Semi-Automated Image Analysis Methodology to Investigate Intracellular Heterogeneity in Immunohistochemical Stained Sections

The discovery of tissue heterogeneity revolutionized the existing knowledge regarding the cellular, molecular, and pathophysiological mechanisms in biomedicine. Therefore, basic science investigations were redirected to encompass observation at the classical and quantum biology levels. Various approaches have been developed to investigate and capture tissue heterogeneity; however, these approaches are costly and incompatible with all types of samples. In this paper, we propose an approach to quantify heterogeneous cellular populations through combining histology and images processing techniques. In this approach, images of immunohistochemically stained sections are processed through color binning of DAB-stained cells (in brown) and non-stained cells (in blue) to select cellular clusters expressing biomarkers of interest. Subsequently, the images were converted to a binary format through threshold modification (threshold 60%) in the grey scale. The cell count was extrapolated from the binary images using the particle analysis tool in ImageJ. This approach was applied to quantify the level of progesterone receptor expression levels in a breast cancer cell line sample. The results of the proposed approach were found to closely reflect those of manual counting. Through this approach, quantitative measures can be added to qualitative observation of subcellular targets expression

Causality Between Market Liquidity and Depth for Energy and Grains

This paper examines the roles of futures prices of crude oil, gasoline, ethanol, corn, soybeans and sugar in the energy-grain nexus. It also investigates the own- and cross-market impacts for lagged grain trading volume and open interest in the energy and grain markets. According to the results, the conventional view, for which the impacts are from oil to gasoline to ethanol to grains in the energy-grain nexus, does not hold well in the long run because the oil price is influenced by gasoline, soybeans and oil. Moreover, gasoline is preceded by only the oil price and ethanol is not foreshadowed by any of the prices. However, in the short run, two-way feedback in both directions exists in all markets. The grain trading volume effect across oil and gasoline is more pronounced in the short run than the long run, satisfying both the overconfidence/disposition and new information hypotheses across markets. The results for the ethanol open interest shows that money flows out of this market in both the short and long run, but no results suggest across market inflows or outflows to the other grain markets.energy;Causality;depth;grains;market liquidity

The Dynamics of Energy-Grain Prices with Open Interest

This paper examines the short- and long-run daily relationships for a grain-energy nexus that includes the prices of corn, crude oil, ethanol, gasoline, soybeans, and sugar, and their open interest. The empirical results demonstrate the presence of these relationships in this nexus, and underscore the importance of ethanol and soybeans in all these relationships. In particular, ethanol and be considered as a catalyst in this nexus because of its significance as a loading factor, a long-run error corrector and a short-run adjuster. Ethanol leads all commodities in the price discovery process in the long run. The negative cross-price open interest effects suggest that there is a money outflow from all commodities in response to increases in open interest positions in the corn futures markets, indicating that active arbitrage activity takes place in those markets. On the other hand, an increase in the soybean open interest contributes to fund inflows in the corn futures market and the other futures markets, leading to more speculative activities in these markets. In connection with open interest, the ethanol market fails because of its thin market. Finally, it is interesting to note that the long-run equilibrium (cointegrating relationship), speeds of adjustment and open interest across markets have strengthened significantly during the 2009-2011 economic recovery period, compared with the full and 2007-2009 Great Recession periods.

Enriched transcriptome analysis of laser capture microdissected populations of single cells to investigate intracellular heterogeneity in immunostained FFPE sections

To investigate intracellular heterogeneity, cell capture of particular cell populations followed by transcriptome analysis has been highly effective in freshly isolated tissues. However, this approach has been quite challenging in immunostained formalin-fixed paraffin-embedded (FFPE) sections. This study aimed at combining the standard pathology techniques, immunostaining and laser capture microdissection, with whole RNA-sequencing and bioinformatics analysis to characterize FFPE breast cancer cell populations with heterogeneous expression of progesterone receptor (PR). Immunocytochemical analysis revealed that 60% of MCF-7 cells admixture highly express PR. Immunocytochemistry-based targeted RNA-seq (ICC-RNAseq) and in silico functional analysis revealed that the PR-high cell population is associated with upregulation in transcripts implicated in immunomodulatory and inflammatory pathways (e.g. NF-κB and interferon signaling). In contrast, the PR-low cell population is associated with upregulation of genes involved in metabolism and mitochondrial processes as well as EGFR and MAPK signaling. These findings were cross-validated and confirmed in FACS-sorted PR high and PR-low MCF-7 cells and in MDA-MB-231 cells ectopically overexpressing PR. Significantly, ICC-RNAseq could be extended to analyze samples captured at specific spatio-temporal states to investigate gene expression profiles using diverse biomarkers. This would also facilitate our understanding of cell population-specific molecular events driving cancer and potentially other diseases

Network traffic analysis for threats detection in the Internet of Things

As the prevalence of the Internet of Things (IoT) continues to increase, cyber criminals are quick to exploit the security gaps that many devices are inherently designed with. Users cannot be expected to tackle this threat alone, and many current solutions available for network monitoring are simply not accessible or can be difficult to implement for the average user, which is a gap that needs to be addressed. This article presents an effective signature-based solution to monitor, analyze, and detect potentially malicious traffic for IoT ecosystems in the typical home network environment by utilizing passive network sniffing techniques and a cloud application to monitor anomalous activity. The proposed solution focuses on two attack and propagation vectors leveraged by the infamous Mirai botnet, namely DNS and Telnet. Experimental evaluation demonstrates the proposed solution can detect 98.35 percent of malicious DNS traffic and 99.33 percent of Telnet traffic for an overall detection accuracy of 98.84 percent

A Systematic Review of the Availability and Efficacy of Countermeasures to Internal Threats in Healthcare Critical Infrastructure

Insider attacks are becoming increasingly detrimental and frequent, affecting critical infrastructure at a massive scale. Recent attacks such as the UK National Health Service (NHS) WannaCry ransomware attack which partly depends on internal users for initial infection highlight the increasing role of the malicious insiders in cyber attack campaigns . The objective of this research is to ascertain the existing technological capability to mitigate insider threats within computer security systems by way of a mixed-method systematic review. Evidence was acquired from major sources of mainstream and grey literature by analysing about 300, 000 papers. Crude aggregated results were analysed across the literature, the results were TPR 0.75, FPR 0.32, σ 0.24 and 0.36 respectively, σ 2 0.06 and 0.13 respectively. In totality, the literature evidence suggests that there is high heterogeneity across crude data indicating that the effectiveness of security measures varies significantly. No solution is able to totally mitigate an insider threat. Themes when set against that data suggest that most, if not all, security measures require breaches to occur before an analysis of malicious activity can prevent it in future through recall. Such a reactive approach is not effective to protect our critical infrastructure including our healthcare systems. Consequently, there is a major theoretical shortfall in current cyber defence architecture

PMT : opposition based learning technique for enhancing metaheuristic algorithms performance

Metaheuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. Nevertheless, many metaheuristic algorithms are still suffering from a low convergence rate because of the poor balance between exploration (i.e. roaming new potential search areas) and exploitation (i.e., exploiting the existing neighbors). In some complex problems, the convergence rate can still be poor owing to becoming trapped in local optima. Opposition-based learning (OBL) has shown promising results to address the aforementioned issue. Nonetheless, OBL-based solutions often consider one particular direction of the opposition. Considering only one direction can be problematic as the best solution may come in any of a multitude of directions. Addressing these OBL limitations, this research proposes a new general OBL technique inspired by a natural phenomenon of parallel mirrors systems called the Parallel Mirrors Technique (PMT). Like existing OBL-based approaches, the PMT generates new potential solutions based on the currently selected candidate. Unlike existing OBL-based techniques, the PMT generates more than one candidate in multiple solution-space directions. To evaluate the PMT’s performance and adaptability, the PMT was applied to four contemporary metaheuristic algorithms, Differential Evolution, Particle Swarm Optimization, Simulated Annealing, and Whale Optimization Algorithm, to solve 15 well-known benchmark functions as well as 2 real world problems based on the welded beam design and pressure vessel design. Experimentally, the PMT shows promising results by accelerating the convergence rate against the original algorithms with the same number of fitness evaluations comparing to the original metaheuristic algorithms in benchmark functions and real-world optimization problems

The Dynamics of Energy-Grain Prices with Open Interest

This paper examines the short- and long-run daily relationships for a grain-energy nexus that includes the prices of corn, crude oil, ethanol, gasoline, soybeans, and sugar, and their open interest. The empirical results demonstrate the presence of these relationships in this nexus, and underscore the importance of ethanol and soybeans in all these relationships. In particular, ethanol and be considered as a catalyst in this nexus because of its significance as a loading factor, a long-run error corrector and a short-run adjuster. Ethanol leads all commodities in the price discovery process in the long run. The negative cross-price open interest effects suggest that there is a money outflow from all commodities in response to increases in open interest positions in the corn futures markets, indicating that active arbitrage activity takes place in those markets. On the other hand, an increase in the soybean open interest contributes to fund inflows in the corn futures market and the other futures markets, leading to more speculative activities in these markets. In connection with open interest, the ethanol market fails because of its thin market. Finally, it is interesting to note that the long-run equilibrium (cointegrating relationship), speeds of adjustment and open interest across markets have strengthened significantly during the 2009-2011 economic recovery period, compared with the full and 2007-2009 Great Recession periods

A Service-Oriented Approach for Sensing in the Internet of Things: Intelligent Transportation Systems and Privacy Use Cases

This paper presents a Sensing-as-a-Service run-time Service Oriented Architecture (SOA), called 3SOA, for the development of Internet of Things (IoT) applications. 3SOA aims to allow interoperability among various IoT platforms and support service-oriented modelling at high levels of abstraction where fundamental SOA theories and techniques are fully integrated into a practical software engineering approach. 3SOA abstracts the dependencies of the middleware programming model from the application logic. This abstraction allows the development efforts to focus on writing the application logic independently from hardware platforms, middleware, and languages in which applications are programmed. To achieve this result, IoT objects are treated as independent entities that may interact with each other using a well-defined message exchange sequence. Each object is defined by the services it provides and the coordination protocol it supports. Objects are then able to coordinate their resources to address the global objectives of the system. To practically validate our proposals, we demonstrate an intelligent transportation system and data privacy functional prototypes as proof of concepts. The use cases show that 3SOA and the presented abstraction language allow the amalgamation of macroprogramming and node-centric programming to develop real-time and efficient applications over IoT

Security challenges of Internet of Underwater Things : a systematic literature review

Water covers approximately 71% of the earth surface, yet much of the underwater world remains unexplored due to technology limitations. Internet of Underwater Things (IoUT) is a network of underwater objects that enables monitoring subsea environment remotely. Underwater Wireless Sensor Network (UWSN) is the main enabling technology for IoUT. UWSNs are characterised by the limitations of the underlying acoustic communication medium, high energy consumption, lack of hardware resources to implement computationally intensive tasks and dynamic network topology due to node mobility. These characteristics render UNWSNs vulnerable to different attacks, such as Wormhole, Sybil, flooding, jamming, spoofing and Denial of Service (DoS) attacks. This article reviews peer-reviewed literature that addresses the security challenges and attacks on UWSNs as well as possible mitigative solutions. Findings show that the biggest contributing factors to security threats in UWSNs are the limited energy supply, the limited communication medium and the harsh underwater communication conditions. Researchers in this field agree that the security measures of terrestrial wireless sensor networks are not directly applicable to UWSNs due to the unique nature of the underwater environment where resource management becomes a significant challenge. This article also outlines future research directions on security and privacy challenges of IoUT and UWSN