designed to discover toxin-like proteins containing AMPs was developed based on the evaluation of the properties and structural peculiarities of amino acid sequences. The algorithm revealed new proteins of the anemone containing antimicrobial candidate sequences, and 10 AMPs verified using high-throughput proteomics were synthesized. The antimicrobial activity of the candidate molecules was experimentally estimated against Gram-positive and -negative bacteria. Ultimately, three peptides exhibited antimicrobial activity against bacterial strains, which suggests that the method can be applied to reveal new AMPs in the venoms of other predators as well.

Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm.

PubMed

Darzi, Soodabeh; Kiong, Tiong Sieh; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Salem, Balasem

2014-01-01

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

PubMed Central

Sieh Kiong, Tiong; Tariqul Islam, Mohammad; Ismail, Mahamod; Salem, Balasem

2014-01-01

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. PMID:25147859

In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses.

PubMed

Schmier, Sonja; Mostafa, Ahmed; Haarmann, Thomas; Bannert, Norbert; Ziebuhr, John; Veljkovic, Veljko; Dietrich, Ursula; Pleschka, Stephan

2015-06-19

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.

In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses

NASA Astrophysics Data System (ADS)

Schmier, Sonja; Mostafa, Ahmed; Haarmann, Thomas; Bannert, Norbert; Ziebuhr, John; Veljkovic, Veljko; Dietrich, Ursula; Pleschka, Stephan

2015-06-01

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.

Density implications of shift compensation postprocessing in holographic storage systems

NASA Astrophysics Data System (ADS)

Menetrier, Laure; Burr, Geoffrey W.

2003-02-01

We investigate the effect of data page misregistration, and its subsequent correction in postprocessing, on the storage density of holographic data storage systems. A numerical simulation is used to obtain the bit-error rate as a function of hologram aperture, page misregistration, pixel fill factors, and Gaussian additive intensity noise. Postprocessing of simulated data pages is performed by a nonlinear pixel shift compensation algorithm [Opt. Lett. 26, 542 (2001)]. The performance of this algorithm is analyzed in the presence of noise by determining the achievable areal density. The impact of inaccurate measurements of page misregistration is also investigated. Results show that the shift-compensation algorithm can provide almost complete immunity to page misregistration, although at some penalty to the baseline areal density offered by a system with zero tolerance to misalignment.

Immune systems are not just for making you feel better: they are for controlling autonomous robots

NASA Astrophysics Data System (ADS)

Rosenblum, Mark

2005-05-01

The typical algorithm for robot autonomous navigation in off-road complex environments involves building a 3D map of the robot's surrounding environment using a 3D sensing modality such as stereo vision or active laser scanning, and generating an instantaneous plan to navigate around hazards. Although there has been steady progress using these methods, these systems suffer from several limitations that cannot be overcome with 3D sensing and planning alone. Geometric sensing alone has no ability to distinguish between compressible and non-compressible materials. As a result, these systems have difficulty in heavily vegetated environments and require sensitivity adjustments across different terrain types. On the planning side, these systems have no ability to learn from their mistakes and avoid problematic environmental situations on subsequent encounters. We have implemented an adaptive terrain classification system based on the Artificial Immune System (AIS) computational model, which is loosely based on the biological immune system, that combines various forms of imaging sensor inputs to produce a "feature labeled" image of the scene categorizing areas as benign or detrimental for autonomous robot navigation. Because of the qualities of the AIS computation model, the resulting system will be able to learn and adapt on its own through interaction with the environment by modifying its interpretation of the sensor data. The feature labeled results from the AIS analysis are inserted into a map and can then be used by a planner to generate a safe route to a goal point. The coupling of diverse visual cues with the malleable AIS computational model will lead to autonomous robotic ground vehicles that require less human intervention for deployment in novel environments and more robust operation as a result of the system's ability to improve its performance through interaction with the environment.

[Dermatologic toxicities of immune checkpoint inhibitors].

PubMed

Sibaud, V; Boulinguez, S; Pagès, C; Riffaud, L; Lamant, L; Chira, C; Boyrie, S; Vigarios, E; Tournier, E; Meyer, N

2018-05-01

The development of immune checkpoint inhibitors (monoclonal antibodies targeting PD-1/PD-L1 or CTLA-4) represents a significant advance in the treatment of multiple cancers. Given their particular mechanism of action, which involves triggering CD4+/CD8+ T-cell activation and proliferation, they are associated with a specific safety profile. Their adverse events are primarily immune-related, and can affect practically all organs. In this context, dermatological toxicity is the most common, though it mostly remains mild to moderate and does not require discontinuation of treatment. More than a third of patients are faced with cutaneous adverse events, usually in the form of a maculopapular rash, pruritus or vitiligo (only in patients treated for melanoma). Much more specific dermatologic disorders, however, may occur such as lichenoid reactions, induced psoriasis, sarcoidosis, auto-immune diseases (bullous pemphigoid, dermatomyositis, alopecia areata), acne-like rash, xerostomia, etc. Rigorous dermatological evaluation is thus mandatory in the case of atypical, persistent/recurrent or severe lesions. In this article, we review the incidence and spectrum of dermatologic adverse events reported with immune checkpoint inhibitors. Finally, a management algorithm is proposed. Copyright © 2018. Published by Elsevier Masson SAS.

Specificity, Privacy, and Degeneracy in the CD4 T Cell Receptor Repertoire Following Immunization

PubMed Central

Sun, Yuxin; Best, Katharine; Cinelli, Mattia; Heather, James M.; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-01-01

T cells recognize antigen using a large and diverse set of antigen-specific receptors created by a complex process of imprecise somatic cell gene rearrangements. In response to antigen-/receptor-binding-specific T cells then divide to form memory and effector populations. We apply high-throughput sequencing to investigate the global changes in T cell receptor sequences following immunization with ovalbumin (OVA) and adjuvant, to understand how adaptive immunity achieves specificity. Each immunized mouse contained a predominantly private but related set of expanded CDR3β sequences. We used machine learning to identify common patterns which distinguished repertoires from mice immunized with adjuvant with and without OVA. The CDR3β sequences were deconstructed into sets of overlapping contiguous amino acid triplets. The frequencies of these motifs were used to train the linear programming boosting (LPBoost) algorithm LPBoost to classify between TCR repertoires. LPBoost could distinguish between the two classes of repertoire with accuracies above 80%, using a small subset of triplet sequences present at defined positions along the CDR3. The results suggest a model in which such motifs confer degenerate antigen specificity in the context of a highly diverse and largely private set of T cell receptors. PMID:28450864

Inflammatory Bowel Disease: Pathophysiology and Current Therapeutic Approaches.

PubMed

Abraham, Bincy P; Ahmed, Tasneem; Ali, Tauseef

2017-01-01

Inflammatory bowel diseases, most commonly categorized as Crohn's disease and ulcerative colitis, are immune mediated chronic inflammatory disorders of the gastrointestinal tract. The etiopathogenesis is multifactorial with different environmental, genetic, immune mediated, and gut microbial factors playing important role. The current goals of therapy are to improve clinical symptoms, control inflammation, prevent complications, and improve quality of life. Different therapeutic agents, with their indications, mechanisms of action, and side effects are discussed in this chapter. Anti-integrin therapy, a newer therapeutic class, with its potential beneficial role in both Crohn's disease and ulcerative colitis is also mentioned. In the end, therapeutic algorithms for both diseases are reviewed.

Identification of biomarkers of response to abatacept in patients with SLE using deconvolution of whole blood transcriptomic data from a phase IIb clinical trial.

PubMed

Bandyopadhyay, Somnath; Connolly, Sean E; Jabado, Omar; Ye, June; Kelly, Sheila; Maldonado, Michael A; Westhovens, Rene; Nash, Peter; Merrill, Joan T; Townsend, Robert M

2017-01-01

To characterise patients with active SLE based on pretreatment gene expression-defined peripheral immune cell patterns and identify clusters enriched for potential responders to abatacept treatment. This post hoc analysis used baseline peripheral whole blood transcriptomic data from patients in a phase IIb trial of intravenous abatacept (~10 mg/kg/month). Cell-specific genes were used with a published deconvolution algorithm to identify immune cell proportions in patient samples, and unsupervised consensus clustering was generated. Efficacy data were re-analysed. Patient data (n=144: abatacept: n=98; placebo: n=46) were grouped into four main clusters (C) by predominant characteristic cells: C1-neutrophils; C2-cytotoxic T cells, B-cell receptor-ligated B cells, monocytes, IgG memory B cells, activated T helper cells; C3-plasma cells, activated dendritic cells, activated natural killer cells, neutrophils; C4-activated dendritic cells, cytotoxic T cells. C3 had the highest baseline total British Isles Lupus Assessment Group (BILAG) scores, highest antidouble-stranded DNA autoantibody levels and shortest time to flare (TTF), plus trends in favour of response to abatacept over placebo: adjusted mean difference in BILAG score over 1 year, -4.78 (95% CI -12.49 to 2.92); median TTF, 56 vs 6 days; greater normalisation of complement component 3 and 4 levels. Differential improvements with abatacept were not seen in other clusters, except for median TTF in C1 (201 vs 109 days). Immune cell clustering segmented disease severity and responsiveness to abatacept. Definition of immune response cell types may inform design and interpretation of SLE trials and treatment decisions. NCT00119678; results.

Modernizing Immunization Practice Through the Use of Cloud Based Platforms.

PubMed

Bell, Cameron; Atkinson, Katherine M; Wilson, Kumanan

2017-04-01

Collection of timely and accurate immunization information is essential for effective immunization programs. Current immunization information systems have important limitations that impact the ability to collect this data. Based on our experience releasing a national immunization app we describe a cloud-based platform that would allow individuals to store their records digitally and exchange these records with public health information systems thus improving the quality of immunization information held by individuals and public health officials.

Quantitative diagnosis of bladder cancer by morphometric analysis of HE images

NASA Astrophysics Data System (ADS)

Wu, Binlin; Nebylitsa, Samantha V.; Mukherjee, Sushmita; Jain, Manu

2015-02-01

In clinical practice, histopathological analysis of biopsied tissue is the main method for bladder cancer diagnosis and prognosis. The diagnosis is performed by a pathologist based on the morphological features in the image of a hematoxylin and eosin (HE) stained tissue sample. This manuscript proposes algorithms to perform morphometric analysis on the HE images, quantify the features in the images, and discriminate bladder cancers with different grades, i.e. high grade and low grade. The nuclei are separated from the background and other types of cells such as red blood cells (RBCs) and immune cells using manual outlining, color deconvolution and image segmentation. A mask of nuclei is generated for each image for quantitative morphometric analysis. The features of the nuclei in the mask image including size, shape, orientation, and their spatial distributions are measured. To quantify local clustering and alignment of nuclei, we propose a 1-nearest-neighbor (1-NN) algorithm which measures nearest neighbor distance and nearest neighbor parallelism. The global distributions of the features are measured using statistics of the proposed parameters. A linear support vector machine (SVM) algorithm is used to classify the high grade and low grade bladder cancers. The results show using a particular group of nuclei such as large ones, and combining multiple parameters can achieve better discrimination. This study shows the proposed approach can potentially help expedite pathological diagnosis by triaging potentially suspicious biopsies.

Chemical Sensor Systems and Associated Algorithms for Fire Detection: A Review.

PubMed

Fonollosa, Jordi; Solórzano, Ana; Marco, Santiago

2018-02-11

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative.

Chemical Sensor Systems and Associated Algorithms for Fire Detection: A Review

PubMed Central

Fonollosa, Jordi

2018-01-01

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative. PMID:29439490

Recommendation system for immunization coverage and monitoring.

PubMed

Bhatti, Uzair Aslam; Huang, Mengxing; Wang, Hao; Zhang, Yu; Mehmood, Anum; Di, Wu

2018-01-02

Immunization averts an expected 2 to 3 million deaths every year from diphtheria, tetanus, pertussis (whooping cough), and measles; however, an additional 1.5 million deaths could be avoided if vaccination coverage was improved worldwide. 1 1 Data source for immunization records of 1.5 M: http://www.who.int/mediacentre/factsheets/fs378/en/ New vaccination technologies provide earlier diagnoses, personalized treatments and a wide range of other benefits for both patients and health care professionals. Childhood diseases that were commonplace less than a generation ago have become rare because of vaccines. However, 100% vaccination coverage is still the target to avoid further mortality. Governments have launched special campaigns to create an awareness of vaccination. In this paper, we have focused on data mining algorithms for big data using a collaborative approach for vaccination datasets to resolve problems with planning vaccinations in children, stocking vaccines, and tracking and monitoring non-vaccinated children appropriately. Geographical mapping of vaccination records helps to tackle red zone areas, where vaccination rates are poor, while green zone areas, where vaccination rates are good, can be monitored to enable health care staff to plan the administration of vaccines. Our recommendation algorithm assists in these processes by using deep data mining and by accessing records of other hospitals to highlight locations with lower rates of vaccination. The overall performance of the model is good. The model has been implemented in hospitals to control vaccination across the coverage area.

Label-free haemogram using wavelength modulated Raman spectroscopy for identifying immune-cell subset

NASA Astrophysics Data System (ADS)

Ashok, Praveen C.; Praveen, Bavishna B.; Campbell, Elaine C.; Dholakia, Kishan; Powis, Simon J.

2014-03-01

Leucocytes in the blood of mammals form a powerful protective system against a wide range of dangerous pathogens. There are several types of immune cells that has specific role in the whole immune system. The number and type of immune cells alter in the disease state and identifying the type of immune cell provides information about a person's state of health. There are several immune cell subsets that are essentially morphologically identical and require external labeling to enable discrimination. Here we demonstrate the feasibility of using Wavelength Modulated Raman Spectroscopy (WMRS) with suitable machine learning algorithms as a label-free method to distinguish between different closely lying immune cell subset. Principal Component Analysis (PCA) was performed on WMRS data from single cells, obtained using confocal Raman microscopy for feature reduction, followed by Support Vector Machine (SVM) for binary discrimination of various cell subset, which yielded an accuracy >85%. The method was successful in discriminating between untouched and unfixed purified populations of CD4+CD3+ and CD8+CD3+ T lymphocyte subsets, and CD56+CD3- natural killer cells with a high degree of specificity. It was also proved sensitive enough to identify unique Raman signatures that allow clear discrimination between dendritic cell subsets, comprising CD303+CD45+ plasmacytoid and CD1c+CD141+ myeloid dendritic cells. The results of this study clearly show that WMRS is highly sensitive and can distinguish between cell types that are morphologically identical.

Review of the systems biology of the immune system using agent-based models.

PubMed

Shinde, Snehal B; Kurhekar, Manish P

2018-06-01

The immune system is an inherent protection system in vertebrate animals including human beings that exhibit properties such as self-organisation, self-adaptation, learning, and recognition. It interacts with the other allied systems such as the gut and lymph nodes. There is a need for immune system modelling to know about its complex internal mechanism, to understand how it maintains the homoeostasis, and how it interacts with the other systems. There are two types of modelling techniques used for the simulation of features of the immune system: equation-based modelling (EBM) and agent-based modelling. Owing to certain shortcomings of the EBM, agent-based modelling techniques are being widely used. This technique provides various predictions for disease causes and treatments; it also helps in hypothesis verification. This study presents a review of agent-based modelling of the immune system and its interactions with the gut and lymph nodes. The authors also review the modelling of immune system interactions during tuberculosis and cancer. In addition, they also outline the future research directions for the immune system simulation through agent-based techniques such as the effects of stress on the immune system, evolution of the immune system, and identification of the parameters for a healthy immune system.

A flexible new method for 3D measurement based on multi-view image sequences

NASA Astrophysics Data System (ADS)

Cui, Haihua; Zhao, Zhimin; Cheng, Xiaosheng; Guo, Changye; Jia, Huayu

2016-11-01

Three-dimensional measurement is the base part for reverse engineering. The paper developed a new flexible and fast optical measurement method based on multi-view geometry theory. At first, feature points are detected and matched with improved SIFT algorithm. The Hellinger Kernel is used to estimate the histogram distance instead of traditional Euclidean distance, which is immunity to the weak texture image; then a new filter three-principle for filtering the calculation of essential matrix is designed, the essential matrix is calculated using the improved a Contrario Ransac filter method. One view point cloud is constructed accurately with two view images; after this, the overlapped features are used to eliminate the accumulated errors caused by added view images, which improved the camera's position precision. At last, the method is verified with the application of dental restoration CAD/CAM, experiment results show that the proposed method is fast, accurate and flexible for tooth 3D measurement.

Small molecules as therapy for uveitis: a selected perspective of new and developing agents.

PubMed

Pleyer, Uwe; Algharably, Engi Abdel-Hady; Feist, Eugen; Kreutz, Reinhold

2017-09-01

Intraocular inflammation (uveitis) remains a significant burden of legal blindness. Because of its immune mediated and chronic recurrent nature, common therapy includes corticosteroids, disease-modifying anti-rheumatic drugs and more recently biologics as immune modulatory agents. The purpose of this article is to identify the role of new treatment approaches focusing on small molecules as therapeutic option in uveitis. Areas covered: A MEDLINE database search was conducted through February 2017 using the terms 'uveitis' and 'small molecule'. To provide ongoing and future perspectives in treatment options, also clinical trials as registered at ClinicalTrials.gov were included. Both, results from experimental as well as clinical research in this field were included. Since this field is rapidly evolving, a selection of promising agents had to be made. Expert opinion: Small molecules may interfere at different steps of the inflammatory cascade and appear as an interesting option in the treatment algorithm of uveitis. Because of their highly targeted molecular effects and their favorable bioavailability with the potential of topical application small molecules hold great promise. Nevertheless, a careful evaluation of these agents has to be made, since current experience is almost exclusively based on experimental uveitis models and few registered trials.

Profiling antibody responses by multiparametric analysis of primary B cells

PubMed Central

Story, Craig M.; Papa, Eliseo; Hu, Chih-Chi Andrew; Ronan, Jehnna L.; Herlihy, Kara; Ploegh, Hidde L.; Love, J. Christopher

2008-01-01

Determining the efficacy of a vaccine generally relies on measuring neutralizing antibodies in sera. This measure cannot elucidate the mechanisms responsible for the development of immunological memory at the cellular level, however. Quantitative profiles that detail the cellular origin, extent, and diversity of the humoral (antibody-based) immune response would improve both the assessment and development of vaccines. Here, we describe a novel approach to collect multiparametric datasets that describe the specificity, isotype, and apparent affinity of the antibodies secreted from large numbers of individual primary B cells (≈103-104). The antibody/antigen binding curves obtained by this approach can be used to classify closely related populations of cells using algorithms for data clustering, and the relationships among populations can be visualized graphically using affinity heatmaps. The technique described was used to evaluate the diversity of antigen-specific antibody-secreting cells generated during an in vivo humoral response to a series of immunizations designed to mimic a multipart vaccination. Profiles correlating primary antibody-producing cells with the molecular characteristics of their secreted antibodies should facilitate both the evaluation of candidate vaccines and, broadly, studies on the repertoires of antibodies generated in response to infectious or autoimmune diseases. PMID:19004776

Visual Recognition of Artifacts by Computer.

DTIC Science & Technology

1979-08-01

detectors , each sensitive to a different group of edge types. An algorithm for selecting the most appropriate detector response in a particular region is...a . *00 . . . . . . . . . . . . . . . . . . . . .* 2 .2. StratwEDGE snco ............. as -o......... . ...... 5 2 *TwO NEW ED)GE DETECTORS 5...27 2.6. Sensitivity , Noise Immunity, and Snape Deformation .. 40 2.7. Conclusions ... . . ......................... ....... 43 3. INTERMEDIATE

A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

PubMed Central

Yao, Yiqing; Xu, Xiaosu

2017-01-01

In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics. PMID:28245549

A RLS-SVM Aided Fusion Methodology for INS during GPS Outages.

PubMed

Yao, Yiqing; Xu, Xiaosu

2017-02-24

In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics.

Enhancing hyperspectral spatial resolution using multispectral image fusion: A wavelet approach

NASA Astrophysics Data System (ADS)

Jazaeri, Amin

High spectral and spatial resolution images have a significant impact in remote sensing applications. Because both spatial and spectral resolutions of spaceborne sensors are fixed by design and it is not possible to further increase the spatial or spectral resolution, techniques such as image fusion must be applied to achieve such goals. This dissertation introduces the concept of wavelet fusion between hyperspectral and multispectral sensors in order to enhance the spectral and spatial resolution of a hyperspectral image. To test the robustness of this concept, images from Hyperion (hyperspectral sensor) and Advanced Land Imager (multispectral sensor) were first co-registered and then fused using different wavelet algorithms. A regression-based fusion algorithm was also implemented for comparison purposes. The results show that the fused images using a combined bi-linear wavelet-regression algorithm have less error than other methods when compared to the ground truth. In addition, a combined regression-wavelet algorithm shows more immunity to misalignment of the pixels due to the lack of proper registration. The quantitative measures of average mean square error show that the performance of wavelet-based methods degrades when the spatial resolution of hyperspectral images becomes eight times less than its corresponding multispectral image. Regardless of what method of fusion is utilized, the main challenge in image fusion is image registration, which is also a very time intensive process. Because the combined regression wavelet technique is computationally expensive, a hybrid technique based on regression and wavelet methods was also implemented to decrease computational overhead. However, the gain in faster computation was offset by the introduction of more error in the outcome. The secondary objective of this dissertation is to examine the feasibility and sensor requirements for image fusion for future NASA missions in order to be able to perform onboard image fusion. In this process, the main challenge of image registration was resolved by registering the input images using transformation matrices of previously acquired data. The composite image resulted from the fusion process remarkably matched the ground truth, indicating the possibility of real time onboard fusion processing.

The optimal dynamic immunization under a controlled heterogeneous node-based SIRS model

NASA Astrophysics Data System (ADS)

Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan

2016-05-01

Dynamic immunizations, under which the state of the propagation network of electronic viruses can be changed by adjusting the control measures, are regarded as an alternative to static immunizations. This paper addresses the optimal dynamical immunization under the widely accepted SIRS assumption. First, based on a controlled heterogeneous node-based SIRS model, an optimal control problem capturing the optimal dynamical immunization is formulated. Second, the existence of an optimal dynamical immunization scheme is shown, and the corresponding optimality system is derived. Next, some numerical examples are given to show that an optimal immunization strategy can be worked out by numerically solving the optimality system, from which it is found that the network topology has a complex impact on the optimal immunization strategy. Finally, the difference between a payoff and the minimum payoff is estimated in terms of the deviation of the corresponding immunization strategy from the optimal immunization strategy. The proposed optimal immunization scheme is justified, because it can achieve a low level of infections at a low cost.

Social network fragmentation and community health.

PubMed

Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

2017-09-05

Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

Rare phenotypes in the understanding of autoimmunity

PubMed Central

Zeissig, Yvonne; Petersen, Britt-Sabina; Franke, Andre; Blumberg, Richard S; Zeissig, Sebastian

2017-01-01

The study of rare phenotypes has a long history in the description of autoimmune disorders. First Mendelian syndromes of idiopathic tissue destruction were defined more than 100 years ago and were later revealed to result from immune-mediated reactivity against self. In the past two decades, continuous advances in sequencing technology and particularly the advent of next-generation sequencing have allowed to define the genetic basis of an ever-growing number of Mendelian forms of autoimmunity. This has provided unique insight into the molecular pathways that govern immunological homeostasis and that are indispensable for the prevention of self-reactive immune-mediated tissue damage and ‘horror autotoxicus’. Here we will discuss selected examples of past and recent investigations into rare phenotypes of autoimmunity that have delineated pathways critical for central and peripheral control of the adaptive immune system. We will outline the implications of these findings for rare and common forms of autoimmunity and will discuss the benefits and potential pitfalls of the integration of next-generation sequencing into algorithms for clinical diagnostics. Because of the concise nature of this review, we will focus on syndromes caused by defects in the control of adaptive immunity as innate immune-mediated autoinflammatory disorders have been covered in excellent recent reviews on Mendelian and polygenic forms of autoimmunity. PMID:27562064

Clinical evaluation of compounds targeting PD-1/PD-L1 pathway for cancer immunotherapy.

PubMed

Lu, Jing; Lee-Gabel, Linda; Nadeau, Michelle C; Ferencz, Thomas M; Soefje, Scott A

2015-12-01

Significant enthusiasm currently exists for new immunotherapeutic strategies: blocking the interaction between programmed death-1 receptor on T-cells and programmed death-ligand 1 on tumor cells to boost immune system stimulation to fight cancer. Immunomodulation with the antiprogrammed death-1/programmed death-ligand 1 monoclonal antibodies has shown to mediate tumor shrinkage and extend overall survival from several pivotal phase I/II studies in melanoma, renal cell carcinoma, and non-small cell lung cancer. This has prompted multiple large ongoing phase III trials with the expectation for fast-track FDA approvals to satisfy unmet medical needs. Compounds targeting the programmed death-1 pathway that are in clinical trials fall into two major categories, namely antiprogrammed death-1 antibodies: Nivolumab, MK-3475, and pidilizumab; and antiprogrammed death-ligand 1 antibodies: MPDL3280A, BMS-936559, MEDI4736, and MSB0010718C. We reviewed the clinical efficacy and safety of each compound based upon major registered clinical trials and published clinical data. Overall, response rate of more than 20% is consistently seen across all these trials, with maximal response of approximately 50% achieved by certain single antiprogrammed death-1 agents or when used in combination with cytotoxic T-lymphocyte antigen-4 blockade. The responses seen are early, durable, and have continued after treatment discontinuation. Immune-related adverse events are the most common side effects seen in these clinical trials. Overall, the skin and gastrointestinal tract are the most common organ systems affected by these compounds while hepatic, endocrine, and neurologic events are less frequent. These side effects are low grade, manageable, and typically resolve within a relatively short time frame with a predictable resolution pattern given proper management. We therefore propose detailed guidelines for management of major immune-related adverse events that are anticipated with antiprogrammed death-1/programmed death-ligand 1 therapies based on general experience with other monoclonal antibodies and the established management algorithms for immune-related adverse events for cytotoxic T-lymphocyte antigen-4 blockade with ipilimumab. We anticipate that the antiprogrammed death-1 strategy will become a viable and crucial clinical strategy for cancer therapy. © The Author(s) 2014.

The Innate Immune Database (IIDB)

PubMed Central

Korb, Martin; Rust, Aistair G; Thorsson, Vesteinn; Battail, Christophe; Li, Bin; Hwang, Daehee; Kennedy, Kathleen A; Roach, Jared C; Rosenberger, Carrie M; Gilchrist, Mark; Zak, Daniel; Johnson, Carrie; Marzolf, Bruz; Aderem, Alan; Shmulevich, Ilya; Bolouri, Hamid

2008-01-01

Background As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site . Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens. Description We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser. Conclusion We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at . PMID:18321385

Nonlinear calibration for petroleum water content measurement using PSO

NASA Astrophysics Data System (ADS)

Li, Mingbao; Zhang, Jiawei

2008-10-01

A new algorithmic for strapdown inertial navigation system (SINS) state estimation based on neural networks is introduced. In training strategy, the error vector and its delay are introduced. This error vector is made of the position and velocity difference between the estimations of system and the outputs of GPS. After state prediction and state update, the states of the system are estimated. After off-line training, the network can approach the status switching of SINS and after on-line training, the state estimate precision can be improved further by reducing network output errors. Then the network convergence is discussed. In the end, several simulations with different noise are given. The results show that the neural network state estimator has lower noise sensitivity and better noise immunity than Kalman filter.

Resolving phase ambiguities in the calibration of redundant interferometric arrays: implications for array design

NASA Astrophysics Data System (ADS)

Kurien, Binoy G.; Tarokh, Vahid; Rachlin, Yaron; Shah, Vinay N.; Ashcom, Jonathan B.

2016-10-01

We provide new results enabling robust interferometric image reconstruction in the presence of unknown aperture piston variation via the technique of redundant spacing calibration (RSC). The RSC technique uses redundant measurements of the same interferometric baseline with different pairs of apertures to reveal the piston variation among these pairs. In both optical and radio interferometry, the presence of phase-wrapping ambiguities in the measurements is a fundamental issue that needs to be addressed for reliable image reconstruction. In this paper, we show that these ambiguities affect recently developed RSC phasor-based reconstruction approaches operating on the complex visibilities, as well as traditional phase-based approaches operating on their logarithm. We also derive new sufficient conditions for an interferometric array to be immune to these ambiguities in the sense that their effect can be rendered benign in image reconstruction. This property, which we call wrap-invariance, has implications for the reliability of imaging via classical three-baseline phase closures as well as generalized closures. We show that wrap-invariance is conferred upon arrays whose interferometric graph satisfies a certain cycle-free condition. For cases in which this condition is not satisfied, a simple algorithm is provided for identifying those graph cycles which prevent its satisfaction. We apply this algorithm to diagnose and correct a member of a pattern family popular in the literature.

[Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

PubMed

Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong

2013-03-01

Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

Defining epitope coverage requirements for T cell-based HIV vaccines: Theoretical considerations and practical applications

PubMed Central

2011-01-01

Background HIV vaccine development must address the genetic diversity and plasticity of the virus that permits the presentation of diverse genetic forms to the immune system and subsequent escape from immune pressure. Assessment of potential HIV strain coverage by candidate T cell-based vaccines (whether natural sequence or computationally optimized products) is now a critical component in interpreting candidate vaccine suitability. Methods We have utilized an N-mer identity algorithm to represent T cell epitopes and explore potential coverage of the global HIV pandemic using natural sequences derived from candidate HIV vaccines. Breadth (the number of T cell epitopes generated) and depth (the variant coverage within a T cell epitope) analyses have been incorporated into the model to explore vaccine coverage requirements in terms of the number of discrete T cell epitopes generated. Results We show that when multiple epitope generation by a vaccine product is considered a far more nuanced appraisal of the potential HIV strain coverage of the vaccine product emerges. By considering epitope breadth and depth several important observations were made: (1) epitope breadth requirements to reach particular levels of vaccine coverage, even for natural sequence-based vaccine products is not necessarily an intractable problem for the immune system; (2) increasing the valency (number of T cell epitope variants present) of vaccine products dramatically decreases the epitope requirements to reach particular coverage levels for any epidemic; (3) considering multiple-hit models (more than one exact epitope match with an incoming HIV strain) places a significantly higher requirement upon epitope breadth in order to reach a given level of coverage, to the point where low valency natural sequence based products would not practically be able to generate sufficient epitopes. Conclusions When HIV vaccine sequences are compared against datasets of potential incoming viruses important metrics such as the minimum epitope count required to reach a desired level of coverage can be easily calculated. We propose that such analyses can be applied early in the planning stages and during the execution phase of a vaccine trial to explore theoretical and empirical suitability of a vaccine product to a particular epidemic setting. PMID:22152192

Towards comprehensive structural motif mining for better fold annotation in the "twilight zone" of sequence dissimilarity

PubMed Central

Jia, Yi; Huan, Jun; Buhr, Vincent; Zhang, Jintao; Carayannopoulos, Leonidas N

2009-01-01

Background Automatic identification of structure fingerprints from a group of diverse protein structures is challenging, especially for proteins whose divergent amino acid sequences may fall into the "twilight-" or "midnight-" zones where pair-wise sequence identities to known sequences fall below 25% and sequence-based functional annotations often fail. Results Here we report a novel graph database mining method and demonstrate its application to protein structure pattern identification and structure classification. The biologic motivation of our study is to recognize common structure patterns in "immunoevasins", proteins mediating virus evasion of host immune defense. Our experimental study, using both viral and non-viral proteins, demonstrates the efficiency and efficacy of the proposed method. Conclusion We present a theoretic framework, offer a practical software implementation for incorporating prior domain knowledge, such as substitution matrices as studied here, and devise an efficient algorithm to identify approximate matched frequent subgraphs. By doing so, we significantly expanded the analytical power of sophisticated data mining algorithms in dealing with large volume of complicated and noisy protein structure data. And without loss of generality, choice of appropriate compatibility matrices allows our method to be easily employed in domains where subgraph labels have some uncertainty. PMID:19208148

Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern.

PubMed

Zhang, Tong-Liang; Ding, Yong-Sheng; Chou, Kuo-Chen

2008-01-07

Compared with the conventional amino acid (AA) composition, the pseudo-amino acid (PseAA) composition as originally introduced for protein subcellular location prediction can incorporate much more information of a protein sequence, so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, based on the concept of PseAA composition, the approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the PseAA components. Also, the immune genetic algorithm (IGA) is applied to search the optimal weight factors in generating the PseAA composition. Thus, for a given protein sequence sample, a 27-D (dimensional) PseAA composition is generated as its descriptor. The fuzzy K nearest neighbors (FKNN) classifier is adopted as the prediction engine. The results thus obtained in predicting protein structural classification are quite encouraging, indicating that the current approach may also be used to improve the prediction quality of other protein attributes, or at least can play a complimentary role to the existing methods in the relevant areas. Our algorithm is written in Matlab that is available by contacting the corresponding author.

Immunization Services for Adolescents within Comprehensive School Health Programs.

ERIC Educational Resources Information Center

Vernon, Mary E.; Bryan, Gloria; Hunt, Pete; Allensworth, Diane; Bradley, Beverly

1997-01-01

Discusses school health services, adolescent immunization, current school immunization practices, and support for school-based immunization programs. Children and adolescents can receive preventive health services, including immunizations and monitoring of immunization levels. Expanding school health services could improve the immunization levels…

Paper-based immune-affinity arrays for detection of multiple mycotoxins in cereals.

PubMed

Li, Li; Chen, Hongpu; Lv, Xiaolan; Wang, Min; Jiang, Xizhi; Jiang, Yifei; Wang, Heye; Zhao, Yongfu; Xia, Liru

2018-03-01

Mycotoxins produced by different species of fungi may coexist in cereals and feedstuffs, and could be highly toxic for humans and animals. For quantification of multiple mycotoxins in cereals, we developed a paper-based mycotoxin immune-affinity array. First, paper-based microzone arrays were fabricated by photolithography. Then, monoclonal mycotoxin antibodies were added in a copolymerization reaction with a cross-linker to form an immune-affinity monolith on the paper-based microzone array. With use of a competitive immune-response format, paper-based mycotoxin immune-affinity arrays were successfully applied to detect mycotoxins in samples. The detection limits for deoxynivalenol, zearalenone, T-2 toxin, and HT-2 toxin were 62.7, 10.8, 0.36, and 0.23 μg·kg -1 , respectively, which meet relevant requirements for these compounds in food. The recovery rates were 81-86% for deoxynivalenol, 89-117% for zearalenone, 79-86% for T-2 toxin, and 78-83% for HT-2 toxin, and showed the paper-based immune-affinity arrays had good reproducibility. In summary, the paper-based mycotoxin immune-affinity array provides a sensitive, rapid, accurate, stable, and convenient platform for detection of multiple mycotoxins in agro-foods. Graphical abstract Paper-based immune-affinity monolithic array. DON deoxynivalenol, HT-2 HT-2 toxin, T-2 T-2 toxin, PEGDA polyethylene glycol diacrylate, ZEN zearalenone.

New development of the image matching algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaoqiang; Feng, Zhao

2018-04-01

To study the image matching algorithm, algorithm four elements are described, i.e., similarity measurement, feature space, search space and search strategy. Four common indexes for evaluating the image matching algorithm are described, i.e., matching accuracy, matching efficiency, robustness and universality. Meanwhile, this paper describes the principle of image matching algorithm based on the gray value, image matching algorithm based on the feature, image matching algorithm based on the frequency domain analysis, image matching algorithm based on the neural network and image matching algorithm based on the semantic recognition, and analyzes their characteristics and latest research achievements. Finally, the development trend of image matching algorithm is discussed. This study is significant for the algorithm improvement, new algorithm design and algorithm selection in practice.

Color image encryption using random transforms, phase retrieval, chaotic maps, and diffusion

NASA Astrophysics Data System (ADS)

Annaby, M. H.; Rushdi, M. A.; Nehary, E. A.

2018-04-01

The recent tremendous proliferation of color imaging applications has been accompanied by growing research in data encryption to secure color images against adversary attacks. While recent color image encryption techniques perform reasonably well, they still exhibit vulnerabilities and deficiencies in terms of statistical security measures due to image data redundancy and inherent weaknesses. This paper proposes two encryption algorithms that largely treat these deficiencies and boost the security strength through novel integration of the random fractional Fourier transforms, phase retrieval algorithms, as well as chaotic scrambling and diffusion. We show through detailed experiments and statistical analysis that the proposed enhancements significantly improve security measures and immunity to attacks.

On Statistical Analysis of Neuroimages with Imperfect Registration

PubMed Central

Kim, Won Hwa; Ravi, Sathya N.; Johnson, Sterling C.; Okonkwo, Ozioma C.; Singh, Vikas

2016-01-01

A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases. To do so, an important first step is to register (or co-register) all of the image data into a common coordinate system. This permits meaningful comparison of the intensities at each voxel across groups (e.g., diseased versus healthy) to evaluate the effects of the disease and/or use machine learning algorithms in a subsequent step. But errors in the underlying registration make this problematic, they either decrease the statistical power or make the follow-up inference tasks less effective/accurate. In this paper, we derive a novel algorithm which offers immunity to local errors in the underlying deformation field obtained from registration procedures. By deriving a deformation invariant representation of the image, the downstream analysis can be made more robust as if one had access to a (hypothetical) far superior registration procedure. Our algorithm is based on recent work on scattering transform. Using this as a starting point, we show how results from harmonic analysis (especially, non-Euclidean wavelets) yields strategies for designing deformation and additive noise invariant representations of large 3-D brain image volumes. We present a set of results on synthetic and real brain images where we achieve robust statistical analysis even in the presence of substantial deformation errors; here, standard analysis procedures significantly under-perform and fail to identify the true signal. PMID:27042168

An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems.

PubMed

Timmis, J; Ismail, A R; Bjerknes, J D; Winfield, A F T

2016-08-01

Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Optical Detection of Degraded Therapeutic Proteins.

PubMed

Herrington, William F; Singh, Gajendra P; Wu, Di; Barone, Paul W; Hancock, William; Ram, Rajeev J

2018-03-23

The quality of therapeutic proteins such as hormones, subunit and conjugate vaccines, and antibodies is critical to the safety and efficacy of modern medicine. Identifying malformed proteins at the point-of-care can prevent adverse immune reactions in patients; this is of special concern when there is an insecure supply chain resulting in the delivery of degraded, or even counterfeit, drug product. Identification of degraded protein, for example human growth hormone, is demonstrated by applying automated anomaly detection algorithms. Detection of the degraded protein differs from previous applications of machine-learning and classification to spectral analysis: only example spectra of genuine, high-quality drug products are used to construct the classifier. The algorithm is tested on Raman spectra acquired on protein dilutions typical of formulated drug product and at sample volumes of 25 µL, below the typical overfill (waste) volumes present in vials of injectable drug product. The algorithm is demonstrated to correctly classify anomalous recombinant human growth hormone (rhGH) with 92% sensitivity and 98% specificity even when the algorithm has only previously encountered high-quality drug product.

A new scanning device in CT with dose reduction potential

NASA Astrophysics Data System (ADS)

Tischenko, Oleg; Xu, Yuan; Hoeschen, Christoph

2006-03-01

The amount of x-ray radiation currently applied in CT practice is not utilized optimally. A portion of radiation traversing the patient is either not detected at all or is used ineffectively. The reason lies partly in the reconstruction algorithms and partly in the geometry of the CT scanners designed specifically for these algorithms. In fact, the reconstruction methods widely used in CT are intended to invert the data that correspond to ideal straight lines. However, the collection of such data is often not accurate due to likely movement of the source/detector system of the scanner in the time interval during which all the detectors are read. In this paper, a new design of the scanner geometry is proposed that is immune to the movement of the CT system and will collect all radiation traversing the patient. The proposed scanning design has a potential to reduce the patient dose by a factor of two. Furthermore, it can be used with the existing reconstruction algorithm and it is particularly suitable for OPED, a new robust reconstruction algorithm.

Artificial Immune System for Flight Envelope Estimation and Protection

DTIC Science & Technology

2014-12-31

Throttle Failure 103 5.3. Estimation Algorithms for Sensor AC 108 5.3.1. Roll Rate Sensor Bias 108...4.13. Reference Features-Pattern for a Roll Rate Sensor Under Low Severity Failure 93 Figure 4.14. Reference Features-Pattern for a Roll Rate...Average PI for Different ACs 134 Figure 6.9. Roll Response Under High Magnitude Stabilator Failure 135 Figure 6.10. Pitch

Control algorithms and applications of the wavefront sensorless adaptive optics

NASA Astrophysics Data System (ADS)

Ma, Liang; Wang, Bin; Zhou, Yuanshen; Yang, Huizhen

2017-10-01

Compared with the conventional adaptive optics (AO) system, the wavefront sensorless (WFSless) AO system need not to measure the wavefront and reconstruct it. It is simpler than the conventional AO in system architecture and can be applied to the complex conditions. Based on the analysis of principle and system model of the WFSless AO system, wavefront correction methods of the WFSless AO system were divided into two categories: model-free-based and model-based control algorithms. The WFSless AO system based on model-free-based control algorithms commonly considers the performance metric as a function of the control parameters and then uses certain control algorithm to improve the performance metric. The model-based control algorithms include modal control algorithms, nonlinear control algorithms and control algorithms based on geometrical optics. Based on the brief description of above typical control algorithms, hybrid methods combining the model-free-based control algorithm with the model-based control algorithm were generalized. Additionally, characteristics of various control algorithms were compared and analyzed. We also discussed the extensive applications of WFSless AO system in free space optical communication (FSO), retinal imaging in the human eye, confocal microscope, coherent beam combination (CBC) techniques and extended objects.

Effects of psycho-behavioral interventions on immune functioning in cancer patients: a systematic review.

PubMed

Tong, Guixian; Geng, Qingqing; Cheng, Jing; Chai, Jing; Xia, Yi; Feng, Rui; Zhang, Lu; Wang, Debin

2014-01-01

This study aimed at summarizing evidence about effects of psycho-behavioral interventions (PBIs) on immune responses among cancer patients and analyzing quality of published studies so as to inform future researches. Literature retrieval utilized both highly inclusive algorithms searching randomized controlled studies published in English and Chinese and manual searching of eligible studies from references of relevant review papers. Two researchers examined the articles selected separately and extracted the information using a pre-designed form for soliciting data about the trials (e.g., sample size, disease status, intervention, immune responses) and quality ratings of the studies. Both narrative descriptions and meta-analysis (via Review manager 5) were used synthesizing the effects of PBIs on immune responses among cancer patients and state of art of the researches in this area. Seventy-six RCTs met inclusion criteria. PBIs implemented were divided into three major categories including psychological state adjustment, physical activity and dietary modification. Immune indicators measured included CD4+ cells, CD8+ cells, CD4/CDC8+ ratio, CD3+ cells, NK cell activity, etc. Effects of PBIs on immune responses documented in individual papers were mixed and pooled analysis of CD4+ cells, CD4+/CD8+ ratio, CD3+ cells, NKCA, IgG, IgM and IL-2 showed modest effects. However, there were huge discrepancies in intervention effects between studies published in English and Chinese and the results should be interpreted with caution. Besides, most studies suffer from some quality flaws concerning blinding, randomization procedures, compliance, attrition and intention-to-treat analyses, etc. Although there are considerable evidences of PBI effects on some immune indicators, the effect sizes are modest and it is still premature to conclude whether PBIs have effects on immune functions among cancer patients. There is a clear need for much more rigorous efforts in this area and future researches should pay particular attention to intervention dose and focus, sample size and comparable immune measures.

Modelling Risk to US Military Populations from Stopping Blanket Mandatory Polio Vaccination.

PubMed

Burgess, Colleen; Burgess, Andrew; McMullen, Kellie

2017-01-01

Transmission of polio poses a threat to military forces when deploying to regions where such viruses are endemic. US-born soldiers generally enter service with immunity resulting from childhood immunization against polio; moreover, new recruits are routinely vaccinated with inactivated poliovirus vaccine (IPV), supplemented based upon deployment circumstances. Given residual protection from childhood vaccination, risk-based vaccination may sufficiently protect troops from polio transmission. This analysis employed a mathematical system for polio transmission within military populations interacting with locals in a polio-endemic region to evaluate changes in vaccination policy. Removal of blanket immunization had no effect on simulated polio incidence among deployed military populations when risk-based immunization was employed; however, when these individuals reintegrated with their base populations, risk of transmission to nondeployed personnel increased by 19%. In the absence of both blanket- and risk-based immunization, transmission to nondeployed populations increased by 25%. The overall number of new infections among nondeployed populations was negligible for both scenarios due to high childhood immunization rates, partial protection against transmission conferred by IPV, and low global disease incidence levels. Risk-based immunization driven by deployment to polio-endemic regions is sufficient to prevent transmission among both deployed and nondeployed US military populations.

Two-way learning with one-way supervision for gene expression data.

PubMed

Wong, Monica H T; Mutch, David M; McNicholas, Paul D

2017-03-04

A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material. The simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively. Initial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.

An immunity-based anomaly detection system with sensor agents.

PubMed

Okamoto, Takeshi; Ishida, Yoshiteru

2009-01-01

This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user's command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

Stable engraftment of human microbiota into mice with a single oral gavage following antibiotic conditioning.

PubMed

Staley, Christopher; Kaiser, Thomas; Beura, Lalit K; Hamilton, Matthew J; Weingarden, Alexa R; Bobr, Aleh; Kang, Johnthomas; Masopust, David; Sadowsky, Michael J; Khoruts, Alexander

2017-08-01

Human microbiota-associated (HMA) animal models relying on germ-free recipient mice are being used to study the relationship between intestinal microbiota and human disease. However, transfer of microbiota into germ-free animals also triggers global developmental changes in the recipient intestine, which can mask disease-specific attributes of the donor material. Therefore, a simple model of replacing microbiota into a developmentally mature intestinal environment remains highly desirable. Here we report on the development of a sequential, three-course antibiotic conditioning regimen that allows sustained engraftment of intestinal microorganisms following a single oral gavage with human donor microbiota. SourceTracker, a Bayesian, OTU-based algorithm, indicated that 59.3 ± 3.0% of the fecal bacterial communities in treated mice were attributable to the donor source. This overall degree of microbiota engraftment was similar in mice conditioned with antibiotics and germ-free mice. Limited surveys of systemic and mucosal immune sites did not show evidence of immune activation following introduction of human microbiota. The antibiotic treatment protocol described here followed by a single gavage of human microbiota may provide a useful, complimentary HMA model to that established in germ-free facilities. The model has the potential for further in-depth translational investigations of microbiota in a variety of human disease states.

Designing a composite correlation filter based on iterative optimization of training images for distortion invariant face recognition

NASA Astrophysics Data System (ADS)

Wang, Q.; Elbouz, M.; Alfalou, A.; Brosseau, C.

2017-06-01

We present a novel method to optimize the discrimination ability and noise robustness of composite filters. This method is based on the iterative preprocessing of training images which can extract boundary and detailed feature information of authentic training faces, thereby improving the peak-to-correlation energy (PCE) ratio of authentic faces and to be immune to intra-class variance and noise interference. By adding the training images directly, one can obtain a composite template with high discrimination ability and robustness for face recognition task. The proposed composite correlation filter does not involve any complicated mathematical analysis and computation which are often required in the design of correlation algorithms. Simulation tests have been conducted to check the effectiveness and feasibility of our proposal. Moreover, to assess robustness of composite filters using receiver operating characteristic (ROC) curves, we devise a new method to count the true positive and false positive rates for which the difference between PCE and threshold is involved.

Quantitative analysis of multiple sclerosis: a feasibility study

NASA Astrophysics Data System (ADS)

Li, Lihong; Li, Xiang; Wei, Xinzhou; Sturm, Deborah; Lu, Hongbing; Liang, Zhengrong

2006-03-01

Multiple Sclerosis (MS) is an inflammatory and demyelinating disorder of the central nervous system with a presumed immune-mediated etiology. For treatment of MS, the measurements of white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) are often used in conjunction with clinical evaluation to provide a more objective measure of MS burden. In this paper, we apply a new unifying automatic mixture-based algorithm for segmentation of brain tissues to quantitatively analyze MS. The method takes into account the following effects that commonly appear in MR imaging: 1) The MR data is modeled as a stochastic process with an inherent inhomogeneity effect of smoothly varying intensity; 2) A new partial volume (PV) model is built in establishing the maximum a posterior (MAP) segmentation scheme; 3) Noise artifacts are minimized by a priori Markov random field (MRF) penalty indicating neighborhood correlation from tissue mixture. The volumes of brain tissues (WM, GM) and CSF are extracted from the mixture-based segmentation. Experimental results of feasibility studies on quantitative analysis of MS are presented.

Enhancing efficiency and quality of statistical estimation of immunogenicity assay cut points through standardization and automation.

PubMed

Su, Cheng; Zhou, Lei; Hu, Zheng; Weng, Winnie; Subramani, Jayanthi; Tadkod, Vineet; Hamilton, Kortney; Bautista, Ami; Wu, Yu; Chirmule, Narendra; Zhong, Zhandong Don

2015-10-01

Biotherapeutics can elicit immune responses, which can alter the exposure, safety, and efficacy of the therapeutics. A well-designed and robust bioanalytical method is critical for the detection and characterization of relevant anti-drug antibody (ADA) and the success of an immunogenicity study. As a fundamental criterion in immunogenicity testing, assay cut points need to be statistically established with a risk-based approach to reduce subjectivity. This manuscript describes the development of a validated, web-based, multi-tier customized assay statistical tool (CAST) for assessing cut points of ADA assays. The tool provides an intuitive web interface that allows users to import experimental data generated from a standardized experimental design, select the assay factors, run the standardized analysis algorithms, and generate tables, figures, and listings (TFL). It allows bioanalytical scientists to perform complex statistical analysis at a click of the button to produce reliable assay parameters in support of immunogenicity studies. Copyright © 2015 Elsevier B.V. All rights reserved.

Self-calibrating generalized phase-shifting interferometry of three phase-steps based on geometric concept of volume enclosed by a surface

NASA Astrophysics Data System (ADS)

Meneses-Fabian, Cruz

2016-12-01

This paper presents a non-iterative, fast, and simple algorithm for phase retrieval, in phase-shifting interferometry of three unknown and unequal phase-steps, based on the geometric concept of the volume enclosed by a surface. This approach can be divided in three stages; first the background is eliminated by the subtraction of two interferograms, for obtaining a secondary pattern; second, a surface is built by the product of two secondary patterns and the volume enclosed by this surface is computed; and third, the ratio between two enclosed volumes is approximated to a constant that depends on the phase-steps, with which a system of equations is established, and its solution allows the measurement of the phase-steps to be obtained. Additional advantages of this approach are its immunity to noise, and its capacity to support high spatial variations in the illumination. This approach is theoretically described and is numerically and experimentally verified.

Immunological correlates for protection against intranasal challenge of Bacillus anthracis spores conferred by a protective antigen-based vaccine in rabbits.

PubMed

Weiss, Shay; Kobiler, David; Levy, Haim; Marcus, Hadar; Pass, Avi; Rothschild, Nili; Altboum, Zeev

2006-01-01

Correlates between immunological parameters and protection against Bacillus anthracis infection in animals vaccinated with protective antigen (PA)-based vaccines could provide surrogate markers to evaluate the putative protective efficiency of immunization in humans. In previous studies we demonstrated that neutralizing antibody levels serve as correlates for protection in guinea pigs (S. Reuveny et al., Infect. Immun. 69:2888-2893, 2001; H. Marcus et al., Infect. Immun. 72:3471-3477, 2004). In this study we evaluated similar correlates for protection by active and passive immunization of New Zealand White rabbits. Full immunization and partial immunization were achieved by single and multiple injections of standard and diluted doses of a PA-based vaccine. Passive immunization was carried out by injection of immune sera from rabbits vaccinated with PA-based vaccine prior to challenge with B. anthracis spores. Immunized rabbits were challenged by intranasal spore instillation with one of two virulent strains (strains Vollum and ATCC 6605). The immune competence was estimated by measuring the level of total anti-PA antibodies, the neutralizing antibody titers, and the conferred protective immunity. The results indicate that total anti-PA antibody titers greater than 1 x 10(5) conferred protection, whereas lower titers (between 10(4) and 10(5)) provided partial protection but failed to predict protection. Neutralizing antibody titers between 500 and 800 provided partial protection, while titers higher than 1,000 conferred protection. In conclusion, this study emphasizes that regardless of the immunization regimen or the time of challenge, neutralizing antibody titers are better predictors of protection than total anti-PA titers.

Broad and Cross-Clade CD4+ T-Cell Responses Elicited by a DNA Vaccine Encoding Highly Conserved and Promiscuous HIV-1 M-Group Consensus Peptides

PubMed Central

Almeida, Rafael Ribeiro; Rosa, Daniela Santoro; Ribeiro, Susan Pereira; Santana, Vinicius Canato; Kallás, Esper Georges; Sidney, John; Sette, Alessandro; Kalil, Jorge; Cunha-Neto, Edecio

2012-01-01

T-cell based vaccine approaches have emerged to counteract HIV-1/AIDS. Broad, polyfunctional and cytotoxic CD4+ T-cell responses have been associated with control of HIV-1 replication, which supports the inclusion of CD4+ T-cell epitopes in vaccines. A successful HIV-1 vaccine should also be designed to overcome viral genetic diversity and be able to confer immunity in a high proportion of immunized individuals from a diverse HLA-bearing population. In this study, we rationally designed a multiepitopic DNA vaccine in order to elicit broad and cross-clade CD4+ T-cell responses against highly conserved and promiscuous peptides from the HIV-1 M-group consensus sequence. We identified 27 conserved, multiple HLA-DR-binding peptides in the HIV-1 M-group consensus sequences of Gag, Pol, Nef, Vif, Vpr, Rev and Vpu using the TEPITOPE algorithm. The peptides bound in vitro to an average of 12 out of the 17 tested HLA-DR molecules and also to several molecules such as HLA-DP, -DQ and murine IAb and IAd. Sixteen out of the 27 peptides were recognized by PBMC from patients infected with different HIV-1 variants and 72% of such patients recognized at least 1 peptide. Immunization with a DNA vaccine (HIVBr27) encoding the identified peptides elicited IFN-γ secretion against 11 out of the 27 peptides in BALB/c mice; CD4+ and CD8+ T-cell proliferation was observed against 8 and 6 peptides, respectively. HIVBr27 immunization elicited cross-clade T-cell responses against several HIV-1 peptide variants. Polyfunctional CD4+ and CD8+ T cells, able to simultaneously proliferate and produce IFN-γ and TNF-α, were also observed. This vaccine concept may cope with HIV-1 genetic diversity as well as provide increased population coverage, which are desirable features for an efficacious strategy against HIV-1/AIDS. PMID:23028895

A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines.

PubMed

Toussaint, Nora C; Dönnes, Pierre; Kohlbacher, Oliver

2008-12-01

Epitope-based vaccines (EVs) have a wide range of applications: from therapeutic to prophylactic approaches, from infectious diseases to cancer. The development of an EV is based on the knowledge of target-specific antigens from which immunogenic peptides, so-called epitopes, are derived. Such epitopes form the key components of the EV. Due to regulatory, economic, and practical concerns the number of epitopes that can be included in an EV is limited. Furthermore, as the major histocompatibility complex (MHC) binding these epitopes is highly polymorphic, every patient possesses a set of MHC class I and class II molecules of differing specificities. A peptide combination effective for one person can thus be completely ineffective for another. This renders the optimal selection of these epitopes an important and interesting optimization problem. In this work we present a mathematical framework based on integer linear programming (ILP) that allows the formulation of various flavors of the vaccine design problem and the efficient identification of optimal sets of epitopes. Out of a user-defined set of predicted or experimentally determined epitopes, the framework selects the set with the maximum likelihood of eliciting a broad and potent immune response. Our ILP approach allows an elegant and flexible formulation of numerous variants of the EV design problem. In order to demonstrate this, we show how common immunological requirements for a good EV (e.g., coverage of epitopes from each antigen, coverage of all MHC alleles in a set, or avoidance of epitopes with high mutation rates) can be translated into constraints or modifications of the objective function within the ILP framework. An implementation of the algorithm outperforms a simple greedy strategy as well as a previously suggested evolutionary algorithm and has runtimes on the order of seconds for typical problem sizes.

Numerical phase retrieval from beam intensity measurements in three planes

NASA Astrophysics Data System (ADS)

Bruel, Laurent

2003-05-01

A system and method have been developed at CEA to retrieve phase information from multiple intensity measurements along a laser beam. The device has been patented. Commonly used devices for beam measurement provide phase and intensity information separately or with a rather poor resolution whereas the MIROMA method provides both at the same time, allowing direct use of the results in numerical models. Usual phase retrieval algorithms use two intensity measurements, typically the image plane and the focal plane (Gerschberg-Saxton algorithm) related by a Fourier transform, or the image plane and a lightly defocus plane (D.L. Misell). The principal drawback of such iterative algorithms is their inability to provide unambiguous convergence in all situations. The algorithms can stagnate on bad solutions and the error between measured and calculated intensities remains unacceptable. If three planes rather than two are used, the data redundancy created confers to the method good convergence capability and noise immunity. It provides an excellent agreement between intensity determined from the retrieved phase data set in the image plane and intensity measurements in any diffraction plane. The method employed for MIROMA is inspired from GS algorithm, replacing Fourier transforms by a beam-propagating kernel with gradient search accelerating techniques and special care for phase branch cuts. A fast one dimensional algorithm provides an initial guess for the iterative algorithm. Applications of the algorithm on synthetic data find out the best reconstruction planes that have to be chosen. Robustness and sensibility are evaluated. Results on collimated and distorted laser beams are presented.

Agouron and immune response to commercialize remune immune-based treatment.

PubMed

James, J S

1998-06-19

Agouron Pharmaceuticals agreed in June to collaborate with The Immune Response Corporation on the final development and marketing of an immune-based treatment for HIV. Remune, the vaccine developed by Dr. Jonas Salk, is currently in Phase III randomized trials with 2,500 patients, and the trials are expected to be completed in April 1999. Immune-based treatments have been difficult to test, as there is no surrogate marker, like viral load, to determine if the drug is working. Agouron agreed to participate in the joint venture after reviewing encouraging results from preliminary trials in which remune was taken in combination with highly active antiretroviral drugs.

Effects of pre-existing orthopoxvirus-specific immunity on the performance of Modified Vaccinia virus Ankara-based influenza vaccines.

PubMed

Altenburg, Arwen F; van Trierum, Stella E; de Bruin, Erwin; de Meulder, Dennis; van de Sandt, Carolien E; van der Klis, Fiona R M; Fouchier, Ron A M; Koopmans, Marion P G; Rimmelzwaan, Guus F; de Vries, Rory D

2018-04-24

The replication-deficient orthopoxvirus modified vaccinia virus Ankara (MVA) is a promising vaccine vector against various pathogens and has an excellent safety record. However, pre-existing vector-specific immunity is frequently suggested to be a drawback of MVA-based vaccines. To address this issue, mice were vaccinated with MVA-based influenza vaccines in the presence or absence of orthopoxvirus-specific immunity. Importantly, protective efficacy of an MVA-based influenza vaccine against a homologous challenge was not impaired in the presence of orthopoxvirus-specific pre-existing immunity. Nonetheless, orthopoxvirus-specific pre-existing immunity reduced the induction of antigen-specific antibodies under specific conditions and completely prevented induction of antigen-specific T cell responses by rMVA-based vaccination. Notably, antibodies induced by vaccinia virus vaccination, both in mice and humans, were not capable of neutralizing MVA. Thus, when using rMVA-based vaccines it is important to consider the main correlate of protection induced by the vaccine, the vaccine dose and the orthopoxvirus immune status of vaccine recipients.

Three list scheduling temporal partitioning algorithm of time space characteristic analysis and compare for dynamic reconfigurable computing

NASA Astrophysics Data System (ADS)

Chen, Naijin

2013-03-01

Level Based Partitioning (LBP) algorithm, Cluster Based Partitioning (CBP) algorithm and Enhance Static List (ESL) temporal partitioning algorithm based on adjacent matrix and adjacent table are designed and implemented in this paper. Also partitioning time and memory occupation based on three algorithms are compared. Experiment results show LBP partitioning algorithm possesses the least partitioning time and better parallel character, as far as memory occupation and partitioning time are concerned, algorithms based on adjacent table have less partitioning time and less space memory occupation.

Mass spectrometry-based proteomic exploration of the human immune system: focus on the inflammasome, global protein secretion, and T cells.

PubMed

Nyman, Tuula A; Lorey, Martina B; Cypryk, Wojciech; Matikainen, Sampsa

2017-05-01

The immune system is our defense system against microbial infections and tissue injury, and understanding how it works in detail is essential for developing drugs for different diseases. Mass spectrometry-based proteomics can provide in-depth information on the molecular mechanisms involved in immune responses. Areas covered: Summarized are the key immunology findings obtained with MS-based proteomics in the past five years, with a focus on inflammasome activation, global protein secretion, mucosal immunology, immunopeptidome and T cells. Special focus is on extracellular vesicle-mediated protein secretion and its role in immune responses. Expert commentary: Proteomics is an essential part of modern omics-scale immunology research. To date, MS-based proteomics has been used in immunology to study protein expression levels, their subcellular localization, secretion, post-translational modifications, and interactions in immune cells upon activation by different stimuli. These studies have made major contributions to understanding the molecular mechanisms involved in innate and adaptive immune responses. New developments in proteomics offer constantly novel possibilities for exploring the immune system. Examples of these techniques include mass cytometry and different MS-based imaging approaches which can be widely used in immunology.

Utilizing cell-based therapeutics to overcome immune evasion in hematologic malignancies.

PubMed

Sun, Chuang; Dotti, Gianpietro; Savoldo, Barbara

2016-06-30

Hematologic malignancies provide a suitable testing environment for cell-based immunotherapies, which were pioneered by the development of allogeneic hematopoietic stem cell transplant. All types of cell-based therapies, from donor lymphocyte infusion to dendritic cell vaccines, and adoptive transfer of tumor-specific cytotoxic T cells and natural killer cells, have been clinically translated for hematologic malignancies. The recent success of chimeric antigen receptor-modified T lymphocytes in B-cell malignancies has stimulated the development of this approach toward other hematologic tumors. Similarly, the remarkable activity of checkpoint inhibitors as single agents has created enthusiasm for potential combinations with other cell-based immune therapies. However, tumor cells continuously develop various strategies to evade their immune-mediated elimination. Meanwhile, the recruitment of immunosuppressive cells and the release of inhibitory factors contribute to the development of a tumor microenvironment that hampers the initiation of effective immune responses or blocks the functions of immune effector cells. Understanding how tumor cells escape from immune attack and favor immunosuppression is essential for the improvement of immune cell-based therapies and the development of rational combination approaches. © 2016 by The American Society of Hematology.

Study of efficient video compression algorithms for space shuttle applications

NASA Technical Reports Server (NTRS)

Poo, Z.

1975-01-01

Results are presented of a study on video data compression techniques applicable to space flight communication. This study is directed towards monochrome (black and white) picture communication with special emphasis on feasibility of hardware implementation. The primary factors for such a communication system in space flight application are: picture quality, system reliability, power comsumption, and hardware weight. In terms of hardware implementation, these are directly related to hardware complexity, effectiveness of the hardware algorithm, immunity of the source code to channel noise, and data transmission rate (or transmission bandwidth). A system is recommended, and its hardware requirement summarized. Simulations of the study were performed on the improved LIM video controller which is computer-controlled by the META-4 CPU.

Modelling Risk to US Military Populations from Stopping Blanket Mandatory Polio Vaccination

PubMed Central

Burgess, Andrew

2017-01-01

Objectives Transmission of polio poses a threat to military forces when deploying to regions where such viruses are endemic. US-born soldiers generally enter service with immunity resulting from childhood immunization against polio; moreover, new recruits are routinely vaccinated with inactivated poliovirus vaccine (IPV), supplemented based upon deployment circumstances. Given residual protection from childhood vaccination, risk-based vaccination may sufficiently protect troops from polio transmission. Methods This analysis employed a mathematical system for polio transmission within military populations interacting with locals in a polio-endemic region to evaluate changes in vaccination policy. Results Removal of blanket immunization had no effect on simulated polio incidence among deployed military populations when risk-based immunization was employed; however, when these individuals reintegrated with their base populations, risk of transmission to nondeployed personnel increased by 19%. In the absence of both blanket- and risk-based immunization, transmission to nondeployed populations increased by 25%. The overall number of new infections among nondeployed populations was negligible for both scenarios due to high childhood immunization rates, partial protection against transmission conferred by IPV, and low global disease incidence levels. Conclusion Risk-based immunization driven by deployment to polio-endemic regions is sufficient to prevent transmission among both deployed and nondeployed US military populations. PMID:29104608

Flight and Operational Medicine Clinic (FOMC) Workflow Analysis

DTIC Science & Technology

2014-03-14

Flight Medicine, Optometry, and Dental ) Base 4 MSME schedules all appointments required in the IFC (i.e., Flight Medicine, Optometry, and Dental ...IT Note: Base 1 Examinee completes Optometry, Dental , and Immunizations on the day of the Flight Medicine appointment Base 2 Examinee...completes Optometry and Immunizations prior to being seen in Flight Medicine Base 4 Examinee completes Optometry, Dental , and Immunizations on the day of

Evaluation of Demons- and FEM-Based Registration Algorithms for Lung Cancer.

PubMed

Yang, Juan; Li, Dengwang; Yin, Yong; Zhao, Fen; Wang, Hongjun

2016-04-01

We evaluated and compared the accuracy of 2 deformable image registration algorithms in 4-dimensional computed tomography images for patients with lung cancer. Ten patients with non-small cell lung cancer or small cell lung cancer were enrolled in this institutional review board-approved study. The displacement vector fields relative to a specific reference image were calculated by using the diffeomorphic demons (DD) algorithm and the finite element method (FEM)-based algorithm. The registration accuracy was evaluated by using normalized mutual information (NMI), the sum of squared intensity difference (SSD), modified Hausdorff distance (dH_M), and ratio of gross tumor volume (rGTV) difference between reference image and deformed phase image. We also compared the registration speed of the 2 algorithms. Of all patients, the FEM-based algorithm showed stronger ability in aligning 2 images than the DD algorithm. The means (±standard deviation) of NMI were 0.86 (±0.05) and 0.90 (±0.05) using the DD algorithm and the FEM-based algorithm, respectively. The means of SSD were 0.006 (±0.003) and 0.003 (±0.002) using the DD algorithm and the FEM-based algorithm, respectively. The means of dH_M were 0.04 (±0.02) and 0.03 (±0.03) using the DD algorithm and the FEM-based algorithm, respectively. The means of rGTV were 3.9% (±1.01%) and 2.9% (±1.1%) using the DD algorithm and the FEM-based algorithm, respectively. However, the FEM-based algorithm costs a longer time than the DD algorithm, with the average running time of 31.4 minutes compared to 21.9 minutes for all patients. The preliminary results showed that the FEM-based algorithm was more accurate than the DD algorithm while compromised with the registration speed. © The Author(s) 2015.

Induction of innate immune signatures following polyepitope protein-glycoprotein B-TLR4&9 agonist immunization generates multifunctional CMV-specific cellular and humoral immunity

PubMed Central

Dasari, Vijayendra; Smith, Corey; Schuessler, Andrea; Zhong, Jie; Khanna, Rajiv

2014-01-01

Recent studies have suggested that a successful subunit human cytomegalovirus (CMV) vaccine requires improved formulation to generate broad-based anti-viral immunity following immunization. Here we report the development of a non-live protein-based vaccine strategy for CMV based on a polyepitope protein and CMV glycoprotein B (gB) adjuvanted with TLR4 and/or TLR9 agonists. The polyepitope protein includes contiguous multiple MHC class I-restricted epitopes with an aim to induce CD8+ T cell immunity, while gB is an important target for CD4+ T cell immunity and neutralizing antibodies. Optimal immunogenicity of this bivalent non-live protein vaccine formulation was dependent upon the co-administration of both the TLR4 and TLR9 agonist, which was associated with the activation of innate immune signatures and the influx of different DC subsets including plasmacytoid DCs and migratory CD8-DEC205+CD103-CD326- langerin-negative dermal DCs into the draining lymph nodes. Furthermore these professional antigen presenting cells also expressed IL-6, IL-12p70, TNFα, and IFNα which play a crucial role in the activation of adaptive immunity. In summary, this study provides a novel platform technology in which broad-based anti-CMV immune responses upon vaccination can be maximized by co-delivery of viral antigens and TLR4 and 9 agonists which induce activation of innate immune signatures and promote potent antigen acquisition and cross-presentation by multiple DC subsets. PMID:24463331

Immunization of Epidemics in Multiplex Networks

PubMed Central

Zhao, Dawei; Wang, Lianhai; Li, Shudong; Wang, Zhen; Wang, Lin; Gao, Bo

2014-01-01

Up to now, immunization of disease propagation has attracted great attention in both theoretical and experimental researches. However, vast majority of existing achievements are limited to the simple assumption of single layer networked population, which seems obviously inconsistent with recent development of complex network theory: each node could possess multiple roles in different topology connections. Inspired by this fact, we here propose the immunization strategies on multiplex networks, including multiplex node-based random (targeted) immunization and layer node-based random (targeted) immunization. With the theory of generating function, theoretical analysis is developed to calculate the immunization threshold, which is regarded as the most critical index for the effectiveness of addressed immunization strategies. Interestingly, both types of random immunization strategies show more efficiency in controlling disease spreading on multiplex Erdös-Rényi (ER) random networks; while targeted immunization strategies provide better protection on multiplex scale-free (SF) networks. PMID:25401755

Immunization of epidemics in multiplex networks.

PubMed

Zhao, Dawei; Wang, Lianhai; Li, Shudong; Wang, Zhen; Wang, Lin; Gao, Bo

2014-01-01

Up to now, immunization of disease propagation has attracted great attention in both theoretical and experimental researches. However, vast majority of existing achievements are limited to the simple assumption of single layer networked population, which seems obviously inconsistent with recent development of complex network theory: each node could possess multiple roles in different topology connections. Inspired by this fact, we here propose the immunization strategies on multiplex networks, including multiplex node-based random (targeted) immunization and layer node-based random (targeted) immunization. With the theory of generating function, theoretical analysis is developed to calculate the immunization threshold, which is regarded as the most critical index for the effectiveness of addressed immunization strategies. Interestingly, both types of random immunization strategies show more efficiency in controlling disease spreading on multiplex Erdös-Rényi (ER) random networks; while targeted immunization strategies provide better protection on multiplex scale-free (SF) networks.

Immunosuppressive Effects of Natural α,β-Unsaturated Carbonyl-Based Compounds, and Their Analogs and Derivatives, on Immune Cells: A Review.

PubMed

Arshad, Laiba; Jantan, Ibrahim; Bukhari, Syed Nasir Abbas; Haque, Md Areeful

2017-01-01

The immune system is complex and pervasive as it functions to prevent or limit infections in the human body. In a healthy organism, the immune system and the redox balance of immune cells maintain homeostasis within the body. The failure to maintain the balance may lead to impaired immune response and either over activity or abnormally low activity of the immune cells resulting in autoimmune or immune deficiency diseases. Compounds containing α,β-unsaturated carbonyl-based moieties are often reactive. The reactivity of these groups is responsible for their diverse pharmacological activities, and the most important and widely studied include the natural compounds curcumin, chalcone, and zerumbone. Numerous studies have revealed the mainly immunosuppressive and anti-inflammatory activities of the aforesaid compounds. This review highlights the specific immunosuppressive effects of these natural α,β-unsaturated carbonyl-based compounds, and their analogs and derivatives on different types of immune cells of the innate (granulocytes, monocytes, macrophages, and dendritic cells) and adaptive (T cells, B cells, and natural killer cells) immune systems. The inhibitory effects of these compounds have been comprehensively studied on neutrophils, monocytes and macrophages but their effects on T cells, B cells, natural killer cells, and dendritic cells have not been well investigated. It is of paramount importance to continue generating experimental data on the mechanisms of action of α,β-unsaturated carbonyl-based compounds on immune cells to provide useful information for ensuing research to discover new immunomodulating agents.

Immunosuppressive Effects of Natural α,β-Unsaturated Carbonyl-Based Compounds, and Their Analogs and Derivatives, on Immune Cells: A Review

PubMed Central

Arshad, Laiba; Jantan, Ibrahim; Bukhari, Syed Nasir Abbas; Haque, Md. Areeful

2017-01-01

The immune system is complex and pervasive as it functions to prevent or limit infections in the human body. In a healthy organism, the immune system and the redox balance of immune cells maintain homeostasis within the body. The failure to maintain the balance may lead to impaired immune response and either over activity or abnormally low activity of the immune cells resulting in autoimmune or immune deficiency diseases. Compounds containing α,β-unsaturated carbonyl-based moieties are often reactive. The reactivity of these groups is responsible for their diverse pharmacological activities, and the most important and widely studied include the natural compounds curcumin, chalcone, and zerumbone. Numerous studies have revealed the mainly immunosuppressive and anti-inflammatory activities of the aforesaid compounds. This review highlights the specific immunosuppressive effects of these natural α,β-unsaturated carbonyl-based compounds, and their analogs and derivatives on different types of immune cells of the innate (granulocytes, monocytes, macrophages, and dendritic cells) and adaptive (T cells, B cells, and natural killer cells) immune systems. The inhibitory effects of these compounds have been comprehensively studied on neutrophils, monocytes and macrophages but their effects on T cells, B cells, natural killer cells, and dendritic cells have not been well investigated. It is of paramount importance to continue generating experimental data on the mechanisms of action of α,β-unsaturated carbonyl-based compounds on immune cells to provide useful information for ensuing research to discover new immunomodulating agents. PMID:28194110

Instant Childhood Immunization Schedule

MedlinePlus

... Recommendations Why Immunize? Vaccines: The Basics Instant Childhood Immunization Schedule Recommend on Facebook Tweet Share Compartir Get ... date. See Disclaimer for additional details. Based on Immunization Schedule for Children 0 through 6 Years of ...

Models for evaluation of relative immunogenic potential of protein particles in biopharmaceutical protein formulations.

PubMed

Johnson, Richard; Jiskoot, Wim

2012-10-01

An immune response to a therapeutic protein that compromises the biopharmaceutical activity or cross-reacts with an endogenous protein is a serious clinical event. The role of protein aggregates and particles in biopharmaceutical formulations in mediating this immune response has gained considerable attention over the recent past. Model systems that could consistently and reliably predict the relative immunogenicity of biopharmaceutical protein formulations would be extremely valuable. Several approaches have been developed in an attempt to provide this insight, including in silico algorithms, in vitro tests utilizing human leukocytes and in vivo animal models. This commentary provides an update of these various approaches as well as the author's perspectives on the pros and cons of these different methods. Copyright © 2012 Wiley Periodicals, Inc.

Diagnosis, Treatment, and Long-Term Management of Kawasaki Disease: A Scientific Statement for Health Professionals From the American Heart Association.

PubMed

McCrindle, Brian W; Rowley, Anne H; Newburger, Jane W; Burns, Jane C; Bolger, Anne F; Gewitz, Michael; Baker, Annette L; Jackson, Mary Anne; Takahashi, Masato; Shah, Pinak B; Kobayashi, Tohru; Wu, Mei-Hwan; Saji, Tsutomu T; Pahl, Elfriede

2017-04-25

Kawasaki disease is an acute vasculitis of childhood that leads to coronary artery aneurysms in ≈25% of untreated cases. It has been reported worldwide and is the leading cause of acquired heart disease in children in developed countries. To revise the previous American Heart Association guidelines, a multidisciplinary writing group of experts was convened to review and appraise available evidence and practice-based opinion, as well as to provide updated recommendations for diagnosis, treatment of the acute illness, and long-term management. Although the cause remains unknown, discussion sections highlight new insights into the epidemiology, genetics, pathogenesis, pathology, natural history, and long-term outcomes. Prompt diagnosis is essential, and an updated algorithm defines supplemental information to be used to assist the diagnosis when classic clinical criteria are incomplete. Although intravenous immune globulin is the mainstay of initial treatment, the role for additional primary therapy in selected patients is discussed. Approximately 10% to 20% of patients do not respond to initial intravenous immune globulin, and recommendations for additional therapies are provided. Careful initial management of evolving coronary artery abnormalities is essential, necessitating an increased frequency of assessments and escalation of thromboprophylaxis. Risk stratification for long-term management is based primarily on maximal coronary artery luminal dimensions, normalized as Z scores, and is calibrated to both past and current involvement. Patients with aneurysms require life-long and uninterrupted cardiology follow-up. These recommendations provide updated and best evidence-based guidance to healthcare providers who diagnose and manage Kawasaki disease, but clinical decision making should be individualized to specific patient circumstances. © 2017 American Heart Association, Inc.

Mathematical fundamentals for the noise immunity of the genetic code.

PubMed

Fimmel, Elena; Strüngmann, Lutz

2018-02-01

Symmetry is one of the essential and most visible patterns that can be seen in nature. Starting from the left-right symmetry of the human body, all types of symmetry can be found in crystals, plants, animals and nature as a whole. Similarly, principals of symmetry are also some of the fundamental and most useful tools in modern mathematical natural science that play a major role in theory and applications. As a consequence, it is not surprising that the desire to understand the origin of life, based on the genetic code, forces us to involve symmetry as a mathematical concept. The genetic code can be seen as a key to biological self-organisation. All living organisms have the same molecular bases - an alphabet consisting of four letters (nitrogenous bases): adenine, cytosine, guanine, and thymine. Linearly ordered sequences of these bases contain the genetic information for synthesis of proteins in all forms of life. Thus, one of the most fascinating riddles of nature is to explain why the genetic code is as it is. Genetic coding possesses noise immunity which is the fundamental feature that allows to pass on the genetic information from parents to their descendants. Hence, since the time of the discovery of the genetic code, scientists have tried to explain the noise immunity of the genetic information. In this chapter we will discuss recent results in mathematical modelling of the genetic code with respect to noise immunity, in particular error-detection and error-correction. We will focus on two central properties: Degeneracy and frameshift correction. Different amino acids are encoded by different quantities of codons and a connection between this degeneracy and the noise immunity of genetic information is a long standing hypothesis. Biological implications of the degeneracy have been intensively studied and whether the natural code is a frozen accident or a highly optimised product of evolution is still controversially discussed. Symmetries in the structure of degeneracy of the genetic code are essential and give evidence of substantial advantages of the natural code over other possible ones. In the present chapter we will present a recent approach to explain the degeneracy of the genetic code by algorithmic methods from bioinformatics, and discuss its biological consequences. The biologists recognised this problem immediately after the detection of the non-overlapping structure of the genetic code, i.e., coding sequences are to be read in a unique way determined by their reading frame. But how does the reading head of the ribosome recognises an error in the grouping of codons, caused by e.g. insertion or deletion of a base, that can be fatal during the translation process and may result in nonfunctional proteins? In this chapter we will discuss possible solutions to the frameshift problem with a focus on the theory of so-called circular codes that were discovered in large gene populations of prokaryotes and eukaryotes in the early 90s. Circular codes allow to detect a frameshift of one or two positions and recently a beautiful theory of such codes has been developed using statistics, group theory and graph theory. Copyright © 2017 Elsevier B.V. All rights reserved.

SPHINX--an algorithm for taxonomic binning of metagenomic sequences.

PubMed

Mohammed, Monzoorul Haque; Ghosh, Tarini Shankar; Singh, Nitin Kumar; Mande, Sharmila S

2011-01-01

Compared with composition-based binning algorithms, the binning accuracy and specificity of alignment-based binning algorithms is significantly higher. However, being alignment-based, the latter class of algorithms require enormous amount of time and computing resources for binning huge metagenomic datasets. The motivation was to develop a binning approach that can analyze metagenomic datasets as rapidly as composition-based approaches, but nevertheless has the accuracy and specificity of alignment-based algorithms. This article describes a hybrid binning approach (SPHINX) that achieves high binning efficiency by utilizing the principles of both 'composition'- and 'alignment'-based binning algorithms. Validation results with simulated sequence datasets indicate that SPHINX is able to analyze metagenomic sequences as rapidly as composition-based algorithms. Furthermore, the binning efficiency (in terms of accuracy and specificity of assignments) of SPHINX is observed to be comparable with results obtained using alignment-based algorithms. A web server for the SPHINX algorithm is available at http://metagenomics.atc.tcs.com/SPHINX/.

A Service Oriented Architecture Approach to Achieve Interoperability between Immunization Information Systems in Iran

PubMed Central

Hosseini, Masoud; Ahmadi, Maryam; Dixon, Brian E.

2014-01-01

Clinical decision support (CDS) systems can support vaccine forecasting and immunization reminders; however, immunization decision-making requires data from fragmented, independent systems. Interoperability and accurate data exchange between immunization information systems (IIS) is an essential factor to utilize Immunization CDS systems. Service oriented architecture (SOA) and Health Level 7 (HL7) are dominant standards for web-based exchange of clinical information. We implemented a system based on SOA and HL7 v3 to support immunization CDS in Iran. We evaluated system performance by exchanging 1500 immunization records for roughly 400 infants between two IISs. System turnaround time is less than a minute for synchronous operation calls and the retrieved immunization history of infants were always identical in different systems. CDS generated reports were accordant to immunization guidelines and the calculations for next visit times were accurate. Interoperability is rare or nonexistent between IIS. Since inter-state data exchange is rare in United States, this approach could be a good prototype to achieve interoperability of immunization information. PMID:25954452

A Service Oriented Architecture Approach to Achieve Interoperability between Immunization Information Systems in Iran.

PubMed

Hosseini, Masoud; Ahmadi, Maryam; Dixon, Brian E

2014-01-01

Clinical decision support (CDS) systems can support vaccine forecasting and immunization reminders; however, immunization decision-making requires data from fragmented, independent systems. Interoperability and accurate data exchange between immunization information systems (IIS) is an essential factor to utilize Immunization CDS systems. Service oriented architecture (SOA) and Health Level 7 (HL7) are dominant standards for web-based exchange of clinical information. We implemented a system based on SOA and HL7 v3 to support immunization CDS in Iran. We evaluated system performance by exchanging 1500 immunization records for roughly 400 infants between two IISs. System turnaround time is less than a minute for synchronous operation calls and the retrieved immunization history of infants were always identical in different systems. CDS generated reports were accordant to immunization guidelines and the calculations for next visit times were accurate. Interoperability is rare or nonexistent between IIS. Since inter-state data exchange is rare in United States, this approach could be a good prototype to achieve interoperability of immunization information.

Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography.

PubMed

Picek, Stjepan; Carlet, Claude; Guilley, Sylvain; Miller, Julian F; Jakobovic, Domagoj

2016-01-01

The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.

Investment in home-based maternal, newborn and child health records improves immunization coverage in Indonesia.

PubMed

Osaki, K; Hattori, T; Kosen, Soewarta; Singgih, Budihardja

2009-08-01

Indonesia Demographic and Health Surveys show that the ownership of home-based immunization records among children aged 12-23 months increased from 30.8% in 1997 and 30.7% in 2002-3 to 37% in 2007. In 2002-3, 70.9% of children who owned records had received all vaccines by the time of the survey, whereas 42.9% of children who did not own records had been fully immunized. An Indonesian ministerial decree of 2004 stated that the Maternal and Child Health Handbook (MCH handbook) was to be the only home-based record of maternal, newborn and child health. The increased immunization coverage seen would be a reflection of MCH handbook implementation, through raising awareness of immunization among community and health personnel and children's parents or guardians and allowing more accurate measurement of immunization coverage.

Induction of complex immune responses and strong protection against retrovirus challenge by adenovirus-based immunization depends on the order of vaccine delivery.

PubMed

Kaulfuß, Meike; Wensing, Ina; Windmann, Sonja; Hrycak, Camilla Patrizia; Bayer, Wibke

2017-02-06

In the Friend retrovirus mouse model we developed potent adenovirus-based vaccines that were designed to induce either strong Friend virus GagL 85-93 -specific CD8 + T cell or antibody responses, respectively. To optimize the immunization outcome we evaluated vaccination strategies using combinations of these vaccines. While the vaccines on their own confer strong protection from a subsequent Friend virus challenge, the simple combination of the vaccines for the establishment of an optimized immunization protocol did not result in a further improvement of vaccine effectivity. We demonstrate that the co-immunization with GagL 85-93 /leader-gag encoding vectors together with envelope-encoding vectors abrogates the induction of GagL 85-93 -specific CD8 + T cells, and in successive immunization protocols the immunization with the GagL 85-93 /leader-gag encoding vector had to precede the immunization with an envelope encoding vector for the efficient induction of GagL 85-93 -specific CD8 + T cells. Importantly, the antibody response to envelope was in fact enhanced when the mice were adenovirus-experienced from a prior immunization, highlighting the expedience of this approach. To circumvent the immunosuppressive effect of envelope on immune responses to simultaneously or subsequently administered immunogens, we developed a two immunizations-based vaccination protocol that induces strong immune responses and confers robust protection of highly Friend virus-susceptible mice from a lethal Friend virus challenge.

CRISPR Detection From Short Reads Using Partial Overlap Graphs.

PubMed

Ben-Bassat, Ilan; Chor, Benny

2016-06-01

Clustered regularly interspaced short palindromic repeats (CRISPR) are structured regions in bacterial and archaeal genomes, which are part of an adaptive immune system against phages. CRISPRs are important for many microbial studies and are playing an essential role in current gene editing techniques. As such, they attract substantial research interest. The exponential growth in the amount of bacterial sequence data in recent years enables the exploration of CRISPR loci in more and more species. Most of the automated tools that detect CRISPR loci rely on fully assembled genomes. However, many assemblers do not handle repetitive regions successfully. The first tool to work directly on raw sequence data is Crass, which requires reads that are long enough to contain two copies of the same repeat. We present a method to identify CRISPR repeats from raw sequence data of short reads. The algorithm is based on an observation differentiating CRISPR repeats from other types of repeats, and it involves a series of partial constructions of the overlap graph. This enables us to avoid many of the difficulties that assemblers face, as we merely aim to identify the repeats that belong to CRISPR loci. A preliminary implementation of the algorithm shows good results and detects CRISPR repeats in cases where other existing tools fail to do so.

Web-based e-learning and virtual lab of human-artificial immune system.

PubMed

Gong, Tao; Ding, Yongsheng; Xiong, Qin

2014-05-01

Human immune system is as important in keeping the body healthy as the brain in supporting the intelligence. However, the traditional models of the human immune system are built on the mathematics equations, which are not easy for students to understand. To help the students to understand the immune systems, a web-based e-learning approach with virtual lab is designed for the intelligent system control course by using new intelligent educational technology. Comparing the traditional graduate educational model within the classroom, the web-based e-learning with the virtual lab shows the higher inspiration in guiding the graduate students to think independently and innovatively, as the students said. It has been found that this web-based immune e-learning system with the online virtual lab is useful for teaching the graduate students to understand the immune systems in an easier way and design their simulations more creatively and cooperatively. The teaching practice shows that the optimum web-based e-learning system can be used to increase the learning effectiveness of the students.

Expression mapping using a retroviral vector for CD8+ T cell epitopes: definition of a Mycobacterium tuberculosis peptide presented by H2-Dd.

PubMed

Aoshi, Taiki; Suzuki, Mina; Uchijima, Masato; Nagata, Toshi; Koide, Yukio

2005-03-01

Identification of CD8+ T cell epitopes is important because detection of specific CD8+ T cells after infection or immunization requires prior knowledge of epitope specificity. Furthermore, identification of CD8+ T cell epitopes permits the development of specific preventive and therapeutic approaches to both infections and tumors. Thus far, CD8+ T cell epitopes have been identified either using an overlapping peptide library covering an entire protein, or using algorithms designed to identify likely peptides that bind to major histocompatibility complex (MHC) class I molecules. The synthesis of overlapping peptides can be prohibitively expensive, and the algorithm programs used to predict CD8+ T cell epitopes are not always accurate. Here we describe a retroviral expression system that specifically allows longer polypeptides and shorter peptides to be expressed in the cytoplasm, and thereby to be processed onto class I MHC molecules. T cells from mice that were immunized with a DNA vaccine encoding MPT-51 were probed against MHC-compatible cell lines retrovirally transduced with overlapping gene fragments encoding 120-140 amino acids of the MPT-51 molecule. After further testing of shorter peptide sequences, we identified a CD8+ T cell epitope using cell lines expressing a relatively small number of algorithm-predicted candidate epitopes. We found that one of the requirements for cell surface display of the 20-mer peptide was the need for cotranslational ubiquitination. The restriction molecule was identified as Dd following transduction with MHC class I genes followed by transduction with the oligonucleotide encoding the epitope. The retroviral expression system described here is cost-effective, particularly if the target molecule is large, and could be adapted to identifying T cell epitopes recognized in infectious disease and against tumor cell antigens.

Immune-based therapies.

PubMed

Lein, B

1995-12-01

Several immune-based HIV therapy studies presented at the Interscience Conference on Antimicrobial Agents Chemotherapy (ICAAC) are summarized. These studies involve the following therapies: HIV-IT, a gene therapy approach to augmenting the body's anti-HIV responses; interferon-alpha n3, a new formulation of alpha interferon with fewer toxicities; transfer of immune responses from one individual to another, also called passive immune therapy; and interleukin-2 (IL-2) in combination with protease inhibitors.

Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees

PubMed Central

Chang, Wan-Yu; Chiu, Chung-Cheng; Yang, Jia-Horng

2015-01-01

In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods. PMID:26393597

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms.

PubMed

Goodswen, Stephen J; Kennedy, Paul J; Ellis, John T

2013-11-02

An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

PubMed Central

2013-01-01

Background An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. Results The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. Conclusions Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory. PMID:24180526

Gradient Evolution-based Support Vector Machine Algorithm for Classification

NASA Astrophysics Data System (ADS)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

TLR9-based immunotherapy for the treatment of allergic diseases.

PubMed

Farrokhi, Shokrollah; Abbasirad, Narjes; Movahed, Ali; Khazaei, Hossein Ali; Pishjoo, Masoud; Rezaei, Nima

2017-03-01

Toll-like receptors (TLRs), a family of pattern recognition receptors expressed on many cell types of innate immunity, recognize the pathogen-associated molecular patterns of microbes. The hygiene hypothesis suggests that a reduced microbial exposure in early childhood increases the susceptibility to allergic diseases due to deviation in development of the immune system. TLRs are key roles in the right and healthy direction of adaptive immunity with the induction of T-helper 2 toward Th1 immune responses and regulatory T cells. TLR ligand CpG-ODN-based immunomodulation is independent of allergen and it mainly affects innate immune system. While, CpG-oligodeoxynucleotide-based vaccination is allergen specific and induces adaptive immune system. The use of agonists of TLR9 in two distinct strategies of immunotherapy, immunomodulation and vaccination, could be presented as the curative method for the treatment of allergic diseases.

Proposed method to construct Boolean functions with maximum possible annihilator immunity

NASA Astrophysics Data System (ADS)

Goyal, Rajni; Panigrahi, Anupama; Bansal, Rohit

2017-07-01

Nonlinearity and Algebraic(annihilator) immunity are two core properties of a Boolean function because optimum values of Annihilator Immunity and nonlinearity are required to resist fast algebraic attack and differential cryptanalysis respectively. For a secure cypher system, Boolean function(S-Boxes) should resist maximum number of attacks. It is possible if a Boolean function has optimal trade-off among its properties. Before constructing Boolean functions, we fixed the criteria of our constructions based on its properties. In present work, our construction is based on annihilator immunity and nonlinearity. While keeping above facts in mind,, we have developed a multi-objective evolutionary approach based on NSGA-II and got the optimum value of annihilator immunity with good bound of nonlinearity. We have constructed balanced Boolean functions having the best trade-off among balancedness, Annihilator immunity and nonlinearity for 5, 6 and 7 variables by the proposed method.

The Ad5 [E1-, E2b-]-based vector: a new and versatile gene delivery platform

NASA Astrophysics Data System (ADS)

Jones, Frank R.; Gabitzsch, Elizabeth S.; Balint, Joseph P.

2015-05-01

Based upon advances in gene sequencing and construction, it is now possible to identify specific genes or sequences thereof for gene delivery applications. Recombinant adenovirus serotype-5 (Ad5) viral vectors have been utilized in the settings of gene therapy, vaccination, and immunotherapy but have encountered clinical challenges because they are recognized as foreign entities to the host. This recognition leads to an immunologic clearance of the vector that contains the inserted gene of interest and prevents effective immunization(s). We have reported on a new Ad5-based viral vector technology that can be utilized as an immunization modality to induce immune responses even in the presence of Ad5 vector immunity. We have reported successful immunization and immunotherapy results to infectious diseases and cancers. This improved recombinant viral platform (Ad5 [E1-, E2b-]) can now be utilized in the development of multiple vaccines and immunotherapies.

In silico and ex vivo approaches indicate immune pressure on capsid and non-capsid regions of coxsackie B viruses in the human system.

PubMed

Kundu, Rhiannon; Knight, Robin; Dunga, Meenakshi; Peakman, Mark

2018-01-01

Coxsackie B Virus (CBV) infection has been linked to the aetiology of type 1 diabetes (T1D) and vaccination has been proposed as prophylaxis for disease prevention. Serum neutralising antibodies and the presence of viral protein and RNA in tissues have been common tools to examine this potential disease relationship, whilst the role of anti-CBV cytotoxic T cell responses and their targets have not been studied. To address this knowledge gap, we augmented conventional HLA-binding predictive algorithm-based epitope discovery by cross-referencing epitopes with sites of positive natural selection within the CBV3 viral genome, identified using mixed effects models of evolution. Eight epitopes for the common MHC class I allele HLA-A*0201 occur at sites that appear to be positively selected. Furthermore, such epitopes span the viral genome, indicating that effective anti-viral responses may not be restricted to the capsid region. To assess the spectrum of IFNy responses in non-diabetic subjects and recently diagnosed type 1 diabetes (T1D) patients, we stimulated PBMC ex vivo with pools of synthetic peptides based on component-restricted sequences identified in silico. We found responders were more likely to recognize multiple rather than a single CBV peptide pool, indicating that the natural course of infection results in multiple targets for effector memory responses, rather than immunodominant epitopes or viral components. The finding that anti-CBV CD8 T cell immunity is broadly targeted has implications for vaccination strategies and studies on the pathogenesis of CBV-linked diseases.

Plasmodium immunomics.

PubMed

Doolan, Denise L

2011-01-01

The Plasmodium parasite, the causative agent of malaria, is an excellent model for immunomic-based approaches to vaccine development. The Plasmodium parasite has a complex life cycle with multiple stages and stage-specific expression of ∼5300 putative proteins. No malaria vaccine has yet been licensed. Many believe that an effective vaccine will need to target several antigens and multiple stages, and will require the generation of both antibody and cellular immune responses. Vaccine efforts to date have been stage-specific and based on only a very limited number of proteins representing <0.5% of the genome. The recent availability of comprehensive genomic, proteomic and transcriptomic datasets from human and selected non-human primate and rodent malarias provide a foundation to exploit for vaccine development. This information can be mined to identify promising vaccine candidate antigens, by proteome-wide screening of antibody and T cell reactivity using specimens from individuals exposed to malaria and technology platforms such as protein arrays, high throughput protein production and epitope prediction algorithms. Such antigens could be incorporated into a rational vaccine development process that targets specific stages of the Plasmodium parasite life cycle with immune responses implicated in parasite elimination and control. Immunomic approaches which enable the selection of the best possible targets by prioritising antigens according to clinically relevant criteria may overcome the problem of poorly immunogenic, poorly protective vaccines that has plagued malaria vaccine developers for the past 25 years. Herein, current progress and perspectives regarding Plasmodium immunomics are reviewed. Copyright © 2010 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Design of polarization insensitive filters with micro- and nano-grating structures

NASA Astrophysics Data System (ADS)

Wang, Wen-liang; Rong, Xiao-hong

2014-03-01

For isotropic dielectric thin films, polarization effect is an inherent characteristic. As it will make the performance of optical-electric system go to bad, such polarization-dependent properties are often intolerable and should be eliminated in many applications. In this paper, based on a micro- and nano-optical structure whose period consists of four parts, a polarization insensitive filter is obtained by combining rigorous wave theory and multi-objective immune optimization algorithm. Its working wavelength is 1315 nm which is often used in laser systems. The results of our design show that TE and TM polarized waves have reflectivities of 0.482 and 0.485, respectively at designed wavelength of 1315 nm. And it denotes that two values are both close to the design values, their difference is only 0.003, and polarization deviation is also very little. Therefore, the designed filter can eliminate the effect of polarization deviation very well at 1315 nm wavelength.

Chaos Through-Wall Imaging Radar

NASA Astrophysics Data System (ADS)

Xu, Hang; Wang, Bingjie; Zhang, Jianguo; Liu, Li; Li, Ying; Wang, Yuncai; Wang, Anbang

2017-12-01

We experimentally demonstrate a chaos through-wall imaging radar using ultra-wideband chaotic-pulse-position modulation (CPPM) microwave signal. The CPPM signal based on logistic map with 1-ns pulse width and 1-GHz bandwidth is implemented by a field programmable gate array (FPGA) and then up-converted as the radar transmitting signal. Two-dimensional image of human objects behind obstacles is obtained by correlation method and back projection algorithm. Our experiments successfully perform through-wall imaging for single and multiple human objects through 20-cm thick wall. The down-range resolution of the proposed radar is 15 cm. Furthermore, the anti-jamming properties of the proposed radar in CPPM jamming, linear frequency-modulated jamming, and Gaussian noise jamming environments are demonstrated by electromagnetic simulations using the finite-difference time-domain. The simulation results show the CPPM microwave signal possesses excellent jamming immunity to the noise and radio frequency interference, which makes it perform superbly in multiradar environments.

In vivo analysis of the time and spatial activation pattern of microglia in the retina following laser-induced choroidal neovascularization.

PubMed

Crespo-Garcia, Sergio; Reichhart, Nadine; Hernandez-Matas, Carlos; Zabulis, Xenophon; Kociok, Norbert; Brockmann, Claudia; Joussen, Antonia M; Strauss, Olaf

2015-10-01

Microglia play a major role in retinal neovascularization and degeneration and are thus potential targets for therapeutic intervention. In vivo assessment of microglia behavior in disease models can provide important information to understand patho-mechanisms and develop therapeutic strategies. Although scanning laser ophthalmoscope (SLO) permits the monitoring of microglia in transgenic mice with microglia-specific GFP expression, there are fundamental limitations in reliable identification and quantification of activated cells. Therefore, we aimed to improve the SLO-based analysis of microglia using enhanced image processing with subsequent testing in laser-induced neovascularization (CNV). CNV was induced by argon laser in MacGreen mice. Microglia was visualized in vivo by SLO in the fundus auto-fluorescence (FAF) mode and verified ex vivo using retinal preparations. Three image processing algorithms based on different analysis of sequences of images were tested. The amount of recorded frames was limiting the effectiveness of the different algorithms. Best results from short recordings were obtained with a pixel averaging algorithm, further used to quantify spatial and temporal distribution of activated microglia in CNV. Morphologically, different microglia populations were detected in the inner and outer retinal layers. In CNV, the peak of microglia activation occurred in the inner layer at day 4 after laser, lacking an acute reaction. Besides, the spatial distribution of the activation changed by the time over the inner retina. No significant time and spatial changes were observed in the outer layer. An increase in laser power did not increase number of activated microglia. The SLO, in conjunction with enhanced image processing, is suitable for in vivo quantification of microglia activation. This surprisingly revealed that laser damage at the outer retina led to more reactive microglia in the inner retina, shedding light upon a new perspective to approach the immune response in the retina in vivo. Copyright © 2015 Elsevier Ltd. All rights reserved.

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes. Part 3

NASA Technical Reports Server (NTRS)

Lin, Shu

1998-01-01

Decoding algorithms based on the trellis representation of a code (block or convolutional) drastically reduce decoding complexity. The best known and most commonly used trellis-based decoding algorithm is the Viterbi algorithm. It is a maximum likelihood decoding algorithm. Convolutional codes with the Viterbi decoding have been widely used for error control in digital communications over the last two decades. This chapter is concerned with the application of the Viterbi decoding algorithm to linear block codes. First, the Viterbi algorithm is presented. Then, optimum sectionalization of a trellis to minimize the computational complexity of a Viterbi decoder is discussed and an algorithm is presented. Some design issues for IC (integrated circuit) implementation of a Viterbi decoder are considered and discussed. Finally, a new decoding algorithm based on the principle of compare-select-add is presented. This new algorithm can be applied to both block and convolutional codes and is more efficient than the conventional Viterbi algorithm based on the add-compare-select principle. This algorithm is particularly efficient for rate 1/n antipodal convolutional codes and their high-rate punctured codes. It reduces computational complexity by one-third compared with the Viterbi algorithm.

The Ocean Colour Climate Change Initiative: III. A Round-Robin Comparison on In-Water Bio-Optical Algorithms

NASA Technical Reports Server (NTRS)

Brewin, Robert J.W.; Sathyendranath, Shubha; Muller, Dagmar; Brockmann, Carsten; Deschamps, Pierre-Yves; Devred, Emmanuel; Doerffer, Roland; Fomferra, Norman; Franz, Bryan; Grant, Mike;

The role of community pharmacy-based vaccination in the USA: current practice and future directions

PubMed Central

Bach, Albert T; Goad, Jeffery A

2015-01-01

Community pharmacy-based provision of immunizations in the USA has become commonplace in the last few decades, with success in increasing rates of immunizations. Community pharmacy-based vaccination services are provided by pharmacists educated in the practice of immunization delivery and provide a convenient and accessible option for receiving immunizations. The pharmacist’s role in immunization practice has been described as serving in the roles of educator, facilitator, and immunizer. With a majority of pharmacist-provided vaccinations occurring in the community pharmacy setting, there are many examples of community pharmacists serving in these immunization roles with successful outcomes. Different community pharmacies employ a number of different models and workflow practices that usually consist of a year-round in-house service staffed by their own immunizing pharmacist. Challenges that currently exist in this setting are variability in scopes of immunization practice for pharmacists across states, inconsistent reimbursement mechanisms, and barriers in technology. Many of these challenges can be alleviated by continual education; working with legislators, state boards of pharmacy, stakeholders, and payers to standardize laws; and reimbursement design. Other challenges that may need to be addressed are improvements in communication and continuity of care between community pharmacists and the patient centered medical home. PMID:29354521

Personalized recommendation via unbalance full-connectivity inference

NASA Astrophysics Data System (ADS)

Ma, Wenping; Ren, Chen; Wu, Yue; Wang, Shanfeng; Feng, Xiang

2017-10-01

Recommender systems play an important role to help us to find useful information. They are widely used by most e-commerce web sites to push the potential items to individual user according to purchase history. Network-based recommendation algorithms are popular and effective in recommendation, which use two types of elements to represent users and items respectively. In this paper, based on consistence-based inference (CBI) algorithm, we propose a novel network-based algorithm, in which users and items are recognized with no difference. The proposed algorithm also uses information diffusion to find the relationship between users and items. Different from traditional network-based recommendation algorithms, information diffusion initializes from users and items, respectively. Experiments show that the proposed algorithm is effective compared with traditional network-based recommendation algorithms.

Generating compact classifier systems using a simple artificial immune system.

PubMed

Leung, Kevin; Cheong, France; Cheong, Christopher

2007-10-01

Current artificial immune system (AIS) classifiers have two major problems: 1) their populations of B-cells can grow to huge proportions, and 2) optimizing one B-cell (part of the classifier) at a time does not necessarily guarantee that the B-cell pool (the whole classifier) will be optimized. In this paper, the design of a new AIS algorithm and classifier system called simple AIS is described. It is different from traditional AIS classifiers in that it takes only one B-cell, instead of a B-cell pool, to represent the classifier. This approach ensures global optimization of the whole system, and in addition, no population control mechanism is needed. The classifier was tested on seven benchmark data sets using different classification techniques and was found to be very competitive when compared to other classifiers.

Microneedle-mediated immunization of an adenovirus-based malaria vaccine enhances antigen-specific antibody immunity and reduces anti-vector responses compared to the intradermal route.

PubMed

Carey, John B; Vrdoljak, Anto; O'Mahony, Conor; Hill, Adrian V S; Draper, Simon J; Moore, Anne C

2014-08-21

Substantial effort has been placed in developing efficacious recombinant attenuated adenovirus-based vaccines. However induction of immunity to the vector is a significant obstacle to its repeated use. Here we demonstrate that skin-based delivery of an adenovirus-based malaria vaccine, HAdV5-PyMSP1₄₂, to mice using silicon microneedles induces equivalent or enhanced antibody responses to the encoded antigen, however it results in decreased anti-vector responses, compared to intradermal delivery. Microneedle-mediated vaccine priming and resultant induction of low anti-vector antibody titres permitted repeated use of the same adenovirus vaccine vector. This resulted in significantly increased antigen-specific antibody responses in these mice compared to ID-treated mice. Boosting with a heterologous vaccine; MVA-PyMSP1₄₂ also resulted in significantly greater antibody responses in mice primed with HAdV5-PyMSP1₄₂ using MN compared to the ID route. The highest protection against blood-stage malaria challenge was observed when a heterologous route of immunization (MN/ID) was used. Therefore, microneedle-mediated immunization has potential to both overcome some of the logistic obstacles surrounding needle-and-syringe-based immunization as well as to facilitate the repeated use of the same adenovirus vaccine thereby potentially reducing manufacturing costs of multiple vaccines. This could have important benefits in the clinical ease of use of adenovirus-based immunization strategies.

Microneedle-mediated immunization of an adenovirus-based malaria vaccine enhances antigen-specific antibody immunity and reduces anti-vector responses compared to the intradermal route

PubMed Central

Carey, John B.; Vrdoljak, Anto; O'Mahony, Conor; Hill, Adrian V. S.; Draper, Simon J.; Moore, Anne C.

2014-01-01

Substantial effort has been placed in developing efficacious recombinant attenuated adenovirus-based vaccines. However induction of immunity to the vector is a significant obstacle to its repeated use. Here we demonstrate that skin-based delivery of an adenovirus-based malaria vaccine, HAdV5-PyMSP142, to mice using silicon microneedles induces equivalent or enhanced antibody responses to the encoded antigen, however it results in decreased anti-vector responses, compared to intradermal delivery. Microneedle-mediated vaccine priming and resultant induction of low anti-vector antibody titres permitted repeated use of the same adenovirus vaccine vector. This resulted in significantly increased antigen-specific antibody responses in these mice compared to ID-treated mice. Boosting with a heterologous vaccine; MVA-PyMSP142 also resulted in significantly greater antibody responses in mice primed with HAdV5-PyMSP142 using MN compared to the ID route. The highest protection against blood-stage malaria challenge was observed when a heterologous route of immunization (MN/ID) was used. Therefore, microneedle-mediated immunization has potential to both overcome some of the logistic obstacles surrounding needle-and-syringe-based immunization as well as to facilitate the repeated use of the same adenovirus vaccine thereby potentially reducing manufacturing costs of multiple vaccines. This could have important benefits in the clinical ease of use of adenovirus-based immunization strategies. PMID:25142082

Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids.

PubMed

Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker

2012-08-01

Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.

Self-adjuvanted mRNA vaccines induce local innate immune responses that lead to a potent and boostable adaptive immunity.

PubMed

Kowalczyk, Aleksandra; Doener, Fatma; Zanzinger, Kai; Noth, Janine; Baumhof, Patrick; Fotin-Mleczek, Mariola; Heidenreich, Regina

2016-07-19

mRNA represents a new platform for the development of therapeutic and prophylactic vaccines with high flexibility with respect to production and application. We have previously shown that our two component self-adjuvanted mRNA-based vaccines (termed RNActive® vaccines) induce balanced immune responses comprising both humoral and cellular effector as well as memory responses. Here, we evaluated the early events upon intradermal application to gain more detailed insights into the underlying mode of action of our mRNA-based vaccine. We showed that the vaccine is taken up in the skin by both non-leukocytic and leukocytic cells, the latter being mostly represented by antigen presenting cells (APCs). mRNA was then transported to the draining lymph nodes (dLNs) by migratory dendritic cells. Moreover, the encoded protein was expressed and efficiently presented by APCs within the dLNs as shown by T cell proliferation and immune cell activation, followed by the induction of the adaptive immunity. Importantly, the immunostimulation was limited to the injection site and lymphoid organs as no proinflammatory cytokines were detected in the sera of the immunized mice indicating a favorable safety profile of the mRNA-based vaccines. Notably, a substantial boostability of the immune responses was observed, indicating that mRNA can be used effectively in repetitive immunization schedules. The evaluation of the immunostimulation following prime and boost vaccination revealed no signs of exhaustion as demonstrated by comparable levels of cytokine production at the injection site and immune cell activation within dLNs. In summary, our data provide mechanistic insight into the mode of action and a rational for the use of mRNA-based vaccines as a promising immunization platform. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Impact of a Line Probe Assay Based Diagnostic Algorithm on Time to Treatment Initiation and Treatment Outcomes for Multidrug Resistant TB Patients in Arkhangelsk Region, Russia.

PubMed

Eliseev, Platon; Balantcev, Grigory; Nikishova, Elena; Gaida, Anastasia; Bogdanova, Elena; Enarson, Donald; Ornstein, Tara; Detjen, Anne; Dacombe, Russell; Gospodarevskaya, Elena; Phillips, Patrick P J; Mann, Gillian; Squire, Stephen Bertel; Mariandyshev, Andrei

2016-01-01

In the Arkhangelsk region of Northern Russia, multidrug-resistant (MDR) tuberculosis (TB) rates in new cases are amongst the highest in the world. In 2014, MDR-TB rates reached 31.7% among new cases and 56.9% among retreatment cases. The development of new diagnostic tools allows for faster detection of both TB and MDR-TB and should lead to reduced transmission by earlier initiation of anti-TB therapy. The PROVE-IT (Policy Relevant Outcomes from Validating Evidence on Impact) Russia study aimed to assess the impact of the implementation of line probe assay (LPA) as part of an LPA-based diagnostic algorithm for patients with presumptive MDR-TB focusing on time to treatment initiation with time from first-care seeking visit to the initiation of MDR-TB treatment rather than diagnostic accuracy as the primary outcome, and to assess treatment outcomes. We hypothesized that the implementation of LPA would result in faster time to treatment initiation and better treatment outcomes. A culture-based diagnostic algorithm used prior to LPA implementation was compared to an LPA-based algorithm that replaced BacTAlert and Löwenstein Jensen (LJ) for drug sensitivity testing. A total of 295 MDR-TB patients were included in the study, 163 diagnosed with the culture-based algorithm, 132 with the LPA-based algorithm. Among smear positive patients, the implementation of the LPA-based algorithm was associated with a median decrease in time to MDR-TB treatment initiation of 50 and 66 days compared to the culture-based algorithm (BacTAlert and LJ respectively, p<0.001). In smear negative patients, the LPA-based algorithm was associated with a median decrease in time to MDR-TB treatment initiation of 78 days when compared to the culture-based algorithm (LJ, p<0.001). However, several weeks were still needed for treatment initiation in LPA-based algorithm, 24 days in smear positive, and 62 days in smear negative patients. Overall treatment outcomes were better in LPA-based algorithm compared to culture-based algorithm (p = 0.003). Treatment success rates at 20 months of treatment were higher in patients diagnosed with the LPA-based algorithm (65.2%) as compared to those diagnosed with the culture-based algorithm (44.8%). Mortality was also lower in the LPA-based algorithm group (7.6%) compared to the culture-based algorithm group (15.9%). There was no statistically significant difference in smear and culture conversion rates between the two algorithms. The results of the study suggest that the introduction of LPA leads to faster time to MDR diagnosis and earlier treatment initiation as well as better treatment outcomes for patients with MDR-TB. These findings also highlight the need for further improvements within the health system to reduce both patient and diagnostic delays to truly optimize the impact of new, rapid diagnostics.

Temporal high-pass non-uniformity correction algorithm based on grayscale mapping and hardware implementation

NASA Astrophysics Data System (ADS)

Jin, Minglei; Jin, Weiqi; Li, Yiyang; Li, Shuo

2015-08-01

In this paper, we propose a novel scene-based non-uniformity correction algorithm for infrared image processing-temporal high-pass non-uniformity correction algorithm based on grayscale mapping (THP and GM). The main sources of non-uniformity are: (1) detector fabrication inaccuracies; (2) non-linearity and variations in the read-out electronics and (3) optical path effects. The non-uniformity will be reduced by non-uniformity correction (NUC) algorithms. The NUC algorithms are often divided into calibration-based non-uniformity correction (CBNUC) algorithms and scene-based non-uniformity correction (SBNUC) algorithms. As non-uniformity drifts temporally, CBNUC algorithms must be repeated by inserting a uniform radiation source which SBNUC algorithms do not need into the view, so the SBNUC algorithm becomes an essential part of infrared imaging system. The SBNUC algorithms' poor robustness often leads two defects: artifacts and over-correction, meanwhile due to complicated calculation process and large storage consumption, hardware implementation of the SBNUC algorithms is difficult, especially in Field Programmable Gate Array (FPGA) platform. The THP and GM algorithm proposed in this paper can eliminate the non-uniformity without causing defects. The hardware implementation of the algorithm only based on FPGA has two advantages: (1) low resources consumption, and (2) small hardware delay: less than 20 lines, it can be transplanted to a variety of infrared detectors equipped with FPGA image processing module, it can reduce the stripe non-uniformity and the ripple non-uniformity.

A review of classification algorithms for EEG-based brain-computer interfaces.

PubMed

Lotte, F; Congedo, M; Lécuyer, A; Lamarche, F; Arnaldi, B

2007-06-01

In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

An improved conscan algorithm based on a Kalman filter

NASA Technical Reports Server (NTRS)

Eldred, D. B.

1994-01-01

Conscan is commonly used by DSN antennas to allow adaptive tracking of a target whose position is not precisely known. This article describes an algorithm that is based on a Kalman filter and is proposed to replace the existing fast Fourier transform based (FFT-based) algorithm for conscan. Advantages of this algorithm include better pointing accuracy, continuous update information, and accommodation of missing data. Additionally, a strategy for adaptive selection of the conscan radius is proposed. The performance of the algorithm is illustrated through computer simulations and compared to the FFT algorithm. The results show that the Kalman filter algorithm is consistently superior.

Immune modulation of i.v. immunoglobulin in women with reproductive failure.

PubMed

Han, Ae R; Lee, Sung K

2018-04-01

The mechanism of maternal immune tolerance of the semi-allogenic fetus has been explored extensively. The immune reaction to defend from invasion by pathogenic microorganisms should be maintained during pregnancy. An imbalance between the immune tolerance to the fetus and immune activation to the pathogenic organisms is associated with poor pregnancy outcomes. This emphasizes that the immune mechanism of successful reproduction is not just immune suppression, but adequate immune modulation. In this review, the action of i.v. immunoglobulin G (IVIg) on the immune system and its efficacy in reproductive failure (RF) was summarized. Also suggested is the indication of IVIg therapy for women with RF. Based on the mechanism of the immune regulation of IVIg and following confirmation of the immune modulation effects of it in various aberrant immune parameters in patients with RF, it is obvious that IVIg is effective in recurrent pregnancy losses and repeated implantation failures with immunologic disturbances. The authors recommend IVIg therapy in patients with RF with aberrant cellular immunologic parameters, including a high natural killer cell proportion and its cytotoxicity or elevated T helper 1 to T helper 2 ratio, based on each clinic's cut-off values. Further clinical studies about the safety of IVIg in the fetus and its efficacy in other immunologic abnormalities of RF are needed.

Viral Infection of Tumors Overcomes Resistance to PD-1-immunotherapy by Broadening Neoantigenome-directed T-cell Responses.

PubMed

Woller, Norman; Gürlevik, Engin; Fleischmann-Mundt, Bettina; Schumacher, Anja; Knocke, Sarah; Kloos, Arnold M; Saborowski, Michael; Geffers, Robert; Manns, Michael P; Wirth, Thomas C; Kubicka, Stefan; Kühnel, Florian

2015-10-01

There is evidence that viral oncolysis is synergistic with immune checkpoint inhibition in cancer therapy but the underlying mechanisms are unclear. Here, we investigated whether local viral infection of malignant tumors is capable of overcoming systemic resistance to PD-1-immunotherapy by modulating the spectrum of tumor-directed CD8 T-cells. To focus on neoantigen-specific CD8 T-cell responses, we performed transcriptomic sequencing of PD-1-resistant CMT64 lung adenocarcinoma cells followed by algorithm-based neoepitope prediction. Investigations on neoepitope-specific T-cell responses in tumor-bearing mice demonstrated that PD-1 immunotherapy was insufficient whereas viral oncolysis elicited cytotoxic T-cell responses to a conserved panel of neoepitopes. After combined treatment, we observed that PD-1-blockade did not affect the magnitude of oncolysis-mediated antitumoral immune responses but a broader spectrum of T-cell responses including additional neoepitopes was observed. Oncolysis of the primary tumor significantly abrogated systemic resistance to PD-1-immunotherapy leading to improved elimination of disseminated lung tumors. Our observations were confirmed in a transgenic murine model of liver cancer where viral oncolysis strongly induced PD-L1 expression in primary liver tumors and lung metastasis. Furthermore, we demonstrated that combined treatment completely inhibited dissemination in a CD8 T-cell-dependent manner. Therefore, our results strongly recommend further evaluation of virotherapy and concomitant PD-1 immunotherapy in clinical studies.

Surgical motion characterization in simulated needle insertion procedures

NASA Astrophysics Data System (ADS)

Holden, Matthew S.; Ungi, Tamas; Sargent, Derek; McGraw, Robert C.; Fichtinger, Gabor

2012-02-01

PURPOSE: Evaluation of surgical performance in image-guided needle insertions is of emerging interest, to both promote patient safety and improve the efficiency and effectiveness of training. The purpose of this study was to determine if a Markov model-based algorithm can more accurately segment a needle-based surgical procedure into its five constituent tasks than a simple threshold-based algorithm. METHODS: Simulated needle trajectories were generated with known ground truth segmentation by a synthetic procedural data generator, with random noise added to each degree of freedom of motion. The respective learning algorithms were trained, and then tested on different procedures to determine task segmentation accuracy. In the threshold-based algorithm, a change in tasks was detected when the needle crossed a position/velocity threshold. In the Markov model-based algorithm, task segmentation was performed by identifying the sequence of Markov models most likely to have produced the series of observations. RESULTS: For amplitudes of translational noise greater than 0.01mm, the Markov model-based algorithm was significantly more accurate in task segmentation than the threshold-based algorithm (82.3% vs. 49.9%, p<0.001 for amplitude 10.0mm). For amplitudes less than 0.01mm, the two algorithms produced insignificantly different results. CONCLUSION: Task segmentation of simulated needle insertion procedures was improved by using a Markov model-based algorithm as opposed to a threshold-based algorithm for procedures involving translational noise.

WS-BP: An efficient wolf search based back-propagation algorithm

NASA Astrophysics Data System (ADS)

Nawi, Nazri Mohd; Rehman, M. Z.; Khan, Abdullah

2015-05-01

Wolf Search (WS) is a heuristic based optimization algorithm. Inspired by the preying and survival capabilities of the wolves, this algorithm is highly capable to search large spaces in the candidate solutions. This paper investigates the use of WS algorithm in combination with back-propagation neural network (BPNN) algorithm to overcome the local minima problem and to improve convergence in gradient descent. The performance of the proposed Wolf Search based Back-Propagation (WS-BP) algorithm is compared with Artificial Bee Colony Back-Propagation (ABC-BP), Bat Based Back-Propagation (Bat-BP), and conventional BPNN algorithms. Specifically, OR and XOR datasets are used for training the network. The simulation results show that the WS-BP algorithm effectively avoids the local minima and converge to global minima.

Evaluation of the performance of existing non-laboratory based cardiovascular risk assessment algorithms

PubMed Central

2013-01-01

Background The high burden and rising incidence of cardiovascular disease (CVD) in resource constrained countries necessitates implementation of robust and pragmatic primary and secondary prevention strategies. Many current CVD management guidelines recommend absolute cardiovascular (CV) risk assessment as a clinically sound guide to preventive and treatment strategies. Development of non-laboratory based cardiovascular risk assessment algorithms enable absolute risk assessment in resource constrained countries. The objective of this review is to evaluate the performance of existing non-laboratory based CV risk assessment algorithms using the benchmarks for clinically useful CV risk assessment algorithms outlined by Cooney and colleagues. Methods A literature search to identify non-laboratory based risk prediction algorithms was performed in MEDLINE, CINAHL, Ovid Premier Nursing Journals Plus, and PubMed databases. The identified algorithms were evaluated using the benchmarks for clinically useful cardiovascular risk assessment algorithms outlined by Cooney and colleagues. Results Five non-laboratory based CV risk assessment algorithms were identified. The Gaziano and Framingham algorithms met the criteria for appropriateness of statistical methods used to derive the algorithms and endpoints. The Swedish Consultation, Framingham and Gaziano algorithms demonstrated good discrimination in derivation datasets. Only the Gaziano algorithm was externally validated where it had optimal discrimination. The Gaziano and WHO algorithms had chart formats which made them simple and user friendly for clinical application. Conclusion Both the Gaziano and Framingham non-laboratory based algorithms met most of the criteria outlined by Cooney and colleagues. External validation of the algorithms in diverse samples is needed to ascertain their performance and applicability to different populations and to enhance clinicians’ confidence in them. PMID:24373202

Consequences of Longterm-Confinement and Hypobaric Hypoxia on Immunity in the Antarctic Concordia Environment

NASA Technical Reports Server (NTRS)

Crucian, Brian; Chouker, Alexander; Pierson, Duane; Mehta, Satish; Stowe, Raymond; Salam, Alex; Sams, Clarence

2010-01-01

This slide presentation reviews the affects of longterm-confinement and hypobaric hypoxia on immunity in the Antarctic Concordia environment. It includes information on spaceflight-associated immune dysregulation, immune-related knowledge gaps, and ground-based space flight analogs.

An AIS-Based E-mail Classification Method

NASA Astrophysics Data System (ADS)

Qing, Jinjian; Mao, Ruilong; Bie, Rongfang; Gao, Xiao-Zhi

This paper proposes a new e-mail classification method based on the Artificial Immune System (AIS), which is endowed with good diversity and self-adaptive ability by using the immune learning, immune memory, and immune recognition. In our method, the features of spam and non-spam extracted from the training sets are combined together, and the number of false positives (non-spam messages that are incorrectly classified as spam) can be reduced. The experimental results demonstrate that this method is effective in reducing the false rate.

Range image registration based on hash map and moth-flame optimization

NASA Astrophysics Data System (ADS)

Zou, Li; Ge, Baozhen; Chen, Lei

2018-03-01

Over the past decade, evolutionary algorithms (EAs) have been introduced to solve range image registration problems because of their robustness and high precision. However, EA-based range image registration algorithms are time-consuming. To reduce the computational time, an EA-based range image registration algorithm using hash map and moth-flame optimization is proposed. In this registration algorithm, a hash map is used to avoid over-exploitation in registration process. Additionally, we present a search equation that is better at exploration and a restart mechanism to avoid being trapped in local minima. We compare the proposed registration algorithm with the registration algorithms using moth-flame optimization and several state-of-the-art EA-based registration algorithms. The experimental results show that the proposed algorithm has a lower computational cost than other algorithms and achieves similar registration precision.

Host genetics contributes to the effectiveness of dendritic cell-based HIV immunotherapy.

PubMed

Reis, Edione C; da Silva, Lais T; da Silva, Wanessa C; Rios, Alexandre; Duarte, Alberto J; Oshiro, Telma M; Crovella, Sergio; Pontillo, Alessandra

2018-04-11

Systems biological analysis has recently revealed how innate immune variants as well as gut microbiota impact the individual response to immunization. HIV-infected (HIV+) patients have a worse response rate after standard vaccinations, possibly due to the immune exhaustion, increased gut permeability and microbial translocation. In the last decade, dendritic cells (DC)-based immunotherapy has been proposed as an alternative approach to control HIV plasma viral load, however clinical trials showed a heterogeneity of immunization response. Hypothesizing that host genetics may importantly affects the outcome of immunotherapy in HIV+ patients, genetic polymorphisms' distribution and gene expression modulation were analyzed in a phase I/II clinical trial of DC-based immunotherapy according to immunization response, and quality of vaccine product (DC). Polymorphisms in genes previously associated with progression of HIV infection to AIDS (i.e.: PARD3B, CCL5) contribute to a better response to immunotherapy in HIV+ individuals, possibly through a systemic effect on host immune system, but also directly on vaccine product. Genes expression profile after immunization correlates with different degrees of immune chronic activation/exhaustion of HIV+ patients (i.e. PD1, IL7RA, EOMES), but also with anti-viral response and DC quality (i.e.: APOBEC3G, IL8, PPIA), suggested that an incompetent individual would have a better vaccine response. These findings showed once more that host genetics can affect the response to DC-based immunotherapy in HIV+ individuals, contributing to the heterogeneity of response observed in concluded trials; and it can be used as predictor of immunization success.

Study on the algorithm of computational ghost imaging based on discrete fourier transform measurement matrix

NASA Astrophysics Data System (ADS)

Zhang, Leihong; Liang, Dong; Li, Bei; Kang, Yi; Pan, Zilan; Zhang, Dawei; Gao, Xiumin; Ma, Xiuhua

2016-07-01

On the basis of analyzing the cosine light field with determined analytic expression and the pseudo-inverse method, the object is illuminated by a presetting light field with a determined discrete Fourier transform measurement matrix, and the object image is reconstructed by the pseudo-inverse method. The analytic expression of the algorithm of computational ghost imaging based on discrete Fourier transform measurement matrix is deduced theoretically, and compared with the algorithm of compressive computational ghost imaging based on random measurement matrix. The reconstruction process and the reconstruction error are analyzed. On this basis, the simulation is done to verify the theoretical analysis. When the sampling measurement number is similar to the number of object pixel, the rank of discrete Fourier transform matrix is the same as the one of the random measurement matrix, the PSNR of the reconstruction image of FGI algorithm and PGI algorithm are similar, the reconstruction error of the traditional CGI algorithm is lower than that of reconstruction image based on FGI algorithm and PGI algorithm. As the decreasing of the number of sampling measurement, the PSNR of reconstruction image based on FGI algorithm decreases slowly, and the PSNR of reconstruction image based on PGI algorithm and CGI algorithm decreases sharply. The reconstruction time of FGI algorithm is lower than that of other algorithms and is not affected by the number of sampling measurement. The FGI algorithm can effectively filter out the random white noise through a low-pass filter and realize the reconstruction denoising which has a higher denoising capability than that of the CGI algorithm. The FGI algorithm can improve the reconstruction accuracy and the reconstruction speed of computational ghost imaging.

Immunizations challenge healthcare personnel and affects immunization rates.

PubMed

Strohfus, Pamela K; Kim, Susan C; Palma, Sara; Duke, Russell A; Remington, Richard; Roberts, Caleb

2017-02-01

This study measured 1. medical office immunization rates and 2. health care personnel competency in managing vaccine practices before and after evidence-based immunization education was provided. This descriptive study compared 32 family medicine and pediatric offices and 178 medical assistants, licensed practical nurses, registered nurses, nurse practitioners, and physicians in knowledge-based testing pre-education, post-education, and 12-months post-education. Immunization rates were assessed before and 18-months post-education. Immunization rates increased 10.3% - 18months post-education; knowledge increased 7.8% - 12months post-education. Family medicine offices, licensed practical nurses, and medical assistants showed significant knowledge deficits before and 12-months post-education. All demographic groups scored less in storage/handling 12-months post-education. This study is one of the first studies to identify competency challenges in effective immunization delivery among medical assistants, licensed practical nurses, and family medicine offices. Formal and continuous education in immunization administration and storage/handling is recommended among these select groups. Copyright © 2016 Elsevier Inc. All rights reserved.

The immunology of smallpox vaccines

PubMed Central

Kennedy, Richard B; Ovsyannikova, Inna G; Jacobson, Robert M; Poland, Gregory A

2010-01-01

In spite of the eradication of smallpox over 30 years ago; orthopox viruses such as smallpox and monkeypox remain serious public health threats both through the possibility of bioterrorism and the intentional release of smallpox and through natural outbreaks of emerging infectious diseases such as monkeypox. The eradication effort was largely made possible by the availability of an effective vaccine based on the immunologically cross-protective vaccinia virus. Although the concept of vaccination dates back to the late 1800s with Edward Jenner, it is only in the past decade that modern immunologic tools have been applied toward deciphering poxvirus immunity. Smallpox vaccines containing vaccinia virus elicit strong humoral and cellular immune responses that confer cross-protective immunity against variola virus for decades after immunization. Recent studies have focused on: establishing the longevity of poxvirus-specific immunity, defining key immune epitopes targeted by T and B cells, developing subunit-based vaccines, and developing genotypic and phenotypic immune response profiles that predict either vaccine response or adverse events following immunization. PMID:19524427

Peak-to-average power ratio reduction in orthogonal frequency division multiplexing-based visible light communication systems using a modified partial transmit sequence technique

NASA Astrophysics Data System (ADS)

Liu, Yan; Deng, Honggui; Ren, Shuang; Tang, Chengying; Qian, Xuewen

2018-01-01

We propose an efficient partial transmit sequence technique based on genetic algorithm and peak-value optimization algorithm (GAPOA) to reduce high peak-to-average power ratio (PAPR) in visible light communication systems based on orthogonal frequency division multiplexing (VLC-OFDM). By analysis of hill-climbing algorithm's pros and cons, we propose the POA with excellent local search ability to further process the signals whose PAPR is still over the threshold after processed by genetic algorithm (GA). To verify the effectiveness of the proposed technique and algorithm, we evaluate the PAPR performance and the bit error rate (BER) performance and compare them with partial transmit sequence (PTS) technique based on GA (GA-PTS), PTS technique based on genetic and hill-climbing algorithm (GH-PTS), and PTS based on shuffled frog leaping algorithm and hill-climbing algorithm (SFLAHC-PTS). The results show that our technique and algorithm have not only better PAPR performance but also lower computational complexity and BER than GA-PTS, GH-PTS, and SFLAHC-PTS technique.

On Parallel Push-Relabel based Algorithms for Bipartite Maximum Matching

DOE Office of Scientific and Technical Information (OSTI.GOV)

Langguth, Johannes; Azad, Md Ariful; Halappanavar, Mahantesh

2014-07-01

We study multithreaded push-relabel based algorithms for computing maximum cardinality matching in bipartite graphs. Matching is a fundamental combinatorial (graph) problem with applications in a wide variety of problems in science and engineering. We are motivated by its use in the context of sparse linear solvers for computing maximum transversal of a matrix. We implement and test our algorithms on several multi-socket multicore systems and compare their performance to state-of-the-art augmenting path-based serial and parallel algorithms using a testset comprised of a wide range of real-world instances. Building on several heuristics for enhancing performance, we demonstrate good scaling for themore » parallel push-relabel algorithm. We show that it is comparable to the best augmenting path-based algorithms for bipartite matching. To the best of our knowledge, this is the first extensive study of multithreaded push-relabel based algorithms. In addition to a direct impact on the applications using matching, the proposed algorithmic techniques can be extended to preflow-push based algorithms for computing maximum flow in graphs.« less

A SAT Based Effective Algorithm for the Directed Hamiltonian Cycle Problem

NASA Astrophysics Data System (ADS)

Jäger, Gerold; Zhang, Weixiong

The Hamiltonian cycle problem (HCP) is an important combinatorial problem with applications in many areas. While thorough theoretical and experimental analyses have been made on the HCP in undirected graphs, little is known for the HCP in directed graphs (DHCP). The contribution of this work is an effective algorithm for the DHCP. Our algorithm explores and exploits the close relationship between the DHCP and the Assignment Problem (AP) and utilizes a technique based on Boolean satisfiability (SAT). By combining effective algorithms for the AP and SAT, our algorithm significantly outperforms previous exact DHCP algorithms including an algorithm based on the award-winning Concorde TSP algorithm.

Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood.

PubMed

Prauße, Maria T E; Lehnert, Teresa; Timme, Sandra; Hünniger, Kerstin; Leonhardt, Ines; Kurzai, Oliver; Figge, Marc Thilo

2018-01-01

Bloodstream infections by the human-pathogenic fungi Candida albicans and Candida glabrata increasingly occur in hospitalized patients and are associated with high mortality rates. The early immune response against these fungi in human blood comprises a concerted action of humoral and cellular components of the innate immune system. Upon entering the blood, the majority of fungal cells will be eliminated by innate immune cells, i.e., neutrophils and monocytes. However, recent studies identified a population of fungal cells that can evade the immune response and thereby may disseminate and cause organ dissemination, which is frequently observed during candidemia. In this study, we investigate the so far unresolved mechanism of fungal immune evasion in human whole blood by testing hypotheses with the help of mathematical modeling. We use a previously established state-based virtual infection model for whole-blood infection with C. albicans to quantify the immune response and identified the fungal immune-evasion mechanism. While this process was assumed to be spontaneous in the previous model, we now hypothesize that the immune-evasion process is mediated by host factors and incorporate such a mechanism in the model. In particular, we propose, based on previous studies that the fungal immune-evasion mechanism could possibly arise through modification of the fungal surface by as of yet unknown proteins that are assumed to be secreted by activated neutrophils. To validate or reject any of the immune-evasion mechanisms, we compared the simulation of both immune-evasion models for different infection scenarios, i.e., infection of whole blood with either C. albicans or C. glabrata under non-neutropenic and neutropenic conditions. We found that under non-neutropenic conditions, both immune-evasion models fit the experimental data from whole-blood infection with C. albicans and C. glabrata . However, differences between the immune-evasion models could be observed for the infection outcome under neutropenic conditions with respect to the distribution of fungal cells across the immune cells. Based on these predictions, we suggested specific experimental studies that might allow for the validation or rejection of the proposed immune-evasion mechanism.

Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood

PubMed Central

Prauße, Maria T. E.; Lehnert, Teresa; Timme, Sandra; Hünniger, Kerstin; Leonhardt, Ines; Kurzai, Oliver; Figge, Marc Thilo

2018-01-01

Bloodstream infections by the human-pathogenic fungi Candida albicans and Candida glabrata increasingly occur in hospitalized patients and are associated with high mortality rates. The early immune response against these fungi in human blood comprises a concerted action of humoral and cellular components of the innate immune system. Upon entering the blood, the majority of fungal cells will be eliminated by innate immune cells, i.e., neutrophils and monocytes. However, recent studies identified a population of fungal cells that can evade the immune response and thereby may disseminate and cause organ dissemination, which is frequently observed during candidemia. In this study, we investigate the so far unresolved mechanism of fungal immune evasion in human whole blood by testing hypotheses with the help of mathematical modeling. We use a previously established state-based virtual infection model for whole-blood infection with C. albicans to quantify the immune response and identified the fungal immune-evasion mechanism. While this process was assumed to be spontaneous in the previous model, we now hypothesize that the immune-evasion process is mediated by host factors and incorporate such a mechanism in the model. In particular, we propose, based on previous studies that the fungal immune-evasion mechanism could possibly arise through modification of the fungal surface by as of yet unknown proteins that are assumed to be secreted by activated neutrophils. To validate or reject any of the immune-evasion mechanisms, we compared the simulation of both immune-evasion models for different infection scenarios, i.e., infection of whole blood with either C. albicans or C. glabrata under non-neutropenic and neutropenic conditions. We found that under non-neutropenic conditions, both immune-evasion models fit the experimental data from whole-blood infection with C. albicans and C. glabrata. However, differences between the immune-evasion models could be observed for the infection outcome under neutropenic conditions with respect to the distribution of fungal cells across the immune cells. Based on these predictions, we suggested specific experimental studies that might allow for the validation or rejection of the proposed immune-evasion mechanism. PMID:29619027

Identifying patterns of immune-related disease: use in disease prevention and management.

PubMed

Dietert, Rodney R; Zelikoff, Judith T

2010-05-01

Childhood susceptibility to diseases linked with immune dysfunction affects over a quarter of the pediatric population in some countries. While this alone is a significant health issue, the actual impact of immune-related diseases extends over a lifetime and involves additional secondary conditions. Some comorbidities are well known (e.g., allergic rhinitis and asthma). However, no systematic approach has been used to identify life-long patterns of immune-based disease where the primary condition arises in childhood. Such information is useful for both disease prevention and treatment approaches. Recent primary research papers as well as review articles were obtained from PubMed, Chem Abstracts, Biosis and from the personal files of the authors. Search words used were: the diseases and conditions shown Figs. 1 and 2 in conjunction with comorbid, comorbidities, pediatric, childhood, adult, immune, immune dysfunction, allergy, autoimmune, inflammatory, infectious, health risks, environment, risk factors. Childhood diseases such as asthma, type-1 diabetes, inflammatory bowel disease, respiratory infections /rhinitis, recurrent otitis media, pediatric celiac, juvenile arthritis and Kawasaki disease are examples of significant childhood health problems where immune dysfunction plays a significant role. Each of these pediatric diseases is associated with increased risk of several secondary conditions, many of which appear only later in life. To illustrate, four prototypes of immune-related disease patterns (i.e., allergy, autoimmunity, inflammation and infectious disease) are shown as tools for: 1) enhanced disease prevention; 2) improved management of immune-based pediatric diseases; and 3) better recognition of underlying pediatric immune dysfunction. Identification of immune-related disease patterns beginning in childhood provides the framework for examining the underlying immune dysfunctions that can contribute to additional diseases in later life. Many pediatric diseases associated with dysfunctional immune responses have been linked with an elevated risk of other diseases or conditions as the child ages. Diseases within a pattern may be interlinked based on underlying immune dysfunctions and/or current therapeutic approaches for managing the entryway diseases. It may be beneficial to consider treatment options for the earliest presenting diseases that will concomitantly reduce the risk of immune-linked secondary conditions. Additionally, improved disease prevention is possible with more relevant and age-specific immune safety testing.

A human tissue-based functional assay platform to evaluate the immune function impact of small molecule inhibitors that target the immune system.

PubMed

St Pierre, Cristina; Guo, Jane; Shin, John D; Engstrom, Laura W; Lee, Hyun-Hee; Herbert, Alan; Surdi, Laura; Baker, James; Salmon, Michael; Shah, Sanjiv; Ellis, J Michael; Houshyar, Hani; Crackower, Michael A; Kleinschek, Melanie A; Jones, Dallas C; Hicks, Alexandra; Zaller, Dennis M; Alves, Stephen E; Ramadas, Ravisankar A

2017-01-01

While the immune system is essential for the maintenance of the homeostasis, health and survival of humans, aberrant immune responses can lead to chronic inflammatory and autoimmune disorders. Pharmacological modulation of drug targets in the immune system to ameliorate disease also carry a risk of immunosuppression that could lead to adverse outcomes. Therefore, it is important to understand the 'immune fingerprint' of novel therapeutics as they relate to current and, clinically used immunological therapies to better understand their potential therapeutic benefit as well as immunosuppressive ability that might lead to adverse events such as infection risks and cancer. Since the mechanistic investigation of pharmacological modulators in a drug discovery setting is largely compound- and mechanism-centric but not comprehensive in terms of immune system impact, we developed a human tissue based functional assay platform to evaluate the impact of pharmacological modulators on a range of innate and adaptive immune functions. Here, we demonstrate that it is possible to generate a qualitative and quantitative immune system impact of pharmacological modulators, which might help better understand and predict the benefit-risk profiles of these compounds in the treatment of immune disorders.

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking †

PubMed Central

Kiku, Daisuke; Okutomi, Masatoshi

2017-01-01

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking. PMID:29194407

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking.

PubMed

Monno, Yusuke; Kiku, Daisuke; Tanaka, Masayuki; Okutomi, Masatoshi

2017-12-01

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking.

Upper-limb prosthetic control using wearable multichannel mechanomyography.

PubMed

Wilson, Samuel; Vaidyanathan, Ravi

2017-07-01

In this paper we introduce a robust multi-channel wearable sensor system for capturing user intent to control robotic hands. The interface is based on a fusion of inertial measurement and mechanomyography (MMG), which measures the vibrations of muscle fibres during motion. MMG is immune to issues such as sweat, skin impedance, and the need for a reference signal that is common to electromyography (EMG). The main contributions of this work are: 1) the hardware design of a fused inertial and MMG measurement system that can be worn on the arm, 2) a unified algorithm for detection, segmentation, and classification of muscle movement corresponding to hand gestures, and 3) experiments demonstrating the real-time control of a commercial prosthetic hand (Bebionic Version 2). Results show recognition of seven gestures, achieving an offline classification accuracy of 83.5% performed on five healthy subjects and one transradial amputee. The gesture recognition was then tested in real time on subsets of two and five gestures, with an average accuracy of 93.3% and 62.2% respectively. To our knowledge this is the first applied MMG based control system for practical prosthetic control.

Active edge maps for medical image registration

NASA Astrophysics Data System (ADS)

Kerwin, William; Yuan, Chun

2001-07-01

Applying edge detection prior to performing image registration yields several advantages over raw intensity- based registration. Advantages include the ability to register multicontrast or multimodality images, immunity to intensity variations, and the potential for computationally efficient algorithms. In this work, a common framework for edge-based image registration is formulated as an adaptation of snakes used in boundary detection. Called active edge maps, the new formulation finds a one-to-one transformation T(x) that maps points in a source image to corresponding locations in a target image using an energy minimization approach. The energy consists of an image component that is small when edge features are well matched in the two images, and an internal term that restricts T(x) to allowable configurations. The active edge map formulation is illustrated here with a specific example developed for affine registration of carotid artery magnetic resonance images. In this example, edges are identified using a magnitude of gradient operator, image energy is determined using a Gaussian weighted distance function, and the internal energy includes separate, adjustable components that control volume preservation and rigidity.

Optimization of cascading failure on complex network based on NNIA

NASA Astrophysics Data System (ADS)

Zhu, Qian; Zhu, Zhiliang; Qi, Yi; Yu, Hai; Xu, Yanjie

2018-07-01

Recently, the robustness of networks under cascading failure has attracted extensive attention. Different from previous studies, we concentrate on how to improve the robustness of the networks from the perspective of intelligent optimization. We establish two multi-objective optimization models that comprehensively consider the operational cost of the edges in the networks and the robustness of the networks. The NNIA (Non-dominated Neighbor Immune Algorithm) is applied to solve the optimization models. We finished simulations of the Barabási-Albert (BA) network and Erdös-Rényi (ER) network. In the solutions, we find the edges that can facilitate the propagation of cascading failure and the edges that can suppress the propagation of cascading failure. From the conclusions, we take optimal protection measures to weaken the damage caused by cascading failures. We also consider actual situations of operational cost feasibility of the edges. People can make a more practical choice based on the operational cost. Our work will be helpful in the design of highly robust networks or improvement of the robustness of networks in the future.

Lactation-Based Maternal Educational Immunity Crosses MHC Class I Barriers and Can Impart Th1 Immunity to Th2-Biased Recipients

PubMed Central

Ghosh, Mrinal K.; Muller, H. Konrad

2017-01-01

We have previously demonstrated lactational transfer of T cell–based immunity from dam to foster pup. In the short term, a significant part of transferred immunity is passive cellular immunity. However, as time progresses, this is replaced by what we have described as maternal educational immunity such that by young adulthood, all immune cells responding to a foster dam immunogen are the product of the foster pup’s thymus. To reduce confounding factors, this original demonstration used congenic/syngeneic dam and foster pup pairs. In this study, we investigated lactational transfer of immunity to Mycobacterium tuberculosis in MHC class I–mismatched animals, as well as from Th1-biased dams to Th2-biased foster pups. Using immunized C57BL/6J dams, lactational transfer to nonimmunized BALB/cJ foster pups resulted in much greater immunity than direct immunization in 5-wk-old pups (ex vivo assay of pup splenocytes). At this age, 82% of immunogen-responding cells in the pup spleen were produced through maternal educational immunity. FVB/NJ nonimmunized foster recipients had a greater number of maternal cells in the spleen and thymus but a much larger percentage was Foxp3+, resulting in equivalent immunity to direct immunization. Depletion of maternal Foxp3+ cells from pup splenocytes illustrated a substantial role for lactationally transferred dam regulatory T cells in suppression of the ex vivo response in FVB/NJ, but not BALB/cJ, recipients. We conclude that lactational transfer of immunity can cross MHC class I barriers and that Th1 immunity can be imparted to Th2-biased offspring; in some instances, it can be greater than that achieved by direct immunization. PMID:28747348

Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

NASA Astrophysics Data System (ADS)

Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

2017-02-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

Increasing Immunization Compliance by Reducing Provisional Admittance

ERIC Educational Resources Information Center

Davis, Wendy S.; Varni, Susan E.; Barry, Sara E.; Frankowski, Barbara L.; Harder, Valerie S.

2016-01-01

Students in Vermont with incomplete or undocumented immunization status are provisionally admitted to schools and historically had a calendar year to resolve their immunization status. The process of resolving these students' immunization status was challenging for school nurses. We conducted a school-based quality improvement effort to increase…

Distributed delays in a hybrid model of tumor-immune system interplay.

PubMed

Caravagna, Giulio; Graudenzi, Alex; d'Onofrio, Alberto

2013-02-01

A tumor is kinetically characterized by the presence of multiple spatio-temporal scales in which its cells interplay with, for instance, endothelial cells or Immune system effectors, exchanging various chemical signals. By its nature, tumor growth is an ideal object of hybrid modeling where discrete stochastic processes model low-numbers entities, and mean-field equations model abundant chemical signals. Thus, we follow this approach to model tumor cells, effector cells and Interleukin-2, in order to capture the Immune surveillance effect. We here present a hybrid model with a generic delay kernel accounting that, due to many complex phenomena such as chemical transportation and cellular differentiation, the tumor-induced recruitment of effectors exhibits a lag period. This model is a Stochastic Hybrid Automata and its semantics is a Piecewise Deterministic Markov process where a two-dimensional stochastic process is interlinked to a multi-dimensional mean-field system. We instantiate the model with two well-known weak and strong delay kernels and perform simulations by using an algorithm to generate trajectories of this process. Via simulations and parametric sensitivity analysis techniques we (i) relate tumor mass growth with the two kernels, we (ii) measure the strength of the Immune surveillance in terms of probability distribution of the eradication times, and (iii) we prove, in the oscillatory regime, the existence of a stochastic bifurcation resulting in delay-induced tumor eradication.

Testicular defense systems: immune privilege and innate immunity

PubMed Central

Zhao, Shutao; Zhu, Weiwei; Xue, Shepu; Han, Daishu

2014-01-01

The mammalian testis possesses a special immunological environment because of its properties of remarkable immune privilege and effective local innate immunity. Testicular immune privilege protects immunogenic germ cells from systemic immune attack, and local innate immunity is important in preventing testicular microbial infections. The breakdown of local testicular immune homeostasis may lead to orchitis, an etiological factor of male infertility. The mechanisms underlying testicular immune privilege have been investigated for a long time. Increasing evidence shows that both a local immunosuppressive milieu and systemic immune tolerance are involved in maintaining testicular immune privilege status. The mechanisms underlying testicular innate immunity are emerging based on the investigation of the pattern recognition receptor-mediated innate immune response in testicular cells. This review summarizes our current understanding of testicular defense mechanisms and identifies topics that merit further investigation. PMID:24954222

Implications for registry-based vaccine effectiveness studies from an evaluation of an immunization registry: a cross-sectional study.

PubMed

Mahon, Barbara E; Shea, Kimberly M; Dougherty, Nancy N; Loughlin, Anita M

2008-05-14

Population-based electronic immunization registries create the possibility of using registry data to conduct vaccine effectiveness studies which could have methodological advantages over traditional observational studies. For study validity, the base population would have to be clearly defined and the immunization status of members of the population accurately recorded in the registry. We evaluated a city-wide immunization registry, focusing on its potential as a tool to study pertussis vaccine effectiveness, especially in adolescents. We conducted two evaluations - one in sites that were active registry participants and one in sites that had implemented an electronic medical record with plans for future direct data transfer to the registry - of the ability to match patients' medical records to registry records and the accuracy of immunization records in the registry. For each site, records from current pediatric patients were chosen randomly. Data regarding pertussis-related immunizations, clinic usage, and demographic and identifying information were recorded; for 11-17-year-old subjects, information on MMR, hepatitis B, and varicella immunizations was also collected. Records were then matched, when possible, to registry records. For records with a registry match, immunization data were compared. Among 350 subjects from sites that were current registry users, 307 (87.7%) matched a registry record. Discrepancies in pertussis-related data were common for up-to-date status (22.6%), number of immunizations (34.7%), dates (10.2%), and formulation (34.4%). Among 442 subjects from sites that planned direct electronic transfer of immunization data to the registry, 393 (88.9%) would have matched a registry record; discrepancies occurred frequently in number of immunizations (11.9%), formulation (29.1%), manufacturer (94.4%), and lot number (95.1%.) Inability to match and immunization discrepancies were both more common in subjects who were older at their first visit to the provider site. For 11-17-year-old subjects, discrepancies were also common for MMR, hepatitis B, and varicella vaccination data. Provider records frequently could not be matched to registry records or had discrepancies in key immunization data. These issues were more common for older children and were present even with electronic data transfer. These results highlight general challenges that may face investigators wishing to use registry-based immunization data for vaccine effectiveness studies, especially in adolescents.

Implications for registry-based vaccine effectiveness studies from an evaluation of an immunization registry: A cross-sectional study

PubMed Central

Mahon, Barbara E; Shea, Kimberly M; Dougherty, Nancy N; Loughlin, Anita M

2008-01-01

Background Population-based electronic immunization registries create the possibility of using registry data to conduct vaccine effectiveness studies which could have methodological advantages over traditional observational studies. For study validity, the base population would have to be clearly defined and the immunization status of members of the population accurately recorded in the registry. We evaluated a city-wide immunization registry, focusing on its potential as a tool to study pertussis vaccine effectiveness, especially in adolescents. Methods We conducted two evaluations – one in sites that were active registry participants and one in sites that had implemented an electronic medical record with plans for future direct data transfer to the registry – of the ability to match patients' medical records to registry records and the accuracy of immunization records in the registry. For each site, records from current pediatric patients were chosen randomly. Data regarding pertussis-related immunizations, clinic usage, and demographic and identifying information were recorded; for 11–17-year-old subjects, information on MMR, hepatitis B, and varicella immunizations was also collected. Records were then matched, when possible, to registry records. For records with a registry match, immunization data were compared. Results Among 350 subjects from sites that were current registry users, 307 (87.7%) matched a registry record. Discrepancies in pertussis-related data were common for up-to-date status (22.6%), number of immunizations (34.7%), dates (10.2%), and formulation (34.4%). Among 442 subjects from sites that planned direct electronic transfer of immunization data to the registry, 393 (88.9%) would have matched a registry record; discrepancies occurred frequently in number of immunizations (11.9%), formulation (29.1%), manufacturer (94.4%), and lot number (95.1%.) Inability to match and immunization discrepancies were both more common in subjects who were older at their first visit to the provider site. For 11–17-year-old subjects, discrepancies were also common for MMR, hepatitis B, and varicella vaccination data. Conclusion Provider records frequently could not be matched to registry records or had discrepancies in key immunization data. These issues were more common for older children and were present even with electronic data transfer. These results highlight general challenges that may face investigators wishing to use registry-based immunization data for vaccine effectiveness studies, especially in adolescents. PMID:18479517

To give or not to give: Approaches to early childhood immunization delivery in Oregon rural primary care practices.

PubMed

Fagnan, Lyle J; Shipman, Scott A; Gaudino, James A; Mahler, Jo; Sussman, Andrew L; Holub, Jennifer

2011-01-01

Little is known about rural clinicians' perspectives regarding early childhood immunization delivery, their adherence to recommended best immunization practices, or the specific barriers they confront. To examine immunization practices, beliefs, and barriers among rural primary care clinicians for children in Oregon and compare those who deliver all recommended immunizations in their practices with those who do not. A mailed questionnaire was sent to all physicians, nurse practitioners, and physician assistants practicing primary care in rural communities throughout Oregon. While 39% of rural clinicians reported delivering all childhood immunizations in their clinic, 43% of clinicians reported that they refer patients elsewhere for some vaccinations, and 18% provided no immunizations in the clinic whatsoever. Leading reasons for referral include inadequate reimbursement, parental request, and storage and stocking difficulties. Nearly a third of respondents reported that they had some level of concern about the safety of immunizations, and 14% reported that concerns about safety were a specific reason for referring. Clinicians who delivered only some of the recommended immunizations were less likely than nonreferring clinicians to have adopted evidence-based best immunization practices. This study of rural clinicians in Oregon demonstrates the prevalence of barriers to primary care based immunization delivery in rural regions. While some barriers may be difficult to overcome, others may be amenable to educational outreach and support. Thus, efforts to improve population immunization rates should focus on promoting immunization "best practices" and enhancing the capacity of practices to provide immunizations and ensuring that any alternative means of delivering immunizations are effective. © 2011 National Rural Health Association.

To Give or Not to Give: Approaches to Early Childhood Immunization Delivery in Oregon Rural Primary Care Practices

PubMed Central

Fagnan, Lyle J.

2010-01-01

Context Little is known about rural clinicians’ perspectives regarding early childhood immunization delivery, their adherence to recommended best immunization practices, or the specific barriers they confront. Purpose To examine immunization practices, beliefs, and barriers among rural primary care clinicians for children in Oregon and compare those who deliver all recommended immunizations in their practices with those who do not. Methods A mailed questionnaire sent to all physicians, nurse practitioners, and physician assistants practicing primary care in rural communities throughout Oregon. Findings While 39% of rural clinicians reported delivering all childhood immunizations in their clinic, 43% of clinicians reported that they refer patients elsewhere for some vaccinations and 18% provided no immunizations in the clinic whatsoever. Leading reasons for referral include inadequate reimbursement, parental request, and storage and stocking difficulties. Nearly a third of respondents reported that they had some level of concern about the safety of immunizations, and 14% reported that concerns about safety were a specific reason for referring. Clinicians who delivered only some of the recommended immunizations were less likely than non-referring clinicians to have adopted evidence-based best immunization practices. Conclusions This study of rural clinicians in Oregon demonstrates the prevalence of barriers to primary-care-based immunization delivery in rural regions. While some barriers may be difficult to overcome, others may be amenable to educational outreach and support. Thus, efforts to improve population immunization rates should focus on promoting immunization “best practices” and enhancing the capacity of practices to provide immunizations and assuring that any alternative means of delivering immunizations are effective. PMID:21967382

iPS-cell derived dendritic cells and macrophages for cancer therapy.

PubMed

Senju, Satoru

2016-08-01

Antibody-based anti-cancer immunotherapy was recently recognized as one of the truly effective therapies for cancer patients. Antibodies against cell surface cancer antigens, such as CD20, and also those against immune-inhibitory molecules called "immune checkpoint blockers", such as CTLA4 or PD1, have emerged. Large-scale clinical trials have confirmed that, in some cases, antibody-based drugs are superior to conventional chemotherapeutic agents. These antibody-based drugs are now being manufactured employing a mass-production system by pharmaceutical companies. Anti-cancer therapy by immune cells, i.e. cell-based immunotherapy, is expected to be more effective than antibody therapy, because immune cells can recognize, infiltrate, and act in cancer tissues more directly than antibodies. In order to achieve cell-based anti-cancer immunotherapy, it is necessary to develop manufacturing systems for mass-production of immune cells. Our group has been studying immunotherapy with myeloid cells derived from ES cells or iPS cells. These pluripotent stem cells can be readily propagated under constant culture conditions, with expansion into a large quantity. We consider these stem cells to be the most suitable cellular source for mass-production of immune cells. This review introduces our studies on anti-cancer therapy with iPS cell-derived dendritic cells and iPS cell-derived macrophages.

Cryptanalysis of "an improvement over an image encryption method based on total shuffling"

NASA Astrophysics Data System (ADS)

Akhavan, A.; Samsudin, A.; Akhshani, A.

2015-09-01

In the past two decades, several image encryption algorithms based on chaotic systems had been proposed. Many of the proposed algorithms are meant to improve other chaos based and conventional cryptographic algorithms. Whereas, many of the proposed improvement methods suffer from serious security problems. In this paper, the security of the recently proposed improvement method for a chaos-based image encryption algorithm is analyzed. The results indicate the weakness of the analyzed algorithm against chosen plain-text.

Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases

PubMed Central

Arakelyan, Arsen; Nersisyan, Lilit; Petrek, Martin; Löffler-Wirth, Henry; Binder, Hans

2016-01-01

Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent. PMID:27200087

Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013)

NASA Astrophysics Data System (ADS)

Wei, Jun; Jiang, Guo-Qing; Liu, Xin

2017-09-01

This study proposed three algorithms that can potentially be used to provide sea surface temperature (SST) conditions for typhoon prediction models. Different from traditional data assimilation approaches, which provide prescribed initial/boundary conditions, our proposed algorithms aim to resolve a flow-dependent SST feedback between growing typhoons and oceans in the future time. Two of these algorithms are based on linear temperature equations (TE-based), and the other is based on an innovative technique involving machine learning (ML-based). The algorithms are then implemented into a Weather Research and Forecasting model for the simulation of typhoon to assess their effectiveness, and the results show significant improvement in simulated storm intensities by including ocean cooling feedback. The TE-based algorithm I considers wind-induced ocean vertical mixing and upwelling processes only, and thus obtained a synoptic and relatively smooth sea surface temperature cooling. The TE-based algorithm II incorporates not only typhoon winds but also ocean information, and thus resolves more cooling features. The ML-based algorithm is based on a neural network, consisting of multiple layers of input variables and neurons, and produces the best estimate of the cooling structure, in terms of its amplitude and position. Sensitivity analysis indicated that the typhoon-induced ocean cooling is a nonlinear process involving interactions of multiple atmospheric and oceanic variables. Therefore, with an appropriate selection of input variables and neuron sizes, the ML-based algorithm appears to be more efficient in prognosing the typhoon-induced ocean cooling and in predicting typhoon intensity than those algorithms based on linear regression methods.

Brief Report: Immune Factors in Autism: A Critical Review.

ERIC Educational Resources Information Center

Krause, Ilan; He, Ziao-Song; Gershwin, M. Eric; Shoenfeld, Yehuda

2002-01-01

This article reviews studies linking autistic disorder with various immune factors. It concludes that although various immune system abnormalities have been reported in children with autism, previous studies are largely association based and it remains difficult to draw conclusions regarding the role of immune factors in the etiopathogenesis of…

An Improved SoC Test Scheduling Method Based on Simulated Annealing Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Jingjing; Shen, Zhihang; Gao, Huaien; Chen, Bianna; Zheng, Weida; Xiong, Xiaoming

2017-02-01

In this paper, we propose an improved SoC test scheduling method based on simulated annealing algorithm (SA). It is our first to disorganize IP core assignment for each TAM to produce a new solution for SA, allocate TAM width for each TAM using greedy algorithm and calculate corresponding testing time. And accepting the core assignment according to the principle of simulated annealing algorithm and finally attain the optimum solution. Simultaneously, we run the test scheduling experiment with the international reference circuits provided by International Test Conference 2002(ITC’02) and the result shows that our algorithm is superior to the conventional integer linear programming algorithm (ILP), simulated annealing algorithm (SA) and genetic algorithm(GA). When TAM width reaches to 48,56 and 64, the testing time based on our algorithm is lesser than the classic methods and the optimization rates are 30.74%, 3.32%, 16.13% respectively. Moreover, the testing time based on our algorithm is very close to that of improved genetic algorithm (IGA), which is state-of-the-art at present.

Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

An Evidence-Based Project Demonstrating Increased School Immunization Compliance Following a School Nurse-Initiated Vaccine Compliance Strategy

ERIC Educational Resources Information Center

Swallow, Wendy; Roberts, Jill C.

2016-01-01

During the 2012-2013 school year, only 66% of students at a Northern Indiana High School were in compliance with school immunization requirements. We report here successful implementation of evidence-based, time, and cost-effective methods aimed at increasing school immunization compliance. A three-stage strategy initiated by the school nurse was…

Research on compressive sensing reconstruction algorithm based on total variation model

NASA Astrophysics Data System (ADS)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

The immune system in space, including Earth-based benefits of space-based research.

PubMed

Sonnenfeld, Gerald

2005-08-01

Exposure to space flight conditions has been shown to result in alterations in immune responses. Changes in immune responses of humans and experimental animals have been shown to be altered during and after space flight of humans and experimental animals or cell cultures of lymphoid cells. Exposure of subjects to ground-based models of space flight conditions, such as hindlimb unloading of rodents or chronic bed rest of humans, has also resulted in changes in the immune system. The relationship of these changes to compromised resistance to infection or tumors in space flight has not been fully established, but results from model systems suggest that alterations in the immune system that occur in space flight conditions may be related to decreases in resistance to infection. The establishment of such a relationship could lead to the development of countermeasures that could prevent or ameliorate any compromises in resistance to infection resulting from exposure to space flight conditions. An understanding of the mechanisms of space flight conditions effects on the immune response and development of countermeasures to prevent them could contribute to the development of treatments for compromised immunity on earth.

Spectrum sensing algorithm based on autocorrelation energy in cognitive radio networks

NASA Astrophysics Data System (ADS)

Ren, Shengwei; Zhang, Li; Zhang, Shibing

2016-10-01

Cognitive radio networks have wide applications in the smart home, personal communications and other wireless communication. Spectrum sensing is the main challenge in cognitive radios. This paper proposes a new spectrum sensing algorithm which is based on the autocorrelation energy of signal received. By taking the autocorrelation energy of the received signal as the statistics of spectrum sensing, the effect of the channel noise on the detection performance is reduced. Simulation results show that the algorithm is effective and performs well in low signal-to-noise ratio. Compared with the maximum generalized eigenvalue detection (MGED) algorithm, function of covariance matrix based detection (FMD) algorithm and autocorrelation-based detection (AD) algorithm, the proposed algorithm has 2 11 dB advantage.

Isobaric Reconstruction of the Baryonic Acoustic Oscillation

NASA Astrophysics Data System (ADS)

Wang, Xin; Yu, Hao-Ran; Zhu, Hong-Ming; Yu, Yu; Pan, Qiaoyin; Pen, Ue-Li

2017-06-01

In this Letter, we report a significant recovery of the linear baryonic acoustic oscillation (BAO) signature by applying the isobaric reconstruction algorithm to the nonlinear matter density field. Assuming only the longitudinal component of the displacement being cosmologically relevant, this algorithm iteratively solves the coordinate transform between the Lagrangian and Eulerian frames without requiring any specific knowledge of the dynamics. For dark matter field, it produces the nonlinear displacement potential with very high fidelity. The reconstruction error at the pixel level is within a few percent and is caused only by the emergence of the transverse component after the shell-crossing. As it circumvents the strongest nonlinearity of the density evolution, the reconstructed field is well described by linear theory and immune from the bulk-flow smearing of the BAO signature. Therefore, this algorithm could significantly improve the measurement accuracy of the sound horizon scale s. For a perfect large-scale structure survey at redshift zero without Poisson or instrumental noise, the fractional error {{Δ }}s/s is reduced by a factor of ˜2.7, very close to the ideal limit with the linear power spectrum and Gaussian covariance matrix.

Non-Invasive UWB Sensing of Astronauts' Breathing Activity

PubMed Central

Baldi, Marco; Cerri, Graziano; Chiaraluce, Franco; Eusebi, Lorenzo; Russo, Paola

2015-01-01

The use of a UWB system for sensing breathing activity of astronauts must account for many critical issues specific to the space environment. The aim of this paper is twofold. The first concerns the definition of design constraints about the pulse amplitude and waveform to transmit, as well as the immunity requirements of the receiver. The second issue concerns the assessment of the procedures and the characteristics of the algorithms to use for signal processing to retrieve the breathing frequency and respiration waveform. The algorithm has to work correctly in the presence of surrounding electromagnetic noise due to other sources in the environment. The highly reflecting walls increase the difficulty of the problem and the hostile scenario has to be accurately characterized. Examples of signal processing techniques able to recover breathing frequency in significant and realistic situations are shown and discussed. PMID:25558995

In silico Derivation of HLA-Specific Alloreactivity Potential from Whole Exome Sequencing of Stem-Cell Transplant Donors and Recipients: Understanding the Quantitative Immunobiology of Allogeneic Transplantation

PubMed Central

Jameson-Lee, Max; Koparde, Vishal; Griffith, Phil; Scalora, Allison F.; Sampson, Juliana K.; Khalid, Haniya; Sheth, Nihar U.; Batalo, Michael; Serrano, Myrna G.; Roberts, Catherine H.; Hess, Michael L.; Buck, Gregory A.; Neale, Michael C.; Manjili, Masoud H.; Toor, Amir Ahmed

2014-01-01

Donor T-cell mediated graft versus host (GVH) effects may result from the aggregate alloreactivity to minor histocompatibility antigens (mHA) presented by the human leukocyte antigen (HLA) molecules in each donor–recipient pair undergoing stem-cell transplantation (SCT). Whole exome sequencing has previously demonstrated a large number of non-synonymous single nucleotide polymorphisms (SNP) present in HLA-matched recipients of SCT donors (GVH direction). The nucleotide sequence flanking each of these SNPs was obtained and the amino acid sequence determined. All the possible nonameric peptides incorporating the variant amino acid resulting from these SNPs were interrogated in silico for their likelihood to be presented by the HLA class I molecules using the Immune Epitope Database stabilized matrix method (SMM) and NetMHCpan algorithms. The SMM algorithm predicted that a median of 18,396 peptides weakly bound HLA class I molecules in individual SCT recipients, and 2,254 peptides displayed strong binding. A similar library of presented peptides was identified when the data were interrogated using the NetMHCpan algorithm. The bioinformatic algorithm presented here demonstrates that there may be a high level of mHA variation in HLA-matched individuals, constituting a HLA-specific alloreactivity potential. PMID:25414699

In silico Derivation of HLA-Specific Alloreactivity Potential from Whole Exome Sequencing of Stem-Cell Transplant Donors and Recipients: Understanding the Quantitative Immunobiology of Allogeneic Transplantation.

PubMed

Jameson-Lee, Max; Koparde, Vishal; Griffith, Phil; Scalora, Allison F; Sampson, Juliana K; Khalid, Haniya; Sheth, Nihar U; Batalo, Michael; Serrano, Myrna G; Roberts, Catherine H; Hess, Michael L; Buck, Gregory A; Neale, Michael C; Manjili, Masoud H; Toor, Amir Ahmed

2014-01-01

Donor T-cell mediated graft versus host (GVH) effects may result from the aggregate alloreactivity to minor histocompatibility antigens (mHA) presented by the human leukocyte antigen (HLA) molecules in each donor-recipient pair undergoing stem-cell transplantation (SCT). Whole exome sequencing has previously demonstrated a large number of non-synonymous single nucleotide polymorphisms (SNP) present in HLA-matched recipients of SCT donors (GVH direction). The nucleotide sequence flanking each of these SNPs was obtained and the amino acid sequence determined. All the possible nonameric peptides incorporating the variant amino acid resulting from these SNPs were interrogated in silico for their likelihood to be presented by the HLA class I molecules using the Immune Epitope Database stabilized matrix method (SMM) and NetMHCpan algorithms. The SMM algorithm predicted that a median of 18,396 peptides weakly bound HLA class I molecules in individual SCT recipients, and 2,254 peptides displayed strong binding. A similar library of presented peptides was identified when the data were interrogated using the NetMHCpan algorithm. The bioinformatic algorithm presented here demonstrates that there may be a high level of mHA variation in HLA-matched individuals, constituting a HLA-specific alloreactivity potential.

Pilot-based parametric channel estimation algorithm for DCO-OFDM-based visual light communications

NASA Astrophysics Data System (ADS)

Qian, Xuewen; Deng, Honggui; He, Hailang

2017-10-01

Due to wide modulation bandwidth in optical communication, multipath channels may be non-sparse and deteriorate communication performance heavily. Traditional compressive sensing-based channel estimation algorithm cannot be employed in this kind of situation. In this paper, we propose a practical parametric channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based visual light communication (VLC) systems based on modified zero correlation code (ZCC) pair that has the impulse-like correlation property. Simulation results show that the proposed algorithm achieves better performances than existing least squares (LS)-based algorithm in both bit error ratio (BER) and frequency response estimation.

New drugs, new toxicities: severe side effects of modern targeted and immunotherapy of cancer and their management.

PubMed

Kroschinsky, Frank; Stölzel, Friedrich; von Bonin, Simone; Beutel, Gernot; Kochanek, Matthias; Kiehl, Michael; Schellongowski, Peter

2017-04-14

Pharmacological and cellular treatment of cancer is changing dramatically with benefits for patient outcome and comfort, but also with new toxicity profiles. The majority of adverse events can be classified as mild or moderate, but severe and life-threatening complications requiring ICU admission also occur. This review will focus on pathophysiology, symptoms, and management of these events based on the available literature.While standard antineoplastic therapy is associated with immunosuppression and infections, some of the recent approaches induce overwhelming inflammation and autoimmunity. Cytokine-release syndrome (CRS) describes a complex of symptoms including fever, hypotension, and skin reactions as well as lab abnormalities. CRS may occur after the infusion of monoclonal or bispecific antibodies (MABs, BABs) targeting immune effectors and tumor cells and is a major concern in recipients of chimeric antigen receptor (CAR) modified T lymphocytes as well. BAB and CAR T-cell treatment may also be compromised by central nervous system (CNS) toxicities such as encephalopathy, cerebellar alteration, disturbed consciousness, or seizures. While CRS is known to be induced by exceedingly high levels of inflammatory cytokines, the pathophysiology of CNS events is still unclear. Treatment with antibodies against inhibiting immune checkpoints can lead to immune-related adverse events (IRAEs); colitis, diarrhea, and endocrine disorders are often the cause for ICU admissions.Respiratory distress is the main reason for ICU treatment in cancer patients and is attributable to infectious agents in most cases. In addition, some of the new drugs are reported to cause non-infectious lung complications. While drug-induced interstitial pneumonitis was observed in a substantial number of patients treated with phosphoinositol-3-kinase inhibitors, IRAEs may also affect the lungs.Inhibitors of angiogenetic pathways have increased the antineoplastic portfolio. However, vessel formation is also essential for regeneration and tissue repair. Therefore, severe vascular side effects, including thromboembolic events, gastrointestinal bleeding or perforation, hypertension, and congestive heart failure, compromise antitumor efficacy.The limited knowledge of the pathophysiology and management of life-threatening complications relating to new cancer drugs presents a need to provide ICU staff, oncologists, and organ specialists with evidence-based algorithms.

A Collaborative Recommend Algorithm Based on Bipartite Community

PubMed Central

Fu, Yuchen; Liu, Quan; Cui, Zhiming

2014-01-01

The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database. PMID:24955393

A Cancer Gene Selection Algorithm Based on the K-S Test and CFS.

PubMed

Su, Qiang; Wang, Yina; Jiang, Xiaobing; Chen, Fuxue; Lu, Wen-Cong

2017-01-01

To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selection (CFS) principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. We adopted support vector machines (SVM) as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR), and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms.

Immunization Information System and Informatics to Promote Immunizations: Perspective From Minnesota Immunization Information Connection.

PubMed

Muscoplat, Miriam Halstead; Rajamani, Sripriya

2017-01-01

The vision for management of immunization information is availability of real-time consolidated data and services for all ages, to clinical, public health, and other stakeholders. This is being executed through Immunization Information Systems (IISs), which are population-based and confidential computerized systems present in most US states and territories. Immunization Information Systems offer many functionalities, such as immunization assessment reports, client follow-up, reminder/recall feature, vaccine management tools, state-supplied vaccine ordering, comprehensive immunization history, clinical decision support/vaccine forecasting and recommendations, data processing, and data exchange. This perspective article will present various informatics tools in an IIS, in the context of the Minnesota Immunization Information Connection.

Hierarchical trie packet classification algorithm based on expectation-maximization clustering.

PubMed

Bi, Xia-An; Zhao, Junxia

2017-01-01

With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm.

Diagnostic Approach to Ocular Toxoplasmosis

PubMed Central

Garweg, Justus G; de Groot-Mijnes, Jolanda DF; Montoya, Jose G

2011-01-01

Toxoplasmic retinochoroiditis is deemed a local event, which may fail to evoke a detectable systemic immune response. A correct diagnosis of the disease is a necessary basis for estimating its clinical burden. This is not so difficult in a typical clinical picture. In atypical cases, further diagnostic efforts are to be installed. Although the aqueous humor may be analyzed for specific antibodies or the presence of parasitic DNA, the DNA burden therein is low, and in rare instances a confirmation would necessitate vitreous sampling. A laboratory confirmation of the diagnosis is frustrated by individual differences in the time elapsing between clinical symptoms and activation of specific antibody production, which may result in false negatives. In congenital ocular toxoplasmosis, a delay in the onset of specific local antibody production could reflect immune tolerance. Herein, the authors attempt to provide a simple and practicable algorithm for a clinically tailored diagnostic approach in atypical instances. PMID:21770803

High-dimensional single-cell analysis reveals the immune signature of narcolepsy.

PubMed

Hartmann, Felix J; Bernard-Valnet, Raphaël; Quériault, Clémence; Mrdjen, Dunja; Weber, Lukas M; Galli, Edoardo; Krieg, Carsten; Robinson, Mark D; Nguyen, Xuan-Hung; Dauvilliers, Yves; Liblau, Roland S; Becher, Burkhard

2016-11-14

Narcolepsy type 1 is a devastating neurological sleep disorder resulting from the destruction of orexin-producing neurons in the central nervous system (CNS). Despite its striking association with the HLA-DQB1*06:02 allele, the autoimmune etiology of narcolepsy has remained largely hypothetical. Here, we compared peripheral mononucleated cells from narcolepsy patients with HLA-DQB1*06:02-matched healthy controls using high-dimensional mass cytometry in combination with algorithm-guided data analysis. Narcolepsy patients displayed multifaceted immune activation in CD4 + and CD8 + T cells dominated by elevated levels of B cell-supporting cytokines. Additionally, T cells from narcolepsy patients showed increased production of the proinflammatory cytokines IL-2 and TNF. Although it remains to be established whether these changes are primary to an autoimmune process in narcolepsy or secondary to orexin deficiency, these findings are indicative of inflammatory processes in the pathogenesis of this enigmatic disease. © 2016 Hartmann et al.

High-dimensional single-cell analysis reveals the immune signature of narcolepsy

PubMed Central

Quériault, Clémence; Krieg, Carsten; Nguyen, Xuan-Hung

2016-01-01

Narcolepsy type 1 is a devastating neurological sleep disorder resulting from the destruction of orexin-producing neurons in the central nervous system (CNS). Despite its striking association with the HLA-DQB1*06:02 allele, the autoimmune etiology of narcolepsy has remained largely hypothetical. Here, we compared peripheral mononucleated cells from narcolepsy patients with HLA-DQB1*06:02-matched healthy controls using high-dimensional mass cytometry in combination with algorithm-guided data analysis. Narcolepsy patients displayed multifaceted immune activation in CD4+ and CD8+ T cells dominated by elevated levels of B cell–supporting cytokines. Additionally, T cells from narcolepsy patients showed increased production of the proinflammatory cytokines IL-2 and TNF. Although it remains to be established whether these changes are primary to an autoimmune process in narcolepsy or secondary to orexin deficiency, these findings are indicative of inflammatory processes in the pathogenesis of this enigmatic disease. PMID:27821550

WEALTH-BASED INEQUALITY IN CHILD IMMUNIZATION IN INDIA: A DECOMPOSITION APPROACH.

PubMed

Debnath, Avijit; Bhattacharjee, Nairita

2018-05-01

SummaryDespite years of health and medical advancement, children still suffer from infectious diseases that are vaccine preventable. India reacted in 1978 by launching the Expanded Programme on Immunization in an attempt to reduce the incidence of vaccine-preventable diseases (VPDs). Although the nation has made remarkable progress over the years, there is significant variation in immunization coverage across different socioeconomic strata. This study attempted to identify the determinants of wealth-based inequality in child immunization using a new, modified method. The present study was based on 11,001 eligible ever-married women aged 15-49 and their children aged 12-23 months. Data were from the third District Level Household and Facility Survey (DLHS-3) of India, 2007-08. Using an approximation of Erreyger's decomposition technique, the study identified unequal access to antenatal care as the main factor associated with inequality in immunization coverage in India.

Immune Cells in Blood Recognize Tumors

Cancer.gov

NCI scientists have developed a novel strategy for identifying immune cells circulating in the blood that recognize specific proteins on tumor cells, a finding they believe may have potential implications for immune-based therapies.

A Model for the Stop Planning and Timetables of Customized Buses

PubMed Central

Yang, Yang

2017-01-01

Customized buses (CBs) are a new mode of public transportation and an important part of diversified public transportation, providing advanced, attractive and user-led service. The operational activity of a CB is planned by aggregating space–time demand and similar passenger travel demands. Based on an analysis of domestic and international research and the current development of CBs in China and considering passenger travel data, this paper studies the problems associated with the operation of CBs, such as stop selection, line planning and timetables, and establishes a model for the stop planning and timetables of CBs. The improved immune genetic algorithm (IIGA) is used to solve the model with regard to the following: 1) multiple population design and transport operator design, 2) memory library design, 3) mutation probability design and crossover probability design, and 4) the fitness calculation of the gene segment. Finally, a real-world example in Beijing is calculated, and the model and solution results are verified and analyzed. The results illustrate that the IIGA solves the model and is superior to the basic genetic algorithm in terms of the number of passengers, travel time, average passenger travel time, average passenger arrival time ahead of schedule and total line revenue. This study covers the key issues involving operational systems of CBs, combines theoretical research and empirical analysis, and provides a theoretical foundation for the planning and operation of CBs. PMID:28056041

A Model for the Stop Planning and Timetables of Customized Buses.

PubMed

Ma, Jihui; Zhao, Yanqing; Yang, Yang; Liu, Tao; Guan, Wei; Wang, Jiao; Song, Cuiying

2017-01-01

Customized buses (CBs) are a new mode of public transportation and an important part of diversified public transportation, providing advanced, attractive and user-led service. The operational activity of a CB is planned by aggregating space-time demand and similar passenger travel demands. Based on an analysis of domestic and international research and the current development of CBs in China and considering passenger travel data, this paper studies the problems associated with the operation of CBs, such as stop selection, line planning and timetables, and establishes a model for the stop planning and timetables of CBs. The improved immune genetic algorithm (IIGA) is used to solve the model with regard to the following: 1) multiple population design and transport operator design, 2) memory library design, 3) mutation probability design and crossover probability design, and 4) the fitness calculation of the gene segment. Finally, a real-world example in Beijing is calculated, and the model and solution results are verified and analyzed. The results illustrate that the IIGA solves the model and is superior to the basic genetic algorithm in terms of the number of passengers, travel time, average passenger travel time, average passenger arrival time ahead of schedule and total line revenue. This study covers the key issues involving operational systems of CBs, combines theoretical research and empirical analysis, and provides a theoretical foundation for the planning and operation of CBs.

Immunological signature of the different clinical stages of the HTLV-1 infection: establishing serum biomarkers for HTLV-1-associated disease morbidity.

PubMed

Starling, Ana Lúcia Borges; Coelho-Dos-Reis, Jordana Grazziela Alves; Peruhype-Magalhães, Vanessa; Pascoal-Xavier, Marcelo Antônio; Gonçalves, Denise Utsch; Béla, Samantha Ribeiro; Lambertucci, José Roberto; Labanca, Ludimila; Souza Pereira, Silvio Roberto; Teixeira-Carvalho, Andréa; Ribas, João Gabriel; Trindade, Bruno Caetano; Faccioli, Lucia Helena; Carneiro-Proietti, Anna Bárbara Freitas; Martins-Filho, Olindo Assis

2015-01-01

This study aimed at establishing the immunological signature and an algorithm for clinical management of the different clinical stages of the HTLV-1-infection based on serum biomarkers. A panel of serum biomarkers was evaluated by four sets of innovative/non-conventional data analysis approaches in samples from 87 HTLV-1 patients: asymptomatic carriers (AC), putative HTLV-1 associated myelopathy/tropical spastic paraparesis (pHAM/TSP) and HAM/TSP. The analysis of cumulative curves and molecular signatures pointed out that HAM/TSP presented a pro-inflammatory profile mediated by CXCL10/LTB-4/IL-6/TNF-α/IFN-γ, counterbalanced by IL-4/IL-10. The analysis of biomarker networks showed that AC presented a strongly intertwined pro-inflammatory/regulatory net with IL-4/IL-10 playing a central role, while HAM/TSP exhibited overall immune response toward a predominant pro-inflammatory profile. At last, the classification and regression trees proposed for clinical practice allowed for the construction of an algorithm to discriminate AC, pHAM and HAM/TSP patients with the elected biomarkers: IFN-γ, TNF-α, IL-10, IL-6, IL-4 and CysLT. These findings reveal a complex interaction among chemokine/leukotriene/cytokine in HTLV-1 infection and suggest the use of the selected but combined biomarkers for the follow-up/diagnosis of disease morbidity of HTLV-1-infected individuals.

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

PubMed

Yang, Shengxiang

2008-01-01

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

PCA-LBG-based algorithms for VQ codebook generation

NASA Astrophysics Data System (ADS)

Tsai, Jinn-Tsong; Yang, Po-Yuan

2015-04-01

Vector quantisation (VQ) codebooks are generated by combining principal component analysis (PCA) algorithms with Linde-Buzo-Gray (LBG) algorithms. All training vectors are grouped according to the projected values of the principal components. The PCA-LBG-based algorithms include (1) PCA-LBG-Median, which selects the median vector of each group, (2) PCA-LBG-Centroid, which adopts the centroid vector of each group, and (3) PCA-LBG-Random, which randomly selects a vector of each group. The LBG algorithm finds a codebook based on the better vectors sent to an initial codebook by the PCA. The PCA performs an orthogonal transformation to convert a set of potentially correlated variables into a set of variables that are not linearly correlated. Because the orthogonal transformation efficiently distinguishes test image vectors, the proposed PCA-LBG-based algorithm is expected to outperform conventional algorithms in designing VQ codebooks. The experimental results confirm that the proposed PCA-LBG-based algorithms indeed obtain better results compared to existing methods reported in the literature.

Optimizing Controlling-Value-Based Power Gating with Gate Count and Switching Activity

NASA Astrophysics Data System (ADS)

Chen, Lei; Kimura, Shinji

In this paper, a new heuristic algorithm is proposed to optimize the power domain clustering in controlling-value-based (CV-based) power gating technology. In this algorithm, both the switching activity of sleep signals (p) and the overall numbers of sleep gates (gate count, N) are considered, and the sum of the product of p and N is optimized. The algorithm effectively exerts the total power reduction obtained from the CV-based power gating. Even when the maximum depth is kept to be the same, the proposed algorithm can still achieve power reduction approximately 10% more than that of the prior algorithms. Furthermore, detailed comparison between the proposed heuristic algorithm and other possible heuristic algorithms are also presented. HSPICE simulation results show that over 26% of total power reduction can be obtained by using the new heuristic algorithm. In addition, the effect of dynamic power reduction through the CV-based power gating method and the delay overhead caused by the switching of sleep transistors are also shown in this paper.

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

PubMed

Konstorum, Anna; Vella, Anthony T; Adler, Adam J; Laubenbacher, Reinhard C

2017-06-01

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development. © 2017 The Author(s).

Hybrid employment recommendation algorithm based on Spark

NASA Astrophysics Data System (ADS)

Li, Zuoquan; Lin, Yubei; Zhang, Xingming

2017-08-01

Aiming at the real-time application of collaborative filtering employment recommendation algorithm (CF), a clustering collaborative filtering recommendation algorithm (CCF) is developed, which applies hierarchical clustering to CF and narrows the query range of neighbour items. In addition, to solve the cold-start problem of content-based recommendation algorithm (CB), a content-based algorithm with users’ information (CBUI) is introduced for job recommendation. Furthermore, a hybrid recommendation algorithm (HRA) which combines CCF and CBUI algorithms is proposed, and implemented on Spark platform. The experimental results show that HRA can overcome the problems of cold start and data sparsity, and achieve good recommendation accuracy and scalability for employment recommendation.

Pharmacists as immunizers: a survey of community pharmacists' willingness to administer adult immunizations.

PubMed

Edwards, Nicholas; Gorman Corsten, Erin; Kiberd, Mathew; Bowles, Susan; Isenor, Jennifer; Slayter, Kathryn; McNeil, Shelly

2015-04-01

Adult immunization rates worldwide fall below desired targets. Pharmacists are highly accessible healthcare providers with the potential to increase immunization rates among adults by administering vaccines in their practice setting. To determine the attitudes of community-based Canadian pharmacists with respect to expanding their scope of practice to include administration of immunizations. An internet-based survey was emailed to community pharmacists across Canada. The survey was piloted through focus groups for qualitative feedback, tested for content validity, and test-retest reliability prior to dissemination. There were 495 responses to the survey. The majority (88 %) agreed that pharmacists as immunizers would increase public access, improve rates (84 %), and be acceptable to the public (72 %). However, only 68 % agreed that pharmacists should be permitted to immunize. The majority of respondents (90 %) agreed that certification in vaccine administration should be required for pharmacists to administer vaccines. Pharmacists identified education, reimbursement, and negative interactions with other providers as barriers to pharmacists administering vaccines. Canadian pharmacists are willing to expand their scope of practice to include immunization. However, implementation requires professional development and certification in vaccine administration.

A prime-boost immunization regimen based on a simian adenovirus 36 vectored multi-stage malaria vaccine induces protective immunity in mice.

PubMed

Fonseca, Jairo A; McCaffery, Jessica N; Kashentseva, Elena; Singh, Balwan; Dmitriev, Igor P; Curiel, David T; Moreno, Alberto

2017-05-31

Malaria remains a considerable burden on public health. In 2015, the WHO estimates there were 212 million malaria cases causing nearly 429,000 deaths globally. A highly effective malaria vaccine is needed to reduce the burden of this disease. We have developed an experimental vaccine candidate (PyCMP) based on pre-erythrocytic (CSP) and erythrocytic (MSP1) stage antigens derived from the rodent malaria parasite P. yoelii. Our protein-based vaccine construct induces protective antibodies and CD4 + T cell responses. Based on evidence that viral vectors increase CD8 + T cell-mediated immunity, we also have tested heterologous prime-boost immunization regimens that included human adenovirus serotype 5 vector (Ad5), obtaining protective CD8 + T cell responses. While Ad5 is commonly used for vaccine studies, the high prevalence of pre-existing immunity to Ad5 severely compromises its utility. Here, we report the use of the novel simian adenovirus 36 (SAd36) as a candidate for a vectored malaria vaccine since this virus is not known to infect humans, and it is not neutralized by anti-Ad5 antibodies. Our study shows that the recombinant SAd36PyCMP can enhance specific CD8 + T cell response and elicit similar antibody titers when compared to an immunization regimen including the recombinant Ad5PyCMP. The robust immune responses induced by SAd36PyCMP are translated into a lower parasite load following P. yoelii infectious challenge when compared to mice immunized with Ad5PyCMP. Copyright © 2017 Elsevier Ltd. All rights reserved.

Pneumococcal surface protein A (PspA) is effective at eliciting T cell-mediated responses during invasive pneumococcal disease in adults

PubMed Central

Baril, L; Dietemann, J; Essevaz-Roulet, M; Béniguel, L; Coan, P; Briles, D E; Guy, B; Cozon, G

2006-01-01

Humoral immune response is essential for protection against invasive pneumococcal disease and this property is the basis of the polysaccharide-based anti-pneumococcal vaccines. Pneumococcal surface protein A (PspA), a cell-wall-associated surface protein, is a promising component for the next generation of pneumococcal vaccines. This PspA antigen has been shown to stimulate an antibody-based immunity. In the present study, we evaluated the capacity of PspA to stimulate CD4+ T cells which are needed for the correct development of a B cell based immune response in humans. Cellular immunity to PspA was evaluated by whole-blood culture with different pneumococcal antigens, followed by flow cytometric detection of activated CD4+CD25+ T cells. T cell-mediated immune responses to recombinant PspA proteins were assessed in acute-phase and convalescent blood from adults with invasive pneumococcal disease and in blood from healthy subjects. All cases had detectable antibodies against PspA on admission. We found that invasive pneumococcal disease induced transient T cell depletion but adaptive immune responses strengthened markedly during convalescence. The increased production of both interleukin (IL)-10 and interferon (IFN)-γ during convalescence suggests that these cytokines may be involved in modulating antibody-based immunity to pneumococcal disease. We demonstrated that PspA is efficient at eliciting T cell immune responses and antibodies to PspA. This study broadens the applicability of recombinant PspA as potent pneumococcal antigen for vaccination against S. pneumoniae. PMID:16879247

Improved targeted immunization strategies based on two rounds of selection

NASA Astrophysics Data System (ADS)

Xia, Ling-Ling; Song, Yu-Rong; Li, Chan-Chan; Jiang, Guo-Ping

2018-04-01

In the case of high degree targeted immunization where the number of vaccine is limited, when more than one node associated with the same degree meets the requirement of high degree centrality, how can we choose a certain number of nodes from those nodes, so that the number of immunized nodes will not exceed the limit? In this paper, we introduce a new idea derived from the selection process of second-round exam to solve this problem and then propose three improved targeted immunization strategies. In these proposed strategies, the immunized nodes are selected through two rounds of selection, where we increase the quotas of first-round selection according the evaluation criterion of degree centrality and then consider another characteristic parameter of node, such as node's clustering coefficient, betweenness and closeness, to help choose targeted nodes in the second-round selection. To validate the effectiveness of the proposed strategies, we compare them with the degree immunizations including the high degree targeted and the high degree adaptive immunizations using two metrics: the size of the largest connected component of immunized network and the number of infected nodes. Simulation results demonstrate that the proposed strategies based on two rounds of sorting are effective for heterogeneous networks and their immunization effects are better than that of the degree immunizations.

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

PubMed

Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

2015-01-01

A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

PubMed

Karayiannis, Nicolaos B; Mukherjee, Amit; Glover, John R; Ktonas, Periklis Y; Frost, James D; Hrachovy, Richard A; Mizrahi, Eli M

2006-04-01

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

An Image Encryption Algorithm Based on Information Hiding

NASA Astrophysics Data System (ADS)

Ge, Xin; Lu, Bin; Liu, Fenlin; Gong, Daofu

Aiming at resolving the conflict between security and efficiency in the design of chaotic image encryption algorithms, an image encryption algorithm based on information hiding is proposed based on the “one-time pad” idea. A random parameter is introduced to ensure a different keystream for each encryption, which has the characteristics of “one-time pad”, improving the security of the algorithm rapidly without significant increase in algorithm complexity. The random parameter is embedded into the ciphered image with information hiding technology, which avoids negotiation for its transport and makes the application of the algorithm easier. Algorithm analysis and experiments show that the algorithm is secure against chosen plaintext attack, differential attack and divide-and-conquer attack, and has good statistical properties in ciphered images.

Infections and the risk of incident giant cell arteritis: a population-based, case-control study.

PubMed

Rhee, Rennie L; Grayson, Peter C; Merkel, Peter A; Tomasson, Gunnar

2017-06-01

Alterations in the immune system and infections are suspected to increase susceptibility to giant cell arteritis (GCA). Recently herpes zoster has been directly implicated in the pathogenesis of GCA. We examined the association between prior infections, in particular herpes zoster, and incident GCA in a population-based cohort. A nested case-control study was performed using an electronic database from the UK. Cases with newly diagnosed GCA were identified using a validated algorithm and compared with age-matched, sex-matched and practice-matched controls. Conditional logistic regression was used to examine the relationship between any infection or herpes zoster infection on the development of GCA after adjusting for potential confounders; results were expressed as incidence rate ratios (IRRs). There were 4559 cases of GCA and 22 795 controls. Any prior infection and herpes zoster were associated with incident GCA (IRR 1.26 (95% CI 1.16 to 1.36), p<0.01; and 1.17 (95% CI 1.04 to 1.32), p<0.01, respectively). A greater number of infections was associated with a higher risk of developing GCA (IRR for 1, 2-4 and ≥5 infections was 1.28, 1.60 and 2.18, respectively). Antecedent infections and, to a lesser extent, herpes zoster infections are modestly associated with incident GCA. These data provide population-level support for the hypothesis that long-standing alterations of the immune system are associated with susceptibility to GCA and suggest that herpes zoster is unlikely to play a major causal role in the pathogenesis of GCA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Country Immunization Information System Assessments - Kenya, 2015 and Ghana, 2016.

PubMed

Scott, Colleen; Clarke, Kristie E N; Grevendonk, Jan; Dolan, Samantha B; Ahmed, Hussein Osman; Kamau, Peter; Ademba, Peter Aswani; Osadebe, Lynda; Bonsu, George; Opare, Joseph; Diamenu, Stanley; Amenuvegbe, Gregory; Quaye, Pamela; Osei-Sarpong, Fred; Abotsi, Francis; Ankrah, Joseph Dwomor; MacNeil, Adam

2017-11-10

The collection, analysis, and use of data to measure and improve immunization program performance are priorities for the World Health Organization (WHO), global partners, and national immunization programs (NIPs). High quality data are essential for evidence-based decision-making to support successful NIPs. Consistent recording and reporting practices, optimal access to and use of health information systems, and rigorous interpretation and use of data for decision-making are characteristics of high-quality immunization information systems. In 2015 and 2016, immunization information system assessments (IISAs) were conducted in Kenya and Ghana using a new WHO and CDC assessment methodology designed to identify root causes of immunization data quality problems and facilitate development of plans for improvement. Data quality challenges common to both countries included low confidence in facility-level target population data (Kenya = 50%, Ghana = 53%) and poor data concordance between child registers and facility tally sheets (Kenya = 0%, Ghana = 3%). In Kenya, systemic challenges included limited supportive supervision and lack of resources to access electronic reporting systems; in Ghana, challenges included a poorly defined subdistrict administrative level. Data quality improvement plans (DQIPs) based on assessment findings are being implemented in both countries. IISAs can help countries identify and address root causes of poor immunization data to provide a stronger evidence base for future investments in immunization programs.

Coarse Alignment Technology on Moving base for SINS Based on the Improved Quaternion Filter Algorithm.

PubMed

Zhang, Tao; Zhu, Yongyun; Zhou, Feng; Yan, Yaxiong; Tong, Jinwu

2017-06-17

Initial alignment of the strapdown inertial navigation system (SINS) is intended to determine the initial attitude matrix in a short time with certain accuracy. The alignment accuracy of the quaternion filter algorithm is remarkable, but the convergence rate is slow. To solve this problem, this paper proposes an improved quaternion filter algorithm for faster initial alignment based on the error model of the quaternion filter algorithm. The improved quaternion filter algorithm constructs the K matrix based on the principle of optimal quaternion algorithm, and rebuilds the measurement model by containing acceleration and velocity errors to make the convergence rate faster. A doppler velocity log (DVL) provides the reference velocity for the improved quaternion filter alignment algorithm. In order to demonstrate the performance of the improved quaternion filter algorithm in the field, a turntable experiment and a vehicle test are carried out. The results of the experiments show that the convergence rate of the proposed improved quaternion filter is faster than that of the tradition quaternion filter algorithm. In addition, the improved quaternion filter algorithm also demonstrates advantages in terms of correctness, effectiveness, and practicability.

DNABIT Compress - Genome compression algorithm.

PubMed

Rajarajeswari, Pothuraju; Apparao, Allam

2011-01-22

Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, "DNABIT Compress" for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that "DNABIT Compress" algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases.

Identification of immunity-related genes in the larvae of Protaetia brevitarsis seulensis (Coleoptera: Cetoniidae) by a next-generation sequencing-based transcriptome analysis.

PubMed

Bang, Kyeongrin; Hwang, Sejung; Lee, Jiae; Cho, Saeyoull

2015-01-01

To identify immune-related genes in the larvae of white-spotted flower chafers, next-generation sequencing was conducted with an Illumina HiSeq2000, resulting in 100 million cDNA reads with sequence information from over 10 billion base pairs (bp) and >50× transcriptome coverage. A subset of 77,336 contigs was created, and ∼35,532 sequences matched entries against the NCBI nonredundant database (cutoff, e < 10(-5)). Statistical analysis was performed on the 35,532 contigs. For profiling of the immune response, samples were analyzed by aligning 42 base sequence tags to the de novo reference assembly, comparing levels in immunized larvae to control levels of expression. Of the differentially expressed genes, 3,440 transcripts were upregulated and 3,590 transcripts were downregulated. Many of these genes were confirmed as immune-related genes such as pattern recognition proteins, immune-related signal transduction proteins, antimicrobial peptides, and cellular response proteins, by comparison to published data. © The Author 2015. Published by Oxford University Press on behalf of the Entomological Society of America.

Impact of early treatment programs on HIV epidemics: An immunity-based mathematical model.

PubMed

Rahman, S M Ashrafur; Vaidya, Naveen K; Zou, Xingfu

2016-10-01

While studies on pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP) have demonstrated substantial advantages in controlling HIV transmission, the overall benefits of the programs with early initiation of antiretroviral therapy (ART) have not been fully understood and are still on debate. Here, we develop an immunity-based (CD4+ T cell count based) mathematical model to study the impacts of early treatment programs on HIV epidemics and the overall community-level immunity. The model is parametrized using the HIV prevalence data from South Africa and fully analyzed for stability of equilibria and infection persistence criteria. Using our model, we evaluate the effects of early treatment on the new infection transmission, disease death, basic reproduction number, HIV prevalence, and the community-level immunity. Our model predicts that the programs with early treatments significantly reduce the new infection transmission and increase the community-level immunity, but the treatments alone may not be enough to eliminate HIV epidemics. These findings, including the community-level immunity, might provide helpful information for proper implementation of HIV treatment programs. Copyright © 2016 Elsevier Inc. All rights reserved.

Modelling the Immune Response to Cancer: An Individual-Based Approach Accounting for the Difference in Movement Between Inactive and Activated T Cells.

PubMed

Macfarlane, Fiona R; Lorenzi, Tommaso; Chaplain, Mark A J

2018-06-01

A growing body of experimental evidence indicates that immune cells move in an unrestricted search pattern if they are in the pre-activated state, whilst they tend to stay within a more restricted area upon activation induced by the presence of tumour antigens. This change in movement is not often considered in the existing mathematical models of the interactions between immune cells and cancer cells. With the aim to fill such a gap in the existing literature, in this work we present a spatially structured individual-based model of tumour-immune competition that takes explicitly into account the difference in movement between inactive and activated immune cells. In our model, a Lévy walk is used to capture the movement of inactive immune cells, whereas Brownian motion is used to describe the movement of antigen-activated immune cells. The effects of activation of immune cells, the proliferation of cancer cells and the immune destruction of cancer cells are also modelled. We illustrate the ability of our model to reproduce qualitatively the spatial trajectories of immune cells observed in experimental data of single-cell tracking. Computational simulations of our model further clarify the conditions for the onset of a successful immune action against cancer cells and may suggest possible targets to improve the efficacy of cancer immunotherapy. Overall, our theoretical work highlights the importance of taking into account spatial interactions when modelling the immune response to cancer cells.

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

DTIC Science & Technology

2018-01-01

ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform by Kwok F Tom Sensors and Electron...1 October 2016–30 September 2017 4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a

Network Community Detection based on the Physarum-inspired Computational Framework.

PubMed

Gao, Chao; Liang, Mingxin; Li, Xianghua; Zhang, Zili; Wang, Zhen; Zhou, Zhili

2016-12-13

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, which is a large amoeba-like cell consisting of a dendritic network of tube-like pseudopodia, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost. Moreover, a computational complexity analysis verifies the scalability of our framework.

Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples

DOE PAGES

Azad, Ariful; Rajwa, Bartek; Pothen, Alex

2016-08-31

We describe algorithms for discovering immunophenotypes from large collections of flow cytometry samples and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations’ characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters), a template consists of generic meta-populations (a group ofmore » homogeneous cell populations obtained from the samples in a class) that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples while ignoring noise and small sample-specific variations. We have applied the template-based scheme to analyze several datasets, including one representing a healthy immune system and one of acute myeloid leukemia (AML) samples. The last task is challenging due to the phenotypic heterogeneity of the several subtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML and were able to distinguish acute promyelocytic leukemia (APL) samples with the markers provided. Clinically, this is helpful since APL has a different treatment regimen from other subtypes of AML. Core algorithms used in our data analysis are available in the flowMatch package at www.bioconductor.org. It has been downloaded nearly 6,000 times since 2014.« less

Worldwide genetic variability of the Duffy binding protein: insights into Plasmodium vivax vaccine development.

PubMed

Nóbrega de Sousa, Taís; Carvalho, Luzia Helena; Alves de Brito, Cristiana Ferreira

2011-01-01

The dependence of Plasmodium vivax on invasion mediated by Duffy binding protein (DBP) makes this protein a prime candidate for development of a vaccine. However, the development of a DBP-based vaccine might be hampered by the high variability of the protein ligand (DBP(II)), known to bias the immune response toward a specific DBP variant. Here, the hypothesis being investigated is that the analysis of the worldwide DBP(II) sequences will allow us to determine the minimum number of haplotypes (MNH) to be included in a DBP-based vaccine of broad coverage. For that, all DBP(II) sequences available were compiled and MNH was based on the most frequent nonsynonymous single nucleotide polymorphisms, the majority mapped on B and T cell epitopes. A preliminary analysis of DBP(II) genetic diversity from eight malaria-endemic countries estimated that a number between two to six DBP haplotypes (17 in total) would target at least 50% of parasite population circulating in each endemic region. Aiming to avoid region-specific haplotypes, we next analyzed the MNH that broadly cover worldwide parasite population. The results demonstrated that seven haplotypes would be required to cover around 60% of DBP(II) sequences available. Trying to validate these selected haplotypes per country, we found that five out of the eight countries will be covered by the MNH (67% of parasite populations, range 48-84%). In addition, to identify related subgroups of DBP(II) sequences we used a Bayesian clustering algorithm. The algorithm grouped all DBP(II) sequences in six populations that were independent of geographic origin, with ancestral populations present in different proportions in each country. In conclusion, in this first attempt to undertake a global analysis about DBP(II) variability, the results suggest that the development of DBP-based vaccine should consider multi-haplotype strategies; otherwise a putative P. vivax vaccine may not target some parasite populations.

Multiplexing T- and B-Cell FLUOROSPOT Assays: Experimental Validation of the Multi-Color ImmunoSpot® Software Based on Center of Mass Distance Algorithm.

PubMed

Karulin, Alexey Y; Megyesi, Zoltán; Caspell, Richard; Hanson, Jodi; Lehmann, Paul V

2018-01-01

Over the past decade, ELISPOT has become a highly implemented mainstream assay in immunological research, immune monitoring, and vaccine development. Unique single cell resolution along with high throughput potential sets ELISPOT apart from flow cytometry, ELISA, microarray- and bead-based multiplex assays. The necessity to unambiguously identify individual T and B cells that do, or do not co-express certain analytes, including polyfunctional cytokine producing T cells has stimulated the development of multi-color ELISPOT assays. The success of these assays has also been driven by limited sample/cell availability and resource constraints with reagents and labor. There are few commercially available test kits and instruments available at present for multi-color FLUOROSPOT. Beyond commercial descriptions of competing systems, little is known about their accuracy in experimental settings detecting individual cells that secrete multiple analytes vs. random overlays of spots. Here, we present a theoretical and experimental validation study for three and four color T- and B-cell FLUOROSPOT data analysis. The ImmunoSpot ® Fluoro-X™ analysis system we used includes an automatic image acquisition unit that generates individual color images free of spectral overlaps and multi-color spot counting software based on the maximal allowed distance between centers of spots of different colors or Center of Mass Distance (COMD). Using four color B-cell FLUOROSPOT for IgM, IgA, IgG1, IgG3; and three/four color T-cell FLUOROSPOT for IL-2, IFN-γ, TNF-α, and GzB, in serial dilution experiments, we demonstrate the validity and accuracy of Fluoro-X™ multi-color spot counting algorithms. Statistical predictions based on the Poisson spatial distribution, coupled with scrambled image counting, permit objective correction of true multi-color spot counts to exclude randomly overlaid spots.

Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Azad, Ariful; Rajwa, Bartek; Pothen, Alex

We describe algorithms for discovering immunophenotypes from large collections of flow cytometry samples and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations’ characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters), a template consists of generic meta-populations (a group ofmore » homogeneous cell populations obtained from the samples in a class) that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples while ignoring noise and small sample-specific variations. We have applied the template-based scheme to analyze several datasets, including one representing a healthy immune system and one of acute myeloid leukemia (AML) samples. The last task is challenging due to the phenotypic heterogeneity of the several subtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML and were able to distinguish acute promyelocytic leukemia (APL) samples with the markers provided. Clinically, this is helpful since APL has a different treatment regimen from other subtypes of AML. Core algorithms used in our data analysis are available in the flowMatch package at www.bioconductor.org. It has been downloaded nearly 6,000 times since 2014.« less

Application of optical coherence tomography for assessment of transcutaneous vaccine delivery

NASA Astrophysics Data System (ADS)

Kamali, T.; Rattanapak, T.; Hook, S.; Meglinski, I.

2012-03-01

Immunization is one of the most efficient and cost-effective means for the prevention of diseases, but most vaccines have to be administered invasively. A novel strategy of inducing an immune response is topical application of vaccines to intact skin. Apart from being a non-invasive route of drug delivery, skin delivery also offers an advantageous mode of immunization due to the ability of skin immune cells to present antigens to the immune system. Topical vaccine penetration through the outermost layers of skin is based on the percutaneous diffusion of lipid-based nano-particles. In the current study we investigate the applicability of Optical Coherence Tomography for monitoring transcutaneous delivery of a peptide vaccine into the skin in vivo.

[Express diagnostics of bovine leucosis by immune sensor based on surface plasmon resonance].

PubMed

Pyrohova, L V; Starodub, M F; Artiukh, V P; Nahaieva, L I; Dobrosol, H I

2002-01-01

An immune sensor based on the surface plasmon resonance (SPR) was developed for express diagnostics of bovine leucosis. The sensor was used for detection of the level of antibodies against bovine leukaemia virus (BLV) in the blood serum. The industrially manufactured BLV antigen for screening test in the agar gel immunodiffusion (AGID) required the additional purification in order to be used in immune sensor analysis. It was shown that immune sensor analysis was more sensitive, rapid and simple in comparison with the traditional AGID test. It was stated that the developed immune sensor was capable to be used for performance of bovine leucosis screening at the farms and the minimal dilution of the serum should be 1:500.

Novel Adjuvants and Immunomodulators for Veterinary Vaccines.

PubMed

Heegaard, Peter M H; Fang, Yongxiang; Jungersen, Gregers

2016-01-01

Adjuvants are crucial for efficacy of vaccines, especially subunit and recombinant vaccines. Rational vaccine design, including knowledge-based and molecularly defined adjuvants tailored for directing and potentiating specific types of host immune responses towards the antigens included in the vaccine is becoming a reality with our increased understanding of innate and adaptive immune activation. This will allow future vaccines to induce immune reactivity having adequate specificity as well as protective and recallable immune effector mechanisms in appropriate body compartments, including mucosal surfaces. Here we describe these new developments and, when possible, relate new immunological knowledge to the many years of experience with traditional, empirical adjuvants. Finally, some protocols are given for production of emulsion (oil-based) and liposome-based adjuvant/antigen formulations.

Modulating the immune system through nanotechnology.

PubMed

Dacoba, Tamara G; Olivera, Ana; Torres, Dolores; Crecente-Campo, José; Alonso, María José

2017-12-01

Nowadays, nanotechnology-based modulation of the immune system is presented as a cutting-edge strategy, which may lead to significant improvements in the treatment of severe diseases. In particular, efforts have been focused on the development of nanotechnology-based vaccines, which could be used for immunization or generation of tolerance. In this review, we highlight how different immune responses can be elicited by tuning nanosystems properties. In addition, we discuss specific formulation approaches designed for the development of anti-infectious and anti-autoimmune vaccines, as well as those intended to prevent the formation of antibodies against biologicals. Copyright © 2017 Elsevier Ltd. All rights reserved.

National Network for Immunization Information

MedlinePlus

NNii's mission is to provide the best possible science-based information to everyone who needs to know the facts about immunization. NNii believes that immunization is one of the most important ways to protect against serious infectious diseases. We ...

High-level generation of polyclonal antibodies by genetic immunization.

PubMed

Chambers, Ross S; Johnston, Stephen Albert

2003-09-01

Antibodies are important tools for investigating the proteome, but current methods for producing them have become a rate-limiting step. A primary obstacle in most methods for generating antibodies or antibody-like molecules is the requirement for at least microgram quantities of purified protein. We have developed a technology for producing antibodies using genetic immunization. Genetic immunization-based antibody production offers several advantages, including high throughput and high specificity. Moreover, antibodies produced from genetically immunized animals are more likely to recognize the native protein. Here we show that a genetic immunization-based system can be used to efficiently raise useful antibodies to a wide range of antigens. We accomplished this by linking the antigen gene to various elements that enhance antigenicity and by codelivering plasmids encoding genetic adjuvants. Our system, which was tested by immunizing mice with >130 antigens, has shown a final success rate of 84%.

Computing border bases using mutant strategies

NASA Astrophysics Data System (ADS)

Ullah, E.; Abbas Khan, S.

2014-01-01

Border bases, a generalization of Gröbner bases, have actively been addressed during recent years due to their applicability to industrial problems. In cryptography and coding theory a useful application of border based is to solve zero-dimensional systems of polynomial equations over finite fields, which motivates us for developing optimizations of the algorithms that compute border bases. In 2006, Kehrein and Kreuzer formulated the Border Basis Algorithm (BBA), an algorithm which allows the computation of border bases that relate to a degree compatible term ordering. In 2007, J. Ding et al. introduced mutant strategies bases on finding special lower degree polynomials in the ideal. The mutant strategies aim to distinguish special lower degree polynomials (mutants) from the other polynomials and give them priority in the process of generating new polynomials in the ideal. In this paper we develop hybrid algorithms that use the ideas of J. Ding et al. involving the concept of mutants to optimize the Border Basis Algorithm for solving systems of polynomial equations over finite fields. In particular, we recall a version of the Border Basis Algorithm which is actually called the Improved Border Basis Algorithm and propose two hybrid algorithms, called MBBA and IMBBA. The new mutants variants provide us space efficiency as well as time efficiency. The efficiency of these newly developed hybrid algorithms is discussed using standard cryptographic examples.

Hierarchical trie packet classification algorithm based on expectation-maximization clustering

PubMed Central

Bi, Xia-an; Zhao, Junxia

2017-01-01

With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm. PMID:28704476

An improved non-uniformity correction algorithm and its hardware implementation on FPGA

NASA Astrophysics Data System (ADS)

Rong, Shenghui; Zhou, Huixin; Wen, Zhigang; Qin, Hanlin; Qian, Kun; Cheng, Kuanhong

2017-09-01

The Non-uniformity of Infrared Focal Plane Arrays (IRFPA) severely degrades the infrared image quality. An effective non-uniformity correction (NUC) algorithm is necessary for an IRFPA imaging and application system. However traditional scene-based NUC algorithm suffers the image blurring and artificial ghosting. In addition, few effective hardware platforms have been proposed to implement corresponding NUC algorithms. Thus, this paper proposed an improved neural-network based NUC algorithm by the guided image filter and the projection-based motion detection algorithm. First, the guided image filter is utilized to achieve the accurate desired image to decrease the artificial ghosting. Then a projection-based moving detection algorithm is utilized to determine whether the correction coefficients should be updated or not. In this way the problem of image blurring can be overcome. At last, an FPGA-based hardware design is introduced to realize the proposed NUC algorithm. A real and a simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. Experimental results indicated that the proposed NUC algorithm can effectively eliminate the fix pattern noise with less image blurring and artificial ghosting. The proposed hardware design takes less logic elements in FPGA and spends less clock cycles to process one frame of image.

Analysis of image thresholding segmentation algorithms based on swarm intelligence

NASA Astrophysics Data System (ADS)

Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

2013-03-01

Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

Improved pulse laser ranging algorithm based on high speed sampling

NASA Astrophysics Data System (ADS)

Gao, Xuan-yi; Qian, Rui-hai; Zhang, Yan-mei; Li, Huan; Guo, Hai-chao; He, Shi-jie; Guo, Xiao-kang

2016-10-01

Narrow pulse laser ranging achieves long-range target detection using laser pulse with low divergent beams. Pulse laser ranging is widely used in military, industrial, civil, engineering and transportation field. In this paper, an improved narrow pulse laser ranging algorithm is studied based on the high speed sampling. Firstly, theoretical simulation models have been built and analyzed including the laser emission and pulse laser ranging algorithm. An improved pulse ranging algorithm is developed. This new algorithm combines the matched filter algorithm and the constant fraction discrimination (CFD) algorithm. After the algorithm simulation, a laser ranging hardware system is set up to implement the improved algorithm. The laser ranging hardware system includes a laser diode, a laser detector and a high sample rate data logging circuit. Subsequently, using Verilog HDL language, the improved algorithm is implemented in the FPGA chip based on fusion of the matched filter algorithm and the CFD algorithm. Finally, the laser ranging experiment is carried out to test the improved algorithm ranging performance comparing to the matched filter algorithm and the CFD algorithm using the laser ranging hardware system. The test analysis result demonstrates that the laser ranging hardware system realized the high speed processing and high speed sampling data transmission. The algorithm analysis result presents that the improved algorithm achieves 0.3m distance ranging precision. The improved algorithm analysis result meets the expected effect, which is consistent with the theoretical simulation.

Molecular Isotopic Distribution Analysis (MIDAs) with Adjustable Mass Accuracy

NASA Astrophysics Data System (ADS)

Alves, Gelio; Ogurtsov, Aleksey Y.; Yu, Yi-Kuo

2014-01-01

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/midas/index.html.

Molecular Isotopic Distribution Analysis (MIDAs) with adjustable mass accuracy.

PubMed

Alves, Gelio; Ogurtsov, Aleksey Y; Yu, Yi-Kuo

2014-01-01

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/midas/index.html.

Synthetic immunology: modulating the human immune system.

PubMed

Geering, Barbara; Fussenegger, Martin

2015-02-01

Humans have manipulated the immune system to dampen or boost the immune response for thousands of years. As our understanding of fundamental immunology and biotechnological methodology accumulates, we can capitalize on this combined knowledge to engineer biological devices with the aim of rationally manipulating the immune response. We address therapeutic approaches based on the principles of synthetic immunology that either ameliorate disorders of the immune system by interfering with the immune response, or improve diverse pathogenic conditions by exploiting immune cell effector functions. We specifically highlight synthetic proteins investigated in preclinical and clinical trials, summarize studies that have used engineered immune cells, and finish with a discussion of possible future therapeutic concepts. Copyright © 2014 Elsevier Ltd. All rights reserved.

Score-Level Fusion of Phase-Based and Feature-Based Fingerprint Matching Algorithms

NASA Astrophysics Data System (ADS)

Ito, Koichi; Morita, Ayumi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

Recursive approach to the moment-based phase unwrapping method.

PubMed

Langley, Jason A; Brice, Robert G; Zhao, Qun

2010-06-01

The moment-based phase unwrapping algorithm approximates the phase map as a product of Gegenbauer polynomials, but the weight function for the Gegenbauer polynomials generates artificial singularities along the edge of the phase map. A method is presented to remove the singularities inherent to the moment-based phase unwrapping algorithm by approximating the phase map as a product of two one-dimensional Legendre polynomials and applying a recursive property of derivatives of Legendre polynomials. The proposed phase unwrapping algorithm is tested on simulated and experimental data sets. The results are then compared to those of PRELUDE 2D, a widely used phase unwrapping algorithm, and a Chebyshev-polynomial-based phase unwrapping algorithm. It was found that the proposed phase unwrapping algorithm provides results that are comparable to those obtained by using PRELUDE 2D and the Chebyshev phase unwrapping algorithm.

PWFQ: a priority-based weighted fair queueing algorithm for the downstream transmission of EPON

NASA Astrophysics Data System (ADS)

Xu, Sunjuan; Ye, Jiajun; Zou, Junni

2005-11-01

In the downstream direction of EPON, all ethernet frames share one downlink channel from the OLT to destination ONUs. To guarantee differentiated services, a scheduling algorithm is needed to solve the link-sharing issue. In this paper, we first review the classical WFQ algorithm and point out the shortcomings existing in the fair queueing principle of WFQ algorithm for EPON. Then we propose a novel scheduling algorithm called Priority-based WFQ (PWFQ) algorithm which distributes bandwidth based on priority. PWFQ algorithm can guarantee the quality of real-time services whether under light load or under heavy load. Simulation results also show that PWFQ algorithm not only can improve delay performance of real-time services, but can also meet the worst-case delay bound requirements.

Patient reminder and recall interventions to improve immunization rates.

PubMed

Jacobson Vann, Julie C; Jacobson, Robert M; Coyne-Beasley, Tamera; Asafu-Adjei, Josephine K; Szilagyi, Peter G

2018-01-18

Immunization rates for children and adults are rising, but coverage levels have not reached optimal goals. As a result, vaccine-preventable diseases still occur. In an era of increasing complexity of immunization schedules, rising expectations about the performance of primary care, and large demands on primary care providers, it is important to understand and promote interventions that work in primary care settings to increase immunization coverage. One common theme across immunization programs in many nations involves the challenge of implementing a population-based approach and identifying all eligible recipients, for example the children who should receive the measles vaccine. However, this issue is gradually being addressed through the availability of immunization registries and electronic health records. A second common theme is identifying the best strategies to promote high vaccination rates. Three types of strategies have been studied: (1) patient-oriented interventions, such as patient reminder or recall, (2) provider interventions, and (3) system interventions, such as school laws. One of the most prominent intervention strategies, and perhaps best studied, involves patient reminder or recall systems. This is an update of a previously published review. To evaluate and compare the effectiveness of various types of patient reminder and recall interventions to improve receipt of immunizations. We searched CENTRAL, MEDLINE, Embase and CINAHL to January 2017. We also searched grey literature and trial registers to January 2017. We included randomized trials, controlled before and after studies, and interrupted time series evaluating immunization-focused patient reminder or recall interventions in children, adolescents, and adults who receive immunizations in any setting. We included no-intervention control groups, standard practice activities that did not include immunization patient reminder or recall, media-based activities aimed at promoting immunizations, or simple practice-based awareness campaigns. We included receipt of any immunizations as eligible outcome measures, excluding special travel immunizations. We excluded patients who were hospitalized for the duration of the study period. We used the standard methodological procedures expected by Cochrane and the Cochrane Effective Practice and Organisation of Care (EPOC) Group. We present results for individual studies as relative rates using risk ratios, and risk differences for randomized trials, and as absolute changes in percentage points for controlled before-after studies. We present pooled results for randomized trials using the random-effects model. The 75 included studies involved child, adolescent, and adult participants in outpatient, community-based, primary care, and other settings in 10 countries.Patient reminder or recall interventions, including telephone and autodialer calls, letters, postcards, text messages, combination of mail or telephone, or a combination of patient reminder or recall with outreach, probably improve the proportion of participants who receive immunization (risk ratio (RR) of 1.28, 95% confidence interval (CI) 1.23 to 1.35; risk difference of 8%) based on moderate certainty evidence from 55 studies with 138,625 participants.Three types of single-method reminders improve receipt of immunizations based on high certainty evidence: the use of postcards (RR 1.18, 95% CI 1.08 to 1.30; eight studies; 27,734 participants), text messages (RR 1.29, 95% CI 1.15 to 1.44; six studies; 7772 participants), and autodialer (RR 1.17, 95% CI 1.03 to 1.32; five studies; 11,947 participants). Two types of single-method reminders probably improve receipt of immunizations based on moderate certainty evidence: the use of telephone calls (RR 1.75, 95% CI 1.20 to 2.54; seven studies; 9120 participants) and letters to patients (RR 1.29, 95% CI 1.21 to 1.38; 27 studies; 81,100 participants).Based on high certainty evidence, reminders improve receipt of immunizations for childhood (RR 1.22, 95% CI 1.15 to 1.29; risk difference of 8%; 23 studies; 31,099 participants) and adolescent vaccinations (RR 1.29, 95% CI 1.17 to 1.42; risk difference of 7%; 10 studies; 30,868 participants). Reminders probably improve receipt of vaccinations for childhood influenza (RR 1.51, 95% CI 1.14 to 1.99; risk difference of 22%; five studies; 9265 participants) and adult influenza (RR 1.29, 95% CI 1.17 to 1.43; risk difference of 9%; 15 studies; 59,328 participants) based on moderate certainty evidence. They may improve receipt of vaccinations for adult pneumococcus, tetanus, hepatitis B, and other non-influenza vaccinations based on low certainty evidence although the confidence interval includes no effect of these interventions (RR 2.08, 95% CI 0.91 to 4.78; four studies; 8065 participants). Patient reminder and recall systems, in primary care settings, are likely to be effective at improving the proportion of the target population who receive immunizations.

Asymptotic analysis of SPTA-based algorithms for no-wait flow shop scheduling problem with release dates.

PubMed

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

Asymptotic Analysis of SPTA-Based Algorithms for No-Wait Flow Shop Scheduling Problem with Release Dates

PubMed Central

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms. PMID:24764774

Survey of gene splicing algorithms based on reads.

PubMed

Si, Xiuhua; Wang, Qian; Zhang, Lei; Wu, Ruo; Ma, Jiquan

2017-11-02

Gene splicing is the process of assembling a large number of unordered short sequence fragments to the original genome sequence as accurately as possible. Several popular splicing algorithms based on reads are reviewed in this article, including reference genome algorithms and de novo splicing algorithms (Greedy-extension, Overlap-Layout-Consensus graph, De Bruijn graph). We also discuss a new splicing method based on the MapReduce strategy and Hadoop. By comparing these algorithms, some conclusions are drawn and some suggestions on gene splicing research are made.

Billing code algorithms to identify cases of peripheral artery disease from administrative data

PubMed Central

Fan, Jin; Arruda-Olson, Adelaide M; Leibson, Cynthia L; Smith, Carin; Liu, Guanghui; Bailey, Kent R; Kullo, Iftikhar J

2013-01-01

Objective To construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD). Methods We extracted all encounters and line item details including PAD-related billing codes at Mayo Clinic Rochester, Minnesota, between July 1, 1997 and June 30, 2008; 22 712 patients evaluated in the vascular laboratory were divided into training and validation sets. Multiple logistic regression analysis was used to create an integer code score from the training dataset, and this was tested in the validation set. We applied a model-based code algorithm to patients evaluated in the vascular laboratory and compared this with a simpler algorithm (presence of at least one of the ICD-9 PAD codes 440.20–440.29). We also applied both algorithms to a community-based sample (n=4420), followed by a manual review. Results The logistic regression model performed well in both training and validation datasets (c statistic=0.91). In patients evaluated in the vascular laboratory, the model-based code algorithm provided better negative predictive value. The simpler algorithm was reasonably accurate for identification of PAD status, with lesser sensitivity and greater specificity. In the community-based sample, the sensitivity (38.7% vs 68.0%) of the simpler algorithm was much lower, whereas the specificity (92.0% vs 87.6%) was higher than the model-based algorithm. Conclusions A model-based billing code algorithm had reasonable accuracy in identifying PAD cases from the community, and in patients referred to the non-invasive vascular laboratory. The simpler algorithm had reasonable accuracy for identification of PAD in patients referred to the vascular laboratory but was significantly less sensitive in a community-based sample. PMID:24166724

General Quantum Meet-in-the-Middle Search Algorithm Based on Target Solution of Fixed Weight

NASA Astrophysics Data System (ADS)

Fu, Xiang-Qun; Bao, Wan-Su; Wang, Xiang; Shi, Jian-Hong

2016-10-01

Similar to the classical meet-in-the-middle algorithm, the storage and computation complexity are the key factors that decide the efficiency of the quantum meet-in-the-middle algorithm. Aiming at the target vector of fixed weight, based on the quantum meet-in-the-middle algorithm, the algorithm for searching all n-product vectors with the same weight is presented, whose complexity is better than the exhaustive search algorithm. And the algorithm can reduce the storage complexity of the quantum meet-in-the-middle search algorithm. Then based on the algorithm and the knapsack vector of the Chor-Rivest public-key crypto of fixed weight d, we present a general quantum meet-in-the-middle search algorithm based on the target solution of fixed weight, whose computational complexity is \\sumj = 0d {(O(\\sqrt {Cn - k + 1d - j }) + O(C_kj log C_k^j))} with Σd i =0 Ck i memory cost. And the optimal value of k is given. Compared to the quantum meet-in-the-middle search algorithm for knapsack problem and the quantum algorithm for searching a target solution of fixed weight, the computational complexity of the algorithm is lower. And its storage complexity is smaller than the quantum meet-in-the-middle-algorithm. Supported by the National Basic Research Program of China under Grant No. 2013CB338002 and the National Natural Science Foundation of China under Grant No. 61502526

Listeria monocytogenes Induces a Virulence-Dependent microRNA Signature That Regulates the Immune Response in Galleria mellonella

PubMed Central

Mannala, Gopala K.; Izar, Benjamin; Rupp, Oliver; Schultze, Tilman; Goesmann, Alexander; Chakraborty, Trinad; Hain, Torsten

2017-01-01

microRNAs (miRNAs) coordinate several physiological and pathological processes by regulating the fate of mRNAs. Studies conducted in vitro indicate a role of microRNAs in the control of host-microbe interactions. However, there is limited understanding of miRNA functions in in vivo models of bacterial infections. In this study, we systematically explored changes in miRNA expression levels of Galleria mellonella larvae (greater-wax moth), a model system that recapitulates the vertebrate innate immunity, following infection with L. monocytogenes. Using an insect-specific miRNA microarray with more than 2000 probes, we found differential expression of 90 miRNAs (39 upregulated and 51 downregulated) in response to infection with L. monocytogenes. We validated the expression of a subset of miRNAs which have mammalian homologs of known or predicted function. In contrast, non-pathogenic L. innocua failed to induce these miRNAs, indicating a virulence-dependent miRNA deregulation. To predict miRNA targets using established algorithms, we generated a publically available G. mellonella transcriptome database. We identified miRNA targets involved in innate immunity, signal transduction and autophagy, including spätzle, MAP kinase, and optineurin, respectively, which exhibited a virulence-specific differential expression. Finally, in silico estimation of minimum free energy of miRNA-mRNA duplexes of validated microRNAs and target transcripts revealed a regulatory network of the host immune response to L. monocytogenes. In conclusion, this study provides evidence for a role of miRNAs in the regulation of the innate immune response following bacterial infection in a simple, rapid and scalable in vivo model that may predict host-microbe interactions in higher vertebrates. PMID:29312175

Identification of immune correlates of protection in Shigella infection by application of machine learning.

PubMed

Arevalillo, Jorge M; Sztein, Marcelo B; Kotloff, Karen L; Levine, Myron M; Simon, Jakub K

2017-10-01

Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical vaccine studies. Our goal is the development of a methodology to identify immunologic correlates of protection using the Shigella challenge as a model. The proposed methodology utilizes the Random Forests (RF) machine learning algorithm as well as Classification and Regression Trees (CART) to detect immune markers that predict protection, identify interactions between variables, and define optimal cutoffs. Logistic regression modeling is applied to estimate the probability of protection and the confidence interval (CI) for such a probability is computed by bootstrapping the logistic regression models. The results demonstrate that the combination of Classification and Regression Trees and Random Forests complements the standard logistic regression and uncovers subtle immune interactions. Specific levels of immunoglobulin IgG antibody in blood on the day of challenge predicted protection in 75% (95% CI 67-86). Of those subjects that did not have blood IgG at or above a defined threshold, 100% were protected if they had IgA antibody secreting cells above a defined threshold. Comparison with the results obtained by applying only logistic regression modeling with standard Akaike Information Criterion for model selection shows the usefulness of the proposed method. Given the complexity of the immune system, the use of machine learning methods may enhance traditional statistical approaches. When applied together, they offer a novel way to quantify important immune correlates of protection that may help the development of vaccines. Copyright © 2017 Elsevier Inc. All rights reserved.

Code-based Diagnostic Algorithms for Idiopathic Pulmonary Fibrosis. Case Validation and Improvement.

PubMed

Ley, Brett; Urbania, Thomas; Husson, Gail; Vittinghoff, Eric; Brush, David R; Eisner, Mark D; Iribarren, Carlos; Collard, Harold R

2017-06-01

Population-based studies of idiopathic pulmonary fibrosis (IPF) in the United States have been limited by reliance on diagnostic code-based algorithms that lack clinical validation. To validate a well-accepted International Classification of Diseases, Ninth Revision, code-based algorithm for IPF using patient-level information and to develop a modified algorithm for IPF with enhanced predictive value. The traditional IPF algorithm was used to identify potential cases of IPF in the Kaiser Permanente Northern California adult population from 2000 to 2014. Incidence and prevalence were determined overall and by age, sex, and race/ethnicity. A validation subset of cases (n = 150) underwent expert medical record and chest computed tomography review. A modified IPF algorithm was then derived and validated to optimize positive predictive value. From 2000 to 2014, the traditional IPF algorithm identified 2,608 cases among 5,389,627 at-risk adults in the Kaiser Permanente Northern California population. Annual incidence was 6.8/100,000 person-years (95% confidence interval [CI], 6.1-7.7) and was higher in patients with older age, male sex, and white race. The positive predictive value of the IPF algorithm was only 42.2% (95% CI, 30.6 to 54.6%); sensitivity was 55.6% (95% CI, 21.2 to 86.3%). The corrected incidence was estimated at 5.6/100,000 person-years (95% CI, 2.6-10.3). A modified IPF algorithm had improved positive predictive value but reduced sensitivity compared with the traditional algorithm. A well-accepted International Classification of Diseases, Ninth Revision, code-based IPF algorithm performs poorly, falsely classifying many non-IPF cases as IPF and missing a substantial proportion of IPF cases. A modification of the IPF algorithm may be useful for future population-based studies of IPF.

Diagnostic Accuracy of FebriDx: A Rapid Test to Detect Immune Responses to Viral and Bacterial Upper Respiratory Infections.

PubMed

Self, Wesley H; Rosen, Jeffrey; Sharp, Stephan C; Filbin, Michael R; Hou, Peter C; Parekh, Amisha D; Kurz, Michael C; Shapiro, Nathan I

2017-10-07

C-reactive protein (CRP) and myxovirus resistance protein A (MxA) are associated with bacterial and viral infections, respectively. We conducted a prospective, multicenter, cross-sectional study of adults and children with febrile upper respiratory tract infections (URIs) to evaluate the diagnostic accuracy of a rapid CRP/MxA immunoassay to identify clinically significant bacterial infection with host response and acute pathogenic viral infection. The reference standard for classifying URI etiology was an algorithm that included throat bacterial culture, upper respiratory PCR for viral and atypical pathogens, procalcitonin, white blood cell count, and bandemia. The algorithm also allowed for physician override. Among 205 patients, 25 (12.2%) were classified as bacterial, 53 (25.9%) as viral, and 127 (62.0%) negative by the reference standard. For bacterial detection, agreement between FebriDx and the reference standard was 91.7%, with FebriDx having a sensitivity of 80% (95% CI: 59-93%), specificity of 93% (89-97%), positive predictive value (PPV) of 63% (45-79%), and a negative predictive value (NPV) of 97% (94-99%). For viral detection, agreement was 84%, with a sensitivity of 87% (75-95%), specificity of 83% (76-89%), PPV of 64% (63-75%), and NPV of 95% (90-98%). FebriDx may help to identify clinically significant immune responses associated with bacterial and viral URIs that are more likely to require clinical management or therapeutic intervention, and has potential to assist with antibiotic stewardship.

Assessment of Child Immunization Coverage and Associated Factors with Full Vaccination among Children Aged 12–23 Months at Mizan Aman Town, Bench Maji Zone, Southwest Ethiopia

PubMed Central

Geremew, Mesfin; Birhanu, Frehiwot

2017-01-01

Immunization remains one of the most important and cost-effective public health interventions to reduce child mortality and morbidity. Globally, it is estimated to avert between 2 and 3 million deaths each year. In Ethiopia, immunization coverage rates stagnated and remained very low for many years. Thus, this study was aimed to assess child immunization coverage and factors associated with full vaccination among children aged 12–23 months in Mizan Aman town. The study design was community-based cross-sectional survey. Data was collected by using pretested structured questionnaire. A total of 322 mothers/caretakers were interviewed. Based on vaccination card and mothers/caretakers' recall, 295 (91.6%) of the children took at least a single dose of vaccine. From total children, 27 (8.4%) were not immunized at all, 159 (49.4%) were partially immunized, and 136 (42.2%) were fully immunized. Mothers/caretakers educational level, fathers' educational level, place of delivery, maternal health care utilization, and mothers/caretakers knowledge about vaccine and vaccine-preventable disease showed significant association with full child immunization. The finding from this study revealed that child immunization coverage in the studied area was low. Thus the town health office and concerned stakeholders need to work more to improve performance of the expanded program on immunization in this area. PMID:29434643

DNABIT Compress – Genome compression algorithm

PubMed Central

Rajarajeswari, Pothuraju; Apparao, Allam

2011-01-01

Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, “DNABIT Compress” for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that “DNABIT Compress” algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases. PMID:21383923

Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

PubMed

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing algorithms in accuracy and efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive power allocation schemes based on IAFS algorithm for OFDM-based cognitive radio systems

NASA Astrophysics Data System (ADS)

Zhang, Shuying; Zhao, Xiaohui; Liang, Cong; Ding, Xu

2017-01-01

In cognitive radio (CR) systems, reasonable power allocation can increase transmission rate of CR users or secondary users (SUs) as much as possible and at the same time insure normal communication among primary users (PUs). This study proposes an optimal power allocation scheme for the OFDM-based CR system with one SU influenced by multiple PU interference constraints. This scheme is based on an improved artificial fish swarm (IAFS) algorithm in combination with the advantage of conventional artificial fish swarm (ASF) algorithm and particle swarm optimisation (PSO) algorithm. In performance comparison of IAFS algorithm with other intelligent algorithms by simulations, the superiority of the IAFS algorithm is illustrated; this superiority results in better performance of our proposed scheme than that of the power allocation algorithms proposed by the previous studies in the same scenario. Furthermore, our proposed scheme can obtain higher transmission data rate under the multiple PU interference constraints and the total power constraint of SU than that of the other mentioned works.

A range-based predictive localization algorithm for WSID networks

NASA Astrophysics Data System (ADS)

Liu, Yuan; Chen, Junjie; Li, Gang

2017-11-01

Most studies on localization algorithms are conducted on the sensor networks with densely distributed nodes. However, the non-localizable problems are prone to occur in the network with sparsely distributed sensor nodes. To solve this problem, a range-based predictive localization algorithm (RPLA) is proposed in this paper for the wireless sensor networks syncretizing the RFID (WSID) networks. The Gaussian mixture model is established to predict the trajectory of a mobile target. Then, the received signal strength indication is used to reduce the residence area of the target location based on the approximate point-in-triangulation test algorithm. In addition, collaborative localization schemes are introduced to locate the target in the non-localizable situations. Simulation results verify that the RPLA achieves accurate localization for the network with sparsely distributed sensor nodes. The localization accuracy of the RPLA is 48.7% higher than that of the APIT algorithm, 16.8% higher than that of the single Gaussian model-based algorithm and 10.5% higher than that of the Kalman filtering-based algorithm.

Evaluation of a social marketing campaign to increase awareness of immunizations for urban low-income children.

PubMed

Ngui, Emmanuel M; Hamilton, Chelsea; Nugent, Melodee; Simpson, Pippa; Willis, Earnestine

2015-02-01

To assess community awareness of childhood immunizations and intent to immunize children after a social marketing immunization campaign. We used 2 interviewer-assisted street-intercept surveys to evaluate awareness of childhood immunizations and intent to immunize low-income children. The "Take Control! Immunize" social marketing campaign was developed using a community-based participatory research approach and used billboards, flyers, and various "walking billboard" (eg, backpacks, pens) to deliver immunization messages in the community settings. Over 85% of community members recalled the "Take Control! Immunize" message. Almost half of those who saw the immunization message indicated that the message motivated them to act, including getting their children immunized or calling their physician to inquire about their children's immunizations status. All respondents indicated that immunizations were important for children and that they were likely or very likely to immunize their children. Respondents who reported that "Take Control!" messages motivated them to act in the first intercept survey were significantly more likely than those in the second intercept to report being likely or very likely to immunize their children. Culturally appropriate social marketing immunization messages in targeted urban settings can increase parental awareness and behavioral intention to immunize children.

Hepatitis B core antigen antibody as an indicator of a low grade carrier state for hepatitis B virus in a Saudi Arabian blood donor population.

PubMed

Bernvil, S S; Andrews, V; Kuhns, M C; McNamara, A L

1997-03-01

Blood donor screening for anti-hepatitis B core antigen (anti-HBc) was introduced as a surrogate marker of non-A, non-B hepatitis prior to the availability of a specific test for hepatitis C. In areas endemic for hepatitis B virus (HBV), such as Saudi Arabia, earlier studies indicated that up to 30% of blood donors might disqualify if screened for anti-HBc. The issue was readdressed in a study of 6035 consecutive first-time Saudi national blood donors in an attempt to identify a subgroup of anti-HBc positive donors who might be at high risk of being low grade carriers of HBV. An isolated anti-HBc of high titer in a donor with a low or absent anti-hepatitis B surface antigen (anti-HBsAg) was taken as an indicator of increased risk of a low grade carrier state. Using this algorithm, an additional 125 (2%) donors would disqualify. HBsAg immune complex assays and polymerase chain reaction of donor samples with an isolated anti-HBc identified two donors with immune complexes and two donors with HBV DNA. All four donor samples expressed over 90% neutralization in the anti-HBc supplementary testing, indicating high titer anti-HBc. These findings seem to support the suggested policy of donor exclusion based on the anti-HBc and anti-HBsAg serology as a means to eliminate low grade carriers of HBV in endemic areas without jeopardizing the blood supply.

Viral Infection of Tumors Overcomes Resistance to PD-1-immunotherapy by Broadening Neoantigenome-directed T-cell Responses

PubMed Central

Woller, Norman; Gürlevik, Engin; Fleischmann-Mundt, Bettina; Schumacher, Anja; Knocke, Sarah; Kloos, Arnold M; Saborowski, Michael; Geffers, Robert; Manns, Michael P; Wirth, Thomas C; Kubicka, Stefan; Kühnel, Florian

2015-01-01

There is evidence that viral oncolysis is synergistic with immune checkpoint inhibition in cancer therapy but the underlying mechanisms are unclear. Here, we investigated whether local viral infection of malignant tumors is capable of overcoming systemic resistance to PD-1-immunotherapy by modulating the spectrum of tumor-directed CD8 T-cells. To focus on neoantigen-specific CD8 T-cell responses, we performed transcriptomic sequencing of PD-1-resistant CMT64 lung adenocarcinoma cells followed by algorithm-based neoepitope prediction. Investigations on neoepitope-specific T-cell responses in tumor-bearing mice demonstrated that PD-1 immunotherapy was insufficient whereas viral oncolysis elicited cytotoxic T-cell responses to a conserved panel of neoepitopes. After combined treatment, we observed that PD-1-blockade did not affect the magnitude of oncolysis-mediated antitumoral immune responses but a broader spectrum of T-cell responses including additional neoepitopes was observed. Oncolysis of the primary tumor significantly abrogated systemic resistance to PD-1-immunotherapy leading to improved elimination of disseminated lung tumors. Our observations were confirmed in a transgenic murine model of liver cancer where viral oncolysis strongly induced PD-L1 expression in primary liver tumors and lung metastasis. Furthermore, we demonstrated that combined treatment completely inhibited dissemination in a CD8 T-cell-dependent manner. Therefore, our results strongly recommend further evaluation of virotherapy and concomitant PD-1 immunotherapy in clinical studies. PMID:26112079

Artificial immune system for diabetes meal plans optimization

NASA Astrophysics Data System (ADS)

Prilianti, K. R.; Callista, P. B.; Setiawan, H.

2017-03-01

Type 2 diabetes mellitus is a disease that occurs because the body lacks of insulin or the insulin produced by the pancreas cannot work effectively such that the glucose level in the blood cannot well controlled. One of the most common causes of diabetes mellitus type 2 is obesity, therefore this disease can be controlled with the appropriate diet regarding to the daily calorie requirement. Hence, the level of blood glucose is maintained. Unfortunately, because the lack of proper diet education and facility, many people cannot work on proper daily healthy diet by their own. In this research Artificial Immune System algorithm was applied to build a model that help diabetes mellitus patient arrange their meal plans. The model can calculate the amount of daily calorie needed and arrange the appropriate daily meal plans based on it. The meal plans vary according to the patient calorie needs. The required input data are age, gender, weight, height, and type of patient daily main activity. The experiments show that this model has a good result. The result is already approaching the patients' daily calorie need, i.e. 97.6% (actual need is not less than 80% and not greater than 100%). Carbohydrate of the meal plan is 55-57% (actual need is not less than 45% and not greater than 60%) whereas the protein approximate 15-18% (actual need is not less than 15% and not greater than 20%) and fat of approximate 22-24% (actual need is not less than 20% and not greater than 25%).

Novel full-spectral flow cytometry with multiple spectrally-adjacent fluorescent proteins and fluorochromes and visualization of in vivo cellular movement.

PubMed

Futamura, Koji; Sekino, Masashi; Hata, Akihiro; Ikebuchi, Ryoyo; Nakanishi, Yasutaka; Egawa, Gyohei; Kabashima, Kenji; Watanabe, Takeshi; Furuki, Motohiro; Tomura, Michio

2015-09-01

Flow cytometric analysis with multicolor fluoroprobes is an essential method for detecting biological signatures of cells. Here, we present a new full-spectral flow cytometer (spectral-FCM). Unlike conventional flow cytometer, this spectral-FCM acquires the emitted fluorescence for all probes across the full-spectrum from each cell with 32 channels sequential PMT unit after dispersion with prism, and extracts the signals of each fluoroprobe based on the spectral shape of each fluoroprobe using unique algorithm in high speed, high sensitive, accurate, automatic and real-time. The spectral-FCM detects the continuous changes in emission spectra from green to red of the photoconvertible protein, KikGR with high-spectral resolution and separates spectrally-adjacent fluoroprobes, such as FITC (Emission peak (Em) 519 nm) and EGFP (Em 507 nm). Moreover, the spectral-FCM can measure and subtract autofluorescence of each cell providing increased signal-to-noise ratios and improved resolution of dim samples, which leads to a transformative technology for investigation of single cell state and function. These advances make it possible to perform 11-color fluorescence analysis to visualize movement of multilinage immune cells by using KikGR-expressing mice. Thus, the novel spectral flow cytometry improves the combinational use of spectrally-adjacent various FPs and multicolor fluorochromes in metabolically active cell for the investigation of not only the immune system but also other research and clinical fields of use. © 2015 International Society for Advancement of Cytometry.

Obtaining the phase in the star test using genetic algorithms

NASA Astrophysics Data System (ADS)

Salazar Romero, Marcos A.; Vazquez-Montiel, Sergio; Cornejo-Rodriguez, Alejandro

2004-10-01

The star test is conceptually perhaps the most basic and simplest of all methods of testing image-forming optical systems, the irradiance distribution at the image of a point source (such as a star) is give for the Point Spread Function, PSF. The PSF is very sensitive to aberrations. One way to quantify the PSF is measuring the irradiance distribution on the image of the source point. On the other hand, if we know the aberrations introduced by the optical systems and utilizing the diffraction theory then we can calculate the PSF. In this work we propose a method in order to find the wavefront aberrations starting from the PSF, transforming the problem of fitting a polynomial of aberrations in a problem of optimization using Genetic Algorithm. Also, we show that this method is immune to the noise introduced in the register or recording of the image. Results of these methods are shown.

A Very Low Cost BCH Decoder for High Immunity of On-Chip Memories

NASA Astrophysics Data System (ADS)

Seo, Haejun; Han, Sehwan; Heo, Yoonseok; Cho, Taewon

BCH(Bose-Chaudhuri-Hoquenbhem) code, a type of block codes-cyclic codes, has very strong error-correcting ability which is vital for performing the error protection on the memory system. BCH code has many kinds of dual algorithms, PGZ(Pererson-Gorenstein-Zierler) algorithm out of them is advantageous in view of correcting the errors through the simple calculation in t value. However, this is problematic when this becomes 0 (divided by zero) in case ν ≠ t. In this paper, the circuit would be simplified by suggesting the multi-mode hardware architecture in preparation that v were 0~3. First, production cost would be less thanks to the smaller number of gates. Second, lessening power consumption could lengthen the recharging period. The very low cost and simple datapath make our design a good choice in small-footprint SoC(System on Chip) as ECC(Error Correction Code/Circuit) in memory system.

Online selective kernel-based temporal difference learning.

PubMed

Chen, Xingguo; Gao, Yang; Wang, Ruili

2013-12-01

In this paper, an online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems. OSKTD includes two online procedures: online sparsification and parameter updating for the selective kernel-based value function. A new sparsification method (i.e., a kernel distance-based online sparsification method) is proposed based on selective ensemble learning, which is computationally less complex compared with other sparsification methods. With the proposed sparsification method, the sparsified dictionary of samples is constructed online by checking if a sample needs to be added to the sparsified dictionary. In addition, based on local validity, a selective kernel-based value function is proposed to select the best samples from the sample dictionary for the selective kernel-based value function approximator. The parameters of the selective kernel-based value function are iteratively updated by using the temporal difference (TD) learning algorithm combined with the gradient descent technique. The complexity of the online sparsification procedure in the OSKTD algorithm is O(n). In addition, two typical experiments (Maze and Mountain Car) are used to compare with both traditional and up-to-date O(n) algorithms (GTD, GTD2, and TDC using the kernel-based value function), and the results demonstrate the effectiveness of our proposed algorithm. In the Maze problem, OSKTD converges to an optimal policy and converges faster than both traditional and up-to-date algorithms. In the Mountain Car problem, OSKTD converges, requires less computation time compared with other sparsification methods, gets a better local optima than the traditional algorithms, and converges much faster than the up-to-date algorithms. In addition, OSKTD can reach a competitive ultimate optima compared with the up-to-date algorithms.

Cutting the Stone: Health Defined in the Era of Value-based Care

PubMed Central

2017-01-01

The immune system contributes to the maintenance of health by preventing and limiting the clinical consequences of infections by pathogenic microorganisms. During the evolution of Homo sapiens, those with the fittest immune system survived. The immune system of Homo sapiens was further improved and adapted by admixture with Neanderthal genes. Nowadays, the human immune system provides adequate protection against the majority of infections. For some 20 infectious diseases, the immune system needs to be improved by vaccination. Vaccination is the number one value-based healthcare intervention and has resulted in global eradication of smallpox. Eradication of poliomyelitis and measles is within reach. A continuous effort will be required for recently emerged pathogens, such as Ebola and HIV, as well as the most difficult - malaria and tuberculosis.   PMID:28348941

Cutting the Stone: Health Defined in the Era of Value-based Care.

PubMed

Rijkers, Ger

2017-02-10

The immune system contributes to the maintenance of health by preventing and limiting the clinical consequences of infections by pathogenic microorganisms. During the evolution of Homo sapiens, those with the fittest immune system survived. The immune system of Homo sapiens was further improved and adapted by admixture with Neanderthal genes. Nowadays, the human immune system provides adequate protection against the majority of infections. For some 20 infectious diseases, the immune system needs to be improved by vaccination. Vaccination is the number one value-based healthcare intervention and has resulted in global eradication of smallpox. Eradication of poliomyelitis and measles is within reach. A continuous effort will be required for recently emerged pathogens, such as Ebola and HIV, as well as the most difficult - malaria and tuberculosis.

The deconvolution of complex spectra by artificial immune system

NASA Astrophysics Data System (ADS)

Galiakhmetova, D. I.; Sibgatullin, M. E.; Galimullin, D. Z.; Kamalova, D. I.

2017-11-01

An application of the artificial immune system method for decomposition of complex spectra is presented. The results of decomposition of the model contour consisting of three components, Gaussian contours, are demonstrated. The method of artificial immune system is an optimization method, which is based on the behaviour of the immune system and refers to modern methods of search for the engine optimization.

Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators.

PubMed

Karayiannis, N B

2000-01-01

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.

Human resource recommendation algorithm based on ensemble learning and Spark

NASA Astrophysics Data System (ADS)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

Design of the OMPS limb sensor correction algorithm

NASA Astrophysics Data System (ADS)

Jaross, Glen; McPeters, Richard; Seftor, Colin; Kowitt, Mark

The Sensor Data Records (SDR) for the Ozone Mapping and Profiler Suite (OMPS) on NPOESS (National Polar-orbiting Operational Environmental Satellite System) contains geolocated and calibrated radiances, and are similar to the Level 1 data of NASA Earth Observing System and other programs. The SDR algorithms (one for each of the 3 OMPS focal planes) are the processes by which the Raw Data Records (RDR) from the OMPS sensors are converted into the records that contain all data necessary for ozone retrievals. Consequently, the algorithms must correct and calibrate Earth signals, geolocate the data, and identify and ingest collocated ancillary data. As with other limb sensors, ozone profile retrievals are relatively insensitive to calibration errors due to the use of altitude normalization and wavelength pairing. But the profile retrievals as they pertain to OMPS are not immune from sensor changes. In particular, the OMPS Limb sensor images an altitude range of > 100 km and a spectral range of 290-1000 nm on its detector. Uncorrected sensor degradation and spectral registration drifts can lead to changes in the measured radiance profile, which in turn affects the ozone trend measurement. Since OMPS is intended for long-term monitoring, sensor calibration is a specific concern. The calibration is maintained via the ground data processing. This means that all sensor calibration data, including direct solar measurements, are brought down in the raw data and processed separately by the SDR algorithms. One of the sensor corrections performed by the algorithm is the correction for stray light. The imaging spectrometer and the unique focal plane design of OMPS makes these corrections particularly challenging and important. Following an overview of the algorithm flow, we will briefly describe the sensor stray light characterization and the correction approach used in the code.

A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Genrang; Lin, ZhengChun

The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

List-Based Simulated Annealing Algorithm for Traveling Salesman Problem.

PubMed

Zhan, Shi-hua; Lin, Juan; Zhang, Ze-jun; Zhong, Yi-wen

2016-01-01

Simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters' setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA) algorithm to solve traveling salesman problem (TSP). LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms.

Bandwidth turbulence control based on flow community structure in the Internet

NASA Astrophysics Data System (ADS)

Wu, Xiaoyu; Gu, Rentao; Ji, Yuefeng

2016-10-01

Bursty flows vary rapidly in short period of time, and cause fierce bandwidth turbulence in the Internet. In this letter, we model the flow bandwidth turbulence process by constructing a flow interaction network (FIN network), with nodes representing flows and edges denoting bandwidth interactions among them. To restrain the bandwidth turbulence in FIN networks, an immune control strategy based on flow community structure is proposed. Flows in community boundary positions are immunized to cut off the inter-community turbulence spreading. By applying this control strategy in the first- and the second-level flow communities separately, 97.2% flows can effectively avoid bandwidth variations by immunizing 21% flows, and the average bandwidth variation degree reaches near zero. To achieve a similar result, about 70%-90% immune flows are needed with targeted control strategy based on flow degrees and random control strategy. Moreover, simulation results showed that the control effect of the proposed strategy improves significantly if the immune flow number is relatively smaller in each control step.

The Five Immune Forces Impacting DNA-Based Cancer Immunotherapeutic Strategy

PubMed Central

Amara, Suneetha; Tiriveedhi, Venkataswarup

2017-01-01

DNA-based vaccine strategy is increasingly realized as a viable cancer treatment approach. Strategies to enhance immunogenicity utilizing tumor associated antigens have been investigated in several pre-clinical and clinical studies. The promising outcomes of these studies have suggested that DNA-based vaccines induce potent T-cell effector responses and at the same time cause only minimal side-effects to cancer patients. However, the immune evasive tumor microenvironment is still an important hindrance to a long-term vaccine success. Several options are currently under various stages of study to overcome immune inhibitory effect in tumor microenvironment. Some of these approaches include, but are not limited to, identification of neoantigens, mutanome studies, designing fusion plasmids, vaccine adjuvant modifications, and co-treatment with immune-checkpoint inhibitors. In this review, we follow a Porter’s analysis analogy, otherwise commonly used in business models, to analyze various immune-forces that determine the potential success and sustainable positive outcomes following DNA vaccination using non-viral tumor associated antigens in treatment against cancer. PMID:28304339

Evaluation of hybrid algorithm for analysis of scattered light using ex vivo nuclear morphology measurements of cervical epithelium

PubMed Central

Ho, Derek; Drake, Tyler K.; Bentley, Rex C.; Valea, Fidel A.; Wax, Adam

2015-01-01

We evaluate a new hybrid algorithm for determining nuclear morphology using angle-resolved low coherence interferometry (a/LCI) measurements in ex vivo cervical tissue. The algorithm combines Mie theory based and continuous wavelet transform inverse light scattering analysis. The hybrid algorithm was validated and compared to traditional Mie theory based analysis using an ex vivo tissue data set. The hybrid algorithm achieved 100% agreement with pathology in distinguishing dysplastic and non-dysplastic biopsy sites in the pilot study. Significantly, the new algorithm performed over four times faster than traditional Mie theory based analysis. PMID:26309741

Research on target tracking algorithm based on spatio-temporal context

NASA Astrophysics Data System (ADS)

Li, Baiping; Xu, Sanmei; Kang, Hongjuan

2017-07-01

In this paper, a novel target tracking algorithm based on spatio-temporal context is proposed. During the tracking process, the camera shaking or occlusion may lead to the failure of tracking. The proposed algorithm can solve this problem effectively. The method use the spatio-temporal context algorithm as the main research object. We get the first frame's target region via mouse. Then the spatio-temporal context algorithm is used to get the tracking targets of the sequence of frames. During this process a similarity measure function based on perceptual hash algorithm is used to judge the tracking results. If tracking failed, reset the initial value of Mean Shift algorithm for the subsequent target tracking. Experiment results show that the proposed algorithm can achieve real-time and stable tracking when camera shaking or target occlusion.

How parents' negative experiences at immunization visits affect child immunization status in a community in New York City.

PubMed

Stockwell, Melissa S; Irigoyen, Matilde; Martinez, Raquel Andres; Findley, Sally

2011-01-01

Little is known about how families' experiences with immunization visits within the medical home may affect children's immunization status. We assessed the association between families' negative immunization experiences within the medical home and underimmunization. We surveyed parents (n = 392) of children aged 2-36 months about immunization experiences at community health centers, hospital-based clinics, private practices, and community-based organizations in New York City. We used Chi-square tests and odds ratios (ORs) to assess the relationship between medical home elements and parental immunization experience ratings. We used multivariable analysis to determine the association between negative experiences during immunization visits and underimmunization, controlling for insurance, maternal education, and receipt of benefits from the Special Supplemental Nutrition Program for Women, Infants, and Children. The majority of children were of Latino race/ethnicity and had Medicaid and a medical home. One-sixth (16.9%) of families reported a previous negative immunization experience, primarily related to the child's reaction, waiting time, and attitudes of medical and office staff. Parents' negative immunization experiences were associated with the absence of four components of the medical home: continuity of care, family-centered care, compassionate care, and comprehensive care. In addition, children in families who reported a negative experience were more likely to have been underimmunized (adjusted OR = 2.00; 95% confidence interval 1.12, 3.58). In a community in New York City, underimmunization of young children was associated with negative immunization experiences. Strategies to improve family experiences with immunization visits within the medical home (particularly around support for the family), medical and ancillary staff attitudes, and reduced waiting time may lead to improved immunization delivery.

Minimalist ensemble algorithms for genome-wide protein localization prediction.

PubMed

Lin, Jhih-Rong; Mondal, Ananda Mohan; Liu, Rong; Hu, Jianjun

2012-07-03

Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi.

Minimalist ensemble algorithms for genome-wide protein localization prediction

PubMed Central

2012-01-01

Background Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. Results This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. Conclusions We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi. PMID:22759391

Home-based child vaccination records--a reflection on form.

PubMed

Brown, David W; Gacic-Dobo, Marta; Young, Stacy L

2014-04-01

Home-based child vaccination records play an important role in documenting immunization services received by children. We report some of the results of a review of home-based vaccination records from 55 countries. In doing so, we categorize records into three groups (vaccination only cards, vaccination plus cards, child health books) and describe differences in characteristics related to the quality of data recorded on immunization. Moreover, we highlight areas of potential concern and areas in need of further research and investigation to improve our understanding of the home-based vaccination record form related to improved data quality from immunization service delivery. Copyright © 2014. Published by Elsevier Ltd.

New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

Hua, Haiyan; Lin, Shuwen

2014-03-01

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Population-based versus practice-based recall for childhood immunizations: a randomized controlled comparative effectiveness trial.

PubMed

Kempe, Allison; Saville, Alison; Dickinson, L Miriam; Eisert, Sheri; Reynolds, Joni; Herrero, Diana; Beaty, Brenda; Albright, Karen; Dibert, Eva; Koehler, Vicky; Lockhart, Steven; Calonge, Ned

2013-06-01

We compared the effectiveness and cost-effectiveness of population-based recall (Pop-recall) versus practice-based recall (PCP-recall) at increasing immunizations among preschool children. This cluster-randomized trial involved children aged 19 to 35 months needing immunizations in 8 rural and 6 urban Colorado counties. In Pop-recall counties, recall was conducted centrally using the Colorado Immunization Information System (CIIS). In PCP-recall counties, practices were invited to attend webinar training using CIIS and offered financial support for mailings. The percentage of up-to-date (UTD) and vaccine documentation were compared 6 months after recall. A mixed-effects model assessed the association between intervention and whether a child became UTD. Ten of 195 practices (5%) implemented recall in PCP-recall counties. Among children needing immunizations, 18.7% became UTD in Pop-recall versus 12.8% in PCP-recall counties (P < .001); 31.8% had documented receipt of 1 or more vaccines in Pop-recall versus 22.6% in PCP-recall counties (P < .001). Relative risk estimates from multivariable modeling were 1.23 (95% confidence interval [CI] = 1.10, 1.37) for becoming UTD and 1.26 (95% CI = 1.15, 1.38) for receipt of any vaccine. Costs for Pop-recall versus PCP-recall were $215 versus $1981 per practice and $17 versus $62 per child brought UTD. Population-based recall conducted centrally was more effective and cost-effective at increasing immunization rates in preschool children.

An oral Sindbis virus replicon-based DNA vaccine containing VP2 gene of canine parvovirus delivered by Escherichia coli elicits immune responses in dogs.

PubMed

Dahiya, S S; Saini, M; Kumar, P; Gupta, P K

2011-01-01

A Sindbis virus replicon-based DNA vaccine containing VP2 gene of canine parvovirus (CPV) was delivered by Escherichia coli to elicit immune responses. The orally immunized dogs developed CPV-specific serum IgG and virus neutralizing antibody responses. The cellular immune responses analyzed using lymphocyte proliferation test and flow cytometry indicated CPV-specific sensitization of both CD3+CD4+ and CD3+CD8+ lymphocytes. This study demonstrated that the oral CPV DNA vaccine delivered by E. coli can be considered as a promising approach for vaccination of dogs against CPV.

Mechanism-based strategies for the management of autoimmunity and immune dysregulation in primary immunodeficiencies

PubMed Central

Walter, Jolan E; Farmer, Jocelyn R; Foldvari, Zsofia; Torgerson, Troy R; Cooper, Megan A

2016-01-01

A broad spectrum of autoimmunity is now well described in patients with primary immunodeficiencies (PIDs). Management of autoimmune disease in the background of PID is particularly challenging given the seemingly discordant goals of immune support and immune suppression. Our growing ability to define the molecular underpinnings of immune dysregulation has facilitated novel targeted therapeutics. This review focuses on mechanism-based treatment strategies for the most common autoimmune and inflammatory complications of PID including autoimmune cytopenias, rheumatologic disease, and gastrointestinal disease. We aim to provide guidance regarding the rational use of these agents in the complex PID patient population. PMID:27836058

A community detection algorithm based on structural similarity

NASA Astrophysics Data System (ADS)

Guo, Xuchao; Hao, Xia; Liu, Yaqiong; Zhang, Li; Wang, Lu

2017-09-01

In order to further improve the efficiency and accuracy of community detection algorithm, a new algorithm named SSTCA (the community detection algorithm based on structural similarity with threshold) is proposed. In this algorithm, the structural similarities are taken as the weights of edges, and the threshold k is considered to remove multiple edges whose weights are less than the threshold, and improve the computational efficiency. Tests were done on the Zachary’s network, Dolphins’ social network and Football dataset by the proposed algorithm, and compared with GN and SSNCA algorithm. The results show that the new algorithm is superior to other algorithms in accuracy for the dense networks and the operating efficiency is improved obviously.

Effectiveness of multiple sclerosis treatment with current immunomodulatory drugs.

PubMed

Milo, Ron

2015-04-01

Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS of a putative autoimmune origin characterized by neurologic dysfunction disseminated in space and time due to demyelination and axonal loss that results in progressive disability. Recent advances in understanding the immune pathogenesis of the disease resulted in the introduction of numerous effective immunomodulatoty drugs having diverse mechanisms of action, modes of administration and risk-benefit profiles. This results in more complex albeit more promising treatment selection and choices. The epidemiology, clinical features, pathogenesis and diagnosis of the disease are discussed. The mode of action and main characteristics of current immunomodulatory drugs for MS and their place in the therapeutic algorithm of the disease based on evidence from clinical trials are described. Speculation on new paradigms, treatment goals and outcome measures aimed at improving the landscape of MS treatment is presented. Multiple disease, drug and patient-related factors should be taken into consideration when selecting the appropriate drug and treatment strategy to the appropriate patient, thus paving the road for personalized medicine in MS.

Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome.

PubMed

Bickhart, Derek M; Rosen, Benjamin D; Koren, Sergey; Sayre, Brian L; Hastie, Alex R; Chan, Saki; Lee, Joyce; Lam, Ernest T; Liachko, Ivan; Sullivan, Shawn T; Burton, Joshua N; Huson, Heather J; Nystrom, John C; Kelley, Christy M; Hutchison, Jana L; Zhou, Yang; Sun, Jiajie; Crisà, Alessandra; Ponce de León, F Abel; Schwartz, John C; Hammond, John A; Waldbieser, Geoffrey C; Schroeder, Steven G; Liu, George E; Dunham, Maitreya J; Shendure, Jay; Sonstegard, Tad S; Phillippy, Adam M; Van Tassell, Curtis P; Smith, Timothy P L

2017-04-01

The decrease in sequencing cost and increased sophistication of assembly algorithms for short-read platforms has resulted in a sharp increase in the number of species with genome assemblies. However, these assemblies are highly fragmented, with many gaps, ambiguities, and errors, impeding downstream applications. We demonstrate current state of the art for de novo assembly using the domestic goat (Capra hircus) based on long reads for contig formation, short reads for consensus validation, and scaffolding by optical and chromatin interaction mapping. These combined technologies produced what is, to our knowledge, the most continuous de novo mammalian assembly to date, with chromosome-length scaffolds and only 649 gaps. Our assembly represents a ∼400-fold improvement in continuity due to properly assembled gaps, compared to the previously published C. hircus assembly, and better resolves repetitive structures longer than 1 kb, representing the largest repeat family and immune gene complex yet produced for an individual of a ruminant species.

Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome

PubMed Central

Bickhart, Derek M.; Rosen, Benjamin D.; Koren, Sergey; Sayre, Brian L.; Hastie, Alex R.; Chan, Saki; Lee, Joyce; Lam, Ernest T.; Liachko, Ivan; Sullivan, Shawn T.; Burton, Joshua N.; Huson, Heather J.; Nystrom, John C.; Kelley, Christy M.; Hutchison, Jana L.; Zhou, Yang; Sun, Jiajie; Crisà, Alessandra; de León, F. Abel Ponce; Schwartz, John C.; Hammond, John A.; Waldbieser, Geoffrey C.; Schroeder, Steven G.; Liu, George E.; Dunham, Maitreya J.; Shendure, Jay; Sonstegard, Tad S.; Phillippy, Adam M.; Van Tassell, Curtis P.; Smith, Timothy P.L.

2018-01-01

The decrease in sequencing cost and increased sophistication of assembly algorithms for short-read platforms has resulted in a sharp increase in the number of species with genome assemblies. However, these assemblies are highly fragmented, with many gaps, ambiguities, and errors, impeding downstream applications. We demonstrate current state of the art for de novo assembly using the domestic goat (Capra hircus), based on long reads for contig formation, short reads for consensus validation, and scaffolding by optical and chromatin interaction mapping. These combined technologies produced the most continuous de novo mammalian assembly to date, with chromosome-length scaffolds and only 649 gaps. Our assembly represents a ~400-fold improvement in continuity due to properly assembled gaps compared to the previously published C. hircus assembly, and better resolves repetitive structures longer than 1 kb, representing the largest repeat family and immune gene complex ever produced for an individual of a ruminant species. PMID:28263316

NASA Tech Briefs, March 2008

NASA Technical Reports Server (NTRS)

2008-01-01

Topics covered include: WRATS Integrated Data Acquisition System; Breadboard Signal Processor for Arraying DSN Antennas; Digital Receiver Phase Meter; Split-Block Waveguide Polarization Twist for 220 to 325 GHz; Nano-Multiplication-Region Avalanche Photodiodes and Arrays; Tailored Asymmetry for Enhanced Coupling to WGM Resonators; Disabling CNT Electronic Devices by Use of Electron Beams; Conical Bearingless Motor/Generators; Integrated Force Method for Indeterminate Structures; Carbon-Nanotube-Based Electrodes for Biomedical Applications; Compact Directional Microwave Antenna for Localized Heating; Using Hyperspectral Imagery to Identify Turfgrass Stresses; Shaping Diffraction-Grating Grooves to Optimize Efficiency; Low-Light-Shift Cesium Fountain without Mechanical Shutters; Magnetic Compensation for Second-Order Doppler Shift in LITS; Nanostructures Exploit Hybrid-Polariton Resonances; Microfluidics, Chromatography, and Atomic-Force Microscopy; Model of Image Artifacts from Dust Particles; Pattern-Recognition System for Approaching a Known Target; Orchestrator Telemetry Processing Pipeline; Scheme for Quantum Computing Immune to Decoherence; Spin-Stabilized Microsatellites with Solar Concentrators; Phase Calibration of Antenna Arrays Aimed at Spacecraft; Ring Bus Architecture for a Solid-State Recorder; and Image Compression Algorithm Altered to Improve Stereo Ranging.

Noninvasive optical monitoring multiple physiological parameters response to cytokine storm

NASA Astrophysics Data System (ADS)

Li, Zebin; Li, Ting

2018-02-01

Cancer and other disease originated by immune or genetic problems have become a main cause of death. Gene/cell therapy is a highlighted potential method for the treatment of these diseases. However, during the treatment, it always causes cytokine storm, which probably trigger acute respiratory distress syndrome and multiple organ failure. Here we developed a point-of-care device for noninvasive monitoring cytokine storm induced multiple physiological parameters simultaneously. Oxy-hemoglobin, deoxy-hemoglobin, water concentration and deep-tissue/tumor temperature variations were simultaneously measured by extended near infrared spectroscopy. Detection algorithms of symptoms such as shock, edema, deep-tissue fever and tissue fibrosis were developed and included. Based on these measurements, modeling of patient tolerance and cytokine storm intensity were carried out. This custom device was tested on patients experiencing cytokine storm in intensive care unit. The preliminary data indicated the potential of our device in popular and milestone gene/cell therapy, especially, chimeric antigen receptor T-cell immunotherapy (CAR-T).

Optical image encryption using chaos-based compressed sensing and phase-shifting interference in fractional wavelet domain

NASA Astrophysics Data System (ADS)

Liu, Qi; Wang, Ying; Wang, Jun; Wang, Qiong-Hua

2018-02-01

In this paper, a novel optical image encryption system combining compressed sensing with phase-shifting interference in fractional wavelet domain is proposed. To improve the encryption efficiency, the volume data of original image are decreased by compressed sensing. Then the compacted image is encoded through double random phase encoding in asymmetric fractional wavelet domain. In the encryption system, three pseudo-random sequences, generated by three-dimensional chaos map, are used as the measurement matrix of compressed sensing and two random-phase masks in the asymmetric fractional wavelet transform. It not only simplifies the keys to storage and transmission, but also enhances our cryptosystem nonlinearity to resist some common attacks. Further, holograms make our cryptosystem be immune to noises and occlusion attacks, which are obtained by two-step-only quadrature phase-shifting interference. And the compression and encryption can be achieved in the final result simultaneously. Numerical experiments have verified the security and validity of the proposed algorithm.

Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review.

PubMed

Du, Jiaying; Gerdtman, Christer; Lindén, Maria

2018-04-06

Motion sensors such as MEMS gyroscopes and accelerometers are characterized by a small size, light weight, high sensitivity, and low cost. They are used in an increasing number of applications. However, they are easily influenced by environmental effects such as temperature change, shock, and vibration. Thus, signal processing is essential for minimizing errors and improving signal quality and system stability. The aim of this work is to investigate and present a systematic review of different signal error reduction algorithms that are used for MEMS gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area. A systematic search was performed with the search engines/databases of the ACM Digital Library, IEEE Xplore, PubMed, and Scopus. Sixteen papers that focus on MEMS gyroscope-related signal processing and were published in journals or conference proceedings in the past 10 years were found and fully reviewed. Seventeen algorithms were categorized into four main groups: Kalman-filter-based algorithms, adaptive-based algorithms, simple filter algorithms, and compensation-based algorithms. The algorithms were analyzed and presented along with their characteristics such as advantages, disadvantages, and time limitations. A user guide to the most suitable signal processing algorithms within this area is presented.

Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review

PubMed Central

Gerdtman, Christer

2018-01-01

Motion sensors such as MEMS gyroscopes and accelerometers are characterized by a small size, light weight, high sensitivity, and low cost. They are used in an increasing number of applications. However, they are easily influenced by environmental effects such as temperature change, shock, and vibration. Thus, signal processing is essential for minimizing errors and improving signal quality and system stability. The aim of this work is to investigate and present a systematic review of different signal error reduction algorithms that are used for MEMS gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area. A systematic search was performed with the search engines/databases of the ACM Digital Library, IEEE Xplore, PubMed, and Scopus. Sixteen papers that focus on MEMS gyroscope-related signal processing and were published in journals or conference proceedings in the past 10 years were found and fully reviewed. Seventeen algorithms were categorized into four main groups: Kalman-filter-based algorithms, adaptive-based algorithms, simple filter algorithms, and compensation-based algorithms. The algorithms were analyzed and presented along with their characteristics such as advantages, disadvantages, and time limitations. A user guide to the most suitable signal processing algorithms within this area is presented. PMID:29642412

Is introducing rapid culture into the diagnostic algorithm of smear-negative tuberculosis cost-effective?

PubMed

Yakhelef, N; Audibert, M; Varaine, F; Chakaya, J; Sitienei, J; Huerga, H; Bonnet, M

2014-05-01

In 2007, the World Health Organization recommended introducing rapid Mycobacterium tuberculosis culture into the diagnostic algorithm of smear-negative pulmonary tuberculosis (TB). To assess the cost-effectiveness of introducing a rapid non-commercial culture method (thin-layer agar), together with Löwenstein-Jensen culture to diagnose smear-negative TB at a district hospital in Kenya. Outcomes (number of true TB cases treated) were obtained from a prospective study evaluating the effectiveness of a clinical and radiological algorithm (conventional) against the alternative algorithm (conventional plus M. tuberculosis culture) in 380 smear-negative TB suspects. The costs of implementing each algorithm were calculated using a 'micro-costing' or 'ingredient-based' method. We then compared the cost and effectiveness of conventional vs. culture-based algorithms and estimated the incremental cost-effectiveness ratio. The costs of conventional and culture-based algorithms per smear-negative TB suspect were respectively €39.5 and €144. The costs per confirmed and treated TB case were respectively €452 and €913. The culture-based algorithm led to diagnosis and treatment of 27 more cases for an additional cost of €1477 per case. Despite the increase in patients started on treatment thanks to culture, the relatively high cost of a culture-based algorithm will make it difficult for resource-limited countries to afford.

Evaluation of Baby Advocate, a childhood immunization reminder system.

PubMed

Ludwig-Beymer, P; Hefferan, C

2001-10-01

Childhood immunizations, based on CDC recommendations, are recognized as a cost effective and health promoting practice. However, ensuring full immunization requires a long-term commitment on the part of parents and providers. This article describes a program at Advocate Health care to increase the percentage of children fully immunized at two years to 90%. Termed Baby Advocate, the program uses a mailed reminder system that includes vaccine and growth and development information along with gifts and incentives. Volume, satisfaction and immunization status data are presented.

Information-based management mode based on value network analysis for livestock enterprises

NASA Astrophysics Data System (ADS)

Liu, Haoqi; Lee, Changhoon; Han, Mingming; Su, Zhongbin; Padigala, Varshinee Anu; Shen, Weizheng

2018-01-01

With the development of computer and IT technologies, enterprise management has gradually become information-based management. Moreover, due to poor technical competence and non-uniform management, most breeding enterprises show a lack of organisation in data collection and management. In addition, low levels of efficiency result in increasing production costs. This paper adopts 'struts2' in order to construct an information-based management system for standardised and normalised management within the process of production in beef cattle breeding enterprises. We present a radio-frequency identification system by studying multiple-tag anti-collision via a dynamic grouping ALOHA algorithm. This algorithm is based on the existing ALOHA algorithm and uses an improved packet dynamic of this algorithm, which is characterised by a high-throughput rate. This new algorithm can reach a throughput 42% higher than that of the general ALOHA algorithm. With a change in the number of tags, the system throughput is relatively stable.

SOTXTSTREAM: Density-based self-organizing clustering of text streams.

PubMed

Bryant, Avory C; Cios, Krzysztof J

2017-01-01

A streaming data clustering algorithm is presented building upon the density-based self-organizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clustering algorithms use a two-phase clustering approach. In the first phase, a micro-clustering solution is maintained online, while in the second phase, the micro-clustering solution is clustered offline to produce a macro solution. By performing self-organization techniques on micro-clusters in the online phase, SOSTREAM is able to maintain a macro clustering solution in a single phase. Leveraging concepts from SOSTREAM, a new density-based self-organizing text stream clustering algorithm, SOTXTSTREAM, is presented that addresses several shortcomings of SOSTREAM. Gains in clustering performance of this new algorithm are demonstrated on several real-world text stream datasets.

A multiplexed microfluidic system for evaluation of dynamics of immune-tumor interactions.

PubMed

Moore, N; Doty, D; Zielstorff, M; Kariv, I; Moy, L Y; Gimbel, A; Chevillet, J R; Lowry, N; Santos, J; Mott, V; Kratchman, L; Lau, T; Addona, G; Chen, H; Borenstein, J T

2018-05-25

Recapitulation of the tumor microenvironment is critical for probing mechanisms involved in cancer, and for evaluating the tumor-killing potential of chemotherapeutic agents, targeted therapies and immunotherapies. Microfluidic devices have emerged as valuable tools for both mechanistic studies and for preclinical evaluation of therapeutic agents, due to their ability to precisely control drug concentrations and gradients of oxygen and other species in a scalable and potentially high throughput manner. Most existing in vitro microfluidic cancer models are comprised of cultured cancer cells embedded in a physiologically relevant matrix, collocated with vascular-like structures. However, the recent emergence of immune checkpoint inhibitors (ICI) as a powerful therapeutic modality against many cancers has created a need for preclinical in vitro models that accommodate interactions between tumors and immune cells, particularly for assessment of unprocessed tumor fragments harvested directly from patient biopsies. Here we report on a microfluidic model, termed EVIDENT (ex vivo immuno-oncology dynamic environment for tumor biopsies), that accommodates up to 12 separate tumor biopsy fragments interacting with flowing tumor-infiltrating lymphocytes (TILs) in a dynamic microenvironment. Flow control is achieved with a single pump in a simple and scalable configuration, and the entire system is constructed using low-sorption materials, addressing two principal concerns with existing microfluidic cancer models. The system sustains tumor fragments for multiple days, and permits real-time, high-resolution imaging of the interaction between autologous TILs and tumor fragments, enabling mapping of TIL-mediated tumor killing and testing of various ICI treatments versus tumor response. Custom image analytic algorithms based on machine learning reported here provide automated and quantitative assessment of experimental results. Initial studies indicate that the system is capable of quantifying temporal levels of TIL infiltration and tumor death, and that the EVIDENT model mimics the known in vivo tumor response to anti-PD-1 ICI treatment of flowing TILs relative to isotype control treatments for syngeneic mouse MC38 tumors.

Diagnostic Strategy Used To Establish Etiologies of Encephalitis in a Prospective Cohort of Patients in England▿

PubMed Central

Ambrose, H. E.; Granerod, J.; Clewley, J. P.; Davies, N. W. S.; Keir, G.; Cunningham, R.; Zuckerman, M.; Mutton, K. J.; Ward, K. N.; Ijaz, S.; Crowcroft, N. S.; Brown, D. W. G.

2011-01-01

The laboratory diagnostic strategy used to determine the etiology of encephalitis in 203 patients is reported. An etiological diagnosis was made by first-line laboratory testing for 111 (55%) patients. Subsequent testing, based on individual case reviews, resulted in 17 (8%) further diagnoses, of which 12 (71%) were immune-mediated and 5 (29%) were due to infection. Seventy-five cases were of unknown etiology. Sixteen (8%) of 203 samples were found to be associated with either N-methyl-d-aspartate receptor or voltage-gated potassium channel complex antibodies. The most common viral causes identified were herpes simplex virus (HSV) (19%) and varicella-zoster virus (5%), while the most important bacterial cause was Mycobacterium tuberculosis (5%). The diagnostic value of testing cerebrospinal fluid (CSF) for antibody was assessed using 139 samples from 99 patients, and antibody was detected in 46 samples from 37 patients. Samples collected at 14 to 28 days were more likely to be positive than samples taken 0 to 6 days postadmission. Three PCR-negative HSV cases were diagnosed by the presence of virus-specific antibody in the central nervous system (CNS). It was not possible to make an etiological diagnosis for one-third of the cases; these were therefore considered to be due to unknown causes. Delayed sampling did not contribute to these cases. Twenty percent of the patients with infections with an unknown etiology showed evidence of localized immune activation within the CNS, but no novel viral DNA or RNA sequences were found. We conclude that a good standard of clinical investigation and thorough first-line laboratory testing allows the diagnosis of most cases of infectious encephalitis; testing for CSF antibodies allows further cases to be diagnosed. It is important that testing for immune-mediated causes also be included in a diagnostic algorithm. PMID:21865429

GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

PubMed

Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

2016-08-01

In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement.

PubMed

Zhao, Shengkui; Jones, Douglas L; Khoo, Suiyang; Man, Zhihong

2014-09-01

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.

Symmetric Stream Cipher using Triple Transposition Key Method and Base64 Algorithm for Security Improvement

NASA Astrophysics Data System (ADS)

Nurdiyanto, Heri; Rahim, Robbi; Wulan, Nur

2017-12-01

Symmetric type cryptography algorithm is known many weaknesses in encryption process compared with asymmetric type algorithm, symmetric stream cipher are algorithm that works on XOR process between plaintext and key, to improve the security of symmetric stream cipher algorithm done improvisation by using Triple Transposition Key which developed from Transposition Cipher and also use Base64 algorithm for encryption ending process, and from experiment the ciphertext that produced good enough and very random.

Evaluation of the immune response in Shitou geese (Anser anser domesticus) following immunization with GPV-VP1 DNA-based and live attenuated vaccines.

PubMed

Deng, Shu-xuan; Cai, Ming-sheng; Cui, Wei; Huang, Jin-lu; Li, Mei-li

2014-01-01

Goose parvovirus (GPV) is a highly contagious and deadly disease for goslings and Muscovy ducklings. To compare the differences in immune response of geese immunized with GPV-VP1 DNA-based and live attenuated vaccines. Shitou geese were immunized once with either 20 μg pcDNA-GPV-VP1 DNA gene vaccine by gene gun bombardment via intramuscular injection, or 300 μg by i.m. injection, or 300 μL live attenuated vaccine by i.m. injection, whereas 300 μg pcDNA3.1 (+) i.m. or 300 μL saline i.m. were used as positive and negative controls, respectively. Each group comprised 28 animals. Peripheral blood samples were collected from 2-210 days after immunization and the proliferation of T lymphocytes, the number of CD4(+) and CD8(+) T cells and the level of IgG assessed. Statistical analysis was performed using a one-way analysis of variance with group multiple comparisons via Tukey's test. The pcDNA-GPV-VP1 DNA and attenuated vaccine induced cellular and humoral responses, and there were no differences between the 20 and 300 μg group in the responses of proliferation of T lymphocyte and the CD8(+) T-cell. However, as to CD4(+) T-cell response and humoral immunity, the 20 μg group performed better than the 300 μg group, which induced better cellular and humoral immunity than live attenuated vaccine. This study showed that it is possible to induce both cellular and humoral response using DNA-based vaccines and that the pcDNA-GPV-VP1 DNA gene vaccine induced better cellular and humoral immunity than live attenuated vaccine.

A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

PubMed

Yan, Kang K; Zhao, Hongyu; Pang, Herbert

2017-12-06

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

Immunity by equilibrium.

PubMed

Eberl, Gérard

2016-08-01

The classical model of immunity posits that the immune system reacts to pathogens and injury and restores homeostasis. Indeed, a century of research has uncovered the means and mechanisms by which the immune system recognizes danger and regulates its own activity. However, this classical model does not fully explain complex phenomena, such as tolerance, allergy, the increased prevalence of inflammatory pathologies in industrialized nations and immunity to multiple infections. In this Essay, I propose a model of immunity that is based on equilibrium, in which the healthy immune system is always active and in a state of dynamic equilibrium between antagonistic types of response. This equilibrium is regulated both by the internal milieu and by the microbial environment. As a result, alteration of the internal milieu or microbial environment leads to immune disequilibrium, which determines tolerance, protective immunity and inflammatory pathology.

Validation of deformable image registration algorithms on CT images of ex vivo porcine bladders with fiducial markers.

PubMed

Wognum, S; Heethuis, S E; Rosario, T; Hoogeman, M S; Bel, A

2014-07-01

The spatial accuracy of deformable image registration (DIR) is important in the implementation of image guided adaptive radiotherapy techniques for cancer in the pelvic region. Validation of algorithms is best performed on phantoms with fiducial markers undergoing controlled large deformations. Excised porcine bladders, exhibiting similar filling and voiding behavior as human bladders, provide such an environment. The aim of this study was to determine the spatial accuracy of different DIR algorithms on CT images of ex vivo porcine bladders with radiopaque fiducial markers applied to the outer surface, for a range of bladder volumes, using various accuracy metrics. Five excised porcine bladders with a grid of 30-40 radiopaque fiducial markers attached to the outer wall were suspended inside a water-filled phantom. The bladder was filled with a controlled amount of water with added contrast medium for a range of filling volumes (100-400 ml in steps of 50 ml) using a luer lock syringe, and CT scans were acquired at each filling volume. DIR was performed for each data set, with the 100 ml bladder as the reference image. Six intensity-based algorithms (optical flow or demons-based) implemented in theMATLAB platform DIRART, a b-spline algorithm implemented in the commercial software package VelocityAI, and a structure-based algorithm (Symmetric Thin Plate Spline Robust Point Matching) were validated, using adequate parameter settings according to values previously published. The resulting deformation vector field from each registration was applied to the contoured bladder structures and to the marker coordinates for spatial error calculation. The quality of the algorithms was assessed by comparing the different error metrics across the different algorithms, and by comparing the effect of deformation magnitude (bladder volume difference) per algorithm, using the Independent Samples Kruskal-Wallis test. The authors found good structure accuracy without dependency on bladder volume difference for all but one algorithm, and with the best result for the structure-based algorithm. Spatial accuracy as assessed from marker errors was disappointing for all algorithms, especially for large volume differences, implying that the deformations described by the registration did not represent anatomically correct deformations. The structure-based algorithm performed the best in terms of marker error for the large volume difference (100-400 ml). In general, for the small volume difference (100-150 ml) the algorithms performed relatively similarly. The structure-based algorithm exhibited the best balance in performance between small and large volume differences, and among the intensity-based algorithms, the algorithm implemented in VelocityAI exhibited the best balance. Validation of multiple DIR algorithms on a novel physiological bladder phantom revealed that the structure accuracy was good for most algorithms, but that the spatial accuracy as assessed from markers was low for all algorithms, especially for large deformations. Hence, many of the available algorithms exhibit sufficient accuracy for contour propagation purposes, but possibly not for accurate dose accumulation.

Optimizing Tumor Microenvironment for Cancer Immunotherapy: β-Glucan-Based Nanoparticles

PubMed Central

Zhang, Mei; Kim, Julian A.; Huang, Alex Yee-Chen

2018-01-01

Immunotherapy is revolutionizing cancer treatment. Recent clinical success with immune checkpoint inhibitors, chimeric antigen receptor T-cell therapy, and adoptive immune cellular therapies has generated excitement and new hopes for patients and investigators. However, clinically efficacious responses to cancer immunotherapy occur only in a minority of patients. One reason is the tumor microenvironment (TME), which potently inhibits the generation and delivery of optimal antitumor immune responses. As our understanding of TME continues to grow, strategies are being developed to change the TME toward one that augments the emergence of strong antitumor immunity. These strategies include eliminating tumor bulk to provoke the release of tumor antigens, using adjuvants to enhance antigen-presenting cell function, and employ agents that enhance immune cell effector activity. This article reviews the development of β-glucan and β-glucan-based nanoparticles as immune modulators of TME, as well as their potential benefit and future therapeutic applications. Cell-wall β-glucans from natural sources including plant, fungi, and bacteria are molecules that adopt pathogen-associated molecular pattern (PAMP) known to target specific receptors on immune cell subsets. Emerging data suggest that the TME can be actively manipulated by β-glucans and their related nanoparticles. In this review, we discuss the mechanisms of conditioning TME using β-glucan and β-glucan-based nanoparticles, and how this strategy enables future design of optimal combination cancer immunotherapies. PMID:29535722

Comparison of the efficiency control of mycotoxins by some optical immune biosensors

NASA Astrophysics Data System (ADS)

Slyshyk, N. F.; Starodub, N. F.

2013-11-01

It was compared the efficiency of patulin control at the application of such optical biosensors which were based on the surface plasmon resonance (SPR) and nano-porous silicon (sNPS). In last case the intensity of the immune reaction was registered by measuring level of chemiluminescence (ChL) or photocurrent of nPS. The sensitivity of this mycotoxin determination by first type of immune biosensor was 0.05-10 mg/L Approximately the same sensitivity as well as the overall time analysis were demonstrated by the immune biosensor based on the nPS too. Nevertheless, the last type of biosensor was simpler in technical aspect and the cost of analysis was cheapest. That is why, it was recommend the nPS based immune biosensor for wide screening application and SPR one for some additional control or verification of preliminary obtained results. In this article a special attention was given to condition of sample preparation for analysis, in particular, micotoxin extraction from potao and some juices. Moreover, it was compared the efficiency of the above mentioned immune biosensors with such traditional approach of mycotoxin determination as the ELISA-method. In the result of investigation and discussion of obtained data it was concluded that both type of the immune biosensors are able to fulfill modern practice demand in respect sensitivity, rapidity, simplicity and cheapness of analysis.

Efficient clustering aggregation based on data fragments.

PubMed

Wu, Ou; Hu, Weiming; Maybank, Stephen J; Zhu, Mingliang; Li, Bing

2012-06-01

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

PubMed Central

Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong

2014-01-01

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458

Effect of a culture-based screening algorithm on tuberculosis incidence in immigrants and refugees bound for the United States: a population-based cross-sectional study.

PubMed

Liu, Yecai; Posey, Drew L; Cetron, Martin S; Painter, John A

2015-03-17

Before 2007, immigrants and refugees bound for the United States were screened for tuberculosis (TB) by a smear-based algorithm that could not diagnose smear-negative/culture-positive TB. In 2007, the Centers for Disease Control and Prevention implemented a culture-based algorithm. To evaluate the effect of the culture-based algorithm on preventing the importation of TB to the United States by immigrants and refugees from foreign countries. Population-based, cross-sectional study. Panel physician sites for overseas medical examination. Immigrants and refugees with TB. Comparison of the increase of smear-negative/culture-positive TB cases diagnosed overseas among immigrants and refugees by the culture-based algorithm with the decline of reported cases among foreign-born persons within 1 year after arrival in the United States from 2007 to 2012. Of the 3 212 421 arrivals of immigrants and refugees from 2007 to 2012, a total of 1 650 961 (51.4%) were screened by the smear-based algorithm and 1 561 460 (48.6%) were screened by the culture-based algorithm. Among the 4032 TB cases diagnosed by the culture-based algorithm, 2195 (54.4%) were smear-negative/culture-positive. Before implementation (2002 to 2006), the annual number of reported cases among foreign-born persons within 1 year after arrival was relatively constant (range, 1424 to 1626 cases; mean, 1504 cases) but decreased from 1511 to 940 cases during implementation (2007 to 2012). During the same period, the annual number of smear-negative/culture-positive TB cases diagnosed overseas among immigrants and refugees bound for the United States by the culture-based algorithm increased from 4 to 629. This analysis did not control for the decline in new arrivals of nonimmigrant visitors to the United States and the decrease of incidence of TB in their countries of origin. Implementation of the culture-based algorithm may have substantially reduced the incidence of TB among newly arrived, foreign-born persons in the United States. None.

Factors Associated with Student Participation in a School-based Hepatitis B Immunization Program.

ERIC Educational Resources Information Center

Goldstein, Susan T.; Cassidy, William M.; Hodgson, Wesley; Mahoney, Francis J.

2001-01-01

Examined relationships between participation in school-based hepatitis B immunization programs and teacher attitudes toward school-based health care and student socioeconomic status (SES). Data on teacher attitudes, student standardized test scores, and student SES indicated that SES was the most important predictor of student participation. The…

Immune System Dysregulation and Latent Herpesvirus Reactivation During Winterover at Concordia Station, Dome C, Antarctica

NASA Technical Reports Server (NTRS)

Crucian, B. E.; Feuerecker, M.; Salam, A. P.; Rybka, A.; Stowe, R. P.; Morrels, M.; Meta, S. K.; Quiriarte, H.; Quintens, Roel; Thieme, U.;