Modelling evolutionary tempo and mode using formal morphological spaces and Markov chain principles.

Evolutionary tempo and mode summarize ancient and controversial subjects of theoretical biology such as gradualism, convergence, contingence, trends, and entrenchment. We employed an integrative methodological approach to explore the evolutionary tempo and mode of Lepidosaurian phalangeal formulae (PFs). This approach involves quantifying the frequencies of morphological changes along an evolutionary trajectory. The five meristic characters encoded by PFs are particularly valuable in revealing evolutionary patterns, owing to their discrete nature and extensive documentation in the literature. Based on a pre-existing dataset of PFs from 649 taxa (35 Lepidosauria families, including fossils), from which there exists a unique repertoire of 53 formulations, our approach simultaneously considers phenetic and phylogenetic data. This culminates in a diagram accounting for the phylogenetic dynamic of evolution traversing across different regions of morphospace. The method involves enumerating phenotypical options, reconstructing phenotypes across the phylogeny, projecting phenotypes onto a morphospace, and constructing a flow network from the frequency of evolutionary transitions between unique phenotypic conditions. This approach links Markovian chains and evolutionary trajectories to formally define parameters that describe the underlying transitions of morphological change. Among other results, we found that (a) PF evolution exhibits a clear trend towards reduction in the phalangeal count and that (b) evolutionary change tends to occur significantly between morphologically similar PFs. Notwithstanding, although minor but not trivial, transitions between distant formulas -jumps- occur. Our results support a pluralistic view including stasis, gradualism, and saltationism discriminating their prevalence in a target character evolution.

Association of state-level prescription drug monitoring program implementation with opioid prescribing transitions in primary care in Australia.

AIMS: This study aimed to evaluate whether voluntary and mandatory prescription drug monitoring program (PDMP) use in Victoria, Australia, had an impact on prescribing behaviour, focusing on individual patients' prescribed opioid doses and transition to prescribing of nonmonitored medications. METHODS: This was a retrospective cross-sectional study using routinely collected primary healthcare data. A 90-day moving average prescribed opioid dose in oral morphine equivalents was used to estimate opioid dosage. A Markov transition matrix was used to describe how patients prescribed medications transitioned between opioid dose groups and other nonopioid treatment options during 3 transition periods: transition between 2 control periods prior to PDMP implementation (T1 to T2); during the voluntary PDMP implementation (T2 to T3); and during mandatory PDMP implementation (T3 to T4). RESULTS: Among patients prescribed opioids in our study, we noted an increased probability of transitioning to not being prescribed opioids during the mandatory PDMP period (T3 to T4). This increase was attributed mainly to the ceasing of low-dose opioid prescribing. Membership in an opioid dose group remained relatively stable for most patients who were prescribed high opioid doses. For those who were only prescribed nonmonitored medications initially, the probability of being prescribed opioids increased during the mandatory PDMP when compared to other transition periods. CONCLUSION: The introduction of PDMP mandates appeared to have an impact on the prescribing for patients who were prescribed low-dose opioids, while its impact on individuals prescribed higher opioid doses was comparatively limited.

Quantitative MRI by nonlinear inversion of the Bloch equations.

PURPOSE: Development of a generic model-based reconstruction framework for multiparametric quantitative MRI that can be used with data from different pulse sequences. METHODS: Generic nonlinear model-based reconstruction for quantitative MRI estimates parametric maps directly from the acquired k-space by numerical optimization. This requires numerically accurate and efficient methods to solve the Bloch equations and their partial derivatives. In this work, we combine direct sensitivity analysis and pre-computed state-transition matrices into a generic framework for calibrationless model-based reconstruction that can be applied to different pulse sequences. As a proof-of-concept, the method is implemented and validated for quantitative T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ mapping with single-shot inversion-recovery (IR) FLASH and IR bSSFP sequences in simulations, phantoms, and the human brain. RESULTS: The direct sensitivity analysis enables a highly accurate and numerically stable calculation of the derivatives. The state-transition matrices efficiently exploit repeating patterns in pulse sequences, speeding up the calculation by a factor of 10 for the examples considered in this work, while preserving the accuracy of native ordinary differential equations solvers. The generic model-based method reproduces quantitative results of previous model-based reconstructions based on the known analytical solutions for radial IR FLASH. For IR bSFFP it produces accurate T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ maps for the National Insitute of Standards and Technology (NIST) phantom in numerical simulations and experiments. Feasibility is also shown for human brain, although results are affected by magnetization transfer effects. CONCLUSION: By developing efficient tools for numerical optimizations using the Bloch equations as forward model, this work enables generic model-based reconstruction for quantitative MRI.

A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection.

Atrial Fibrillation (AFib) is a heart condition that occurs when electrophysiological malformations within heart tissues cause the atria to lose coordination with the ventricles, resulting in "irregularly irregular" heartbeats. Because symptoms are subtle and unpredictable, AFib diagnosis is often difficult or delayed. One possible solution is to build a system which predicts AFib based on the variability of R-R intervals (the distances between two R-peaks). This research aims to incorporate the transition matrix as a novel measure of R-R variability, while combining three segmentation schemes and two feature importance measures to systematically analyze the significance of individual features. The MIT-BIH dataset was first divided into three segmentation schemes, consisting of 5-s, 10-s, and 25-s subsets. In total, 21 various features, including the transition matrix features, were extracted from these subsets and used for the training of 11 machine learning classifiers. Next, permutation importance and tree-based feature importance calculations determined the most predictive features for each model. In summary, with Leave-One-Person-Out Cross Validation, classifiers under the 25-s segmentation scheme produced the best accuracies; specifically, Gradient Boosting (96.08%), Light Gradient Boosting (96.11%), and Extreme Gradient Boosting (96.30%). Among eleven classifiers, the three gradient boosting models and Random Forest exhibited the highest overall performance across all segmentation schemes. Moreover, the permutation and tree-based importance results demonstrated that the transition matrix features were most significant with longer subset lengths.

Stability and spread: A novel method for quantifying transitions within multivariate binary time series data.

We present a novel method for quantifying transitions within multivariate binary time series data, using a sliding series of transition matrices, to derive metrics of stability and spread. We define stability as the trace of a transition matrix divided by the sum of all observed elements within that matrix. We define spread as the number of all non-zero cells in a transition matrix divided by the number of all possible cells in that matrix. We developed this method to allow investigation into high-dimensional, sparse data matrices for which existing binary time series methods are not designed. Results from 1728 simulations varying six parameters suggest that unique information is captured by both metrics, and that stability and spread values have a moderate inverse association. Further, simulations suggest that this method can be reliably applied to time series with as few as nine observations per person, where at least five consecutive observations construct each overlapping transition matrix, and at least four time series variables compose each transition matrix. A pre-registered application of this method using 4 weeks of ecological momentary assessment data (N = 110) showed that stability and spread in the use of 20 emotion regulation strategies predict next timepoint affect after accounting for affect and anxiety's auto-regressive and cross-lagged effects. Stability, but not spread, also predicted next timepoint anxiety. This method shows promise for meaningfully quantifying two unique aspects of switching behavior in multivariate binary time series data.

Multiple Sclerosis Progressive Courses: A Clinical Cohort Long-Term Disability Progression Study.

OBJECTIVES: Improving the understanding of multiple sclerosis (MS) mechanism and disability progression over time is essential to assess the value of healthcare interventions. Poor or no data on disability progression are available for progressive courses. This study aims to fill this gap. METHODS: An observational cohort study of patients with primary MS (PPMS) and secondary progressive MS (SPMS) was conducted on 2 Italian MS centers disease registries over an observational time of 34 years. Annual transition probabilities among Expanded Disability Status Scale (EDSS) states were estimated using continuous Markov models. A sensitivity analysis was performed in relation to clinical characteristic associated to disability progression. RESULTS: The study cohort included 758 patients (274 PPMS and 434 SPMS) with a median follow-up of 8.2 years. Annual transition probability matrices of SPMS and PPMS reported different annual probabilities to move within EDSS levels. Excluding EDSS associated to relapse events or patient with relapses, the annual probability of staying stable in an EDSS level increased in both disease courses even not significantly. CONCLUSIONS: This study provides estimates of annual disability progression as EDSS changes for PPMS and SPMS. These estimates could be a useful tool for healthcare decision makers and clinicians to properly assess impact of clinical interventions.

Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices.

Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms.

Image Encryption Scheme Based on Multiscale Block Compressed Sensing and Markov Model.

Many image encryption schemes based on compressed sensing have the problem of poor quality of decrypted images. To deal with this problem, this paper develops an image encryption scheme by multiscale block compressed sensing. The image is decomposed by a three-level wavelet transform, and the sampling rates of coefficient matrices at all levels are calculated according to multiscale block compressed sensing theory and the given compression ratio. The first round of permutation is performed on the internal elements of the coefficient matrices at all levels. Then the coefficient matrix is compressed and combined. The second round of permutation is performed on the combined matrix based on the state transition matrix. Independent diffusion and forward-backward diffusion between pixels are used to obtain the final cipher image. Different sampling rates are set by considering the difference of information between an image's low- and high-frequency parts. Therefore, the reconstruction quality of the decrypted image is better than that of other schemes, which set one sampling rate on an entire image. The proposed scheme takes full advantage of the randomness of the Markov model and shows an excellent encryption effect to resist various attacks.

The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model.

BACKGROUND: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. METHODS: Data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. RESULTS: The optimistic scenario (non-Hubei model, daily increment rate of - 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of - 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of - 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. CONCLUSION: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration.

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.

Implications of iterative communication for biological system performance.

The performance of integrated biological systems can often be described by the behavior of component subunits: the proportion of subunits performing an activity, and the rate of recruitment to the activity, can be relevant to system performance. We develop a model for activation of subunits (receivers) to a task when activation requires repeated signals (iterative communication). The model predicts how system performance will be affected by the parameters of iterative communication. Receiver activation is influenced by the frequency of stimulation, by forgetting about past interactions, and by the number of stimuli needed to activate the receivers. These parameters, along with the probability of activated receivers returning to a de-activated state, modulate the system-wide time course of activation and the steady-state proportion of activated receivers. Parameters can interact to affect system-wide activation, and multiple parameter combinations can yield similar patterns of activation. Group performance is less variable at higher stimulation frequencies and in systems with greater numbers of receivers. Biological constraints on iterative communication, such as time and energy costs, may limit the parameter values that are feasible for a given system. Iterative communication parameters may be subject to natural selection at the system (group) level because they affect system performance.

A Multi-Scale Approach to Simulate the Nonlinear Optical Response of Molecular Nanomaterials.

Nonlinear optics is essential for many recent photonic technologies. Here, a novel multi-scale approach is introduced to simulate the nonlinear optical response of molecular nanomaterials combining ab initio quantum-chemical and classical Maxwell-scattering computations. In this approach, the first hyperpolarizability tensor is computed with time-dependent density-functional theory and incorporated into a multi-scattering formalism that considers the optical interaction between neighboring molecules. Such incorporation is achieved by a novel object: the Hyper-Transition(T)-matrix. With this object at hand, the nonlinear optical response from single molecules and also from entire photonic devices can be computed, including the full tensorial and dispersive nature of the optical response of the molecules, as well as the optical interaction between different molecules as, for example, in the lattice of a molecular crystal. To demonstrate the applicability of the novel approach, the generation of a second-harmonic signal from a thin film of an Urea molecular crystal is computed and compared to more traditional simulations. Furthermore, an optical cavity is designed, which enhances the second-harmonic response of the molecular film up to more than two orders of magnitude. This approach is highly versatile and accurate and can be the working horse for the future exploration of nonlinear photonic molecular materials in structured photonic environments.

Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of geographic homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and could inform interventions to reduce risky-prescribing (e.g., should interventions target groups of physicians or select physicians at random). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques - groups of actors that are fully connected to each other - such as closed triangles in the case of three actors), this would further strengthen the case for targeting of select physicians for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing in both the state-wide and multiple HRR sub-networks, and that the level of homophily varied across HRRs. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology could be applied to arbitrary shared-patient networks and even more generally to other kinds of network data that underlies other kinds of social phenomena.

Changes in the land-use landscape pattern and ecological network of Xuzhou planning area.

Ongoing rapid urbanization has triggered significant changes in land use, rendering landscape patterns adversely impacted and certain habitat patches degraded. Ecological networks have consequently contracted overall. As such, an investigation into how land-use landscape patterns and ecological networks change over time and space is of major significance for ecological restoration and regional sustainability. Taking Xuzhou Planning Area as a case study, we examined spatiotemporal changes and features of the landscape pattern by employing the land-use change degree, the land-use transition matrix, and quantified landscape pattern indices. An ecological network analysis, which studies the changes in network connectivity and robustness, as well as their causes and contributors, was undertaken to probe into the features and trends of spatiotemporal changes in the land-use landscape pattern and ecological network amid expeditious urbanization. Analysis results unveiled the following: (1) From 1985 to 2020, there was a decline in the area of farmland, forest, and grassland, accompanied by an increase in land for construction, water bodies, and unused land. The southwestern research area witnessed farmland substantially give way to land for construction for this period, and the most dramatic change in land use occurred between 2000 and 2010. (2) The area of dominant patches in the research area shrank, along with more fragmented, complex landscapes. The land for construction was emerging as the dominant landscape by area, whereas patches of farmland, forest, grassland, and water bodies became less connected. (3) The ecological network was densely linked in the northeast, with sparser connections in the southwest. Spatial shrinkage was observed in the research area's southwestern and central ecological corridors. Overall, the number of ecological sources and corridors rose and subsequently dropped before a rebound. (4) The ecological network grew more connected and robust from 1985 through 1990, as portions of farmland were converted into water bodies, which led to an increase in ecological sources. Given a reduction in ecological sources and corridors in the southwestern and central regions between 1990 and 2010, network connectivity and robustness declined, which was reversed from 2010 onward with the addition of two ecological sources-Pan'an Lake and Dugong Lake. With an optimal ecological network in 1990, however, it deteriorated significantly by 2010. The research area saw the minimum value of its network connectivity indices of network stability index (α), evenness index (ß), and connectivity index (γ), in 2010, when its ecological network was highly fragmented and vulnerable, attributing to a strong contrast between the maximal connected subgraph's relative size and connectivity robustness. The research findings can lay scientific groundwork for addressing ecological issues, restoring landscape patterns, and developing ecological networks amid urbanization.

Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo.

With the specter of accelerating climate change, securing access to potable water has become a critical global challenge. Atmospheric water harvesting (AWH) through metal-organic frameworks (MOFs) emerges as one of the promising solutions. The standard numerical methods applied for rapid and efficient screening for optimal sorbents face significant limitations in the case of water adsorption (slow convergence and inability to overcome high energy barriers). To address these challenges, we employed grand canonical transition matrix Monte Carlo (GC-TMMC) methodology and proposed an efficient interpolation scheme that significantly reduces the number of required simulations while maintaining accuracy of the results. Through the example of water adsorption in three MOFs: MOF-303, MOF-LA2-1, and NU-1000, we show that the extrapolation of the free energy landscape allows for prediction of the adsorption properties over a continuous range of pressure and temperature. This innovative and versatile method provides rich thermodynamic information, enabling rapid, large-scale computational screening of sorbents for adsorption, applicable for a variety of sorbents and gases. As the presented methodology holds strong applicative potential, we provide alongside this paper a modified version of the RASPA2 code with a ghost swap move implementation and a Python library designed to minimize the user's input for analyzing data derived from the TMMC simulations.

Altered spreading of fast aperiodic brain waves relates to disease duration in Amyotrophic Lateral Sclerosis.

OBJECTIVE: To test the hypothesis that patients affected by Amyotrophic Lateral Sclerosis (ALS) show an altered spatio-temporal spreading of neuronal avalanches in the brain, and that this may related to the clinical picture. METHODS: We obtained the source-reconstructed magnetoencephalography (MEG) signals from thirty-six ALS patients and forty-two healthy controls. Then, we used the construct of the avalanche transition matrix (ATM) and the corresponding network parameter nodal strength to quantify the changes in each region, since this parameter provides key information about which brain regions are mostly involved in the spreading avalanches. RESULTS: ALS patients presented higher values of the nodal strength in both cortical and sub-cortical brain areas. This parameter correlated directly with disease duration. CONCLUSIONS: In this work, we provide a deeper characterization of neuronal avalanches propagation in ALS, describing their spatio-temporal trajectories and identifying the brain regions most likely to be involved in the process. This makes it possible to recognize the brain areas that take part in the pathogenic mechanisms of ALS. Furthermore, the nodal strength of the involved regions correlates directly with disease duration. SIGNIFICANCE: Our results corroborate the clinical relevance of aperiodic, fast large-scale brain activity as a biomarker of microscopic changes induced by neurophysiological processes.

Labour Market Dynamics and Worker Flows in India: Impact of Covid-19.

Tracking and analyzing the labour market dynamics at regular, frequent intervals is critical. However, this was not possible for India, a large emerging economy with a significant population undergoing demographic transition, due to a paucity of data. We use the new dataset Centre for Monitoring Indian Economy (CMIE)-Consumer Pyramids Household Survey (CPHS) and use a panel to create Labour Flow Charts and Transition Matrices for India from January 2019 to December 2021. To the best of our knowledge, this is the first time these were created for India. We then use that to look at the impact of Covid-19 on the Indian labour market. We not only look at transitions between employment, unemployment and out of labour force, but also across types of employment-full-time and part-time. The rich data also allows us to consider heterogeneity in the labour market and look at the differential impact of the pandemic across different education groups and gender. From the labour flow charts and transition probabilities, we find that while all groups have been impacted, the magnitude of the impact is different across groups. The recovery is also uneven, and the extent depends on education levels. Further, we do an event study analysis to examine the likelihood of getting a full-time job across different educational and gender groups. Men, on average, enjoy a higher likelihood of getting a full-time job than women. The likelihood coefficients also go up with increasing educational qualifications. Looking at skill heterogeneity, while the likelihood of getting a full-time job either goes down for most groups during the pandemic or the change is minuscule, strikingly it goes up for those with no education, for both men and women. The likelihood coefficients remain elevated for men even after the restrictions are removed, and that for women reverts to the level seen before the pandemic. Finally, this paper provides a way to continuously monitor the dynamics of the labour market as data is released in the regular intervals in the future, which would be of great value for researchers and policymakers alike.

A Multi-Scale Approach for Modeling the Optical Response of Molecular Materials Inside Cavities.

The recent fabrication advances in nanoscience and molecular materials point toward a new era where material properties are tailored in silico for target applications. To fully realize this potential, accurate and computationally efficient theoretical models are needed for: a) the computer-aided design and optimization of new materials before their fabrication; and b) the accurate interpretation of experiments. The development of such theoretical models is a challenging multi-disciplinary problem where physics, chemistry, and material science are intertwined across spatial scales ranging from the molecular to the device level, that is, from ångströms to millimeters. In photonic applications, molecular materials are often placed inside optical cavities. Together with the sought-after enhancement of light-molecule interactions, the cavities bring additional complexity to the modeling of such devices. Here, a multi-scale approach that, starting from ab initio quantum mechanical molecular simulations, can compute the electromagnetic response of macroscopic devices such as cavities containing molecular materials is presented. Molecular time-dependent density-functional theory calculations are combined with the efficient transition matrix based solution of Maxwell's equations. Some of the capabilities of the approach are demonstrated by simulating surface metal-organic frameworks -in-cavity and J-aggregates-in-cavity systems that have been recently investigated experimentally, and providing a refined understanding of the experimental results.

Dynamic Transitions of Pediatric Sepsis: A Markov Chain Analysis.

Pediatric sepsis is a heterogeneous disease with varying physiological dynamics associated with recovery, disability, and mortality. Using risk scores generated from a sepsis prediction model to define illness states, we used Markov chain modeling to describe disease dynamics over time by describing how children transition among illness states. We analyzed 18,666 illness state transitions over 157 pediatric intensive care unit admissions in the 3 days following blood cultures for suspected sepsis. We used Shannon entropy to quantify the differences in transition matrices stratified by clinical characteristics. The population-based transition matrix based on the sepsis illness severity scores in the days following a sepsis diagnosis can describe a sepsis illness trajectory. Using the entropy based on Markov chain transition matrices, we found a different structure of dynamic transitions based on ventilator use but not age group. Stochastic modeling of transitions in sepsis illness severity scores can be useful in describing the variation in transitions made by patient and clinical characteristics.

Random and systematic change analysis in land use change at the category level-A case study on Mu Us area of China.

Random and systematic change analysis is gradually becoming a common method for effectively detecting land use change signals from land transition matrix, but most researches focus only on the change characteristics at the transition level. This paper attempted to distinguish random and systematic changes at the category level, and to clarify the meanings of these two types of changes, as well as their indicative significances of change causes. This paper first calculated the random expected value of change area at the category level, and the deviation of the actual change area from the expected value. Then we proposed a method for setting a threshold of the deviation to clearly distinguish random and systematic changes. This method could eliminate the influence of land use classification errors on the distinction. Through analyzing the mathematical formulas of random expected area, this paper further clarified the meanings of random and systematic changes as well as their indicative significances to change causes. Land use change in Mu Us area of China was used as a case study. Practice showed that detecting and analyzing the random and systematic signals at the category level could accurately determine the change trend of land use, which could help to explore the relationship between land use change and external influences, especially human activities.