Insights into the unique roles of dermal white adipose tissue (dWAT) in wound healing

Dermal white adipose tissue (dWAT) is a newly recognized layer of adipocytes within the reticular dermis of the skin. In many mammals, this layer is clearly separated by panniculus carnosus from subcutaneous adipose tissue (sWAT). While, they concentrated around the hair shaft and follicle, sebaceous gland, and arrector pili muscle, and forms a very specific cone geometry in human. Both the anatomy and the histology indicate that dWAT has distinct development and functions. Different from sWAT, the developmental origin of dWAT shares a common precursor with dermal fibroblasts during embryogenesis. Therefore, when skin injury happens and mature adipocytes in dWAT are exposed, they may undergo lipolysis and dedifferentiate into fibroblasts to participate in wound healing as embryogenetic stage. Studies using genetic strategies to selectively ablate dermal adipocytes observed delayed revascularization and re-epithelialization in wound healing. This review specifically summarizes the hypotheses of the functions of dWAT in wound healing. First, lipolysis of dermal adipocytes could contribute to wound healing by regulating inflammatory macrophage infiltration. Second, loss of dermal adipocytes occurs at the wound edge, and adipocyte-derived cells then become ECM-producing wound bed myofibroblasts during the proliferative phase of repair. Third, mature dermal adipocytes are rich resources for adipokines and cytokines and could release them in response to injury. In addition, the dedifferentiated dermal adipocytes are more sensitive to redifferentiation protocol and could undergo expansion in infected wound. We then briefly introduce the roles of dWAT in protecting the skin from environmental challenges: production of an antimicrobial peptide against infection. In the future, we believe there may be great potential for research in these areas: (1) taking advantage of the plasticity of dermal adipocytes and manipulating them in wound healing; (2) investigating the precise mechanism of dWAT expansion in infected wound healing

Prevalence of insomnia symptoms and their associated factors in patients treated in outpatient clinics of four general hospitals in Guangzhou, China

Background: Data on the prevalence of insomnia symptoms in medical outpatient clinics in China are lacking. This study examined the prevalence of insomnia symptoms and their socio-demographic correlates in patients treated at medical outpatient clinics affiliated with four general hospitals in Guangzhou, a large metropolis in southern China. Method: A total of 4399 patients were consecutively invited to participate in the study. Data on insomnia and its socio-demographic correlates were collected with standardized questionnaires. Results: The prevalence of any type of insomnia symptoms was 22.1% (95% confidence interval (CI): 20.9–23.3%); the prevalence of difficulty initiating sleep was 14.3%, difficulty maintaining sleep was 16.2%, and early morning awakening was 12.4%. Only 17.5% of the patients suffering from insomnia received sleeping pills. Multiple logistic regression analysis revealed that male gender, education level, rural residence, and being unemployed or retired were negatively associated with insomnia symptoms, while lacking health insurance, older age and more severe depressive symptoms were positively associated with insomnia symptoms. Conclusions: Insomnia symptoms are common in patients attending medical outpatient clinics in Guangzhou. Increasing awareness of sleep hygiene measures, regular screening and psychosocial and pharmacological interventions for insomnia are needed in China. Trial registration: ChiCTR-INR-16008066. Registered 8 March 2016

Connecting Multi-modal Contrastive Representations

Multi-modal Contrastive Representation learning aims to encode different modalities into a semantically aligned shared space. This paradigm shows remarkable generalization ability on numerous downstream tasks across various modalities. However, the reliance on massive high-quality data pairs limits its further development on more modalities. This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR). Specifically, given two existing MCRs pre-trained on (A, B) and (B, C) modality pairs, we project them to a new space and use the data from the overlapping modality B to aligning the two MCRs in the new space. Meanwhile, since the modality pairs (A, B) and (B, C) are already aligned within each MCR, the connection learned by overlapping modality can also be transferred to non-overlapping modality pair (A, C). To unleash the potential of C-MCR, we further introduce a semantic-enhanced inter- and intra-MCR connection method. We first enhance the semantic consistency and completion of embeddings across different modalities for more robust alignment. Then we utilize the inter-MCR alignment to establish the connection, and employ the intra-MCR alignment to better maintain the connection for inputs from non-overlapping modalities. To demonstrate the effectiveness of C-MCR, we connect CLIP and CLAP via texts to derive audio-visual representations, and integrate CLIP and ULIP via images for 3D-language representations. Remarkably, without using any paired data, C-MCR for audio-visual achieves state-of-the-art performance on audio-image retrieval, audio-visual source localization, and counterfactual audio-image recognition tasks. Furthermore, C-MCR for 3D-language also attains advanced zero-shot 3D point cloud classification accuracy on ModelNet40.Comment: NeurIPS 202

Exploring the mass and redshift dependence of the cluster pressure profile with stacks on thermal SZ maps

We provide novel constraints on the parameters defining the universal pressure profile (UPP) within clusters of galaxies, and explore their dependence on the cluster mass and redshift, from measurements of Sunyaev-Zel'dovich Compton-$y$ profiles. We employ both the $\textit{Planck}$ 2015 MILCA and the ACT-DR4 $y$ maps over the common $\sim 2,100\,\text{deg}^2$ footprint. We combine existing cluster catalogs based on KiDS, SDSS and DESI observations, for a total of 23,820 clusters spanning the mass range $10^{14.0}\,\text{M}_{\odot}<M_{500}<10^{15.1}\,\text{M}_{\odot}$ and the redshift range $0.02<z<0.98$. We split the clusters into three independent bins in mass and redshift; for each combination we detect the stacked SZ cluster signal and extract the mean $y$ angular profile. The latter is predicted theoretically adopting a halo model framework, and MCMCs are employed to estimate the UPP parameters, the hydrostatic mass bias $b_{\rm h}$ and possible cluster miscentering effects. We constrain $[P_0,c_{500},\alpha,\beta]$ to $[5.9,2.0,1.8,4.9]$ with $\textit{Planck}$ and to $[3.8,1.3,1.0,4.4]$ with ACT using the full cluster sample, in agreement with previous findings. We do not find any compelling evidence for a residual mass or redshift dependence, thus expanding the validity of the cluster pressure profile over much larger $M_{500}$ and $z$ ranges; this is the first time the model has been tested on such a large (complete and representative) cluster sample. Finally, we obtain loose constraints on the hydrostatic mass bias in the range 0.2-0.3, again in broad agreement with previous works.Comment: 39 pages, 22 figures. Accepted for publication in Apj

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe