Cognitive Network Technology Research Articles

Underwater Multi-Channel MAC with Cognitive Acoustics for Distributed Underwater Acoustic Networks.

The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture.

A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks.

Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks. To address this problem, in this paper a new initialization mechanism is proposed to establish a communication link and set up a sensor network without adopting spectrum holes to convey control information. Specifically, firstly a transmission channel model for analyzing the maximum accessible capacity for three different polices in a fading environment is discussed. Secondly, a hybrid spectrum access algorithm based on a reinforcement learning model is proposed for the power allocation problem of both the transmission channel and the control channel. Finally, extensive simulations have been conducted and simulation results show that this new algorithm provides a significant improvement in terms of the tradeoff between the control channel reliability and the efficiency of the transmission channel.

Research on Cognition of Heterogeneous Wireless Networks

With the broadband development of mobile network and the mobility development of broadband networks, all-IP based heterogeneous networks coexistence and integration is becoming an important feature of next generation wireless networks.This Paper firstly summarized the evolution and development tendency of heterogeneous wireless networks, then introduced and analyzed the cognitive radio and cognitive network technologies. Also the difficult Problems for future cognitive heterogeneous networks were also addressed on.

Joint power and spectrum allocation in multi-hop cognitive radio networks

Various cognitive network technologies are developed rapidly. In the article, the power and spectrum allocation in multi-hop cognitive radio network (CRN) with linear topology is investigated. The overall goal is to minimize outage probability and promote spectrum utility, including total reward and fairness, while meeting the limits of total transmit power and interference threshold to primary user simultaneously. The problem is solved with convex optimization and artificial bee colony (ABC) algorithm jointly. Simulation shows that the proposed scheme not only minimizes outage probability, but also realizes a better use of spectrum.

Development of High-Content Assays for Kidney Progenitor Cell Expansion in Transgenic Zebrafish

Reactivation of genes normally expressed during organogenesis is a characteristic of kidney regeneration. Enhancing this reactivation could potentially be a therapeutic target to augment kidney regeneration. The inductive events that drive kidney organogenesis in zebrafish are similar to the initial steps in mammalian kidney organogenesis. Therefore, quantifying embryonic signals that drive zebrafish kidney development is an attractive strategy for the discovery of potential novel therapeutic modalities that accelerate kidney regeneration. The Lim1 homeobox protein, Lhx1, is a marker of kidney development that is also expressed in the regenerating kidneys after injury. Using a fluorescent Lhx1a-EGFP transgene whose phenotype faithfully recapitulates that of the endogenous protein, we developed a high-content assay for Lhx1a-EGFP expression in transgenic zebrafish embryos employing an artificial intelligence–based image analysis method termed cognition network technology (CNT). Implementation of the CNT assay on high-content readers enabled automated real-time in vivo time-course, dose-response, and variability studies in the developing embryo. The Lhx1a assay was complemented with a kidney-specific secondary CNT assay that enables direct measurements of the embryonic renal tubule cell population. The integration of fluorescent transgenic zebrafish embryos with automated imaging and artificial intelligence–based image analysis provides an in vivo analysis system for structure-activity relationship studies and de novo discovery of novel agents that augment innate regenerative processes.

Automated Quantitative RNA in Situ Hybridization for Resolution of Equivocal and Heterogeneous ERBB2 (HER2) Status in Invasive Breast Carcinoma

Patient management based on HER2 status in breast carcinoma is an archetypical example of personalized medicine but remains hampered by equivocal testing and intratumoral heterogeneity. We developed a fully automated, quantitative, bright-field in situ hybridization technique (RNAscope), applied it to quantify single-cell HER2 mRNA levels in 132 invasive breast carcinomas, and compared the results with those by real-time quantitative PCR (qPCR) and Food and Drug Administration-approved methods, including fluorescence in situ hybridization (FISH), IHC, chromogenic in situ hybridization, and dual in situ hybridization. Both RNAscope and qPCR were 97.3% concordant with FISH in cases in which FISH results were unequivocal. RNAscope was superior to qPCR in cases with intratumoral heterogeneity or equivocal FISH results. This novel assay may enable ultimate HER2 status resolution as a reflex test for current testing algorithms. Quantitative in situ RNA measurement at the single-cell level may be broadly applicable in companion diagnostic applications.

Embedding cognition in wireless network management: an experimental perspective

The Future Internet promises to pervade our everyday lives by interweaving an increased variety of access technologies (cellular broadband, wireless hotspots, short range radios). Cognitive network management is a promising approach to cope with such access diversity and to enable dynamic adaptation of the network configurations and parameters. This article reveals the insights and conclusions of a unique real life implementation of a cognitive architecture, comprising software agents and artificial intelligence algorithms, and its deployment within a heterogeneous access network composed of a Broadband WiMAX base station and WiFi access points. We discuss the architectural challenges that this approach poses, and present the distributed software architecture we implemented. We detail the experimental access network we deployed and the algorithms that enable channel reselection and vertical assisted handover. We conclude with our findings and recommendations for the deployment of cognitive network management architectures and technologies in the Future Internet.

Additive Microglia-Mediated Neuronal Injury Caused by Amyloid-β and Bacterial TLR Agonists in Murine Neuron-Microglia Co-Cultures Quantified by an Automated Image Analysis using Cognition Network Technology

Activated microglia is considered to be involved in the progression of Alzheimer's disease (AD). We investigated the effect of amyloid-β(1-40) (Aβ(40) and exogenous agonists of Toll-like receptor (TLR) 1/2 (Pam(3)CSK(4)) and TLR4 (LPS) on neurons in primary murine neuron-microglia co-cultures. Neuronal viability, assessed by quantifying the number of intact neuronal extensions and their crossings using a newly developed Definiens Cognition Network Technology-based method, was significantly decreased after treatment with Pam(3)CSK(4), LPS, and Aβ(40). Combined treatment with Aβ(40) and Pam(3)CSK(4) or LPS had an additive effect. Hence, in patients with AD, synergistic microglial activation by Aβ and bacterial products during infections might contribute to disease progression.

P1.01 Identification of Prognostic Factors Using Image Analysis of HER2 Expression by Immunohistochemistry in Adenocarcinoma of the Oesophagogastric Junction

BackgroundSince adenocarcinoma of the oesophagogastric junction is known to show human epidermal growth factor receptor 2 (HER2) overexpression we investigated the potential of IHC stained cancer tissue to provide information about disease free (DFS) and overall survival (OS) times. We compare the prognostic value of a visually assessed scoring algorithm derived from Dako HercepTestTM with results provided by data mining information from quantitative image analysis. MethodsThree tissue microarrays (TMAs) comprising 391 cores from tissue samples of 150 patients were analysed. After IHC staining with HER2 antibody the TMAs were scanned with Zeiss MIRAX slide scanner (20x objective). Fully automated image analysis using the Definiens Cognition Network Technology® segmented and classified cells, nuclei, cytoplasm and membrane objects, and determined on a per cell basis shape, texture and color properties. Those were correlated with known DFS/OS times using a multivariate regression analysis within the R statistical software. Based on this predictive model, the patient population was divided in one group with good and one with poor prognosis by imposing a threshold on the predicted survival times. The corresponding groups obtained by the pathologist scoring were HER2 score 0, 1 + , 2+ versus HER2 score 3+. ResultKaplan-Meier Analysis revealed a significant (DFS: p < 2.4x10- 6; OS: p < 2.5x10- 7) prognostic value for the two groups generated by data mining image analysis results, whereas the visually assessed score was not significant (p > 0.1). ConclusionData mining quantitative image analysis may provide a more accurate evaluation of HER2 evaluation than a visual assessment of tissue samples. The quantification of HER2 overexpression by image analysis may be also highly valuable for the prediction of anti-HER2 therapy in combating this cancer type.Acknowledgement: The authors thanks Dr. Annette Feuchtinger and Prof. Axel Walch from the HelmholtzCenter Munich for image data and valuable pathology expert consultation.

Weighted Sum Rate Maximization in OFDM Based Cooperative Cognitive Radios: A Joint Optimization Approach

Improving utilization of the radio spectrum is the main goal of Cognitive Radio Networks (CRN). Recent studies made use of cooperative relay technology in cognitive networks, to increase transmission diversity gain. In this paper we consider an OFDM based cooperative cognitive network with a pair of Source-Destination nodes as the primary user (PU), and a pair of Source-Destination nodes—which is assisted with a relay—as the secondary (cognitive) user (SU). Both primary and secondary users share a same spectrum. In a two hop transmission, the source transmits in the first hop, and the half-duplex relay decodes the message, re-encodes and forwards it to the destination in the second hop on a different subcarrier. The cognitive network obeys an underlay paradigm where the SU is allowed to transmit simultaneously with PU, while its power is limited such that the interference caused for PU does not exceed a defined temperature. Under this constraint, a joint subcarrier pairing and power allocation is proposed for SU to maximize its weighted sum rate. The problem is transformed to a convex optimization problem and solved in the dual domain. Then an algorithm to achieve feasible solutions is used based on the optimization results. Through extensive simulations, we compare the spectrum utilization of the proposed approach with the existing ones, and show that interestingly the proposed method improves the weighted sum rate of SU.

An Analytical Approach Differentiates Between Individual and Collective Cancer Invasion

Tumour cells employ a variety of mechanisms to invade their environment and to form metastases. An important property is the ability of tumour cells to transition between individual cell invasive mode and collective mode. The switch from collective to individual cell invasion in the breast was shown recently to determine site of subsequent metastasis. Previous studies have suggested a range of invasion modes from single cells to large clusters. Here, we use a novel image analysis method to quantify and categorise invasion. We have developed a process using automated imaging for data collection, unsupervised morphological examination of breast cancer invasion using cognition network technology (CNT) to determine how many patterns of invasion can be reliably discriminated. We used Bayesian network analysis to probabilistically connect morphological variables and therefore determine that two categories of invasion are clearly distinct from one another. The Bayesian network separated individual and collective invading cell groups based on the morphological measurements, with the level of cell-cell contact the most discriminating morphological feature. Smaller invading groups were typified by smoother cellular surfaces than those invading collectively in larger groups. Interestingly, elongation was evident in all invading cell groups and was not a specific feature of single cell invasion as a surrogate of epithelial-mesenchymal transition. In conclusion, the combination of cognition network technology and Bayesian network analysis provides an insight into morphological variables associated with transition of cancer cells between invasion modes. We show that only two morphologically distinct modes of invasion exist.

An analytical approach differentiates between individual and collective cancer invasion.

Tumour cells employ a variety of mechanisms to invade their environment and to form metastases. An important property is the ability of tumour cells to transition between individual cell invasive mode and collective mode. The switch from collective to individual cell invasion in the breast was shown recently to determine site of subsequent metastasis. Previous studies have suggested a range of invasion modes from single cells to large clusters. Here, we use a novel image analysis method to quantify and categorise invasion. We have developed a process using automated imaging for data collection, unsupervised morphological examination of breast cancer invasion using cognition network technology (CNT) to determine how many patterns of invasion can be reliably discriminated. We used Bayesian network analysis to probabilistically connect morphological variables and therefore determine that two categories of invasion are clearly distinct from one another. The Bayesian network separated individual and collective invading cell groups based on the morphological measurements, with the level of cell-cell contact the most discriminating morphological feature. Smaller invading groups were typified by smoother cellular surfaces than those invading collectively in larger groups. Interestingly, elongation was evident in all invading cell groups and was not a specific feature of single cell invasion as a surrogate of epithelial-mesenchymal transition. In conclusion, the combination of cognition network technology and Bayesian network analysis provides an insight into morphological variables associated with transition of cancer cells between invasion modes. We show that only two morphologically distinct modes of invasion exist.

Development of automated imaging and analysis for zebrafish chemical screens.

We demonstrate the application of image-based high-content screening (HCS) methodology to identify small molecules that can modulate the FGF/RAS/MAPK pathway in zebrafish embryos. The zebrafish embryo is an ideal system for in vivo high-content chemical screens. The 1-day old embryo is approximately 1mm in diameter and can be easily arrayed into 96-well plates, a standard format for high throughput screening. During the first day of development, embryos are transparent with most of the major organs present, thus enabling visualization of tissue formation during embryogenesis. The complete automation of zebrafish chemical screens is still a challenge, however, particularly in the development of automated image acquisition and analysis. We previously generated a transgenic reporter line that expresses green fluorescent protein (GFP) under the control of FGF activity and demonstrated their utility in chemical screens 1. To establish methodology for high throughput whole organism screens, we developed a system for automated imaging and analysis of zebrafish embryos at 24-48 hours post fertilization (hpf) in 96-well plates 2. In this video we highlight the procedures for arraying transgenic embryos into multiwell plates at 24hpf and the addition of a small molecule (BCI) that hyperactivates FGF signaling 3. The plates are incubated for 6 hours followed by the addition of tricaine to anesthetize larvae prior to automated imaging on a Molecular Devices ImageXpress Ultra laser scanning confocal HCS reader. Images are processed by Definiens Developer software using a Cognition Network Technology algorithm that we developed to detect and quantify expression of GFP in the heads of transgenic embryos. In this example we highlight the ability of the algorithm to measure dose-dependent effects of BCI on GFP reporter gene expression in treated embryos.

Development of automated imaging and analysis for zebrafish chemical screens.

We demonstrate the application of image-based high-content screening (HCS) methodology to identify small molecules that can modulate the FGF/RAS/MAPK pathway in zebrafish embryos. The zebrafish embryo is an ideal system for in vivo high-content chemical screens. The 1-day old embryo is approximately 1mm in diameter and can be easily arrayed into 96-well plates, a standard format for high throughput screening. During the first day of development, embryos are transparent with most of the major organs present, thus enabling visualization of tissue formation during embryogenesis. The complete automation of zebrafish chemical screens is still a challenge, however, particularly in the development of automated image acquisition and analysis. We previously generated a transgenic reporter line that expresses green fluorescent protein (GFP) under the control of FGF activity and demonstrated their utility in chemical screens 1. To establish methodology for high throughput whole organism screens, we developed a system for automated imaging and analysis of zebrafish embryos at 24-48 hours post fertilization (hpf) in 96-well plates 2. In this video we highlight the procedures for arraying transgenic embryos into multiwell plates at 24hpf and the addition of a small molecule (BCI) that hyperactivates FGF signaling 3. The plates are incubated for 6 hours followed by the addition of tricaine to anesthetize larvae prior to automated imaging on a Molecular Devices ImageXpress Ultra laser scanning confocal HCS reader. Images are processed by Definiens Developer software using a Cognition Network Technology algorithm that we developed to detect and quantify expression of GFP in the heads of transgenic embryos. In this example we highlight the ability of the algorithm to measure dose-dependent effects of BCI on GFP reporter gene expression in treated embryos.

Cognition Network Technology prototype of a CAD system for mammography to assist radiologists by finding similar cases in a reference database

We present a new approach for computer-aided detection and diagnosis in mammography based on Cognition Network Technology (CNT). Originally designed for image processing, CNT has been extended to also perform context- and knowledge-driven analysis of tabular data. For the first time using this technology, an application was created and evaluated for fully automatic searching of patient cases from a reference database of verified findings. The application aims to support radiologists in providing cases of similarity and relevance to a given query case. It adopts an extensible and knowledge-driven concept as a similarity measure. As a preprocessing step, all input images from more than 400 patients were fully automatically segmented and the resulting objects classified--this includes the complete breast shape, the position of the mammilla, the pectoral muscle, and various potential candidate objects for suspicious mass lesions. For the similarity search, collections of object properties and metadata from many patients were combined into a single table analysis project. Extended CNT allows for a convenient implementation of knowledge-based structures, for example, by meaningfully linking detected objects in different breast views that might represent identical lesions. Objects from alternative segmentation methods are also be considered, so as to collectively become a sufficient set of base-objects for identifying suspicious mass lesions. For 80% of 112 patient cases with suspicious lesions, the system correctly identified at least one corresponding mass lesion as an object of interest. In this database, consisting of 1,024 images from a total of 303 patients, an average of 0.66 false-positive objects per image were detected. An additional testing database contained 480 images from 120 patients, 15 of whom were annotated with suspicious mass lesions. Here, 47% (7 out of 15) of these were detected automatically with 1.13 false-positive objects per image. A diagnosis is predicted for each patient case by applying a majority vote from the reference findings of the ten most similar cases. Two separate evaluation scenarios suggest a fraction of correct predictions of respectively 79 and 76%. Cognition Network Technology was extended to process table data, making it possible to access and relate records from different images and non-image sources, such as demographic patient data or parameters from clinical examinations. A prototypal application enables efficient searching of a patient and image database for similar patient cases. Using concepts of knowledge-driven configuration and flexible extension, the application illustrates a path to a new generation of future CAD systems.

Automated Analysis and Detailed Quantification of Biomedical Images Using Definiens Cognition Network Technology&amp;#174;

Biomedicine has seen tremendous advances in the field of image acquisition. The generation of digital images of high information content has become so straightforward and efficient that the volume of images accumulating in biomedical disciplines is posing significant challenges. Until now, conventional image analysis solutions are generally pixel-based and limited in the amount of information that they extract. However, a software system enabling the complex analysis of biomedical images should not impose restrictions on detection, classification and quantification of structures, but rather allow unlimited freedom to answer exhaustively all conceivable questions about the interactions and relationships between structures. Crucial to this is the precise and robust segmentation of relevant structures in digital micrographs. This challenge involves bringing structure, morphology and context into play. Based on the Definiens Cognition Network Technology, solutions have been deployed for use in biomedicine. The technology is object-oriented, multi-scale, context-driven and knowledge-based. Images are interpreted on the properties of networked image objects, which results in numerous advantages. This approach enables users to bring in detailed expert knowledge and enables complex analyses to be performed with unprecedented accuracy, even on poor quality data or for structures exhibiting heterogeneous properties or variable phenotypes. Extracted structures are the basis for detailed morphometric, structural and relational measurements which can be exported for each individual structure. These data can be used for decision support or correlated against experimental or molecular data, thus bridging classical biomedicine with molecular biology. An overview of the technology is provided with examples from different biomedical applications.

Spectrum Access Games and Strategic Learning in Cognitive Radio Networks for Delay-Critical Applications

With the current proliferation of high bandwidth and delay-sensitive multimedia applications and services, each wireless user will try to maximize its utility by acquiring as much spectrum resources as possible unless a preemptive mechanism exists in the network. Thus, emerging solutions for dynamic spectrum access in cognitive radio networks will need to adopt market-based approaches in order to effectively regulate the available resources. In this paper, we show how various centralized and decentralized spectrum access markets can be designed based on a stochastic game framework, where wireless users (also referred to as secondary users) can compete over time for the dynamically available transmission opportunities (spectrum ldquoholesrdquo). When operating in such spectrum access ldquomarkets,rdquo wireless users become selfish, autonomous agents that strategically interact in order to acquire the necessary spectrum opportunities. We also show how wireless users can successfully compete with each other for the limited and time-varying spectrum opportunities, given the experienced dynamics in the wireless network, by optimizing both their external actions (e.g., the resource bids, power and channel used for transmission, etc.) and their internal actions (e.g., the modulation schemes, etc.). To determine their optimal actions in an informationally decentralized setting, users will need to learn and model directly or indirectly the other users' responses to their external actions. We studied the outcome of various dynamic interactions among self-interested wireless users possessing different knowledge and determine that the proposed framework can lead to multiuser communication systems that achieve new measures of efficiency, rationality and fairness. Lastly, our illustrative results show that the presented game-theoretic solution for wireless resource management enables users deploying enhanced (ldquosmarterrdquo) learning and communication algorithms and being able to make efficient use of the spectrum resources can derive higher utilities. This presents the designers of wireless devices and systems with important incentives to endow their next-generation products and services with enhanced capabilities to gather information, learn, and strategically compete for resources in the emerging spectrum resource markets made possible by the cognitive radio network technologies.

Automated image‐based phenotypic analysis in zebrafish embryos

Presently, the zebrafish is the only vertebrate model compatible with contemporary paradigms of drug discovery. Zebrafish embryos are amenable to automation necessary for high-throughput chemical screens, and optical transparency makes them potentially suited for image-based screening. However, the lack of tools for automated analysis of complex images presents an obstacle to using the zebrafish as a high-throughput screening model. We have developed an automated system for imaging and analyzing zebrafish embryos in multi-well plates regardless of embryo orientation and without user intervention. Images of fluorescent embryos were acquired on a high-content reader and analyzed using an artificial intelligence-based image analysis method termed Cognition Network Technology (CNT). CNT reliably detected transgenic fluorescent embryos (Tg(fli1:EGFP)(y1)) arrayed in 96-well plates and quantified intersegmental blood vessel development in embryos treated with small molecule inhibitors of anigiogenesis. The results demonstrate it is feasible to adapt image-based high-content screening methodology to measure complex whole organism phenotypes.

Cognition Network Technology for a Fully Automated 3D Segmentation of Liver Tumors

The Definiens Cognition Network Technology is applied to detect automatically tumors in a human liver. On the basis of a test data set containing ten tumors we show first quantitative results which are compared to manual segmentations provided by medical experts.

Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology

Automatic segmentation of different types of tissue from magnetic resonance images is of great importance for clinical and research applications, particularly large-scale and longitudinal studies of brain pathology. We developed a fully automated algorithm for the segmentation of lateral ventricles from cranial magnetic resonance images. This problem is of interest in the study of schizophrenia, dementia and other neuropsychiatric disorders. Our algorithm achieves comparable results to expert human raters. The theoretical approach, which is based on an emerging object-oriented technology that has been adapted and evaluated to process 3D data for the first time, may, in the future, be transferred to other important problems of magnetic resonance image analysis like gray/white matter segmentation.