Science Classroom Inquiry (SCI) Simulations: A Novel Method to Scaffold Science Learning

Science education is progressively more focused on employing inquiry-based learning methods in the classroom and increasing scientific literacy among students. However, due to time and resource constraints, many classroom science activities and laboratory experiments focus on simple inquiry, with a step-by-step approach to reach predetermined outcomes. The science classroom inquiry (SCI) simulations were designed to give students real life, authentic science experiences within the confines of a typical classroom. The SCI simulations allow students to engage with a science problem in a meaningful, inquiry-based manner. Three discrete SCI simulations were created as website applications for use with middle school and high school students. For each simulation, students were tasked with solving a scientific problem through investigation and hypothesis testing. After completion of the simulation, 67% of students reported a change in how they perceived authentic science practices, specifically related to the complex and dynamic nature of scientific research and how scientists approach problems. Moreover, 80% of the students who did not report a change in how they viewed the practice of science indicated that the simulation confirmed or strengthened their prior understanding. Additionally, we found a statistically significant positive correlation between students’ self-reported changes in understanding of authentic science practices and the degree to which each simulation benefitted learning. Since SCI simulations were effective in promoting both student learning and student understanding of authentic science practices with both middle and high school students, we propose that SCI simulations are a valuable and versatile technology that can be used to educate and inspire a wide range of science students on the real-world complexities inherent in scientific study.     

A Reliable Parameter to Standardize the Scoring of Stem Cell Spheres

Sphere formation assay is widely used in selection and enrichment of normal stem cells or cancer stem cells (CSCs), also known as tumor initiating cells (TICs), based on their ability to grow in serum-free suspension culture for clonal proliferation. However, there is no standardized parameter to accurately score the spheres, which should be reflected by both the number and size of the spheres. Here we define a novel parameter, designated as Standardized Sphere Score (SSS), which is expressed by the total volume of selected spheres divided by the number of cells initially plated. SSS was validated in quantification of both tumor spheres from cancer cell lines and embryonic bodies (EB) from mouse embryonic stem cells with high sensitivity and reproducibility.     

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.     

Quasi-Monopolar Stimulation: A Novel Electrode Design Configuration for Performance Optimization of a Retinal Neuroprosthesis

In retinal neuroprostheses, spatial interaction between electric fields from various electrodes – electric crosstalk – may occur in multielectrode arrays during simultaneous stimulation of the retina. Depending on the electrode design and placement, this crosstalk can either enhance or degrade the functional characteristics of a visual prosthesis. To optimize the device performance, a balance must be satisfied between the constructive interference of crosstalk on dynamic range and power consumption and its negative effect on artificial visual acuity. In the present computational modeling study, we have examined the trade-off in these positive and negative effects using a range of currently available electrode array configurations, compared to a recently proposed stimulation strategy – the quasi monopolar (QMP) configuration – in which the return current is shared between local bipolar guards and a distant monopolar electrode. We evaluate the performance of the QMP configuration with respect to the implantation site and electrode geometry parameters. Our simulation results demonstrate that the beneficial effects of QMP are only significant at electrode-to-cell distances greater than the electrode dimensions. Possessing a relatively lower activation threshold, QMP was found to be superior to the bipolar configuration in terms of providing a relatively higher visual acuity. However, the threshold for QMP was more sensitive to the topological location of the electrode in the array, which may need to be considered when programming the manner in which electrode are simultaneously activated. This drawback can be offset with a wider dynamic range and lower power consumption of QMP. Furthermore, the ratio of monopolar return current to total return can be used to adjust the functional performance of QMP for a given implantation site and electrode parameters. We conclude that the QMP configuration can be used to improve visual information-to-stimulation mapping in a visual prosthesis, while maintaining low power consumption.     

A Meaningful Attempt: Applying Dielectric Barrier Discharge Plasma to Induce Polarization of Macrophages

Macrophage polarization plays an important role in many macrophage-related diseases. This study was designed to preliminarily explore the effects of dielectric barrier discharge (DBD) plasma on the polarization direction and cell activity of macrophages with different phenotypes (ie, M0, M1, and M2). The M1 macrophage marker inducible nitric oxide synthase (iNOS) and M2 macrophage marker cluster of differentiation 206 (CD206) were detected by western blot (WB). The effects of DBD plasma on macrophage viability were analyzed by using a cell counting kit-8 detection kit. M0, M1, and M2 macrophages exhibited a decrease in iNOS expression and an increase in CD206 expression after the DBD plasma intervention. Additionally, the decrease in macrophage viability remained non-significant after initiating the intervention. DBD plasma can promote the transformation of M0 and M1 macrophages to M2 macrophages, and can further enhance the expression of the M2 macrophage phenotype marker CD206. Our study not only demonstrates the potential therapeutic value of DBD plasma for macrophage-related diseases, but it also provides a new direction for research to improve the treatment of macrophage-related diseases. © 2023 Bioelectromagnetics Society.     

Dynamics of Dual Prism Adaptation: Relating Novel Experimental Results to a Minimalistic Neural Model

In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo–motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements (‘dual–adaptation’). A fundamental question is how the adaptation schedule determines the acquisition speed of new skills. Given a fixed number of movements in two different environments, will dual–adaptation be faster if switches (‘phase changes’) between the environments occur more frequently? We investigated the dynamics of dual–adaptation under different training schedules in a virtual pointing experiment. Surprisingly, we found that acquisition speed of dual visuo–motor mappings in a pointing task is largely independent of the number of phase changes. Next, we studied the neuronal mechanisms underlying this result and other key phenomena of dual–adaptation by relating model simulations to experimental data. We propose a simple and yet biologically plausible neural model consisting of a spatial mapping from an input layer to a pointing angle which is subjected to a global gain modulation. Adaptation is performed by reinforcement learning on the model parameters. Despite its simplicity, the model provides a unifying account for a broad range of experimental data: It quantitatively reproduced the learning rates in dual–adaptation experiments for both direct effect, i.e. adaptation to prisms, and aftereffect, i.e. behavior after removal of prisms, and their independence on the number of phase changes. Several other phenomena, e.g. initial pointing errors that are far smaller than the induced optical shift, were also captured. Moreover, the underlying mechanisms, a local adaptation of a spatial mapping and a global adaptation of a gain factor, explained asymmetric spatial transfer and generalization of prism adaptation, as observed in other experiments.     

Sum of High-Risk Gene Mutation (SHGM): A Novel Attempt to Assist Differential Diagnosis for Adrenocortical Carcinoma with Benign Adenoma,Based on Detection of Mutations of Nine Target Genes

There has been no research on applying gene detection to differential diagnosis of adrenocortical carcinoma (ACC). We attempted to explore a novel auxiliary method for differential diagnosis between ACC with benign adrenocortical adenoma (ACA), based on mutations of target genes in tissues. Nine genes were chosen as target genes, including TP53, CTNNB1, ARMC5, PRKAR1A, ZNRF3, RB1, APC, MEN1, and RPL22. Exons sequencing of target genes were performed in 98 cases of tissue samples by FastTarget technology, including 41 ACC tissues, 32 ACA tissues, and 25 normal adrenal gland tissues. Significant mutations were detected and identified, and the clinical information was collected, for further comparative analysis and application to assist differential diagnosis of ACC. We identified 132 significant gene mutations and 227 significant mutation sites in 37 ACC tissues, much more than ACA and normal adrenal gland tissues. Mutation rates of 6 genes in ACC tissues were obviously higher than ACA tissues, including ZNRF3, ARMC5, TP53, APC, RB1, and PRKAR1A, regarded as high-risk genes. The sum of mutated high-risk genes detected in each sample was denominated sum of high-risk gene mutation (SHGM), and the rates of SHGM?>?0 and SHGM?>?1 in ACC tissues were 73.0% and 62.2%, respectively, both obviously higher than those in ACA tissues, with significant statistic differences. Especially for 8 cases of ACC with diameter?<?5 cm, SHGM?>?0 and SHGM?>?1 were found in 6 samples (75%) and 4 samples (50%), respectively. However, no relevance was found between SHGM and clinical characteristics of ACC. We identified 6 high-risk genes in ACC tissues, with significantly higher mutation rates than ACA or normal adrenal gland tissues. The sum of mutated high-risk genes detected in ACC tissues was denominated SHGM, which was potential to assist the differential diagnosis of ACC with ACA, especially for the small-size ACC.

     

From Molecular to Process Simulation: Novel Approaches to the Prediction of Phase Equilibria and PVT Behavior Based on Molecular/Quantum Mechanics and Molecular Dynamics Simulations

Abstract

Actually, in modern process simulators, more than 75% of the code implemented is dedicated to physical properties estimation, calculation and predictions. Data banks storing pure component parameters and binary interaction parameters for phase equilibrium calculations are extensively used and continuously implemented in actual process simulators. This gives an idea of the important role physical properties availability plays in process simulation.

In this paper we propose a new way for coupling molecular and process simulation. The basic machinery is to resort to molecular/quantum mechanics and molecular dynamics simulation techniques for generating the parameters of some equations of state that will subsequently be used for the prediction of phase equilibria and PVT behavior of small and polymeric molecules as well. This information, in turn, will be used as input in the process simulator, thus creating a final and well-defined bridge between molecular and process simulations in chemical engineering.     

Multi-Action Planning for Threat Management: A Novel Approach for the Spatial Prioritization of Conservation Actions

Planning for the remediation of multiple threats is crucial to ensure the long term persistence of biodiversity. Limited conservation budgets require prioritizing which management actions to implement and where. Systematic conservation planning traditionally assumes that all the threats in priority sites are abated (fixed prioritization approach). However, abating only the threats affecting the species of conservation concerns may be more cost-effective. This requires prioritizing individual actions independently within the same site (independent prioritization approach), which has received limited attention so far. We developed an action prioritization algorithm that prioritizes multiple alternative actions within the same site. We used simulated annealing to find the combination of actions that remediate threats to species at the minimum cost. Our algorithm also accounts for the importance of selecting actions in sites connected through the river network (i.e., connectivity). We applied our algorithm to prioritize actions to address threats to freshwater fish species in the Mitchell River catchment, northern Australia. We compared how the efficiency of the independent and fixed prioritization approach varied as the importance of connectivity increased. Our independent prioritization approach delivered more efficient solutions than the fixed prioritization approach, particularly when the importance of achieving connectivity was high. By spatially prioritizing the specific actions necessary to remediate the threats affecting the target species, our approach can aid cost-effective habitat restoration and land-use planning. It is also particularly suited to solving resource allocation problems, where consideration of spatial design is important, such as prioritizing conservation efforts for highly mobile species, species facing climate change-driven range shifts, or minimizing the risk of threats spreading across different realms.     

A Method to Predict Blood-Brain Barrier Permeability of Drug-Like Compounds Using Molecular Dynamics Simulations

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (P_eff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R² = 0.94) and logPS (R² = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.     

A novel method using CFD to optimize the inner structure parameters of flat photobioreactors

The relationship between growth rates of algae and structure parameters of closed flat-panel photobioreactors was investigated. Ad/Ar (cross-section area of the downcomer/cross-section area of the riser), h₀ (clearance from the upper edge of the baffles to the water level), and h₁ (clearance from the lower edge of the baffles to the bottom of the reactor) were selected as the inner structure parameters. CFD (Computational Fluid Dynamics) was used to simulate the hydrodynamic parameter (TKE)_d (turbulence kinetic energy of the downcomers) and the secondary parameters ε (ratio between td and tc), tc (cycle time), and DZ (dead zones), which were deduced from the hydrodynamic parameters mentioned above. The effects of (TKE)_d, ε, tc, DZ, and the inner structure parameters on cell growth of Isochrysis galbana 3011 were analyzed using data collected in 15-L airlift flat-panel photobioreactors. A model was developed to predict algae cell growth based on these inner structure parameters, thereby providing a new method for photobioreactor optimization. Validity of the model was confirmed by experimental data of I. galbana 3011 cultured in 15-L and 300-L photobioreactors, respectively. Finally, the prospect of applying CFD to photobioreactor optimization was discussed.     

Using Molecular Dynamics Simulations on Crambin to Evaluate the Suitability of Different Continuum Dielectric and Hydrogen Atom Models for Protein Simulations

Abstract

Molecular dynamics simulations of enzymes with enough explicit waters of solvation to realistically account for solute-solvent interactions can burden the computational resources required to perform the simulation by more than two orders of magnitude. Since enzyme simulations even with an implicit solvation model can be imposing for a supercomputer, it is important to assess the suitability of different continuum dielectric models for protein simulations. A series of 100-picosecond molecular dynamics simulations were performed on the X-ray crystal structure of the protein crambin to examine how well computed structures, obtained using seven continuum dielectric and two hydrogen atom models, agreed with the X-ray structure. The best level of agreement between computed and experimental structures was obtained using a constant dielectric of 2 and the all-hydrogen model. Continuum dielectric models of 1,1^*r, and 2^*r also led to computed structures in reasonably good agreement with the X-ray structure. In all cases, the all-hydrogen model gave better agreement than the united atom model, although, in one case, the difference was not significant. Dielectric models of 4, 80, and 4^*r with either hydrogen model yielded significantly poorer fits. It is especially noteworthy that the observed trends did not semiquantitatively converge until about 50 picoseconds into the simulations, suggesting that validation studies for protein calculations based on energy minimizations or short simulations should be viewed with caution.     

A Method to Predict Blood-Brain Barrier Permeability of Drug-Like Compounds Using Molecular Dynamics Simulations

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (P_eff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R² = 0.94) and logPS (R² = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.     

Genomic and Enzymatic Results Show Bacillus cellulosilyticus Uses a Novel Set of LPXTA Carbohydrases to Hydrolyze Polysaccharides

Background

Alkaliphilic Bacillus species are intrinsically interesting due to the bioenergetic problems posed by growth at high pH and high salt. Three alkaline cellulases have been cloned, sequenced and expressed from Bacillus cellulosilyticus N-4 (Bcell) making it an excellent target for genomic sequencing and mining of biomass-degrading enzymes.

Methodology/Principal Findings

The genome of Bcell is a single chromosome of 4.7 Mb with no plasmids present and three large phage insertions. The most unusual feature of the genome is the presence of 23 LPXTA membrane anchor proteins; 17 of these are annotated as involved in polysaccharide degradation. These two values are significantly higher than seen in any other Bacillus species. This high number of membrane anchor proteins is seen only in pathogenic Gram-positive organisms such as Listeria monocytogenes or Staphylococcus aureus. Bcell also possesses four sortase D subfamily 4 enzymes that incorporate LPXTA-bearing proteins into the cell wall; three of these are closely related to each other and unique to Bcell. Cell fractionation and enzymatic assay of Bcell cultures show that the majority of polysaccharide degradation is associated with the cell wall LPXTA-enzymes, an unusual feature in Gram-positive aerobes. Genomic analysis and growth studies both strongly argue against Bcell being a truly cellulolytic organism, in spite of its name. Preliminary results suggest that fungal mycelia may be the natural substrate for this organism.

Conclusions/Significance

Bacillus cellulosilyticus N-4, in spite of its name, does not possess any of the genes necessary for crystalline cellulose degradation, demonstrating the risk of classifying microorganisms without the benefit of genomic analysis. Bcell is the first Gram-positive aerobic organism shown to use predominantly cell-bound, non-cellulosomal enzymes for polysaccharide degradation. The LPXTA-sortase system utilized by Bcell may have applications both in anchoring cellulases and other biomass-degrading enzymes to Bcell itself and in anchoring proteins other Gram-positive organisms.     

A Novel Brief Therapy for Patients Who Attempt Suicide: A 24-months Follow-Up Randomized Controlled Study of the Attempted Suicide Short Intervention Program (ASSIP)

Background

Attempted suicide is the main risk factor for suicide and repeated suicide attempts. However, the evidence for follow-up treatments reducing suicidal behavior in these patients is limited. The objective of the present study was to evaluate the efficacy of the Attempted Suicide Short Intervention Program (ASSIP) in reducing suicidal behavior. ASSIP is a novel brief therapy based on a patient-centered model of suicidal behavior, with an emphasis on early therapeutic alliance.

Methods and Findings

Patients who had recently attempted suicide were randomly allocated to treatment as usual (n = 60) or treatment as usual plus ASSIP (n = 60). ASSIP participants received three therapy sessions followed by regular contact through personalized letters over 24 months. Participants considered to be at high risk of suicide were included, 63% were diagnosed with an affective disorder, and 50% had a history of prior suicide attempts. Clinical exclusion criteria were habitual self-harm, serious cognitive impairment, and psychotic disorder. Study participants completed a set of psychosocial and clinical questionnaires every 6 months over a 24-month follow-up period.The study represents a real-world clinical setting at an outpatient clinic of a university hospital of psychiatry. The primary outcome measure was repeat suicide attempts during the 24-month follow-up period. Secondary outcome measures were suicidal ideation, depression, and health-care utilization. Furthermore, effects of prior suicide attempts, depression at baseline, diagnosis, and therapeutic alliance on outcome were investigated.During the 24-month follow-up period, five repeat suicide attempts were recorded in the ASSIP group and 41 attempts in the control group. The rates of participants reattempting suicide at least once were 8.3% (n = 5) and 26.7% (n = 16). ASSIP was associated with an approximately 80% reduced risk of participants making at least one repeat suicide attempt (Wald χ² ₁ = 13.1, 95% CI 12.4–13.7, p < 0.001). ASSIP participants spent 72% fewer days in the hospital during follow-up (ASSIP: 29 d; control group: 105 d; W = 94.5, p = 0.038). Higher scores of patient-rated therapeutic alliance in the ASSIP group were associated with a lower rate of repeat suicide attempts. Prior suicide attempts, depression, and a diagnosis of personality disorder at baseline did not significantly affect outcome. Participants with a diagnosis of borderline personality disorder (n = 20) had more previous suicide attempts and a higher number of reattempts.Key study limitations were missing data and dropout rates. Although both were generally low, they increased during follow-up. At 24 months, the group difference in dropout rate was significant: ASSIP, 7% (n = 4); control, 22% (n = 13). A further limitation is that we do not have detailed information of the co-active follow-up treatment apart from participant self-reports every 6 months on the setting and the duration of the co-active treatment.

Conclusions

ASSIP, a manual-based brief therapy for patients who have recently attempted suicide, administered in addition to the usual clinical treatment, was efficacious in reducing suicidal behavior in a real-world clinical setting. ASSIP fulfills the need for an easy-to-administer low-cost intervention. Large pragmatic trials will be needed to conclusively establish the efficacy of ASSIP and replicate our findings in other clinical settings.

Trial registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT02505373","term_id":"NCT02505373"}}NCT02505373     

The Effect of Chemical Information on the Spatial Distribution of Fruit Flies: I Model Results

Animal aggregation is a general phenomenon in ecological systems. Aggregations are generally considered as an evolutionary advantageous state in which members derive the benefits of protection and mate choice, balanced by the costs of limiting resources and competition. In insects, chemical information conveyance plays an important role in finding conspecifics and forming aggregations. In this study, we describe a spatio-temporal simulation model designed to explore and quantify the effects of these infochemicals, i.e., food odors and an aggregation pheromone, on the spatial distribution of a fruit fly (Drosophila melanogaster) population, where the lower and upper limit of local population size are controlled by an Allee effect and competition. We found that during the spatial expansion and strong growth of the population, the use of infochemicals had a positive effect on population size. The positive effects of reduced mortality at low population numbers outweighed the negative effects of increased mortality due to competition. At low resource densities, attraction toward infochemicals also had a positive effect on population size during recolonization of an area after a local population crash, by decreasing the mortality due to the Allee effect. However, when the whole area was colonized and the population was large, the negative effects of competition on population size were larger than the positive effects of the reduction in mortality due to the Allee effect. The use of infochemicals thus has mainly positive effects on population size and population persistence when the population is small and during the colonization of an area. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.     

Novel Microbiological and Spatial Statistical Methods to Improve Strength of Epidemiological Evidence in a Community-Wide Waterborne Outbreak

Failures in the drinking water distribution system cause gastrointestinal outbreaks with multiple pathogens. A water distribution pipe breakage caused a community-wide waterborne outbreak in Vuorela, Finland, July 2012. We investigated this outbreak with advanced epidemiological and microbiological methods. A total of 473/2931 inhabitants (16%) responded to a web-based questionnaire. Water and patient samples were subjected to analysis of multiple microbial targets, molecular typing and microbial community analysis. Spatial analysis on the water distribution network was done and we applied a spatial logistic regression model. The course of the illness was mild. Drinking untreated tap water from the defined outbreak area was significantly associated with illness (RR 5.6, 95% CI 1.9–16.4) increasing in a dose response manner. The closer a person lived to the water distribution breakage point, the higher the risk of becoming ill. Sapovirus, enterovirus, single Campylobacter jejuni and EHEC O157:H7 findings as well as virulence genes for EPEC, EAEC and EHEC pathogroups were detected by molecular or culture methods from the faecal samples of the patients. EPEC, EAEC and EHEC virulence genes and faecal indicator bacteria were also detected in water samples. Microbial community sequencing of contaminated tap water revealed abundance of Arcobacter species. The polyphasic approach improved the understanding of the source of the infections, and aided to define the extent and magnitude of this outbreak.     

A Multiscale Approach to the Migration of Cancer Stem Cells: Mathematical Modelling and Simulations

We propose a multiscale model for the invasion of the extracellular matrix by two types of cancer cells, the differentiated cancer cells and the cancer stem cells. We investigate the epithelial mesenchymal-like transition between them being driven primarily by the epidermal growth factors. We moreover take into account the transdifferentiation program of the cancer stem cells towards the cancer-associated fibroblast cells as well as the fibroblast-driven remodelling of the extracellular matrix. The proposed haptotaxis model combines the macroscopic phenomenon of the invasion of the extracellular matrix by both types of cancer cells with the microscopic dynamics of the epidermal growth factors. We analyse our model in a component-wise manner and compare our findings with the literature. We investigate pathological situations regarding the epidermal growth factors and accordingly propose “mathematical-treatment” scenarios to control the aggressiveness of the tumour.