In recent years, polysubstance-related admission to substance abuse treatment facilities is increasing. The Prescription Drug Monitoring Program (PDMP) is used to monitor the prescription of controlled substances. This study aims to investigate the effect of PDMP on the polysubstance-related admission rate.

The benefits and efficacy of control programs for herds infected with Mycobacterium avium subsp. paratuberculosis (MAP) have been investigated under various contexts. However, most previous research investigated paratuberculosis control programs in isolation, without modeling the potential association with other dairy diseases. This paper evaluated the benefits of MAP control programs when the herd is also affected by mastitis, a common disease causing the largest losses in dairy production. The effect of typically suggested MAP controls were estimated under the assumption that MAP infection increased the rate of clinical mastitis. We evaluated one hundred twenty three control strategies comprising various combinations of testing, culling, and hygiene, and found that the association of paratuberculosis with mastitis alters the ranking of specific MAP control programs, but only slightly alters the cost-benefit difference of particular MAP control components, as measured by the distribution of net present value of a representative U.S. dairy operation. In particular, although testing and culling for MAP resulted in a reduction in MAP incidence, that control led to lower net present value (NPV) per cow. When testing was used, ELISA was more economically beneficial than alternative testing regimes, especially if mastitis was explicitly modeled as more likely in MAP-infected animals, but ELISA testing was only significantly associated with higher NPV if mastitis was not included in the model at all. Additional hygiene was associated with a lower NPV per cow, although it lowered MAP prevalence. Overall, the addition of an increased risk of mastitis in MAP-infected animals did not change model recommendations as much as failing to consider.

Fresh produce supply chains present variable and diverse conditions that are relevant to food quality and safety because they may favor microbial growth and survival following contamination. This study presents the development of a simulation and visualization framework to model microbial dynamics on fresh produce moving through postharvest supply chain processes. The postharvest supply chain with microbial travelers (PSCMT) tool provides a modular process modeling approach and graphical user interface to visualize microbial populations and evaluate practices specific to any fresh produce supply chain. The resulting modeling tool was validated with empirical data from an observed tomato supply chain from Mexico to the United States, including the packinghouse, distribution center, and supermarket locations, as an illustrative case study. Due to data limitations, a model-fitting exercise was conducted to demonstrate the calibration of model parameter ranges for microbial indicator populations, i.e., mesophilic aerobic microorganisms (quantified by aerobic plate count and here termed APC) and total coliforms (TC). Exploration and analysis of the parameter space refined appropriate parameter ranges and revealed influential parameters for supermarket indicator microorganism levels on tomatoes. Partial rank correlation coefficient analysis determined that APC levels in supermarkets were most influenced by removal due to spray water washing and microbial growth on the tomato surface at postharvest locations, while TC levels were most influenced by growth on the tomato surface at postharvest locations. Overall, this detailed mechanistic dynamic model of microbial behavior is a unique modeling tool that complements empirical data and visualizes how postharvest supply chain practices influence the fate of microbial contamination on fresh produce.IMPORTANCE Preventing the contamination of fresh produce with foodborne pathogens present in the environment during production and postharvest handling is an important food safety goal. Since studying foodborne pathogens in the environment is a complex and costly endeavor, computer simulation models can help to understand and visualize microorganism behavior resulting from supply chain activities. The postharvest supply chain with microbial travelers (PSCMT) model, presented here, provides a unique tool for postharvest supply chain simulations to evaluate microbial contamination. The tool was validated through modeling an observed tomato supply chain. Visualization of dynamic contamination levels from harvest to the supermarket and analysis of the model parameters highlighted critical points where intervention may prevent microbial levels sufficient to cause foodborne illness. The PSCMT model framework and simulation results support ongoing postharvest research and interventions to improve understanding and control of fresh produce contamination.

4 School of Biological Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK 5 Anglia Ruskin IT Research Institute (ARITI), Anglia Ruskin University, Cambridge, CB1 1PT, UK 6 Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK. Corresponding author: rosemary.bass@northumbria.ac.uk, telephone +44 (0)191 2274899, no fax number.

Johne's disease (JD) is a chronic enteric disease in cattle caused by Mycobacterium avian subsp. paratuberculosis (MAP). Eradicating JD is a difficult task due to the long incubation period of MAP, inefficient diagnostic tests, and delayed clinical signs. Effective control strategies can help farmers to reduce prevalence, but those most acceptable to farmers combine specific information about lactation performance and testing results, which existing models do not provide. This paper presents an individual-based model of MAP infection dynamics and assesses the relative performance of the applied alternative control strategies. The base dairy herd model included the daily life events of a dairy cow and reflects several current dairy management processes. We then integrated MAP infection dynamics into the model. The model adopted four different test-based control strategies based on risk-based culling decisions and three hygiene scenarios. The model tracked the source of each infection and quantified the efficacy of each control strategy in reducing the risks of different transmission routes. The results suggest that risk-based culling can reduce prevalence compared with no control, but cannot eliminate the infection. Overall, this work provides not only a valuable tool to investigate MAP transmission dynamics but also offers adaptability to model similar infectious diseases.

Wireless sensor networks are generally deployed in inhospitable terrain where the lightweight nodes are expected to detect an event and send relevant data to the Base Station (BS). However in such remote places it is generally not feasible to replace batteries quite often. Clustering is an approach to minimize energy consumption of such network. However the approach should be flexible enough to avoid excessive battery drain of the cluster heads particularly those near the BS and to address topology changes as nodes die out. So, in this paper an Energy Aware Sleep Scheduling Clustering based Routing scheme (EASSCR) for Wireless Sensor Network is proposed. Here some nodes are put to sleep to prolong the network lifetime. EASSCR selects a node as a cluster head if its residual energy is more than system average energy and if a node???s remaining energy reaches five percent of its initial energy then it directly sends data to BS avoiding node failure during data collection or aggregation. The goals of this scheme are, increase stability period of network, and minimize loss of sensed data. Performance analysis show that EASSCR has significant improvement over existing protocols LEACH and SEP in terms of lifetime of network and data units gathered at BS.

Most cancer treatments efficacy depends on tumor metastasis suppression, where tumor suppressor genes play an important role. Maspin (Mammary Serine Protease Inhibitor), an non-inhibitory serpin has been reported as a potential tumor suppressor to influence cell migration, adhesion, proliferation and apoptosis in in vitro and in vivo experiments in last two decades. Lack of computational investigations hinders its ability to go through clinical trials. Previously, we reported first computational model for maspin effects on tumor growth using artificial neural network and cellular automata paradigm with in vitro data support. This paper extends the previous in silico model by encompassing how maspin influences cell migration and the cell-extracellular matrix interaction in subcellular level. A feedforward neural network was used to define each cell behavior (proliferation, quiescence, apoptosis) which followed a cell-cycle algorithm to show the microenvironment impacts over tumor growth. Furthermore, the model concentrates how the in silico experiments results can further confirm the fact that maspin reduces cell migration using specific in vitro data verification method. The data collected from in vitro and in silico experiments formulates an unsupervised learning problem which can be solved by using different clustering algorithms. A density based clustering technique was developed to measure the similarity between two datasets based on the number of links between instances. Our proposed clustering algorithm first finds the nearest neighbors of each instance, and then redefines the similarity between pairs of instances in terms of how many nearest neighbors share the two instances. The number of links between two instances is defined as the number of common neighbors they have. The results showed significant resemblances with in vitro experimental data. The results also offer a new insight into the dynamics of maspin and establish as a metastasis suppressor gene for further molecular research.

Cancer is one of the biggest killers in the western world; every two minutes someone is diagnosed with cancer in the UK. Tumour growth and progression is a complex biological process, normally beginning with genetic mutations in a single cell. It starts with the early or avascular phase where growth is limited by nutrient diffusion, then the vascular stage where angiogenesis occurs to stimulate blood vessel production by the secretion of tumour angiogenesis factors and finally the metastasitic phase where the tumour spreads from the site of origin to distant sites around the body. While considering these events at the cellular level, these processes involve many microenvironment parameters like oxygen concentration, hypoglycaemia, acidity, hypoxia (lack of oxygen), cell-cell adhesion, cell migration and cell-extracellular matrix interactions. In this paper, a computational model is proposed which considered hypoxia as a microenvironment constraint of tumour growth. The model is built on two dimensional cellular automata grid and artificial neural network is considered for establishing signaling network of tumour cells. Each tumour cell can take its own decision in this model. A hypoxia impact was implemented in the model by varying different oxygen concentrations. The results show that hypoxia was introduced in the tumour mass due to lack of oxygen. The model measured tumour invasion and the number of apoptotic cells to support that hypoxia has a critical impacts on avascular tumour growth. This model could inform a better understanding of the impacts of hypoxia in tumour growth from the computational point of view.

Maspin (Mammary Serine Protease Inhibitor) is a non-inhibitory member of the serpin family of protease inhibitors that influences multiple cellular functions including adhesion, migration, and invasion in tumour malignancy. In this paper, we describe a computational model of the effects of exogenous maspin on cellular proliferation, migration and invasion. To date, the whole cellular mechanisms of maspin impact on cellular behaviors have not been clearly defined in any computational model. In this paper, we have used a feedforward artificial neural network to define cell signaling and cellular automata approach to define the each and every cell behavior. Results show that maspin reduces migration and invasion by 10-40% and 15-30% respectively; confirmed by published in vitro data. This is the first attempt to model the effects of maspin using a computational model to verify in vitro data. This will provide new insights into the tumour suppressive properties of maspin and inform the development of novel cancer therapies.

Maspin (SERPINB5) is a type II metastasis suppressor that influences multiple cellular functions. To date, maspin has been shown to increase adhesion and apoptosis and to decrease cell migration, proliferation, invasion and metastases in tumour malignancy. At the subcellular level, maspin influences morphological changes in the cell cytoskeleton which regulates complex biological processes including cell migration, cell adhesion and EMT (epithelial to mesenchymal transition). Here non-Euclidian fractal and image analyses have been applied to measure changes in the actin cytoskeleton using confocal microscopy images to confirm the effects of maspin. Results show that maspin contributes to maintaining the regular epithelial like shape, increases cell-cell adhesion and restricts tumour cells from showing the pre-migration and EMT characteristics. Characterization of these changes in the actin cytoskeleton using microscopic image analysis will establish maspin as a potential prognostic marker in future.

This paper presents an investigation into the development of an optimal chemotherapy drug(s) scheduling scheme to control the drug doses to be infused to the patient's body. The current standard of practice of treatment is based on empirical evidence gathered from preclinical and clinical trials carried out during the drug development process. In general, most chemotherapy drugs used in cancer treatments are toxic agents and usually have narrow therapeutic indices; dose levels at which these drugs significantly kill the cancerous cells are close to those levels at which harmful toxic side effects occur. Therefore, an effective chemotherapy treatment protocol requires advanced automation and treatment design tools for use in clinical practice and the challenges inherent to complex biomedical systems and clinical deployment of technology (Parker, 2009). An optimum but effective drug scheduling requires suitable balancing between the beneficial and toxic side effects. Conventional clinical methods very often fail to find right drug doses that balance between these two constraints due to their inherent conflicting nature. A Multi-objective Genetic Algorithm Optimization (MOGA) process is employed to find the desired drug concentration at tumour sites that trade-off between the conflicting objectives. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control the drug to be infused to the patient's body and MOGA is used to find suitable/acceptable drug concentration at tumour site and parameters of the controller. Cell cycle specific cancer tumour models have been used in this work to show the effects of drug(s) on different cell populations, drug concentrations and toxic side effects. Results show that the applied multi-objective optimization approach can produce a wide range of solutions that trade-off between cell killing and toxic side effects and satisfy associated goals of chemotherapy treatment. Depending on the physiological state of the patient and state of the cancer, the oncologist can pick the right solution suitable for the patient. The chemotherapy drug schedules obtained by the proposed treatment protocols appears to be continuous on the time (day) scale, i.e., specific amount of drugs to be administered to the patient on daily basis which can be termed as Metronomics in nature. The dose duration and the interval period between dose applications can be adjusted in the proposed scheme either by setting the sampling time of closed-loop I-PD controller to any value depending on the state of the patient and disease (model parameters) or by using genetic optimization process aiming to minimize/maximize treatment objectives and satisfying treatment constraints. Regarding the total duration of the treatment, clinical knowledge can be utilized giving emphasis on physiological state of the patient, state of the tumour and disease. Moreover, the total duration of the treatment can also be found/determined for specific values of model parameters describing physiological state of the patient, state of the tumour and disease through multi-objective optimization process. It is noted that the proposed scheme offered the best treatment performance as compared to the reported work available so far. Moreover, robustness analysis shows that the control scheme is highly stable and robust despite the model uncertainties; from small to wide range, and the percentage of proliferating cell reduction is almost same as it is found with optimum model parameters without having any uncertainty.

This paper presents a design method for optimal cancer chemotherapy schedules using genetic algorithm (GA). The main objective of chemotherapy is to reduce the number of cancer cells or eradicate completely, if possible, after a predefined time with minimum toxic side effects which is difficult to achieve using conventional clinical methods due to narrow therapeutic indices of chemotherapy drugs. Three drug scheduling schemes are proposed where GA is used to optimize the doses and schedules by satisfying several treatment constraints. Finally, a clinically relevant dose scheme with periodic nature is proposed. Here Martin’s model is used to test the designed treatment schedules and observe cell population, drug concentration and toxicity during the treatment. The number of cancer cells is found zero at the end of the treatment for all three cases with acceptable toxicity. So the proposed design method clearly shows effectiveness in planning chemotherapy schedules.

In this paper, a design method for optimal cancer chemotherapy schedules via genetic algorithm (GA) is presented. The design targets the key objective of chemotherapy to minimize the size of cancer tumor after a predefined time with keeping toxic side effects in limit. This is a difficult target to achieve using conventional clinical methods due to poor therapeutic indices of existing anti-cancer drugs. Moreover, there are clinical limitations in treatment administration to maintain continuous treatment. Besides, carefully decided rest periods are recommended to for patient’s comfort. Three intermittent drug scheduling schemes are presented in this paper where GA is used to optimize the dose quantities and timings by satisfying several treatment constraints. All three schemes are found to be effective in total elimination of cancer tumor after an agreed treatment length. The number of cancer cells is found zero at the end of the treatment for all three cases with tolerable toxicity. Finally, two of the schemes, “Fixed interval variable dose (FIVD) and “Periodic dose” that are periodic in characteristic have been emphasized due to their additional simplicity in administration along with friendliness to patients. responses to the designed treatment schedules. Therefore the proposed design method is capable of planning effective, simple, patient friendly and acceptable chemotherapy schedules.

Cancer metastasis is a complex multistep process which allows cancer cells to establish new tumours in distant organs. The process of metastasis involves cell migration and invasion; it is what makes cancer a fatal disease. The efficiency of most cancer treatments depends on metastasis suppression. Maspin is a type II tumour metastasis suppressor which has multiple cellular effects. It has been described as a key regulatory protein in both the intracellular and extracellular environments. Maspin has been shown to reduce cell migration, invasion, proliferation and angiogenesis, and increase apoptosis and cell–cell adhesion in in vitro and in vivo experiments. The clinical data regarding the predictive effects of maspin expression are variable. To date, the whole cellular mechanisms that maspin uses to influence tumour cell behaviours have not been clearly defined. The diversity of the effects of maspin motivated us to develop an intelligent model to investigate its effects on cellular proliferation and migration. This paper reports a hybrid model of solid tumour growth in order to investigate the impact of maspin on the growth and evolutionary dynamics of the cancer cell. A feed-forward neural network was used to model the behaviours (proliferation, quiescence, apoptosis and/or movement) of each cell, which has been suggested as a suitable model of cell signalling pathways. Results show that maspin reduces migration by 10–40%, confirmed by published in vitro data. The model also shows a reduction in cell proliferation by 20–30% in the presence of maspin. So far, this is the first attempt to model the effect of maspin in a computational model to verify in vitro data. This will provide new insights into the tumour suppressive properties of maspin and inform the development of novel cancer therapies.

Maspin (Mammary Serine Protease Inhibitor) is a non-inhibitory serpin with multiple cellular effects that is a type II tumour metastasis suppressor. Maspin has been shown to reduce cell migration, invasion, proliferation and angiogenesis, and increase apoptosis and adhesion. In this paper, we report the development of a mathematical model of the effects of maspin on cellular proliferation and migration. An artificial neural network has been used to model the unknown cell signalling to determine the cells fate. Results show that maspin reduces migration by between 10-35%; confirmed by published in vitro data. From our knowledge, this is the first attempt to model maspin effects in a computational model to verify in vitro data. This will provide new insights into to the tumour suppressive properties of maspin and inform the development of novel cancer therapy.

Cancer is one of the biggest killers in the western world; every two minutes someone is diagnosed with cancer in the UK. Personalized treatment of cancer, which simply means selecting a treatment best suited to an individual involving the integration and translation of several new technologies in clinical care of patients. Conventional cancer treatments include surgery, radiotherapy and chemotherapy. Among these, therapeutically treatment requires optimal control of radiation/drug to minimize toxic effect and in turn to minimize side effect. We propose a hybrid prediction model consist of avascular tumour growth model from a tumour image and intelligent drug scheduling schema for drug penetration. Our main aim is to develop an intelligent decision support system which helps to analyze the tumour microenvironment constraints like cell-cell adhesion, cell movement, extra-cellular matrix (ECM) and optimal solutions of drug scheduling problem. Hypoxia and drug resistance are also incorporated in the model to achieve the predictive results for every patient as both of them considered as the main reason for chemotherapy and radiotherapy treatment failure. Finally, our goal is to provide a dynamic and effective personalized cancer treatment model to support the oncologist for making right decisions to the right patient at the right time.

The potential for the use of in-silico models of disease in progression monitoring is becoming increasingly recognised, as well as its contribution to the development of complete curative processes. In this paper we report the development of a hybrid cellular automaton model to mimic the growth of avascular tumours, including the infusion of a bioreductive drug to study the effects of protein binding on drug transportation. The growth model is operated within an extracellular tumour microenvironment. An artificial Neural Network based scheme was implemented that modelled the behaviours of each cell (proliferation, quiescence, apoptosis and/or movement) based on the complex heterogeneous microenvironment; consisting of oxygen, glucose, hydrogen ions, inhibitory factors and growth factors. To validate the growth model results, we conducted experiments with multicellular tumour spheroids. These results showed good agreement with the predicted growth dynamics. The outcome of the avascular tumour growth model suggested that tumour microenvironments have a strong impact on cell behaviour. To address the problem of cellular proteins acting as resistive factors preventing efficient drug penetration, a bioreactive drug (tirapazamine) was added to the system. This allowed us to study the drug penetration through multicellular layers of tissue after its binding to cellular proteins. The results of the in vitro model suggested that the proteins reduce the toxicity of the drug, reducing its efficacy for the most severely hypoxic fractions furthest from a functional blood vessel. Finally this research provides a unique comparison of in vitro tumour growth with an intelligent in silico model to measure bioreductive drug availability inside tumour tissue through a set of experiments.