Classification of Highly Nonlinear Boolean Power Functions with a Randomised Algorithm for Checking Normality

A Boolean function is called normal if it is constant on flats of certain dimensions. This property is relevant for the construction and analysis of cryptosystems. This paper presents an asymmetric Monte Carlo algorithm to determine whether a given Boolean function is normal. Our algorithm is far faster than the best known (deterministic) algorithm of Daum et al. In a first phase, it checks for flats of low dimension whether the given Boolean function is constant on them and combines such flats to flats of higher dimension in a second phase. This way, the algorithm is much faster than exhaustive search. Moreover, the algorithm benefits from randomising the first phase. In addition, by evaluating several flats implicitly in parallel, the time-complexity of the algorithm decreases further. As an application, we determine the level of normality for several, highly nonlinear Boolean power functions

Linking quantitative radiology to molecular mechanism for improved vascular disease therapy selection and follow-up

Objective: Therapeutic advancements in atherosclerotic cardiovascular disease have improved the prevention of ischemic stroke and myocardial infarction. However, diagnostic methods for atherosclerotic plaque phenotyping to aid individualized therapy are lacking. In this thesis, we aimed to elucidate plaque biology through the analysis of computed-tomography angiography (CTA) with sufficient sensitivity and specificity to capture the differentiated drivers of the disease. We then aimed to use such data to calibrate a systems biology model of atherosclerosis with adequate granularity to be clinically relevant. Such development may be possible with computational modeling, but given, the multifactorial biology of atherosclerosis, modeling must be based on complete biological networks that capture protein-protein interactions estimated to drive disease progression. Approach and Results: We employed machine intelligence using CTA paired with a molecular assay to determine cohort-level associations and individual patient predictions. Examples of predicted transcripts included ion transporters, cytokine receptors, and a number of microRNAs. Pathway analyses elucidated enrichment of several biological processes relevant to atherosclerosis and plaque pathophysiology. The ability of the models to predict plaque gene expression from CTAs was demonstrated using sequestered patients with transcriptomes of corresponding lesions. We further performed a case study exploring the relationship between biomechanical quantities and plaque morphology, indicating the ability to determine stress and strain from tissue characteristics. Further, we used a uniquely constituted plaque proteomic dataset to create a comprehensive systems biology disease model, which was finally used to simulate responses to different drug categories in individual patients. Individual patient response was simulated for intensive lipid-lowering, anti-inflammatory drugs, anti-diabetic, and combination therapy. Plaque tissue was collected from 18 patients with 6735 proteins at two locations per patient. 113 pathways were identified and included in the systems biology model of endothelial cells, vascular smooth muscle cells, macrophages, lymphocytes, and the integrated intima, altogether spanning 4411 proteins, demonstrating a range of 39-96% plaque instability. Simulations of drug responses varied in patients with initially unstable lesions from high (20%, on combination therapy) to marginal improvement, whereas patients with initially stable plaques showed generally less improvement, but importantly, variation across patients. Conclusion: The results of this thesis show that atherosclerotic plaque phenotyping by multi-scale image analysis of conventional CTA can elucidate the molecular signatures that reflect atherosclerosis. We further showed that calibrated system biology models may be used to simulate drug response in terms of atherosclerotic plaque instability at the individual level, providing a potential strategy for improved personalized management of patients with cardiovascular disease. These results hold promise for optimized and personalized therapy in the prevention of myocardial infarction and ischemic stroke, which warrants further investigations in larger cohorts

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Investigating age at onset in bipolar disorder

Bipolar disorder (BD) is a complex mental health disorder with a heterogenous clinical course, diagnostic delays, high relapse rates, and sub-optimal treatment outcomes. Age at onset (AAO) has been proposed as a useful specifier for defining more homogeneous BD subgroups that can inform clinical course and symptom profiles. This thesis aimed to investigate the utility of AAO as a clinical specifier by defining and validating AAO subgroups; identifying predictive factors for BD AAO; investigating the relationship between premorbid factors, AAO, and functional outcomes; and examining the association between AAO and mood instability. Using mixed methods across four experimental chapters, this thesis provides novel insights into the role of AAO in BD. Chapter 2 presents a systematic review of AAO distributions in BD and provides a recommended AAO definition. Chapter 3 uses machine learning approaches to explore predictive factors for BD AAO. Chapter 4 investigates the potential pathways between premorbid factors, BD AAO, and functional outcomes using prospective data. Chapter 5 examines the association between AAO and mood instability using longitudinal mood monitoring data. Findings reveal a trimodal distribution of AAO in BD, with distinct early-life risk factors, which may represent potential causal pathways to clinical outcomes. Additionally, mood instability is identified as a promising target for intervention in the clinical trajectory of BD. These results have important theoretical and practical implications, informing early intervention strategies and providing further evidence for the distinctiveness of AAO subgroups. The thesis concludes with a general discussion of the findings and implications for future research. Overall, this thesis provides valuable insights into the role of AAO in BD, highlights the importance of identifying more homogeneous subgroups to improve diagnosis and treatment outcomes, and underscores the need for continued research in this area

Assembly Line

An assembly line is a manufacturing process in which parts are added to a product in a sequential manner using optimally planned logistics to create a finished product in the fastest possible way. It is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The present edited book is a collection of 12 chapters written by experts and well-known professionals of the field. The volume is organized in three parts according to the last research works in assembly line subject. The first part of the book is devoted to the assembly line balancing problem. It includes chapters dealing with different problems of ALBP. In the second part of the book some optimization problems in assembly line structure are considered. In many situations there are several contradictory goals that have to be satisfied simultaneously. The third part of the book deals with testing problems in assembly line. This section gives an overview on new trends, techniques and methodologies for testing the quality of a product at the end of the assembling line

Diffuse Reflectance Spectroscopy to Quantify In Vivo Tissue Optical Properties: Applications in Human Epithelium and Subcutaneous Murine Colon Cancer

Colorectal cancer is the 4th most common and 2nd deadliest cancer. Problems exist with predicting which patients will respond best to certain therapy regimens. Diffuse reflectance spectroscopy has been suggested as a candidate to optically monitor a patient’s early response to therapy and has been received favorably in experimentally managing other cancers such as breast and skin. In this dissertation, two diffuse reflectance spectroscopy probes were designed: one with a combined high-resolution microendoscopy modality, and one that was optimized for acquiring data from subcutaneous murine tumors. For both probes, percent errors for estimating tissue optical properties (reduced scattering coefficient and absorption coefficient) were less than 5% and 10%, respectively. Then, studies on tissue-simulating phantoms were performed to test probe sensitivity and to serve as testing platforms for investigators in biomedical optics. Next, the diffuse reflectance spectroscopy probe was applied to subcutaneous murine colon tumors (n=61) undergoing either antibody immunotherapy or standard 5-fluorouracil chemotherapy. Mice treated with a combination of these therapies showed reduced tumor growth compared to saline control, isotype control, immunotherapy, and chemotherapy groups (p\u3c0.001, \u3c0.001, \u3c0.001, and 0.046, respectively) 7 days post-treatment. Additionally, at 7 days post-treatment, oxyhemoglobin, a marker currently being explored as a functional prognostic cancer marker, trended to increase in immunotherapy, chemotherapy, and combination therapy groups compared to controls (p=0.315, 0.149, and 0.190). Also of interest, an oxyhemoglobin flare (averageincrease of 1.44x from baseline, p=0.03 compared to controls) was shown in tumors treated with chemotherapy, indicating that diffuse reflectance spectroscopy may be useful as a complimentary tool to monitor early tumor therapeutic response in colon cancer. However, subject-to-subject variability was high and studies correlating survival to early oxyhemoglobin flares are suggested

Security of Ubiquitous Computing Systems

The chapters in this open access book arise out of the EU Cost Action project Cryptacus, the objective of which was to improve and adapt existent cryptanalysis methodologies and tools to the ubiquitous computing framework. The cryptanalysis implemented lies along four axes: cryptographic models, cryptanalysis of building blocks, hardware and software security engineering, and security assessment of real-world systems. The authors are top-class researchers in security and cryptography, and the contributions are of value to researchers and practitioners in these domains. This book is open access under a CC BY license