Hair Growth Is Induced by Blockade of Macrophage-derived Oncostatin M and Downstream Jak-stat5 Signaling in Hair Follicle Stem Cells

Our lab recently described a role for JAK-STAT signaling in the maintenance of quiescence during the murine hair cycle. Research into signaling pathways and cytokines/growth factors involved in the mammalian hair cycle has not focused extensively on the JAK-STAT pathway. In this thesis, I investigated the upstream effector(s) and downstream mechanisms of JAK-STAT signaling in the HFSC during telogen, using a variety of methods, including murine conditional mutants of the JAK-STAT pathway, pharmacological and immunological techniques. The mechanism through which OSM exerts this effect is via JAK-STAT5 signaling downstream of the OSM receptor, which is antagonized by pharmacological JAK inhibition. Conditional epidermal ablation of OSMR or STAT5 during early- and mid-telogen (P42 – P60) shortens the telogen phase significantly, and inhibition of macrophages by way of neutralizing antibodies, small molecule inhibitors, and genetic ablation (with Csf1r-CreER::R26-iDTR mice) during telogen also promotes hair growth. Single-cell RNA sequencing of dermal immune cells across murine telogen identified a distinct subset of TREM2+ macrophages that are enriched for OSM, and gene-set analysis suggests these “trichophages” are similar to the microglia of the central nervous system. I show that this distinct subset of TREM2+ macrophages predominate during early- and mid-telogen, where they produce Oncostatin M (OSM), which is sufficient to maintain quiescence of hair follicle stem cells (HFSCs). Proliferation of HFSCs and hair growth is associated with depletion of this subset of TREM2+ macrophages. Interestingly, macrophage markers and OSM were found to be upregulated in the balding scalp of males with androgenetic alopecia, suggesting that this mechanism is physiologically relevant in the control of human hair cycling

A near-optimal change-detection based algorithm for piecewise-stationary combinatorial semi-bandits

We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps. We propose an algorithm, GLR-CUCB, which incorporates an efficient combinatorial semi-bandit algorithm, CUCB, with an almost parameter-free change-point detector, the Generalized Likelihood Ratio Test (GLRT). Our analysis shows that the regret of GLR-CUCB is upper bounded by O(√NKT log T), where N is the number of piecewise-stationary segments, K is the number of base arms, and T is the number of time steps. As a complement, we also derive a nearly matching regret lower bound on the order of Ω(√NKT), for both piecewise-stationary multi-armed bandits and combinatorial semi-bandits, using information-theoretic techniques and judiciously constructed piecewise-stationary bandit instances. Our lower bound is tighter than the best available regret lower bound, which is Ω(√T). Numerical experiments on both synthetic and real-world datasets demonstrate the superiority of GLR-CUCB compared to other state-of-the-art algorithms

Cross-sectional analysis of critical risk factors for PPP water projects in China

© 2014 American Society of Civil Engineers. During the past decades in China, the traditional state monopoly has experienced difficulties in meeting the huge demand for new infrastructure and improvement in service levels, engendering the growth of different forms and degrees of private sector involvement. Since the 1990s, China has started experimenting with the public-private partnership (PPP) delivery method in the water supply sector. However, many problems stemming from unsuccessful risk management have been encountered in PPP applications that have eventually led to project failure. This paper aims to identify and evaluate typical risks associated with PPP projects in the Chinese water supply sector. A literature review, a Delphi survey, and face-to-face interviews were used to achieve these objectives. Finally, a register of 16 critical risk factors (CRFs) of water PPP projects in China was established. The findings revealed that completion risk, inflation, and price change risk have a higher impact on Chinese water PPP projects, whereas government corruption, an imperfect law and supervision system, and a change in market demand have a lower impact on the water supply sector. The findings can help project stakeholders to improve the efficiency of privatization in public utility service and provide private investors with a better understanding while they participate in the enormous Chinese water market through the PPP mode

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

This paper aims to investigate the open research problem of uncovering the social behaviors of LLM-based agents. To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game. While previous studies have conducted preliminary investigations into gameplay with LLM agents, there lacks research on their social behaviors. In this paper, we present a novel framework designed to seamlessly adapt to Avalon gameplay. The core of our proposed framework is a multi-agent system that enables efficient communication and interaction among agents. We evaluate the performance of our framework based on metrics from two perspectives: winning the game and analyzing the social behaviors of LLM agents. Our results demonstrate the effectiveness of our framework in generating adaptive and intelligent agents and highlight the potential of LLM-based agents in addressing the challenges associated with dynamic social environment interaction. By analyzing the social behaviors of LLM agents from the aspects of both collaboration and confrontation, we provide insights into the research and applications of this domain

CO2Vec: Embeddings of co-ordered networks based on mutual reinforcement

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient framework to mitigate popularity bias from a gradient perspective. Specifically, we first normalize each user embedding and record accumulated gradients of users and items via popularity bias measures in model training. To address the popularity bias issues, we develop a gradient-based embedding adjustment approach used in model testing. This strategy is generic, model-agnostic, and can be seamlessly integrated into most existing recommender systems. Our extensive experiments on two classic recommendation models and four real-world datasets demonstrate the effectiveness of our method over state-of-the-art debiasing baselines.Comment: Recommendation System, Popularity Bia

Expression quantitative trait loci are highly sensitive to cellular differentiation state

Blood cell development from multipotent hematopoietic stem cells to specialized blood cells is accompanied by drastic changes in gene expression for which the triggers remain mostly unknown. Genetical genomics is an approach linking natural genetic variation to gene expression variation, thereby allowing the identification of genomic loci containing gene expression modulators (eQTLs). In this paper, we used a genetical genomics approach to analyze gene expression across four developmentally close blood cell types collected from a large number of genetically different but related mouse strains. We found that, while a significant number of eQTLs (365) had a consistent “static” regulatory effect on gene expression, an even larger number were found to be very sensitive to cell stage. As many as 1,283 eQTLs exhibited a “dynamic” behavior across cell types. By looking more closely at these dynamic eQTLs, we show that the sensitivity of eQTLs to cell stage is largely associated with gene expression changes in target genes. These results stress the importance of studying gene expression variation in well-defined cell populations. Only such studies will be able to reveal the important differences in gene regulation between different ce