Influence of Tension During Chilling on Pre-Rigor Excised Bovine Muscles

Food Scienc

Sexed up: theorizing the sexualization of culture

This paper reviews and examines emerging academic approaches to the study of ‘sexualized culture’; an examination made necessary by contemporary preoccupations with sexual values, practices and identities, the emergence of new forms of sexual experience and the apparent breakdown of rules, categories and regulations designed to keep the obscene at bay. The paper maps out some key themes and preoccupations in recent academic writing on sex and sexuality, especially those relating to the contemporary or emerging characteristics of sexual discourse. The key issues of pornographication and democratization, taste formations, postmodern sex and intimacy, and sexual citizenship are explored in detail. </p

Integrating barcoded neuroanatomy with spatial transcriptional profiling reveals cadherin correlates of projections shared across the cortex

Functional circuits consist of neurons with diverse axonal projections and gene expression. Understanding the molecular signature of projections requires high-throughput interrogation of both gene expression and projections to multiple targets in the same cells at cellular resolution, which is difficult to achieve using current technology. Here, we introduce BARseq2, a technique that simultaneously maps projections and detects multiplexed gene expression by in situ sequencing. We determined the expression of cadherins and cell-type markers in 29,933 cells, and the projections of 3,164 cells in both the mouse motor cortex and auditory cortex. Associating gene expression and projections in 1,349 neurons revealed shared cadherin signatures of homologous projections across the two cortical areas. These cadherins were enriched across multiple branches of the transcriptomic taxonomy. By correlating multi-gene expression and projections to many targets in single neurons with high throughput, BARseq2 provides a path to uncovering the molecular logic underlying neuronal circuits

Replicability of spatial gene expression atlas data from the adult mouse brain

Background Spatial gene expression is particularly interesting in the mammalian brain, with the potential to serve as a link between many data types. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain sub-divisions, between the Allen Institute’s in situ hybridization data from the adult mouse brain (ABA) and a similar dataset collected using Spatial Transcriptomics (ST). With the advent of tractable spatial techniques, for the first time we are able to benchmark the Allen Institute’s whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. Results We use LASSO, linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen reference atlas labels are classifiable using transcription, but that performance is higher in the ABA than ST. Further, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bi-directionally. Finally, while an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. Conclusions In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability

Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.

High-throughput, spatially resolved gene expression techniques are poised to be transformative across biology by overcoming a central limitation in single-cell biology: the lack of information on relationships that organize the cells into the functional groupings characteristic of tissues in complex multicellular organisms. Spatial expression is particularly interesting in the mammalian brain, which has a highly defined structure, strong spatial constraint in its organization, and detailed multimodal phenotypes for cells and ensembles of cells that can be linked to mesoscale properties such as projection patterns, and from there, to circuits generating behavior. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain subdivisions, between the Allen Institute's in situ hybridization data from the adult mouse brain (Allen Brain Atlas (ABA)) and a similar dataset collected using spatial transcriptomics (ST). With the advent of tractable spatial techniques, for the first time, we are able to benchmark the Allen Institute's whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. We use regularized linear regression (LASSO), linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen Reference Atlas labels are classifiable using transcription in both data sets, but that performance is higher in the ABA than in ST. Furthermore, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bidirectionally. While an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability ultimately providing a valuable reference set for understanding the cell biology of the brain

Comparative hydrodynamic and nanoscale imaging study on the interactions of teicoplanin-A2 and bovine submaxillary mucin as a model ocular mucin

Glycopeptide antibiotics are regularly used in ophthalmology to treat infections of Gram-positive bacteria. Aggregative interactions of antibiotics with mucins however can lead to long exposure and increases the risk of resistant species. This study focuses on the evaluation of potential interactions of the last line of defence glycopeptide antibiotic teicoplanin with an ocular mucin model using precision matrix free hydrodynamic and microscopic techniques: sedimentation velocity in the analytical ultracentrifuge (SV-AUC), dynamic light scattering (DLS) and atomic force microscopy (AFM). For the mixtures of teicoplanin at higher doses (1.25 mg/mL and 12.5 mg/mL), it was shown to interact and aggregate with bovine submaxillary mucin (BSM) in the distributions of both sedimentation coefficients by SV-AUC and hydrodynamic radii by DLS. The presence of aggregates was confirmed by AFM for higher concentrations. We suggest that teicoplanin eye drop formulations should be delivered at concentrations of < 1.25 mg/mL to avoid potentially harmful aggregations

Self-association of the glycopeptide antibiotic teicoplanin A2 in aqueous solution studied by molecular hydrodynamics

AbstractThe natural glycopeptide antibiotic teicoplanin is used for the treatment of serious Gram-positive related bacterial infections and can be administered intravenously, intramuscularly, topically (ocular infections), or orally. It has also been considered for targeting viral infection by SARS-CoV-2. The hydrodynamic properties of teicoplanin A2 (M1 = 1880 g/mol) were examined in phosphate chloride buffer (pH 6.8, I = 0.10 M) using sedimentation velocity and sedimentation equilibrium in the analytical ultracentrifuge together with capillary (rolling ball) viscometry. In the concentration range, 0–10 mg/mL teicoplanin A2 was found to self-associate plateauing &gt; 1 mg/mL to give a molar mass of (35,400 ± 1000) g/mol corresponding to ~ (19 ± 1) mers, with a sedimentation coefficient s20, w =  ~ 4.65 S. The intrinsic viscosity [$$\eta$$ η ] was found to be (3.2 ± 0.1) mL/g: both this, the value for s20,w and the hydrodynamic radius from dynamic light scattering are consistent with a globular macromolecular assembly, with a swelling ratio through dynamic hydration processes of ~ 2. </jats:p

Widespread somatic L1 retrotransposition occurs early during gastrointestinal cancer evolution

Somatic L1 retrotransposition events have been shown to occur in epithelial cancers. Here, we attempted to determine how early somatic L1 insertions occurred during the development of gastrointestinal (GI) cancers. Using L1-targeted resequencing (L1-seq), we studied different stages of four colorectal cancers arising from colonic polyps, seven pancreatic carcinomas, as well as seven gastric cancers. Surprisingly, we found somatic L1 insertions not only in all cancer types and metastases but also in colonic adenomas, well-known cancer precursors. Some insertions were also present in low quantities in normal GI tissues, occasionally caught in the act of being clonally fixed in the adjacent tumors. Insertions in adenomas and cancers numbered in the hundreds, and many were present in multiple tumor sections, implying clonal distribution. Our results demonstrate that extensive somatic insertional mutagenesis occurs very early during the development of GI tumors, probably before dysplastic growth