Effect of mixing and spatial dimension on the glass transition

We study the influence of composition changes on the glass transition of binary hard disc and hard sphere mixtures in the framework of mode coupling theory. We derive a general expression for the slope of a glass transition line. Applied to the binary mixture in the low concentration limits, this new method allows a fast prediction of some properties of the glass transition lines. The glass transition diagram we find for binary hard discs strongly resembles the random close packing diagram. Compared to 3D from previous studies, the extension of the glass regime due to mixing is much more pronounced in 2D where plasticization only sets in at larger size disparities. For small size disparities we find a stabilization of the glass phase quadratic in the deviation of the size disparity from unity.Comment: 13 pages, 8 figures, Phys. Rev. E (in print

Using integrative taxonomy to distinguish cryptic halfbeak species and interpret distribution patterns, fisheries landings, and speciation

Context. Species classification disputes can be resolved using integrative taxonomy, which involves the use of both phenotypic and genetic information to determine species boundaries. Aims. Our aim was to clarify species boundaries of two commercially important cryptic species of halfbeak (Hemiramphidae), whose distributions overlap in south-eastern Australia, and assist fisheries management. Methods. We applied an integrative taxonomic approach to clarify species boundaries and assist fisheries management. Key results. Mitochondrial DNA and morphological data exhibited significant differences between the two species. The low level of mitochondrial DNA divergence, coupled with the lack of difference in the nuclear DNA, suggests that these species diverged relatively recently (c. 500 000 years ago) when compared with other species within the Hyporhamphus genus (>2.4 million years ago). Genetic differences between the species were accompanied by differences in modal gill raker counts, mean upper- jaw and preorbital length, and otolith shape. Conclusions. On the basis of these genetic and morphological differences, as well as the lack of morphological intergradation between species along the overlapping boundaries of their geographical distributions, we propose that Hyporhamphus australis and Hyporhamphus melanochir remain valid species. Implications. This study has illustrated the need for an integrative taxonomic approach when assessing species boundaries and has provided a methodological framework for studying other cryptic fish species in a management context

Digital Orthopaedics: A Glimpse Into the Future in the Midst of a Pandemic.

BackgroundThe response to COVID-19 catalyzed the adoption and integration of digital health tools into the health care delivery model for musculoskeletal patients. The change, suspension, or relaxation of Medicare and federal guidelines enabled the rapid implementation of these technologies. The expansion of payment models for virtual care facilitated its rapid adoption. The authors aim to provide several examples of digital health solutions utilized to manage orthopedic patients during the pandemic and discuss what features of these technologies are likely to continue to provide value to patients and clinicians following its resolution.ConclusionThe widespread adoption of new technologies enabling providers to care for patients remotely has the potential to permanently change the expectations of all stakeholders about the way care is provided in orthopedics. The new era of Digital Orthopaedics will see a gradual and nondisruptive integration of technologies that support the patient's journey through the successful management of their musculoskeletal disease

pGQL: A probabilistic graphical query language for gene expression time courses

<p>Abstract</p> <p>Background</p> <p>Timeboxes are graphical user interface widgets that were proposed to specify queries on time course data. As queries can be very easily defined, an exploratory analysis of time course data is greatly facilitated. While timeboxes are effective, they have no provisions for dealing with noisy data or data with fluctuations along the time axis, which is very common in many applications. In particular, this is true for the analysis of gene expression time courses, which are mostly derived from noisy microarray measurements at few unevenly sampled time points. From a data mining point of view the robust handling of data through a sound statistical model is of great importance.</p> <p>Results</p> <p>We propose probabilistic timeboxes, which correspond to a specific class of Hidden Markov Models, that constitutes an established method in data mining. Since HMMs are a particular class of probabilistic graphical models we call our method Probabilistic Graphical Query Language. Its implementation was realized in the free software package pGQL. We evaluate its effectiveness in exploratory analysis on a yeast sporulation data set.</p> <p>Conclusions</p> <p>We introduce a new approach to define dynamic, statistical queries on time course data. It supports an interactive exploration of reasonably large amounts of data and enables users without expert knowledge to specify fairly complex statistical models with ease. The expressivity of our approach is by its statistical nature greater and more robust with respect to amplitude and frequency fluctuation than the prior, deterministic timeboxes.</p

Dynamic shifts in the composition of resident and recruited macrophages influence tissue remodeling in NASH

Macrophage-mediated inflammation is critical in the pathogenesis of non-alcoholic steatohepatitis (NASH). Here, we describe that, with high-fat, high-sucrose-diet feeding, mature TIM

Steatosis drives monocyte-derived macrophage accumulation in human metabolic dysfunction-associated fatty liver disease

BACKGROUND & AIMS: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a common complication of obesity with a hallmark feature of hepatic steatosis. Recent data from animal models of MAFLD have demonstrated substantial changes in macrophage composition in the fatty liver. In humans, the relationship between liver macrophage heterogeneity and liver steatosis is less clear. METHODS: Liver tissue from 21 participants was collected at time of bariatric surgery and analysed using flow cytometry, immunofluorescence, and H&E microscopy. Single-cell RNA sequencing was also conducted on a subset of samples (n = 3). Intrahepatic triglyceride content was assessed via MRI and tissue histology. Mouse models of hepatic steatosis were used to investigate observations made from human liver tissue. RESULTS: We observed variable degrees of liver steatosis with minimal fibrosis in our participants. Single-cell RNA sequencing revealed four macrophage clusters that exist in the human fatty liver encompassing Kupffer cells and monocyte-derived macrophages (MdMs). The genes expressed in these macrophage subsets were similar to those observed in mouse models of MAFLD. Hepatic CD14 CONCLUSIONS: The human liver in MAFLD contains macrophage subsets that align well with those that appear in mouse models of fatty liver disease. Recruited myeloid cells correlate well with the degree of liver steatosis in humans. MdMs appear to participate in lipid uptake during early stages of MALFD. IMPACT AND IMPLICATIONS: Metabolic dysfunction associated fatty liver disease (MAFLD) is extremely common; however, the early inflammatory responses that occur in human disease are not well understood. In this study, we investigated macrophage heterogeneity in human livers during early MAFLD and demonstrated that similar shifts in macrophage subsets occur in human disease that are similar to those seen in preclinical models. These findings are important as they establish a translational link between mouse and human models of disease, which is important for the development and testing of new therapeutic approaches for MAFLD

Modifiable predictors of depression following childhood maltreatment: a systematic review and meta-analysis

Although maltreatment experiences in childhood increase the risk for depression, not all maltreated children become depressed. This review aims to systematically examine the existing literature to identify modifiable factors that increase vulnerability to, or act as a buffer against, depression, and could therefore inform the development of targeted interventions. Thirteen databases (including Medline, PsychINFO, SCOPUS) were searched (between 1984 and 2014) for prospective, longitudinal studies published in English that included at least 300 participants and assessed associations between childhood maltreatment and later depression. The study quality was assessed using an adapted Newcastle-Ottawa Scale checklist. Meta-analyses (random effects models) were performed on combined data to estimate the effect size of the association between maltreatment and depression. Meta-regressions were used to explore effects of study size and quality. We identified 22 eligible articles (N=12 210 participants), of which 6 examined potential modifiable predictors of depression following maltreatment. No more than two studies examined the same modifiable predictor; therefore, it was not possible to examine combined effects of modifiable predictors with meta-regression. It is thus difficult to draw firm conclusions from this study, but initial findings indicate that interpersonal relationships, cognitive vulnerabilities and behavioral difficulties may be modifiable predictors of depression following maltreatment. There is a lack of well-designed, prospective studies on modifiable predictors of depression following maltreatment. A small amount of initial research suggests that modifiable predictors of depression may be specific to maltreatment subtypes and gender. Corroboration and further investigation of causal mechanisms is required to identify novel targets for intervention, and to inform guidelines for the effective treatment of maltreated children

Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions

<p>Abstract</p> <p>Background</p> <p>Epigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner.</p> <p>Results</p> <p>We propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age.</p> <p>Conclusion</p> <p>Our proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data.</p

Lineage Abundance Estimation for SARS-CoV-2 in Wastewater Using Transcriptome Quantification Techniques

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable