Coordinated analysis of age, sex, and education effects on change in MMSE scores

Objectives. We describe and compare the expected performance trajectories of older adults on the Mini-Mental Status Examination (MMSE) across six independent studies from four countries in the context of a collaborative network of longitudinal studies of aging. A coordinated analysis approach is used to compare patterns of change conditional on sample composition differences related to age, sex, and education. Such coordination accelerates evaluation of particular hypotheses. In particular, we focus on the effect of educational attainment on cognitive decline.Method. Regular and Tobit mixed models were fit to MMSE scores from each study separately. The effects of age, sex, and education were examined based on more than one centering point.Results. Findings were relatively consistent across studies. On average, MMSE scores were lower for older individuals and declined over time. Education predicted MMSE score, but, with two exceptions, was not associated with decline in MMSE over time.Conclusion. A straightforward association between educational attainment and rate of cognitive decline was not supported. Thoughtful consideration is needed when synthesizing evidence across studies, as methodologies adopted and sample characteristics, such as educational attainment, invariably differ. © 2012 The Author

Nonlinear response of dense colloidal suspensions under oscillatory shear: Mode-coupling theory and FT-rheology experiments

Using a combination of theory, experiment and simulation we investigate the nonlinear response of dense colloidal suspensions to large amplitude oscillatory shear flow. The time-dependent stress response is calculated using a recently developed schematic mode-coupling-type theory describing colloidal suspensions under externally applied flow. For finite strain amplitudes the theory generates a nonlinear response, characterized by significant higher harmonic contributions. An important feature of the theory is the prediction of an ideal glass transition at sufficiently strong coupling, which is accompanied by the discontinuous appearance of a dynamic yield stress. For the oscillatory shear flow under consideration we find that the yield stress plays an important role in determining the non linearity of the time-dependent stress response. Our theoretical findings are strongly supported by both large amplitude oscillatory (LAOS) experiments (with FT-rheology analysis) on suspensions of thermosensitive core-shell particles dispersed in water and Brownian dynamics simulations performed on a two-dimensional binary hard-disc mixture. In particular, theory predicts nontrivial values of the exponents governing the final decay of the storage and loss moduli as a function of strain amplitude which are in excellent agreement with both simulation and experiment. A consistent set of parameters in the presented schematic model achieves to jointly describe linear moduli, nonlinear flow curves and large amplitude oscillatory spectroscopy

Collaborative learning exercises for Teaching Protein Mass Spectrometry [post-print]

A collaborative learning module for teaching protein mass spectrometry has been developed to overcome common obstacles to incorporating the modern topic of biological mass spectrometry into the undergraduate chemistry curriculum. Protein mass spectrometry data is provided to eliminate the need for expensive instrumentation, and an instructor’s manual gives necessary details for those unfamiliar with the topic. The first section provides background information on proteins and the field of proteomics. The second section describes the use of electrospray ionization to determine the molecular weight of a protein. The third section shows how to identify a protein using peptide mass mapping, and the fourth section describes tandem MS experiments for de novo peptide sequencing. Each section also includes lessons on the analytical instrumentation used to make mass measurements including electrospray ionization, matrix assisted laser desorption ionization, and time-of-flight mass spectrometry. The module includes preclass reading assignments and small group problem solving exercises to be used during class sessions. The module was implemented over several semesters at both a small liberal arts college and a large research university. Assessment data from both institutions suggest that the module is effective in helping students to learn about mass-spectrometry-based proteomics. This freely available resource will assist instructors in introducing these topics to the undergraduate curriculum

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research

Sequence analysis of pooled bacterial samples enables identification of strain variation in group A streptococcus

Knowledge of the genomic variation among different strains of a pathogenic microbial species can help in selecting optimal candidates for diagnostic assays and vaccine development. Pooled sequencing (Pool-seq) is a cost effective approach for population level genetic studies that require large numbers of samples such as various strains of a microbe. To test the use of Pool-seq in identifying variation, we pooled DNA of 100 Streptococcus pyogenes strains of different emm types in two pools, each containing 50 strains. We used four variant calling tools (Freebayes, UnifiedGenotyper, SNVer, and SAMtools) and one emm1 strain, SF370, as a reference genome. In total 63719 SNPs and 164 INDELs were identified in the two pools concordantly by at least two of the tools. Majority of the variants (93.4%) from six individually sequenced strains used in the pools could be identified from the two pools and 72.3% and 97.4% of the variants in the pools could be mined from the analysis of the 44 complete Str. pyogenes genomes and 3407 sequence runs deposited in the European Nucleotide Archive respectively. We conclude that DNA sequencing of pooled samples of large numbers of bacterial strains is a robust, rapid and cost-efficient way to discover sequence variation.Peer reviewe

Review of Student-Built Spectroscopy Instrumentation Projects

Copyright © 2020 American Chemical Society and Division of Chemical Education, Inc. One challenge of teaching chemical analysis is the proliferation of sophisticated, but often impenetrable, instrumentation in the modern laboratory. Complex instruments, and the software that runs them, distance students from the physical and chemical processes that generate the analytical signal. A solution to this challenge is the introduction of a student-driven instrument-building project. Visible absorbance spectroscopy is well-suited to such a project due to its relative simplicity and the ubiquity of absorbance measurements. This Article reviews simple instructor- A nd student-built instruments for spectroscopy, providing an overview of common designs, components, and applications. This comprehensive summary includes options that are suitable for in-person or remote learning with K-12 students and undergraduates in general chemistry, analytical chemistry, instrumental analysis, and electronics courses

Helicobacter suis binding to carbohydrates on human and porcine gastric mucins and glycolipids occurs via two modes

Helicobacter suis colonizes the stomach of most pigs and is the most prevalent non-Helicobacter pylori Helicobacter species found in the human stomach. In the human host, H. suis contributes to the development of chronic gastritis, peptic ulcer disease and MALT lymphoma, whereas in pigs it is associated with gastritis, decreased growth and ulcers. Here, we demonstrate that the level of H. pylori and H. suis binding to human and pig gastric mucins varies between individuals with species dependent specificity. The binding optimum of H. pylori is at neutral pH whereas that of H. suis has an acidic pH optimum, and the mucins that H. pylori bind to are different than those that H. suis bind to. Mass spectrometric analysis of mucin O-glycans from the porcine mucin showed that individual variation in binding is reflected by a difference in glycosylation; of 109 oligosaccharide structures identified, only 14 were present in all examined samples. H. suis binding to mucins correlated with glycans containing sulfate, sialic acid and terminal galactose. Among the glycolipids present in pig stomach, binding to lactotetraosylceramide (Gal beta 3GlcNAc beta 3Gal beta 4Glc beta 1Cer) was identified, and adhesion to Gal beta 3GlcNAc beta 3Gal beta 4Glc at both acidic and neutral pH was confirmed using other glycoconjugates. Together with that H. suis bound to DNA (used as a proxy for acidic charge), we conclude that H. suis has two binding modes: one to glycans terminating with Gal beta 3GlcNAc, and one to negatively charged structures. Identification of the glycan structures H. suis interacts with can contribute to development of therapeutic strategies alternative to antibiotics

Identification and quantification of microplastics in wastewater using focal plane array-based reflectance micro-FT-IR imaging

Microplastics (<5 mm) have been documented in environmental samples on a global scale. While these pollutants may enter aquatic environments via wastewater treatment facilities, the abundance of microplastics in these matrices has not been investigated. Although efficient methods for the analysis of microplastics in sediment samples and marine organisms have been published, no methods have been developed for detecting these pollutants within organic-rich wastewater samples. In addition, there is no standardized method for analyzing microplastics isolated from environmental samples. In many cases, part of the identification protocol relies on visual selection before analysis, which is open to bias. In order to address this, a new method for the analysis of microplastics in wastewater was developed. A pretreatment step using 30% hydrogen peroxide (H2O2) was employed to remove biogenic material, and focal plane array (FPA)-based reflectance micro-Fourier-transform (FT-IR) imaging was shown to successfully image and identify different microplastic types (polyethylene, polypropylene, nylon-6, polyvinyl chloride, polystyrene). Microplastic-spiked wastewater samples were used to validate the methodology, resulting in a robust protocol which was nonselective and reproducible (the overall success identification rate was 98.33%). The use of FPA-based micro-FT-IR spectroscopy also provides a considerable reduction in analysis time compared with previous methods, since samples that could take several days to be mapped using a single-element detector can now be imaged in less than 9 h (circular filter with a diameter of 47 mm). This method for identifying and quantifying microplastics in wastewater is likely to provide an essential tool for further research into the pathways by which microplastics enter the environment.This work is funded by a NERC (Natural Environment Research Council) CASE studentship (NE/K007521/1) with contribution from industrial partner Fera Science Ltd., United Kingdom. The authors would like to thank Peter Vale, from Severn Trent Water Ltd, for providing access to and additionally Ashley Howkins (Brunel University London) for providing travel and assistance with the sampling of the Severn Trent wastewater treatment plant in Derbyshire, UK. We are grateful to Emma Bradley and Chris Sinclair for providing helpful suggestions for our research

Soluble aggregates present in cerebrospinal fluid change in size and mechanism of toxicity during Alzheimer’s disease progression

Abstract: Soluble aggregates of amyloid-β (Aβ) have been associated with neuronal and synaptic loss in Alzheimer’s disease (AD). However, despite significant recent progress, the mechanisms by which these aggregated species contribute to disease progression are not fully determined. As the analysis of human cerebrospinal fluid (CSF) provides an accessible window into the molecular changes associated with the disease progression, we characterised soluble aggregates present in CSF samples from individuals with AD, mild cognitive impairment (MCI) and healthy controls using a range of sensitive biophysical methods. We used super-resolution imaging and atomic force microscopy to characterise the size and structure of the aggregates present in CSF and correlate this with their ability to permeabilise lipid membranes and induce an inflammatory response. We found that these aggregates are extremely heterogeneous and exist in a range of sizes, varying both structurally and in their mechanisms of toxicity during the disease progression. A higher proportion of small aggregates of Aβ that can cause membrane permeabilization are found in MCI CSF; in established AD, a higher proportion of the aggregates were larger and more prone to elicit a pro-inflammatory response in glial cells, while there was no detectable change in aggregate concentration. These results show that large aggregates, some longer than 100 nm, are present in the CSF of AD patients and suggest that different neurotoxic mechanisms are prevalent at different stages of AD