We advance two-photon microscopy for near diffraction-limited imaging down to 850 μm below the pia in awake mice. Our approach combines direct wavefront sensing of descanned fluorescence from Cy5.5-dextran in brain microvessels, which forms a guide star, and adaptive optics to compensate for tissue-induced aberrations of the wavefront. We achieve high signal-to-noise records of glutamate release from thalamocortical axons and calcium transients in spines of layer 5b basal dendrites during active sensing.

Aims/IntroductionTo investigate the effect of telomere shortening and other predictive factors of non‐alcoholic fatty liver disease (NAFLD) in type 2 diabetes mellitus patients in a 6‐year prospective cohort study.Materials and MethodsA total of 70 type 2 diabetes mellitus (mean age 57.8 ± 6.7 years) patients without NAFLD were included in the study, and 64 of them were successfully followed up 6 years later, excluding four cases with significant alcohol consumption. NAFLD was diagnosed by the hepatorenal ratio obtained by a quantitative ultrasound method using NIH image analysis software. The 39 individuals that developed NAFLD were allocated to group A, and the 21 individuals that did not develop NAFLD were allocated to group B. Fluorescent real‐time quantitative polymerase chain reaction was used to measure telomere length.ResultsThere was no significant difference between the two groups in baseline telomere length; however, at the end of the 6th year, telomere length had become shorter in group A compared with group B. There were significant differences between these two groups in baseline body mass index, waistline, systolic blood pressure, glycated hemoglobin and fasting C‐peptide level. In addition, the estimated indices of baseline insulin resistance increased in group A. Fasting insulin level, body mass index, systolic blood pressure at baseline and the shortening of telomere length were independent risk factors of NAFLD in type 2 diabetes mellitus patients.ConclusionsTelomere length became shorter in type 2 diabetes mellitus patients who developed NAFLD over the course of 6 years. Type 2 diabetes mellitus patients who developed NAFLD had more serious insulin resistance compared with those who did not develop NAFLD a long time ago.

Background: The aim of the present study was to explore whether the triglyceride to high density lipoprotein cholesterol ratio [log (TG)/HDL-C] and peripheral blood leukocytes DNA telomere length could predict future islet beta cell function decreased in Chinese type 2 diabetes mellitus (T2DM) during a 6-year cohort. Methods: Sixty T2DM patients (without insulin treatment at baseline) were included in the 6-year cohort study. Peripheral blood leukocytes DNA telomere length, HbA1c, blood lipid profile, fatty fat acid, glucose, insulin and C peptide (3 h after a mixed meal) were determined. Delta C peptide area under curve (Delta CP AUC) was used to reflect change in beta cell secretion function (Delta CP AUC = baseline CP AUC -CP AUC after 6 years). Subjects were divided into slow decrease of beta cell function group (Delta CP AUCslow group) and fast decrease group (Delta CP AUCfast group) according to median of Delta CP AUC. Baseline demographic characteristics, clinical variables between two groups were compared. Correlations between baseline data and Delta CP AUC were analyzed. Results: Baseline log (TG)/HDL-C was positively correlated with Delta CP AUC (r = 0.306, P = 0.027); log (TG)/HDL-C in Delta CP AUCfast group was higher than that in Delta CP AUCslow group (0.103 ± 0.033 vs 0.083 ± 0.030, P = 0.027). There was no significant difference in DNA telomere length between the two groups. Change in DNA telomere length over 6 years was not significantly correlated with baseline blood lipid. Conclusions: In Chinese T2DM patients, high baseline log (TG)/HDL-C ratio predicts fast progression of islet beta cell dysfunction. It may be a simple index to predict progression speed of islet beta cell dysfunction.

Given a union of non-linear manifolds, non-linear subspace clustering or manifold clustering aims to cluster data points based on manifold structures and also learn to parameterize each manifold as a linear subspace in a feature space. Deep neural networks have the potential to achieve this goal under highly non-linear settings given their large capacity and flexibility. We argue that achieving manifold clustering with neural networks requires two essential ingredients: a domain-specific constraint that ensures the identification of the manifolds, and a learning algorithm for embedding each manifold to a linear subspace in the feature space. This work shows that many constraints can be implemented by data augmentation. For subspace feature learning, Maximum Coding Rate Reduction (MCR 2 ) objective can be used. Putting them together yields Neural Manifold Clustering and Embedding (NMCE), a novel method for general purpose manifold clustering, which significantly outperforms autoencoder-based deep subspace clustering. Further, on more challenging natural image datasets, NMCE can also outperform other algorithms specifically designed for clustering. Qualitatively, we demonstrate that NMCE learns a meaningful and interpretable feature space. As the formulation of NMCE is closely related to several important Self-supervised learning (SSL) methods, we believe this work can help us build a deeper understanding on SSL representation learning.