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This paper suggests a strategy (C5) for assessing cloud and IoT system (CIS) dependability, availability, and cybersecurity based on the continuous collection, comparison, choice, and combination of Markov and semi-Markov models (MMs and SMMs). It proposes the systematic building of an adequate and accurate model to evaluate CISs considering (1) continuous evolution of the model(s) together with systems induced by changes in the CIS or physical and cyber environment parameters; (2) the necessity of collecting data on faults, failures, vulnerabilities, cyber-attacks, privacy violations, and patches to obtain actual data for assessment; (3) renewing the model set based on analysis of CIS operation; (4) the possibility of choice and utilizing “off-the-shelf” models with understandable techniques for their development to assure improved accuracy of assessment; (5) renewing the models during application of CIS by time, component or mixed combining, taking into consideration different operation and maintenance events. The results obtained were algorithms for data collection and analysis, choice, and combining appropriate MM and SMMs and their different types, such as multi-fragmental and multiphase models, considering changing failure rates, cyber-attack parameters, periodical maintenance, etc. To provide and verify the approach, several private and public clouds and IoT systems were researched and discussed in the context of C5 and proposed algorithms.

The resurgent component of the voltage-gated sodium current (I NaR ) is a depolarizing conductance, revealed on membrane hyperpolarizations following brief depolarizing voltage steps, which has been shown to contribute to regulating the firing properties of numerous neuronal cell types throughout the central and peripheral nervous systems. Although mediated by the same voltage-gated sodium (Nav) channels that underlie the transient and persistent Nav current components, the gating mechanisms that contribute to the generation of I NaR remain unclear. Here, we characterized Nav currents in mouse cerebellar Purkinje neurons, and used tailored voltage-clamp protocols to define how the voltage and the duration of the initial membrane depolarization affect the amplitudes and kinetics of I NaR . Using the acquired voltage-clamp data, we developed a novel Markov kinetic state model with parallel (fast and slow) inactivation pathways and, we show that this model reproduces the properties of the resurgent, as well as the transient and persistent, Nav currents recorded in (mouse) cerebellar Purkinje neurons. Based on the acquired experimental data and the simulations, we propose that resurgent Na + influx occurs as a result of fast inactivating Nav channels transitioning into an open/conducting state on membrane hyperpolarization, and that the decay of I NaR reflects the slow accumulation of recovered/opened Nav channels into a second, alternative and more slowly populated, inactivated state. Additional simulations reveal that extrinsic factors that affect the kinetics of fast or slow Nav channel inactivation and/or impact the relative distribution of Nav channels in the fast- and slow-inactivated states, such as the accessory Navβ4 channel subunit, can modulate the amplitude of I NaR .

Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual’s brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual’s time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine. Watching how people behave over time can provide insights into their personality, mental health, as well as how they think and problem solve. Like behaviour, brain activity patterns constantly change, both at rest and in response to external events. These changes might reveal crucial information about a person that cannot be seen when looking at a single snapshot or an average of brain activity. It has been difficult for researchers to predict individual traits from the overarching dynamic patterns of brain activity measured using brain scans and other imaging tools. This is due to the patterns being too complex to be analyzed directly. Mathematical models like the Hidden Markov Model can describe dynamic patterns in brain activity, such as how different brain areas’ activity and interaction with one another changes over time. To use this type of model to predict individual traits, Ahrends et al. combined it with a machine learning technique known as the Fisher kernel. Using this combination of techniques to model dynamic patterns of brain activity based on scans from 1,000 resting people allowed the researchers to successfully predict an individual’s age and their score in various cognitive tests. This approach was shown to more accurately predict traits than alternative methods. In the future, researchers may use this new modeling technique to search for markers of disease in dynamic brain activity patterns. For example, this could provide information about the progression of neuropsychiatric diseases over time. It may also help neuroscientists study how dynamic brain activity patterns contribute to individual cognitive performance.

Background Long-term cost-effectiveness analyses of health behaviour interventions to effectively manage type 2 diabetes mellitus (T2DM) in low-income countries are crucial for minimising economic burden and optimising resource allocation. Therefore, this study aimed to estimate the long-term cost-effectiveness of implementing a health behaviour intervention to manage T2DM in Nepal. Methods A Markov model in combination with a decision tree was developed to compare the costs and outcomes of the health behaviour intervention against usual care among 481 (238-intervention and 243-control) participants from healthcare system and societal perspectives. The model integrates empirical trial data, with published data to inform parameters not collected during the trial. The model estimated costs, quality-adjusted life years (QALYs) and cost-effectiveness over 5 years, 10 years, 20 years, 30 years and a lifetime time horizons with 3% annual discounting. Sub-group, scenarios, both one-way and two-way analyses and probabilistic sensitivity analyses (PSA) were performed to assess the impact of uncertainty in the model under the threshold of 3 times gross domestic product (GDP) per capita (i.e., US $4140) for Nepal. Results Base-case analysis with lifetime horizon showed that the health behaviour intervention compared to usual care improved QALYs by 3.88 and increased costs by US $4293 per patient, with an incremental cost-effectiveness ratio (ICER) of US $1106 per QALY gained from a healthcare system perspective. From a societal perspective, QALYs also improved by 3.88 and costs increased by US $4550, with an ICER of US $1173 per QALY gained. Furthermore, the intervention demonstrated ICERs of US $636, US $678, US $637, and US $632 per QALY gained over 5-, 10-, 20-, and 30-year time horizons, respectively, from a healthcare system perspective, and US $719, US $766, US $659, and US $716 per QALY gained from a societal perspective . In the PSA, the probability of the health behaviour intervention being cost-effective was over 57%. Conclusions The health behaviour intervention for managing T2DM was cost-effective over a lifetime horizon compared to usual care. To maximise its impact, this intervention should be scaled up nationwide, and future research is warranted to assess the long-term cost-effectiveness across diverse settings in low-income countries. Trial registration Australia and New Zealand Clinical Trial Registry (ACTRN12621000531819). Graphical Abstract

Introduction Several studies have proved that Polygenic Risk Score (PRS) is a potential candidate for realizing precision screening. The effectiveness of low-dose computed tomography (LDCT) screening for lung cancer has been proved to reduce lung cancer specific and overall mortality, but the cost-effectiveness of diverse screening strategies remained unclear. Methods The comparative cost-effectiveness analysis used a Markov state-transition model to assess the potential effect and costs of the screening strategies incorporating PRS or not. A hypothetical cohort of 300,000 heavy smokers entered the study at age 50–74 years and were followed up until death or age 79 years. The model was run with a cycle length of 1 year. All the transition probabilities were validated and the performance value of PRS was extracted from published literature. A societal perspective was adopted and cost parameters were derived from databases of local medical insurance bureau. Sensitivity analyses and scenario analyses were conducted. Results The strategy incorporating PRS was estimated to obtain an ICER of CNY 156,691.93 to CNY 221,741.84 per QALY gained compared with non-screening with the initial start age range across 50–74 years. The strategy that screened using LDCT alone from 70–74 years annually could obtain an ICER of CNY 80,880.85 per QALY gained, which was the most cost-effective strategy. The introduction of PRS as an extra eligible criteria was associated with making strategies cost-saving but also lose the capability of gaining more LYs compared with LDCT screening alone. Conclusion The PRS-based conjunctive screening strategy for lung cancer screening in China was not cost-effective using the willingness-to-pay threshold of 1 time Gross Domestic Product (GDP) per capita, and the optimal screening strategy for lung cancer still remains to be LDCT screening for now. Further optimization of the screening modality can be useful to consider adoption of PRS and prospective evaluation remains a research priority.

Body size and age are crucial factors influencing reproductive capacity and success. As females grow, their reproductive investment and success often increase due to improved overall physiological condition and experience gained through successive reproductive events. While much of this work has been conducted on birds and mammals, surprisingly little is known on how body size affects nesting decisions in other long-lived vertebrates. We monitored the movements and nesting behaviour of 57 wild female estuarine crocodiles Crocodylus porosus over a 10-year period (and across consecutive nesting seasons) using externally mounted satellite tags, implanted acoustic transmitters and a network of submerged acoustic receivers. Applying Hidden Markov models to the telemetry-derived location data revealed that female nesting behaviours could be split into three distinct states: (i) ranging movements within home ranges and at nesting sites; (ii) migrations to and from nesting sites; (iii) and nesting/nest guarding. We found that during migration events, larger females migrated further and remained away from dry season territories for longer periods than smaller individuals. Furthermore, not only were migratory movements stimulated by increases in rainfall, larger females migrated to nest sites at lower rainfall thresholds than smaller females. We provide some of the first evidence of body size influencing nesting decisions in an ectothermic vertebrate, with shifts likely resulting from an increased willingness to invest in nest protection among larger and more experienced females.

Background Emergency Medical Service (EMS) is a very crucial aspect of the healthcare system in providing urgent management and transportation of patients during emergencies. The sustainability of the services is however greatly impacted by the quality and age of ambulances. While this has led to numerous replacement policy recommendations, the implementations are often limited due to a lack of evidence and financial constraints. This study thus aims to develop a cost-effectiveness model and testing the model by evaluating the cost-effectiveness of 10-year and 15-year compulsory ambulance replacement strategies in public healthcare for the Malaysian Ministry of Health (MOH). Methods A Markov model was developed to estimate the cost and outcomes ambulance replacement strategies over a period of 20 years. The model was tested using two alternative strategies of 10-year and 15-year. Model inputs were derived from published literature and local study. Model development and economic analysis were accomplished using Microsoft Excel 2016. The outcomes generated were costs per year, the number of missed trips and the number of lives saved, in addition to the Incremental Cost-Effectiveness Ratio (ICER). One-Way Deterministic Sensitivity Analysis (DSA) and Probabilistic Sensitivity Analysis (PSA) were conducted to identify the key drivers and to assess the robustness of the model. Results Findings showed that the most expensive strategy, which is the implementation of 10 years replacement strategy was more cost-effective than 15 years ambulance replacement strategy, with an ICER of MYR 11,276.61 per life saved. While an additional MYR 13.0 million would be incurred by switching from a 15- to 10-year replacement strategy, this would result in 1,157 deaths averted or additional live saved per year. Sensitivity analysis showed that the utilization of ambulances and the mortality rate of cases unattended by ambulances were the key drivers for the cost-effectiveness of the replacement strategies. Conclusions The cost-effectiveness model developed suggests that an ambulance replacement strategy of every 10 years should be considered by the MOH in planning sustainable EMS. While this model may have its own limitation and may require some modifications to suit the local context, it can be used as a guide for future economic evaluations of ambulance replacement strategies and further exploration of alternative solutions.

Background Loss of CpG dinucleotides in genomic DNA through methylation-induced mutation is characteristic of vertebrates and plants. However, these and other eukaryotic phyla show a range of other dinucleotide frequency biases with currently uncharacterized underlying mutational or selection mechanisms. We developed a parameterized Markov process to identify what neighbour context-dependent mutations best accounted for patterns of dinucleotide frequency biases in genomic and cytoplasmically expressed mRNA sequences of different vertebrates, other eukaryotic groups and RNA viruses that infect them. Results Consistently, 11- to 14-fold greater frequencies of the methylation-associated mutation of C to T upstream of G (depicted as C→T,G) than other transitions best modelled dinucleotide frequencies in mammalian genomic DNA. However, further mutations such as G→T,T (5-fold greater than the default transversion rate) were required to account for the full spectrum of dinucleotide frequencies in mammalian sequence datasets. Consistent with modeling predictions for these two mutations, instability of both CpG and CpT dinucleotides was identified through SNP frequency analysis of human DNA sequences. Different sets of context-dependent mutations were modelled in other eukaryotes with non-methylated genomic DNA. In contrast to genomic DNA, best-fit models of dinucleotide frequencies in transcribed RNA sequences expressed in the cytoplasm from all organisms were dominated by mutations that eliminated UpA dinucleotides, observations consistent with cytoplasmically driven selection for mRNA stability. Surprisingly, mRNA sequences from organisms with methylated genomes showed evidence for additional selection against CpG through further context-dependent mutations (eg. C→A,G). Similar mutation or selection processes were identified among single-stranded mammalian RNA viruses; these potentially account for their previously described but unexplained under-representations of CpG and UpA dinucleotides. Conclusions Methods we have developed identify mutational processes and selection pressures in organisms that provide new insights into nucleotide compositional constraints and a wealth of biochemical and evolutionarily testable predictions for the future.

Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual’s brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N ​= ​89) that we can predict the spatial and spectral content of an individual’s task response using features estimated from the individual’s resting MEG data. This works by learning when transient spectral ‘bursts’ or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals. •Using AR-Hidden-Markov-Modeling, we predict diverse task responses from MEG resting state only.•On a group-level, events identified from resting state only can reconstruct group task responses.•Learning the mapping of these states onto task data enables prediction of single unseen subjects.•Genetically closer subjects show better predictability to each other than unrelated subjects.

The aim of this paper is to analyze the performance of a Garbage data collecting sensor network system (GDCSNS) through mathematical modelling and a reliability approach. The reliability assessment of such a system is essential to ensuring that it can collect data related to garbage at different locations consistently and accurately. After the determination of the reliability measures of the system, the next aim is to identify the weakest sensors of the system so that a timely maintenance strategy for the weakest sensor can be planned to avoid disruption in the collection of data from the sensor systems. In the considered system, three sensors have been installed at various location in the city that send the information to the center office (hub point) and then from the center office to the person who is responsible for collecting the garbage from the location and dumping it at some predefined places. These sensors collect data related to garbage level, weight, and other information and send it to computers at the city's central office. Markov modelling has been used to model the system. Based on the mathematical model, a state transition diagram and a set of Kolmogorov time-dependent differential equations have been obtained. The various state probabilities (explicit expressions) related to the performance of the system, namely, Reliability, Mean time to failure, have been obtained to understand the different maintenance policies that can be used. A sensitivity analysis has also been performed to determine the weakest sensor among the sensors.