Spatial proteomics and transcriptomics of the maternal-fetal interface in placenta accreta spectrum.

In severe Placenta Accreta Spectrum (PAS), trophoblasts gain deep access in the myometrium (placenta increta). This study investigated alterations at the fetal-maternal interface in PAS cases using a systems biology approach consisting of immunohistochemistry, spatial transcriptomics and proteomics. We identified spatial variation in the distribution of CD4, CD3 and CD8 T-cells at the maternal-interface in placenta increta cases.

PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images.

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal.

Radiomic study of antenatal prediction of severe placenta accreta spectrum from MRI.

Objectives: We previously demonstrated the potential of radiomics for the prediction of severe histological placenta accreta spectrum (PAS) subtypes using T2-weighted MRI. We aim to validate our model using an additional dataset. Secondly, we explore whether the performance is improved using a new approach to develop a new multivariate radiomics model.

Towards a unified approach for unsupervised brain MRI Motion Artefact Detection with few shot Anomaly Detection.

Automated Motion Artefact Detection (MAD) in Magnetic Resonance Imaging (MRI) is a field of study that aims to automatically flag motion artefacts in order to prevent the requirement for a repeat scan. In this paper, we identify and tackle the three current challenges in the field of automated MAD; (1) reliance on fully-supervised training, meaning they require specific examples of Motion Artefacts (MA), (2) inconsistent use of benchmark datasets across different works and use of private datasets for testing and training of newly proposed MAD techniques and (3) a lack of sufficiently large datasets for MRI MAD. To address these challenges, we demonstrate how MAs can be identified by formulating the problem as an unsupervised Anomaly Detection (AD) task.

Radiomics-based prediction of FIGO grade for placenta accreta spectrum.

Background: Placenta accreta spectrum (PAS) is a rare, life-threatening complication of pregnancy. Predicting PAS severity is critical to individualise care planning for the birth. We aim to explore whether radiomic analysis of T2-weighted magnetic resonance imaging (MRI) can predict severe cases by distinguishing between histopathological subtypes antenatally.

Bone segmentation in contrast enhanced whole-body computed tomography.

Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging. In contrast enhanced whole-body scans accurate automatic segmentation is particularly difficult as low dose whole body protocols reduce image quality and make contrast enhanced regions more difficult to separate when relying on differences in pixel intensities. This paper outlines a U-net architecture with novel preprocessing techniques, based on the windowing of training data and the modification of sigmoid activation threshold selection to successfully segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans.

Enterprise imaging and big data: A review from a medical physics perspective.

In recent years enterprise imaging (EI) solutions have become a core component of healthcare initiatives, while a simultaneous rise in big data has opened up a number of possibilities in how we can analyze and derive insights from large amounts of medical data. Together they afford us a range of opportunities that can transform healthcare in many fields. This paper provides a review of recent developments in EI and big data in the context of medical physics.

Anatomically accurate model of EMG during index finger flexion and abduction derived from diffusion tensor imaging.

This study presents a modelling framework in which information on muscle fiber direction and orientation during contraction is derived from diffusion tensor imaging (DTI) and incorporated in a computational model of the surface electromyographic (EMG) signal. The proposed model makes use of the principle of reciprocity to simultaneously calculate the electric potentials produced at the recording electrode by charges distributed along an arbitrary number of muscle fibers within the muscle, allowing for a computationally efficient evaluation of extracellular motor unit action potentials. The approach is applied to the complex architecture of the first dorsal interosseous (FDI) muscle of the hand to simulate EMG during index finger flexion and abduction.

Integrative Approach for a Transformative Freshman-Level STEM Curriculum.

In 2014 Wesley College adopted a unified undergraduate program of evidence-based high-impact teaching practices. Through foundation and federal and state grant support, the college completely revised its academic core curriculum and strengthened its academic support structures by including a comprehensive early alert system for at-risk students. In this core, science, technology, engineering, and mathematics (STEM) faculty developed fresh manifestations of integrated concept-based introductory courses and revised upper-division STEM courses around student-centered learning.

Short Synthetic Terminators for Improved Heterologous Gene Expression in Yeast.

Terminators play an important role both in completing the transcription process and impacting mRNA half-life. As such, terminators are an important synthetic component considered in applications such as heterologous gene expression and metabolic engineering. Here, we describe a panel of short (35-70 bp) synthetic terminators that can be used for modulating gene expression in yeast.

Design of synthetic yeast promoters via tuning of nucleosome architecture.

Model-based design of biological parts is a critical goal of synthetic biology, especially for eukaryotes. Here we demonstrate that nucleosome architecture can have a role in defining yeast promoter activity and utilize a computationally-guided approach that can enable both the redesign of endogenous promoter sequences and the de novo design of synthetic promoters. Initially, we use our approach to reprogram native promoters for increased expression and evaluate their performance in various genetic contexts.

A condition-specific codon optimization approach for improved heterologous gene expression in Saccharomyces cerevisiae.

Background: Heterologous gene expression is an important tool for synthetic biology that enables metabolic engineering and the production of non-natural biologics in a variety of host organisms. The translational efficiency of heterologous genes can often be improved by optimizing synonymous codon usage to better match the host organism. However, traditional approaches for optimization neglect to take into account many factors known to influence synonymous codon distributions.

Use of expression-enhancing terminators in Saccharomyces cerevisiae to increase mRNA half-life and improve gene expression control for metabolic engineering applications.

Control of gene and protein expression of both endogenous and heterologous genes is a key component of metabolic engineering. While a large amount of work has been published characterizing promoters for this purpose, less effort has been exerted to elucidate the role of terminators in yeast. In this study, we characterize over 30 terminators for use in metabolic engineering applications in Saccharomyces cerevisiae and determine mRNA half-life changes to be the major cause of the varied protein and transcript expression level.

Metabolic engineering of muconic acid production in Saccharomyces cerevisiae.

The dicarboxylic acid muconic acid has garnered significant interest due to its potential use as a platform chemical for the production of several valuable consumer bio-plastics including nylon-6,6 and polyurethane (via an adipic acid intermediate) and polyethylene terephthalate (PET) (via a terephthalic acid intermediate). Many process advantages (including lower pH levels) support the production of this molecule in yeast. Here, we present the first heterologous production of muconic acid in the yeast Saccharomyces cerevisiae.

Characterization of plasmid burden and copy number in Saccharomyces cerevisiae for optimization of metabolic engineering applications.

Many metabolic engineering and genetic engineering applications in yeast rely on the use of plasmids. Despite their pervasive use and the diverse collections available, there is a fundamental lack of understanding of how commonly used DNA plasmids affect the cell's ability to grow and how the choice of plasmid components can influence plasmid load and burden. In this study, we characterized the major attributes of the 2 micron and centromeric plasmids typically used in yeast by examining the impact of choice of selection marker, promoter, origin of replication, and strain ploidy on conferred growth rates and plasmid copy number.

Semiautomatic region-of-interest validation at the femur in (18)F-fluoride PET/CT.

Unlabelled: The assessment of regional skeletal metabolism using (18)F-fluoride PET ((18)F-PET) requires segmentation of the tissue region of interest (ROI). The aim of this study was to validate a novel approach to define multiple ROIs at the proximal femur similar to those used in dual x-ray absorptiometry. Regions were first drawn on low-dose CT images acquired as a routine part of the PET/CT study and transferred to the (18)F-PET images for the quantitative analysis of bone turnover.

A method for improved clustering and classification of microscopy images using quantitative co-localization coefficients.

Background: The localization of proteins to specific subcellular structures in eukaryotic cells provides important information with respect to their function. Fluorescence microscopy approaches to determine localization distribution have proved to be an essential tool in the characterization of unknown proteins, and are now particularly pertinent as a result of the wide availability of fluorescently-tagged constructs and antibodies. However, there are currently very few image analysis options able to effectively discriminate proteins with apparently similar distributions in cells, despite this information being important for protein characterization.

Expanding the chemical palate of cells by combining systems biology and metabolic engineering.

The field of Metabolic Engineering has recently undergone a transformation that has led to a rapid expansion of the chemical palate of cells. Now, it is conceivable to produce nearly any organic molecule of interest using a cellular host. Significant advances have been made in the production of biofuels, biopolymers and precursors, pharmaceuticals and nutraceuticals, and commodity and specialty chemicals.

Comparison of six quantitative methods for the measurement of bone turnover at the hip and lumbar spine using 18F-fluoride PET-CT.

Aim: The aim of this study was to evaluate the relationship between different quantification methods used for the measurement of bone plasma clearance (K(i)) using F-PET at the hip and lumbar spine.

Methods: Twelve healthy postmenopausal women aged 52-71 years were recruited. Each participant underwent 60-min dynamic F-PET scans at the lumbar spine and hip on two separate occasions with an injected activity of 90 and 180 MBq, respectively.

Using flux balance analysis to guide microbial metabolic engineering.

Metabolic engineers modify biological systems through the use of modern molecular biology tools in order to obtain desired phenotypes. However, due to the extreme complexity and interconnectedness of metabolism in all organisms, it is often difficult to a priori predict which changes will yield the optimal results. Flux balance analysis (FBA) is a mathematical approach that uses a genomic-scale metabolic network models to afford in silico prediction and optimization of metabolic changes.

Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images.

Background: Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate.

Validation of image-derived arterial input functions at the femoral artery using 18F-fluoride positron emission tomography.

Introduction: The use of image-derived arterial input functions (IDAIF) for the dynamic quantification of bone metabolism using 18F-fluoride positron emission tomography 18F-PET is an attractive alternative to direct arterial blood sampling.

Purposes: (a) To validate a method for obtaining the IDAIF by imaging the femoral artery against a method for deriving the IDAIF at the aorta that was previously validated against direct arterial sampling. (b) To compare the accuracy of bone plasma clearance measurements (Ki) at the total hip site obtained using the femoral artery IDAIF against Ki values at the same site obtained using the aorta IDAIF.