Understanding the role of regulatory RNAs in human skin wound healing

Human skin wound healing is characterized by four phases in a timely manner, including hemostasis, inflammation, proliferation, and remodeling. Various cell types are involved in the biological process. Keratinocytes that constitute around 95% of epidermal cells recruit immune cells by secreting pro-inflammatory cytokines/chemokines and undergo re-epithelialization in the proliferation phase. Ribonucleic acids (RNAs) without protein-coding capacity, defined as noncoding RNAs, consist of the majority of transcription output, are indispensable for multiple biological processes and are critical during disease contexts. Due to their cell and context specificity, noncoding RNAs present a therapeutic potential. However, revealing their underlying mechanism in the skin wound healing is the prerequisite. In this thesis, we identified and comprehensively characterized the role of long noncoding RNAs (lncRNAs) (Paper I, III, IV) and microRNAs (miRNAs) (Paper II) in human skin wound healing. Paper I identified Wound And Keratinocyte Migration-Associated lncRNA 1 (WAKMAR1), and it was upregulated during wound healing but deficient in nonhealing wounds. WAKMAR1 silencing inhibited keratinocyte migration and re-epithelialization of human ex vivo wounds, whereas its overexpression promoted cell migration. Moreover, we revealed that the WAKMAR1-regulated network composed of pro-migratory genes was driven by E2F Transcription Factor 1 (E2F1). Further mechanistic investigation showed that WAKMAR1 enhanced E2F1 expression by hijacking DNA methyltransferases (DNMTs) and reducing methylation at the E2F1 promoter. This study demonstrates that WAKMAR1 is essential for keratinocyte migration and re-epithelialization of human ex vivo wounds, and its deficiency may be associated with delayed healing. Paper II aimed to identify clinically relevant miRNAs and develop an open database for future studies in skin wound healing. We performed the comprehensive and integrative small and long RNA sequencing analysis in human skin, normal wounds collected at different healing phases, and venous ulcers (VUs). We found 17 VU-relevant miRNAs, whose targets were overrepresented in the VU-specific signature. The upregulated miRNAs in VU were predicted to promote inflammatory response but impair cell proliferation, but the downregulated miRNAs might be needed for cell proliferation and migration. We tested the combined effects of miR-34a-5p, miR-424-5p, and miR-516-5p upregulated in VU. Simultaneous overexpression of miR-34a-5p and miR-424-5p had stronger inhibitory effects on keratinocyte proliferation and migration, whereas the combination of miR-34a-5p and miR-516b-5p promoted the expression of the pro-inflammatory Chemokine (C-C Motif) Ligand 20 (CCL20). Overall, our study identifies VU-relevant miRNAs and demonstrates that their abnormal expression may contribute to the pathogenesis of nonhealing wounds. Paper III investigated the role of the HOXC13 Antisense RNA (HOXC13-AS) in epidermal differentiation. LncRNA HOXC13-AS was specifically expressed in human skin and downregulated in the early phases of wound healing. We analyzed our single-cell RNA sequencing in the human skin and found that HOXC13-AS was highly expressed in the differentiated keratinocytes. Furthermore, we showed that HOXC13-AS was decreased by the epidermal growth factor receptor (EGFR) signaling pathway but gradually increased during keratinocyte differentiation. Transcriptomic analysis and functional assays indicated that HOXC13-AS promoted keratinocyte differentiation using differentiation models in vitro and organotypic epidermis. Mechanistically, we revealed that HOXC13-AS physically interacted with COPI Coat Complex Subunit Alpha (COPA) which is essential for the retrograde transport from the Golgi to the endoplasmic reticulum (ER). HOXC13-AS hijacked COPA, which interfered with the retrograde transport, promoting ER stress and keratinocyte differentiation. Rescue assays confirmed that the role of HOXC13-AS in keratinocyte differentiation was dependent on COPA. Overall, this study demonstrates HOXC13-AS as a molecule of importance for epidermal differentiation. Paper IV focused on lncRNA SNHG26, which plays a key role in the transition from inflammation to proliferation during wound healing. SNHG26 was upregulated during wound healing, and Snhg26 knockout mice showed delayed re-epithelialization. By single-cell RNA sequencing analysis, we found decreased migratory but increased inflammatory keratinocyte progenitors in the wound edge of Snhg26 deficient mice. Moreover, we confirmed that SNHG26 enhanced cell proliferation and migration but inhibited inflammatory response in human keratinocytes and ex vivo wounds. Mechanistically, we demonstrated that Interleukin Enhancer Binding Factor 2 (ILF2) physically interacted with SNHG26 using RNA pulldown and RNA immunoprecipitation (RIP). Chromatin immunoprecipitation (ChIP) and chromatin isolation by RNA purification (ChIRP) sequencing showed that SNHG26 guided ILF2 from the inflammatory genomic loci to the Laminin Subunit Beta 3 (LAMB3) genomic locus, switching the gene network and facilitating the inflammatory-to-proliferative state transition of keratinocyte progenitors. This study provides compelling evidence for SNHG26 being a crucial regulator for human skin wound healing

E3^3Pose: Energy-Efficient Edge-assisted Multi-camera System for Multi-human 3D Pose Estimation

Multi-human 3D pose estimation plays a key role in establishing a seamless connection between the real world and the virtual world. Recent efforts adopted a two-stage framework that first builds 2D pose estimations in multiple camera views from different perspectives and then synthesizes them into 3D poses. However, the focus has largely been on developing new computer vision algorithms on the offline video datasets without much consideration on the energy constraints in real-world systems with flexibly-deployed and battery-powered cameras. In this paper, we propose an energy-efficient edge-assisted multiple-camera system, dubbed E$^3$Pose, for real-time multi-human 3D pose estimation, based on the key idea of adaptive camera selection. Instead of always employing all available cameras to perform 2D pose estimations as in the existing works, E$^3$Pose selects only a subset of cameras depending on their camera view qualities in terms of occlusion and energy states in an adaptive manner, thereby reducing the energy consumption (which translates to extended battery lifetime) and improving the estimation accuracy. To achieve this goal, E$^3$Pose incorporates an attention-based LSTM to predict the occlusion information of each camera view and guide camera selection before cameras are selected to process the images of a scene, and runs a camera selection algorithm based on the Lyapunov optimization framework to make long-term adaptive selection decisions. We build a prototype of E$^3$Pose on a 5-camera testbed, demonstrate its feasibility and evaluate its performance. Our results show that a significant energy saving (up to 31.21%) can be achieved while maintaining a high 3D pose estimation accuracy comparable to state-of-the-art methods

Cellular receptor binding and entry of human papillomavirus

Human papillomaviruses (HPVs), recognized as the etiological agents for the skin, plantar, genital, and laryngopharyngeal wart, have been previously in numerous studies demonstrated to present a close link between HPV infection and certain human cancers, some putative candidates of HPV cell receptor and possible pathways of cell entry proposed. This review was to highlight the investigations and remaining questions regarding the binding and entry process

Generating Efficient Training Data via LLM-based Attribute Manipulation

In this paper, we propose a novel method, Chain-of-Thoughts Attribute Manipulation (CoTAM), to guide few-shot learning by carefully crafted data from Large Language Models (LLMs). The main idea is to create data with changes only in the attribute targeted by the task. Inspired by facial attribute manipulation, our approach generates label-switched data by leveraging LLMs to manipulate task-specific attributes and reconstruct new sentences in a controlled manner. Instead of conventional latent representation controlling, we implement chain-of-thoughts decomposition and reconstruction to adapt the procedure to LLMs. Extensive results on text classification and other tasks verify the advantage of CoTAM over other LLM-based text generation methods with the same number of training examples. Analysis visualizes the attribute manipulation effectiveness of CoTAM and presents the potential of LLM-guided learning with even less supervision

Genetic Evolution and Molecular Selection of the HE Gene of Influenza C Virus

Influenza C virus (ICV) was first identified in humans and swine, but recently also in cattle, indicating a wider host range and potential threat to both the livestock industry and public health than was originally anticipated. The ICV hemagglutinin-esterase (HE) glycoprotein has multiple functions in the viral replication cycle and is the major determinant of antigenicity. Here, we developed a comparative approach integrating genetics, molecular selection analysis, and structural biology to identify the codon usage and adaptive evolution of ICV. We show that ICV can be classified into six lineages, consistent with previous studies. The HE gene has a low codon usage bias, which may facilitate ICV replication by reducing competition during evolution. Natural selection, dinucleotide composition, and mutation pressure shape the codon usage patterns of the ICV HE gene, with natural selection being the most important factor. Codon adaptation index (CAI) and relative codon deoptimization index (RCDI) analysis revealed that the greatest adaption of ICV was to humans, followed by cattle and swine. Additionally, similarity index (SiD) analysis revealed that swine exerted a stronger evolutionary pressure on ICV than humans, which is considered the primary reservoir. Furthermore, a similar tendency was also observed in the M gene. Of note, we found HE residues 176, 194, and 198 to be under positive selection, which may be the result of escape from antibody responses. Our study provides useful information on the genetic evolution of ICV from a new perspective that can help devise prevention and control strategies

Deconstructing Human Capital to Construct Hierarchical Nestedness

Modern economies generate immensely diverse complex goods and services by coordinating efforts and know-how of people in vast networks that span across the globe. This increasing complexity puts us under the pressure of acquiring an ever-increasing specialized and yet diverse skill portfolio in order to stay effective members of a complex economy. Here, we analyze the skill portfolios of workers in an effort to understand the latent structure and evolution of these portfolios. Analyzing the U.S. survey data (2003-2019) and 20 million resumes, we uncover a tree structure of vertical skill dependencies such that skills that only a few jobs need (specialized) are located at the leaves under the broadly demanded (general skills). The resulting structure exhibits an unbalanced tree shape. The unbalanced shape allows the further categorization of specialized skills: nested branching out of a deeply rooted sturdy trunk reflecting a dense web of common prerequisites, and un-nested lacking such support. Our longitudinal analyses show individuals indeed become more specialized, going down the nested paths as moving up the career ladder to enjoy higher wage premiums. The specialization, however, is most likely accompanied by demands for a higher level of general skills, and furthermore, specialization without the strengthening of general skills is deprived of wage premiums. We examine the geographic and demographic distribution of skills to explain disparities in wealth. Finally, historical changes in occupation skill requirements show these branches have become more fragmented over the decade, suggesting the increasing labor gap.Comment: 26 pages, 7 figure

[CLS] Token is All You Need for Zero-Shot Semantic Segmentation

In this paper, we propose an embarrassingly simple yet highly effective zero-shot semantic segmentation (ZS3) method, based on the pre-trained vision-language model CLIP. First, our study provides a couple of key discoveries: (i) the global tokens (a.k.a [CLS] tokens in Transformer) of the text branch in CLIP provide a powerful representation of semantic information and (ii) these text-side [CLS] tokens can be regarded as category priors to guide CLIP visual encoder pay more attention on the corresponding region of interest. Based on that, we build upon the CLIP model as a backbone which we extend with a One-Way [CLS] token navigation from text to the visual branch that enables zero-shot dense prediction, dubbed \textbf{ClsCLIP}. Specifically, we use the [CLS] token output from the text branch, as an auxiliary semantic prompt, to replace the [CLS] token in shallow layers of the ViT-based visual encoder. This one-way navigation embeds such global category prior earlier and thus promotes semantic segmentation. Furthermore, to better segment tiny objects in ZS3, we further enhance ClsCLIP with a local zoom-in strategy, which employs a region proposal pre-processing and we get ClsCLIP+. Extensive experiments demonstrate that our proposed ZS3 method achieves a SOTA performance, and it is even comparable with those few-shot semantic segmentation methods.Comment: 8 pages,6 figure