A model for the dynamics of bipolar disorders

Bipolar disorders are characterized by recurrent, alternating episodes of mania and depression. To examine the dynamical bases of this cyclical illness we consider a minimal model for bipolar disorders based on the observation that the two poles of the disease are mutually exclusive. We assume that the propensities to mania and depression, which are correlated with the activity of two putative neural circuits that promote, respectively, the manic or the depressive state, inhibit each other. When mutual inhibition is sufficiently strong, the model predicts bistability: the bipolar system is then either in a depressive or in a manic state and can display abrupt switches between these stable states. We consider two simple mechanisms which, when added to mutual inhibition, allow the model to pass from bistability to oscillations. Self-sustained oscillations provide a mechanism for the spontaneous, recurrent switching between mania and depression. The model can generate oscillations with a variety of waveforms, including simple periodic oscillations with comparable or unequal durations of the manic and depressive episodes, or small-amplitude oscillations around one of the two states preceding large-amplitude periodic changes in the propensities to mania or depression. The model provides a theoretical framework that covers the bipolar spectrum, i.e., cycling between the two poles of the disease, or evolution to either mania or depression or to an intermediate state without alternating between the two poles of the disease. The model accounts for the clinical observation that antidepressants can trigger the transition to mania or increase the frequency of bipolar cycling.     

Temporal progress model of metabolic syndrome for clinical decision support system

The development of an integrated and personalized healthcare system is becoming an important issue in the modern healthcare industry. One of main objectives of integrated healthcare system is to effectively manage patients having chronic diseases that require long term care and its temporal information plays an important role to manage the statuses of diseases. Thus, a patient having chronic disease needs to visit the hospital periodically, which generates large volume of medical examination data. Among the various chronic diseases, metabolic syndrome (MS) has become a popular chronic disease in many countries. There have been efforts to develop an MS risk quantification and prediction model and to integrate it into personalized healthcare system, so as to predict the risk of having MS in the future. However, the development of methods for temporal progress management of metabolic syndrome has not been widely investigated. This paper proposes a method for identifying the temporal progress of MS patients' status based on the chronological clustering methodology. To investigate the temporal changes of disease status, we develop a chronological distance variance model that quantifies the difference of areal similarity degree (ASD) values between estimated and examined results of MS risk factors. We evaluate the clinical effectiveness of the temporal progress model by using sample subjects' examination results that have been measured for 10 years. We further elaborate the accuracy of the proposed temporal progress estimation method by using multiple linear regression method. Then, we develop a tier-based patients' MS status classification based on the chronological distance variance. The tier classification is based on the sensitivity for temporal change of MS status according to different values of control range of chronological distance variance. Our proposed temporal change identification method and patients' tier classification are expected to be incorporated with the integrated healthcare systems to help physicians with identifying the temporal progress of MS patients' health status and MS patients with self-management at home environments.     

A model for odour thresholds

Odour detection thresholds, that we have previously obtained, have been analysed by a general equation for selective transport. It is shown that such selective transport can account for some 77% of the total effect. The remainder is due to a specific size effect, that might involve odour-binding proteins, and a specific effect for aldehydes and carboxylic acids. Our analysis raises the question of whether selective transport is physically separable from the specific effects of receptor activation. The model predicts a chemical cut-off in odour detection along any homologous series.     

A system dynamics model of island biogeography

The MacArthur-Wilson equilibrium theory of island biogeography has been one of the more influential concepts in modern biogeography and ecology. In this paper, we synthesize the theory and examine effects of different immigration/extinction rate-species diversity curves on original predictions from the theory by using the System Dynamics simulation modeling approach. Moreover, we develop a comprehensive and generic System Dynamics model to incorporate a variety of recent modifications and extensions of the theory, including area effect, distance effect, competition effect, habitat diversity effect, target effect, and rescue effect. Through computer simulation with STELLA, a more profound understanding of the theory of island biogeography can be gained. The System Dynamics modeling approach is especially appropriate for such a study because it maximizes the utilization of the ecological data by incorporating qualitative information so that a complex, imprecisely-defined ecological system can be studied quantitatively, effectively, and comprehensively. Our simulation results show that different monotonic rate-species diversity curves do not affect the essence of the theory of island biogeography, while the magnitude of equilibrium species diversity may be greatly affected. Non-monotonic rate-species diversity curves may result in potential multiple equilibria of species diversity. In addition, our model suggests that a non-monotonic relationship may exist between the equilibrium turnover rate and island area and between the equilibrium turnover rate and distance.     

A mathematical model of population dynamics for Batesian mimicry system

We analyse a mathematical model of the population dynamics among a mimic, a corresponding model, and their common predator populations. Predator changes its search-and-attack probability by forming and losing its search image. It cannot distinguish the mimic from the model. Once a predator eats a model individual, it comes to omit both the model and the mimic species from its diet menu. If a predator eats a mimic individual, it comes to increase the search-and-attack probability for both model and mimic. The predator may lose the repulsive/attractive search image with a probability per day. By analysing our model, we can derive the mathematical condition for the persistence of model and mimic populations, and then get the result that the condition for the persistence of model population does not depend on the mimic population size, while the condition for the persistence of mimic population does depend the predator's memory of search image.     

A mathematical model of population dynamics for Batesian mimicry system

We analyse a mathematical model of the population dynamics among a mimic, a corresponding model, and their common predator populations. Predator changes its search-and-attack probability by forming and losing its search image. It cannot distinguish the mimic from the model. Once a predator eats a model individual, it comes to omit both the model and the mimic species from its diet menu. If a predator eats a mimic individual, it comes to increase the search-and-attack probability for both model and mimic. The predator may lose the repulsive/attractive search image with a probability per day. By analysing our model, we can derive the mathematical condition for the persistence of model and mimic populations, and then get the result that the condition for the persistence of model population does not depend on the mimic population size, while the condition for the persistence of mimic population does depend the predator's memory of search image.     

A mathematical model for interpretable clinical decision support with applications in gynecology

Background

Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.

Methods and Findings

We propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.

Conclusions

The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data.     

A clinical scoring system for detection of patients with pheochromocytomas

We analyzed the medical records of patients admitted to 11 hospitals over a 15-year period, looking for those with metabolically active sporadic pheochromocytomas (Group A) and those in whom the diagnosis was highly suspect but excluded (Group B). Fifty-three patients in Group A and 25 patients in Group B were found. We then devised a scoring system based on the presence or absence of typical symptoms and signs (SSS) and another which also included the results of routine 24-hour urine studies for catecholamines or metabolites (SSLS). The point values given for each symptom and sign were based on those felt to be most characteristic of the disease and points were subtracted if the typical manifestation was absent. Additional points were given if the symptom or sign were paroxysmal. In a high-risk population, the sensitivity of the SSS and the SSLS was 96 percent and the specificity was 64 percent and 88 percent, respectively. The predictive value of a positive SSS was 85 percent and of a positive SSLS was 94 percent. In a large group of patients with essential hypertension (Group C) only 17 of 385 (4.4 percent) had a positive SSS and only one of 385 had a positive SSLS. We feel this scoring system can help detect those hypertensive patients in whom further extensive and potentially invasive evaluation is warranted.     

A data mining based clinical decision support system for survival in lung cancer

BackgroundA clinical decision support system (CDSS ) has been designed to predict the outcome (overall survival) by extracting and analyzing information from routine clinical activity as a complement to clinical guidelines in lung cancer patients.Materials and methodsProspective multicenter data from 543 consecutive (2013–2017) lung cancer patients with 1167 variables were used for development of the CDSS. Data Mining analyses were based on the XGBoost and Generalized Linear Models algorithms. The predictions from guidelines and the CDSS proposed were compared.ResultsOverall, the highest (> 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p < 0.05). For instance, using the guidelines, the AUC for predicting survival was 0.60 while the predictive power of the CDSS enhanced the AUC up to 0.84 (p = 0.0009). In terms of histology, there was only a statistically significant difference when comparing the AUCs of small cell lung cancer patients (0.96) and all lung cancer patients with longer (≥ 18 months) follow up (0.80; p < 0.001).ConclusionsThe CDSS successfully showed potential for enhancing prediction of survival. The CDSS could assist physicians in formulating evidence-based management advice in patients with lung cancer, guiding an individualized discussion according to prognosis.     

A thermal model for human thresholds of microwave-evoked warmth sensations

Human thresholds for skin sensations of warmth were measured at frequencies from 2.45 to 94 GHz. By solving the one-dimensional bioheat equation, we calculated the temperature increase at the skin surface or at a depth of 175 μm at incident power levels corresponding to the observed thresholds. The thermal analysis suggests that the thresholds correspond to a localized temperature increase of about 0.07 °C at and near the surface of the skin. We also found that, even at the highest frequency of irradiation, the depth at which the temperature receptors are located is not a relevant parameter, as long as it is within 0.3 mm of the surface. Over the time range of the simulation, the results of the thermal model are insensitive to blood flow, but sensitive to thermal conduction; and this sensitivity increases strongly with frequency. We conclude with an analysis of the effect of thermal conduction on surface temperature rise, which becomes a dominant factor at microwave frequencies over 10 GHz. Bioelectromagnetics 18:578–583, 1997. © 1997 Wiley-Liss, Inc.     

Dynamic logistic state space prediction model for clinical decision making

Prediction modeling for clinical decision making is of great importance and needed to be updated frequently with the changes of patient population and clinical practice. Existing methods are either done in an ad hoc fashion, such as model recalibration or focus on studying the relationship between predictors and outcome and less so for the purpose of prediction. In this article, we propose a dynamic logistic state space model to continuously update the parameters whenever new information becomes available. The proposed model allows for both time-varying and time-invariant coefficients. The varying coefficients are modeled using smoothing splines to account for their smooth trends over time. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at prespecified time intervals, which allows for better approximation of the underlying binomial density function. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply the method to predict 1 year survival after lung transplantation using the United Network for Organ Sharing data.     

Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: A proof-of-concept study

Background  

Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. Without the appropriate detection strategies, these seizure episodes can dramatically affect the quality of life for those afflicted. The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies.     

A quick method for computing approximate thresholds for quantitative trait loci detection

This article proposes a quick method for computing approximate threshold levels that control the genome-wise type I error rate of tests for quantitative trait locus (QTL) detection in interval mapping (IM) and composite interval mapping (CIM). The procedure is completely general, allowing any population structure to be handled, e.g., BC(1), advanced backcross, F(2), and advanced intercross lines. Its main advantage is applicability in complex situations where no closed form approximate thresholds are available. Extensive simulations demonstrate that the method works well over a range of situations. Moreover, the method is computationally inexpensive and may thus be used as an alternative to permutation procedures. For given values of the likelihood-ratio (LR)-profile, computations involve just a few seconds on a Pentium PC. Computations are simple to perform, requiring only the values of the LR statistics (or LOD scores) of a QTL scan across the genome as input. For CIM, the window size and the position of cofactors are also needed. For the approximation to work well, it is suggested that scans be performed with a relatively small step size between 1 and 2 cM.     

A Markov decision model for computer-aided instruction

A mathematical model for computer-aided instruction is developed. It is assumed that the course is divided into a hierarchy of levels of difficulty. These levels are such that if a student is able to perform successfully at a given level of difficulty, he can also perform successfully at all levels of lesser difficulty. Furthermore, if student performs successfully at one level, it increases his probability of being able to perform successfully at the next higher level of difficulty. Given the initial vector of probabilities for successful performance at each level, the vector describing how these probabilities change with successful performances at each level, and the expected times it takes to attempt a successful performance at each level, this model computes an instructional sequence that minimizes the expected time required for the student to complete the course by performing successfully at the highest level of difficulty. Dynamic programming is used to find this sequence.     

一类大尺度系统中昆虫种群时空动态模拟方法

本文介绍了一类描述大尺度作物系统害虫种群时空动态的模型,该模型包含了寄主作物,昆虫密度以及天敌间的相互作用,并简要介绍了该模型的一些模拟结果在IPM中的应用。     

Population dynamics of plant-parasite interactions: thresholds for invasion

Thresholds are derived for the invasion of plant populations by parasites. The theory is developed for a generic model that takes into account two features characteristic of plant-parasite interactions: a dual source of inoculum (infection from primary or externally introduced inoculum and secondary infection from contact between susceptible and infected host tissue) and a host response to infection load. Each of the threshold criteria is shown to be the sum of the individual components for primary and secondary infection. This indicates that if parasite invasion is not possible through primary or secondary infection alone, when the two modes of transmission are combined, the parasite may be able to invade. The invasion criteria demonstrate that there is a threshold population of susceptible hosts below which the parasite is unable to invade. If there are nonlinearities in the population dynamics (arising through either the transmission process or the host response), there are also threshold densities for the infected hosts and parasite populations below which invasion does not occur. The implications of the results for the control of plant disease are discussed.     

A simple and reliable method for clinical assessment of odor thresholds

We investigated whether presenting of dilutions of phenyl ethyl alcohol at random succession according to the method of constant stimuli can replace the standard procedure of presenting a various number of dilutions in a staircase paradigm. Forty-six men and 44 women, aged 19-76 years, participated in this study. Phenyl ethyl alcohol was diluted in a ratio of 1:2, starting from 4%. Presentation of the odorant followed a three-alternative, temporal forced-choice paradigm with two blanks in addition to the odorant. Twenty dilutions were administered in a randomized order. Odor threshold was obtained by logistic regression of the correct and incorrect identifications of the probe containing the odorant. Thresholds were also calculated on the basis of the first 16 dilution steps only. Results from these procedures were compared with 'gold-standard' threshold assessment employing a three-alternative, temporal forced-choice staircase paradigm with seven reversals using 16 dilutions of phenyl ethyl alcohol. The method of constant stimuli took a shorter and less variable testing time than the staircase technique. The use of 20 dilution steps provided no better results than the use of 16 steps. The method of constant stimuli exhibited a good test-retest reliability (r = 0.7; P < 0.001) comparable to that of the staircase method and provided unbiased results highly correlated (r = 0.8; P < 0.001) with those of the staircase technique with similar inter-test variability. Applying 16 dilutions (1:2 steps) of phenyl ethyl alcohol at random succession in a three-alternative, temporal forced-choice paradigm is thus a simple and reliable procedure for the reproducible assessment of odor thresholds that may be contemplated as an alternative to the 'gold-standard' staircase method of clinical odor threshold assessment.     

A new MSPQC system for rapid detection of pathogens in clinical samples

A new multi-channel series piezoelectric quartz crystal (MSPQC) system for detection of pathogens in clinical sample was proposed. Some factors, which affect the detection of pathogens by using MSPQC, were all investigated. A total of 650 clinical samples were detected by MSPQC and compared with licensed BACTEC 9120 system (Becton Dickinson Diagnostic Instrument Systems, Sparks, MD, USA) simultaneously in the Third Xiangya Hospital of Central South University, China. When the incubation period was 5 days, two systems had similar detected results: the MSPQC system detected 123 growth of 650 (18.92%) bottles while the BACTEC 9120 detected 125 growth of 650 (19.23%) bottles. The MSPQC had 2 false-positive signals and 2 false-negative signals. However, BACTEC 9120 had 3 false-positive signals and 0 false-negative signals. Further identifications of bacteria were run by VITEK-2 (bioMérieux China Ltd.), 5% sheep blood trypticase soy agar (SBA) and chocolate agar (CA). Comparing with BACTEC 9120, MSPQC system possesses following advantages: shorter average detection time, less blood volume needed, less false-positive results and low cost. It can also provide information in real time. So MSPQC has a wonderful perspective in clinical application.     

A model for protein-DNA interaction dynamics

We map a simplified version of the protein-DNA interaction problem into an Ising-model in a random magnetic field. The model includes a "head" which moves along the chain while interacting with the underlying spins. The head moves by using the statistical fluctuations of base openings. A Monte Carlo (MC) simulation of this model reveals the possibility of biased diffusion in one direction, followed by sequence identification and binding. The model provides some insight into the mechanisms used by some repressor proteins to diffuse and bind to specific DNA-binding sites.