A term systems of systems (SoS) refers to a setup in which a number of independent systems collaborate to create a value that each of them is unable to achieve independently. Complexity of a SoS structure is higher compared to its constitute systems that brings challenges in analyzing its critical properties such as security. An SoS can be seen as a set of connected systems or services that needs to be adequately protected. Communication between such systems or services can be considered as a service itself, and it is the paramount for establishment of a SoS as it enables connections, dependencies, and a cooperation. Given that reliable and predictable communication contributes directly to a correct functioning of an SoS, communication as a service is one of the main assets to consider. Protecting it from malicious adversaries should be one of the highest priorities within SoS design and operation. This study aims to investigate the attack propagation problem in terms of service-guarantees through the decomposition into sub-services enriched with preconditions and postconditions at the service levels. Such analysis is required as a prerequisite for an efficient SoS risk assessment at the design stage of the SoS development life cycle to protect it from possibly high impact attacks capable of affecting safety of systems and humans using the system.

Real-time adaptive systems are complex systems capable to adapt their behavior to changing conditions in the environment, and/or internal state changes. Highly dynamic and possibly unpredictable environments, and uncertain operating conditions call for new paradigms of software design, and run-time adaptation mechanisms, to overcome the lack of knowledge at design time. Main application areas include vehicles or robots that need to collaborate to achieve a common task, e.g., minimize fuel consumption, moving objects at a construction site, or performing a set of operations in a factory. Moreover, these vehicles or robots need to interact and possibly collaborate with humans in a safe way, e.g., avoiding accidents or collisions, and prevent hazardous situations that may harm humans and/or machines. % This paper proposes a framework for developing safe and secure adaptive collaborative systems, with run-time guarantees. To enable this, our focus is on requirement engineering and safety assurance techniques to capture the specific safety and security properties for the collaborative system, and to provide an assurance case guaranteeing that the system is sufficiently safe. Moreover, the paper proposes an architecture and behavioral models to analyze the requirements at run-time. Finally, we design a suitable deployment platform to perform the run-time analysis and planning while guaranteeing the real-time constraints.

Abstract Motivation Despite the remarkable advances in sequencing and computational techniques, noise in the data and complexity of the underlying biological mechanisms render deconvolution of the phylogenetic relationships between cancer mutations difficult. Besides that, the majority of the existing datasets consist of bulk sequencing data of single tumor sample of an individual. Accurate inference of the phylogenetic order of mutations is particularly challenging in these cases and the existing methods are faced with several theoretical limitations. To overcome these limitations, new methods are required for integrating and harnessing the full potential of the existing data. Results We introduce a method called Hintra for intra-tumor heterogeneity detection. Hintra integrates sequencing data for a cohort of tumors and infers tumor phylogeny for each individual based on the evolutionary information shared between different tumors. Through an iterative process, Hintra learns the repeating evolutionary patterns and uses this information for resolving the phylogenetic ambiguities of individual tumors. The results of synthetic experiments show an improved performance compared to two state-of-the-art methods. The experimental results with a recent Breast Cancer dataset are consistent with the existing knowledge and provide potentially interesting findings. Availability and implementation The source code for Hintra is available at https://github.com/sahandk/HINTRA.

Abstract We study a stochastic lattice model describing the dynamics of a group chasing and escaping between two species in an environment that contains obstacles. The Monte Carlo simulations are carried out on a two-dimensional square lattice. Obstacles are represented by non-overlapping lattice shapes that are randomly placed on the lattice. The model includes smart pursuit (chasers to targets) and evasion (targets from chasers). Both species can affect their movement by visual perception within their finite sighting range σ . We concentrate here on the role that density and shape of the obstacles plays in the time evolution of the number of targets, N T ( t ) . Temporal evolution of the number of targets N T ( t ) is found to be stretched-exponential, of the form N T ( t ) = N T ( 0 ) − δ N T ( ∞ ) 1 − exp [ − ( t ∕ τ ) β ] , regardless of whether the obstacles are present or not. The characteristic timescale τ is found to decrease with the initial density of targets ρ 0 T according to a power-law, i.e., τ ∝ ( ρ 0 T ) − γ . Furthermore, temporal dependences of the number of targets N T ( t ) are compared for various combinations of chasers and targets with different sighting ranges, σ = 1 , 2 , in order to analyze the relationship between the ability of species and the capture dynamics in the presence of obstacles.

Background: Preclinical ultrasound (US) and contrast-enhanced ultrasound (CEUS) imaging have long been used in oncology to noninvasively measure tumor volume and vascularity. While the value of preclinical US has been repeatedly demonstrated, these modalities are not without several key limitations that make them unattractive to cancer researchers, including: high user-variability, low throughput, and limited imaging field-of-view (FOV). Herein, we present a novel robotic preclinical US/CEUS system that addresses these limitations and demonstrates its use in evaluating tumors in 3D in a rodent model. Methods: The imaging system was designed to allow seamless whole-body 3D imaging, which requires rodents to be imaged without physical contact between the US transducer and the animal. To achieve this, a custom dual-element transducer was mounted on a robotic carriage, submerged in a hydrocarbon fluid, and the reservoir sealed with an acoustically transmissive top platform. Eight NOD/scid/gamma (NSG) female mice were injected subcutaneously in the flank with 8×109 786-O human clear-cell renal cell carcinoma (ccRCC) cells. Weekly imaging commenced after tumors reached a size of 150 mm3 and continued until tumors reached a maximum size of 1 cm3 (∼4-5 weeks). An additional six nude athymic female mice were injected subcutaneously in the flank with 7 × 105 SVR angiosarcoma cells to perform an inter-operator variability study. Imaging consisted of 3D B-mode (conventional ultrasound) of the whole abdomen ( Results: Wide-field US images reconstructed from 3D volumetric data showed superior FOV over conventional US. Several anatomical landmarks could be identified within each image surrounding the tumor, including the liver, small intestines, bladder, and inguinal lymph nodes. Tumor boundaries were clearly delineated in both B-mode and BVD images, with BVD images showing heterogeneous microvessel density at later timepoints suggesting tumor necrosis. Excellent agreement was measured for both inter-reader and inter-operator experiments, with alpha coefficients of 0.914 (95% CI: 0.824-0.948) and 0.959 (0.911-0.981), respectively. Conclusion: We have demonstrated a novel preclinical US imaging system that can accurately and consistently evaluate tumors in rodent models. The system leverages cost-effective robotic technology, and a new scanning paradigm that allows for easy and reproducible data acquisition to enable wide-field, 3D, multi-parametric ultrasound imaging. Note: This abstract was not presented at the meeting. Citation Format: Tomasz Czernuszewicz, Virginie Papadopoulou, Juan D. Rojas, Rajalekha Rajamahendiran, Jonathan Perdomo, James Butler, Max Harlacher, Graeme O9Connell, Dzenan Zukic, Paul A. Dayton, Stephen Aylward, Ryan C. Gessner. A preclinical ultrasound platform for widefield 3D imaging of rodent tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1955.

Many techniques have been developed to cancel the ventricular interference in atrial electrograms (AEG) during atrial fibrillation. In particular, average beat subtraction (ABS) and interpolation are among those mostly adopted. However, ABS usually leaves high power residues and discontinuity at the borders, whereas interpolation totally substitutes the residual activity with a forecasting that might fail at the center of the cancellation segment. In this study, we proposed a new algorithm to refine the ventricular estimate provided by ABS, in such a way that the residual activity should likely be distributed as the local atrial activity. Briefly, the local atrial activity is first modeled with an autoregressive (AR) process, then the estimate is refined by maximizing the log likelihood of the atrial residual activity according to the fitted AR model. We tested the new algorithm on both synthetic and real AEGs, and compared the performance with other four algorithms (two variants of ABS, interpolation and zero substitution). On synthetic data, our algorithm outperformed all the others in terms of average root mean square error (0.043 vs 0.046 for interpolation; p < 0.05). On real data, our methodology outperformed two variants of ABS (p < 0.05) and performed similarly to interpolation when considering the high power residues left (both < 5%), and the log likelihood with the fitted AR model.

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the dataset. Using 11 oral glucose tolerance test (OGTT) measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using Minimum Redundancy Maximum Relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual’s plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80 % and a sensitivity of 80.09 % obtained on a holdout set.

Outlier detection represents the problem of finding patterns in data that does not fit in expected behaviour. In this paper, outlier detection is done over real transactional data set of the distribution company. Outlier detection is done over time-series data, and over an ordered number of products that can be found within transactions. Unsupervised techniques and methods, S-H-ESD and LOF, are applied because data set is unlabelled. Implementation is performed in R language, and web application dashboard using R Shiny is made. Based on collected results, a proposal for creating the outlier detection and prevention system is made, and ideas for further improvements and additional analysis are given.

The identification of association rules is the problem of finding associations between different items in the same transactions. In this paper, performance comparison of different variants of Apriori, FP-Growth and ECLAT algorithms was performed over the real transactional data set of the distribution company by using R programming language and its appropriate packages, and the results obtained are later on explained. Then, the identification and visualization of the association rules of the said real data set was performed.

With the growth of the internet of things (IoT), many challenges like information security and privacy, interoperability/standard, and regulatory and legal issues are arising. This work focused on the information security issue, which is one of the primary challenges faced by connected systems that needs to be resolved without impairing system behaviour. Information, which is made available on the Internet by the things, varies from insensitive information (e.g., readings from outdoor temperature sensors) to extremely sensitive information (e.g., video stream from a camera) and needs to be secured over the Internet. Things which utilise cameras as a source of information pertain to a subclass of the IoT called IoVT (internet of video things). This paper presents secured and unsecured video latency measurement results over the Internet for a marine ROV (remotely operated vehicle). A LabVIEW field programmable gate arrays (FPGAs)-based bump-in-the-wire (BITW) secure core is used to provide an AES (advanced encryption standard)-enabled security feature on the video stream of an IoVT node (ROV equipped with a live-feed camera). The designed LabVIEW-based software architecture provides an option to enable/disable the AES encryption for the video transmission. The latency effects of embedding encryption on the stream with real-time constraints are measured and presented. It is found that the encryption mechanism used does not greatly influence the video feedback performance of the observed IoVT node, which is critical for real-time secure video communication for ROV remote control and piloting. The video latency measurement results are taken using 128, 256 and 512 bytes block lengths of AES for both H.264 and MJPEG encoding schemes transmitted over both TCP and UDP transmission protocols. The latency measurement is performed in two scenarios (i.e., with matching equipment and different equipment on either end of the transmission).

The interaction of robot teams and single human in teleoperation scenarios is beneficial in cooperative tasks, for example, the manipulation of heavy and large objects in remote or dangerous environments. The main control challenge of the interaction is its asymmetry, arising because robot teams have a relatively high number of controllable degrees of freedom compared to the human operator. Therefore, we propose a control scheme that establishes the interaction on spaces of reduced dimensionality taking into account the low number of human command and feedback signals imposed by haptic devices. We evaluate the suitability of wearable haptic fingertip devices for multi-contact teleoperation in a user study. The results show that the proposed control approach is appropriate for human–robot team interaction and that the wearable haptic fingertip devices provide suitable assistance in cooperative manipulation tasks.

We propose a linear state estimation (SE) model with complex coefficients and variables suitable for processing large-scale data in electric power systems observable by phasor measurement units. The presented model is based on factor graphs and solved using the belief propagation (BP) algorithm. The proposed algorithm is placed in the non-overlapping multi-area SE scenario without a central coordinator. The communication between areas is asynchronous, where neighboring areas exchange only “beliefs” about specific state variables. Presented architecture directly exploits system sparsity, can be flexibly paralellized and results in substantially lower computational complexity compared to traditional SE solutions. Finally, we discuss performances of the BP-based SE algorithm using power systems with 118, 1354 and 9241 buses.

It is known that physical activity levels (PA levels) decline during adolescence, but there is a lack of knowledge on possible predictors of changes in PA levels in this period of life. This study aimed to prospectively investigate the relationship between sociodemographic and behavioral factors (predictors), PA levels and changes in PA levels in older adolescents from Bosnia and Herzegovina. The sample comprised 872 participants (404 females) tested at baseline (16 years of age) and at follow-up (18 years of age). Predictors were sociodemographic characteristics (age, gender, socioeconomic status, urban/rural residence, paternal and maternal education level) and variables of substance misuse (consumption of cigarettes, alcohol and illicit drugs). The PA level, as measured by the Physical Activity Questionnaire for Adolescents (PAQ-A), was observed as a criterion. Boys had higher PAQ-A scores than girls at baseline and follow-up. Paternal education levels were correlated with PAQ-A scores at baseline (Spearman’s R: 0.18, 0.15 and 0.14, p < 0.05, for the total sample, females and males, respectively) and at follow-up (Spearman’s R: 0.12, p < 0.01 for the total sample). Logistic regression, which was used to calculate changes in PA levels between baseline and follow-up as a binomial criterion (PA decline vs. PA incline), evidenced a higher likelihood of PA incline in adolescents whose mothers were more educated (OR: 1.29, 95% CI: 1.05–1.60) and who live in urban communities (OR: 1.56, 95% CI: 1.16–2.10). The consumption of illicit drugs at baseline was evidenced as a factor contributing to the lower likelihood of PA incline (OR: 0.36, 95% CI: 0.14–0.92). The negative relationship between illicit drug consumption and PA decline could be a result of a large number of children who quit competitive sports in this period of life. In achieving appropriate PA-levels, special attention should be placed on children whose mothers are not highly educated, who live in rural communities, and who report the consumption of illicit drugs. The results highlighted the importance of studying correlates of PA levels and changes in PA levels during adolescence.

AIM Present study analyses the co-localisation of RIP5 with FGFR1, FGFR2 and HIP2 in the developing kidney, as RIP5 is a major determinant of urinary tract development, downstream of FGF-signaling. METHODS Paraffin embedded human kidney tissues of 16 conceptuses between the 6th-22th developmental week were analysed using double-immunofluorescence method with RIP5/FGFR1/FGFR2 and HIP2 markers. Quantification of positive cells were performed using Kruskal-Wallis test. RESULTS In the 6th week of kidney development RIP5 (89.6%) and HIP2 (39.6%) are strongly expressed in the metanephric mesenchyme. FGFR1 shows moderate/strong expression in the developing nephrons (87.3%) and collecting ducts (70.5%) (p < 0.05). RIP5/FGFR1 co-localized at the marginal zone and the ureteric bud with predominant FGFR1 expression. FGFR2 (26.1%) shows similar expression pattern as FGFR1 (70.5%) in the same kidney structures. RIP5/FGFR2 co-localized at the marginal zone and the collecting ducts (predominant expression of FGFR2). HIP2 is strongly expressed in collecting ducts (96.7%), and co-localized with RIP5. In 10th week, RIP5 expression decrease (74.2%), while the pattern of expression of RIP5 and FGFR1 in collecting ducts (33.4% and 91.9%) and developing nephrons (21.9% and 32.4%) (p < 0.05) is similar to that in the 6th developmental week. Ureter is moderately expressing RIP5 while FGFR1 is strongly expressed in the ureteric wall. FGFR2 is strongly expressed in the collecting ducts (84.3%) and ureter. HIP2 have 81.1% positive cells in the collecting duct. RIP5/FGFR1 co-localize in collecting ducts and Henley's loop. CONCLUSIONS The expression pattern of RIP5, FGFR1, FGFR2 and HIP2 in the human kidney development might indicate their important roles in metanephric development and ureteric muscle layer differentiation through FGF signaling pathways.

Objective. Childhood-onset systemic lupus erythematosus (cSLE) is usually a more severe and aggressive disease than adult-onset SLE (aSLE), but cellular and subcellular reasons for these differences are not well understood. The present study analyzed Th subsets, STAT1/STAT5 signaling response, and cytokine profiles of cSLE. Methods. FOXP3+ regulatory (Treg) and effector Th subsets, expression and phosphorylation of STAT1/STAT5 in Th, and cytokine profiles were measured in the peripheral blood of patients with cSLE and healthy controls (HC), using flow cytometry and immunoassay on a biochip. Results. Significant correlation between expression of the activation marker HLA-DR and decreased Th counts, an increase in the percentage of FOXP3+ Th, and a decrease in the activated Treg (aTreg) subset among them were found in cSLE. In contrast to our previous findings in aSLE, no significant differences in percentages and a significant decrease in the numbers of the naive-resting Treg (rTreg) subset compared to HC were found. The percentages of CD25− cells, possibly reflecting interleukin 2 depletion, were significantly increased in cSLE aTreg, but not in the rTreg subset. Consistent with the results of our previous studies in aSLE, increased expression of STAT1, along with significant correlation between decreased Th counts and their increased basal phosphorylation of STAT5, were also found in cSLE. Conclusion. Our results suggest that the key difference in Treg homeostasis between cSLE and aSLE is in the rTreg subset. However, perturbed aTreg homeostasis, increased levels of STAT1 protein, and homeostatic STAT5 signaling appear to be intrinsic characteristics of the disease, present in cSLE and aSLE alike.