A hidden Markov model for detecting confinement in single particle tracking trajectories

State-of-the-art single-particle tracking (SPT) techniques can generate long trajectories with high temporal and spatial resolution. This offers the possibility of mechanistically interpreting particle movements and behavior in membranes. To this end, a number of statistical techniques have been developed that partition SPT trajectories into states with distinct diffusion signatures, allowing a statistical analysis of diffusion state dynamics and switching behavior. Here, we develop a confinement model, within a hidden Markov framework, that switches between phases of free diffusion and confinement in a harmonic potential well. By using a Markov chain Monte Carlo algorithm to fit this model, automated partitioning of individual SPT trajectories into these two phases is achieved, which allows us to analyze confinement events. We demonstrate the utility of this algorithm on a previously published interferometric scattering microscopy data set, in which gold-nanoparticle-tagged ganglioside GM1 lipids were tracked in model membranes. We performed a comprehensive analysis of confinement events, demonstrating that there is heterogeneity in the lifetime, shape, and size of events, with confinement size and shape being highly conserved within trajectories. Our observations suggest that heterogeneity in confinement events is caused by both individual nanoparticle characteristics and the binding-site environment. The individual nanoparticle heterogeneity ultimately limits the ability of interferometric scattering microscopy to resolve molecule dynamics to the order of the tag size; homogeneous tags could potentially allow the resolution to be taken below this limit by deconvolution methods. In a wider context, the presented harmonic potential well confinement model has the potential to detect and characterize a wide variety of biological phenomena, such as hop diffusion, receptor clustering, and lipid rafts

An exploration of parental narratives in the context of a child's diagnosis of autism spectrum disorder

Section A presents a critical review of current literature regarding the parental experiences of receiving, and making sense of, a diagnosis of an Autism Spectrum Disorder (ASD) for their child where there is no learning disability present. It begins with an overview of the current debates in the diagnosis of ASD and considers how parents make sense of a diagnosis. The review then evaluates papers pertaining to parents' experiences of receiving a diagnosis for their child, their experience of living with ASD and the efficacy of post-diagnostic psychoeducation interventions. A consideration of the clinical and research implications of these findings concludes the section. Section B provides the findings of a narrative study examining the development of parental narratives following the diagnosis of their child with high functioning autism or Asperger’s syndrome. Seven parents who were participating in a post-diagnostic psychoeducation group were interviewed across two time points. The findings highlight the parents’ development of a new framework enabling the creation of an alternative personal and family narrative. The implications this has on parental well-being are discussed and recommendations are made for future research to build on these initial findings. Section C provides a critical appraisal and reflective account of the study presented in section B. This includes consideration of development of research skills, limitations of the study, clinical implications, and future research ideas

Combined Diffusion-Relaxometry MRI to Identify Dysfunction in the Human Placenta

Purpose: A combined diffusion-relaxometry MR acquisition and analysis pipeline for in-vivo human placenta, which allows for exploration of coupling between T2* and apparent diffusion coefficient (ADC) measurements in a sub 10 minute scan time. Methods: We present a novel acquisition combining a diffusion prepared spin-echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in-vivo, including both healthy controls and participants with various pregnancy complications. We estimate the joint T2*-ADC spectra using an inverse Laplace transform. Results: T2*-ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas. Conclusions: Combined T2*-diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T2*-ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs

Molecular and Cellular Biology Animations: Development and Impact on Student Learning

Educators often struggle when teaching cellular and molecular processes because typically they have only two-dimensional tools to teach something that plays out in four dimensions. Learning research has demonstrated that visualizing processes in three dimensions aids learning, and animations are effective visualization tools for novice learners and aid with long-term memory retention. The World Wide Web Instructional Committee at North Dakota State University has used these research results as an inspiration to develop a suite of high-quality animations of molecular and cellular processes. Currently, these animations represent transcription, translation, bacterial gene expression, messenger RNA (mRNA) processing, mRNA splicing, protein transport into an organelle, the electron transport chain, and the use of a biological gradient to drive adenosine triphosphate synthesis. These animations are integrated with an educational module that consists of First Look and Advanced Look components that feature captioned stills from the animation representing the key steps in the processes at varying levels of complexity. These animation-based educational modules are available via the World Wide Web at http://vcell.ndsu.edu/animations. An in-class research experiment demonstrated that student retention of content material was significantly better when students received a lecture coupled with the animations and then used the animation as an individual study activity

Moving Toward Social Justice Through Sport: An Exploration into the Ability of Intercollegiate Coaches of Women’s Teams to Effect Social Change

At the present time, a great number of American schools and certainly the majority of American sport teams are not taking on the responsibility of teaching young women and men the value of cooperation, democratic citizenship, and critical thinking. Because of this, critical educators have begun advocating for a movement in the education system called critical pedagogy, in which it has been theorized that schools can become sites for social transformation and emancipation (McLaren, 2003). There is a similar movement in physical education teacher education programs (Fernandez-Balboa, 1997; Cushion, Armour, & Jones, 2003), but no such actions have been taken in the sport arena. As a result of this dearth, this paper puts forth a model of athletic praxis which promotes social transformation through sport. The model for athletic praxis is based on the data from an empirical study that was designed to explore the spaces and perceived barriers identified by intercollegiate coaches of women’s teams when it comes to the issue of addressing social difference and justice with their athletes. The data was subsequently infused with a model called cultural studies as praxis (Wright, 2002) that currently exists in the education field. Athletic praxis consists of three components: theoretical preparation, service learning for social justice, and structured reflection. It is argued in this dissertation that, by incorporating the components of athletic praxis into the sport setting, female athletes could develop a heightened sense of civic responsibility during their collegiate career. In this way, sport has the ability to play a key role in an individual’s education toward democratic citizenship. Integrating these principles into sport could result in large groups of young women who feel a sense of responsibility to their surrounding community and who see themselves as potential agents of social change. As such, the athletic arena could become another means of working toward social justice in our society

Symposium abstracts: Thresholds and permeability in performance

Publisher PD

Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection

This paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. Current approaches in experimental design focus on model-parameter estimation and require specification of a particular model, whereas in imaging, other tasks may drive the design. Furthermore, such approaches often lead to intractable optimization problems in real-world imaging applications. Here we present a new paradigm for experimental design that simultaneously optimizes the design (set of image channels) and trains a machine-learning model to execute a user-specified image-analysis task. The approach obtains data densely-sampled over the measurement space (many image channels) for a small number of acquisitions, then identifies a subset of channels of prespecified size that best supports the task. We propose a method: TADRED for TAsk-DRiven Experimental Design in imaging, to identify the most informative channel-subset whilst simultaneously training a network to execute the task given the subset. Experiments demonstrate the potential of TADRED in diverse imaging applications: several clinically-relevant tasks in magnetic resonance imaging; and remote sensing and physiological applications of hyperspectral imaging. Results show substantial improvement over classical experimental design, two recent application-specific methods within the new paradigm, and state-of-the-art approaches in supervised feature selection. We anticipate further applications of our approach. Code is available: Code Link

Mechanistic investigation of developmental copper chemotherapeutics

The quest for new metal-based anticancer agents, alternative to clinically established chemotherapeutics, has been motivated by deficiencies observed in current treatment regimes. Coupled with the approach of sophisticated and targeted drug design, there is a clear need for comprehending the underlying biomolecular and cellular responses of new developmental therapeutics. Reported herein is a detailed analysis of redox active developmental metallodrugs containing 1,10-phenanthroline (Phen) ligands and their action as novel cytotoxins of human cancers. This body of research describes mechanistic investigations into the oxidative nuclease activity and redox-targeting properties of new Cu(II) and Mn(II) phenanthroline chemo-types. A number of the Cu(II) complexes have been developed and examined, in collaboration with the National Cancer Institute, USA, for their ability to induce cytotoxicity within a wide variety of cancer cells. To uncover these properties, a range of molecular biology and biophysical techniques were employed including, flow cytometry, confocal microscopy, electrophoresis, and immunohistochemistry. Replacing auxiliary 1,10-phenanthroline with phenazine-type (N,Nʹ) ligands in mononuclear systems, [Cu(N,Nʹ)(Phen)]2+ , was found to enhance intercalation and oxidative DNA scission in vitro. Alternatively, incorporation of dicarboxylates (O,Oʹ) has shown to increase redox potential and stability, thereby targeting both mitochondrial and genomic DNA in human ovarian cancer cells, SKOV3. Increased nuclearity and varying rigidity was explored in dinuclear chemo-types ([Cu2(O,Oʹ)(Phen) 4 ]2+) through the addition of aliphatic and aromatic bridging dicarboxylate ligands. In combination with NCI-60 analysis, the dinulcear complexes were shown to enhance both geno- and cyto-toxic effects when compared to the mononuclear analogue, leading to an apoptotic mode of cellular death; activated through intrinsic mitochondrial machinery. Finally, exchange of the metal centre in the form of di-manganese(II) complex significantly influenced the mode of programmed cell death, activating autophagic catabolism and self-digestion

Generalised Super Resolution for Quantitative MRI Using Self-supervised Mixture of Experts

Multi-modal and multi-contrast imaging datasets have diverse voxel-wise intensities. For example, quantitative MRI acquisition protocols are designed specifically to yield multiple images with widely-varying contrast that inform models relating MR signals to tissue characteristics. The large variance across images in such data prevents the use of standard normalisation techniques, making super resolution highly challenging. We propose a novel self-supervised mixture-of-experts (SS-MoE) paradigm for deep neural networks, and hence present a method enabling improved super resolution of data where image intensities are diverse and have large variance. Unlike the conventional MoE that automatically aggregates expert results for each input, we explicitly assign an input to the corresponding expert based on the predictive pseudo error labels in a self-supervised fashion. A new gater module is trained to discriminate the error levels of inputs estimated by Multiscale Quantile Segmentation. We show that our new paradigm reduces the error and improves the robustness when super resolving combined diffusion-relaxometry MRI data from the Super MUDI dataset. Our approach is suitable for a wide range of quantitative MRI techniques, and multi-contrast or multi-modal imaging techniques in general. It could be applied to super resolve images with inadequate resolution, or reduce the scanning time needed to acquire images of the required resolution. The source code and the trained models are available at https://github.com/hongxiangharry/SS-MoE

Generalised Hierarchical Bayesian Microstructure Modelling for Diffusion MRI

Microstructure imaging combines tailored diffusion MRI acquisition protocols with a mathematical model to give insights into subvoxel tissue features. The model is typically fit voxel-by-voxel to the MRI image with least squares minimisation to give voxelwise maps of parameters relating to microstructural features, such as diffusivities and tissue compartment fractions. However, this fitting approach is susceptible to voxelwise noise, which can lead to erroneous values in parameter maps. Data-driven Bayesian hierarchical modelling defines prior distributions on parameters and learns them from the data, and can hence reduce such noise effects. Bayesian hierarchical modelling has been demonstrated for microstructure imaging with diffusion MRI, but only for a few, relatively simple, models. In this paper, we generalise hierarchical Bayesian modelling to a wide range of multi-compartment microstructural models, and fit the models with a Markov chain Monte Carlo (MCMC) algorithm. We implement our method by utilising Dmipy, a microstructure modelling software package for diffusion MRI data. Our code is available at github.com/PaddySlator/dmipy-bayesian