GPR37 Signaling Modulates Migration of Olfactory Ensheathing Cells and Gonadotropin Releasing Hormone Cells in Mice

Gonadotropin releasing hormone (GnRH) neurons, part of the hypothalamic-pituitary-gonadal axis, regulate reproduction. Prenatally, GnRH neurons migrate into the brain from the nasal placode along terminal nerve fibers, intermixed with olfactory sensory axons and olfactory ensheathing cells (OECs). An expression analysis from embryonic GnRH neurons identified the G protein-coupled receptor 37 (GPR37 or PAEL-r). GPR37 has been linked to (1) juvenile Parkinson’s disease in humans, (2) oligodendrocyte differentiation, and (3) Wnt/β-catenin signaling during neurogenesis. In this study, the role of GPR37 was investigated in the developing GnRH/olfactory system. PCR and immunocytochemistry confirmed expression of GPR37 in migrating GnRH neurons as well as in OECs. Inhibition of GPR37 signaling in nasal explants attenuated GnRH neuronal migration and OEC movement. Examination of GPR37 deficient mice revealed a decrease in the olfactory bulb nerve layer and attenuated/delayed maturation and migration of GnRH neurons into the brain. These data demonstrate a developmental role for GPR37 signaling in neural migration.Significance StatementReproduction is controlled by gonadotrophin releasing hormone (GnRH) neurons located in the central nervous system. Embryonically, GnRH neurons originate in the nasal/olfactory placode and migrate into the brain on axonal tracks from cells in the vomeronasal organ, intermixed with olfactory sensory axons and olfactory ensheathing cells (OECs). An expression analysis from embryonic GnRH neurons identified the G protein-coupled receptor 37. Here we show that inhibition of GPR37 signaling in nasal explants and mutant mice attenuated GnRH neuronal migration. Signaling via GPR37 also perturbed OEC movement, resulting in a decrease in the olfactory bulb nerve layer in vivo. Together, these results identify a new role for GPR37 signaling during development – modulating cell migration

The global impact of the International Federation of Clinical Chemistry and Laboratory Medicine, Education and Management Division: engaging stakeholders and assessing HbA1c quality in a multicentre study across China

Background: Diabetes mellitus is a major global issue and high quality testing is essential for the diagnosis and treatment of the disease. The IFCC Committee for the Education in the Utility of Biomarkers in Diabetes (C-EUBD) plays a global role in improving knowledge and understanding around diabetes testing. This paper describes a multi-stakeholder approach, to improving diagnostic and therapeutic testing for diabetes, using a multicentre study in China as an example of the global impact of the group. Methods: Educational workshops were developed to support the scientific aims of the study in which 30 centres around China received identical, fresh frozen whole blood samples with values assigned using IFCC secondary reference methods and undertook precision (EP-5) and trueness studies. Performance was assessed using sigma metrics. Results: A successful multi-stakeholder group was developed and sustained throughout the study through several educational workshops, which enabled the formation of a long-term collaboration with key opinion leaders and policy makers in China. All 30 centres showed good performance with within and between laboratory coefficient of variations (CVs) below 3% in SI units at both low and high haemoglobin A1c (HbA1c) levels. All individual laboratories met the criteria of a sigma of two or more at a total allowable error (TAE) of 5 mmol/mol (0.46% NGSP). Conclusions: The study led to a successful multi-partner approach to improving diabetes testing in China. All centres involved in the study meeting the published IFCC quality criteria, paving the way for future clinical trials and an expanded role for HbA1c testing across the country

Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation

Diffusion models have proven to be highly effective in image and video generation; however, they still face composition challenges when generating images of varying sizes due to single-scale training data. Adapting large pre-trained diffusion models for higher resolution demands substantial computational and optimization resources, yet achieving a generation capability comparable to low-resolution models remains elusive. This paper proposes a novel self-cascade diffusion model that leverages the rich knowledge gained from a well-trained low-resolution model for rapid adaptation to higher-resolution image and video generation, employing either tuning-free or cheap upsampler tuning paradigms. Integrating a sequence of multi-scale upsampler modules, the self-cascade diffusion model can efficiently adapt to a higher resolution, preserving the original composition and generation capabilities. We further propose a pivot-guided noise re-schedule strategy to speed up the inference process and improve local structural details. Compared to full fine-tuning, our approach achieves a 5X training speed-up and requires only an additional 0.002M tuning parameters. Extensive experiments demonstrate that our approach can quickly adapt to higher resolution image and video synthesis by fine-tuning for just 10k steps, with virtually no additional inference time.Comment: Project Page: https://guolanqing.github.io/Self-Cascade

Glutamic acid decarboxylase autoantibodies are dominant but insufficient to identify most Chinese with adult-onset non-insulin requiring autoimmune diabetes: LADA China study 5.

AIMS: Adult-onset autoimmune diabetes is prevalent in China, in contrast to childhood-onset type 1 diabetes mellitus. Islet autoantibodies are the most important immune biomarkers to diagnose autoimmune diabetes. We assayed four different islet autoantibodies in recently diagnosed adult non-insulin-requiring diabetes Chinese subjects to investigate the best antibody assay strategy for the correct diagnosis of these subjects. METHODS: LADA China study is a nation-wide multicenter study conducted in diabetes patients from 46 university-affiliated hospitals in China. Non-insulin-treated newly diagnosed adult diabetes patients (n = 2388) were centrally assayed for glutamic acid decarboxylase autoantibody (GADA), protein tyrosine phosphatase-2 autoantibody (IA-2A), and zinc transporter 8 autoantibody (ZnT8A) by radioligand assay and insulin autoantibody (IAA) by microtiter plate radioimmunoassay. Clinical data were determined locally. RESULTS: Two hundred and six (8.63 %) subjects were autoantibody positive, of which GADA identified 5.78 % (138/2388) of the total, but only 67 % (138/206) of the autoimmune cases. IA-2A, ZnT8A, and IAA were found in 1.51, 1.84, and 1.26 % of the total study subjects, respectively. When assaying three islet autoantibodies, the most effective strategy was the combination of GADA, ZnT8A, and IAA, which could identify 92.2 % (190/206) autoimmune diabetes patients. The clinical data showed that those subjects with positive GADA had lower random C-peptide than autoantibody negative subjects (P < 0.05). CONCLUSIONS: As with Europeans, GADA is the dominant autoantibody in this form of autoimmune diabetes in China, but in contrast to Europeans, screening should include other diabetes-associated autoantibodies

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Asymptotically Symmetric Metrics and Ricci Flows

This thesis presents a comprehensive investigation into the properties of asymptoti- cally hyperbolic manifolds and provides an exact definition for asymptotically symmetric manifolds.Chapter 1 begins with a thorough classification of symmetric spaces of non-compact type, as detailed in Section 1.1. Utilizing parabolic geometry, we then explore the boundary geometry of symmetric spaces of non-compact type, aiming to precisely define asymptotically symmetric manifolds in Section 1.2. Chapter 2 focuses on the perturbation existence of asymptotically hyperbolic Einstein manifolds. Following the methodology proposed by O. Biquard, we present the concep- tual proof of perturbation existence for general asymptotically symmetric manifolds, as outlined in their work [5]. In Chapter 3, we examine the stability of asymptotically hyperbolic Einstein manifolds under normalized Ricci flow. Drawing on R. Bamler’s research [1], we establish a reduction of the stability problem to estimating the heat kernel for the Lichnerowicz operator (refer to Lemma 3.2.2). Furthermore, we discuss the underlying ideas behind proving these heat kernel estimates. Finally, in the last chapter, we introduce our improved result on long-time existence, building upon the work presented in [42]. This enhancement in long-time existence demonstrates the significant contributions made by this thesis

How to Promote a Smart City Effectively? An Evaluation Model and Efficiency Analysis of Smart Cities in China

With the rapid development of smart cities, smart city evaluation is receiving an increasing amount of attention. However, the link between the evaluation results of smart cities and the decision making of urban construction roadmap is still relatively lacking. Therefore, it is necessary to quantitatively analyze the evaluation results, to support cities to formulate specific measures for effectively improving their smartness construction. The era of big data gives us the opportunity to evaluate and improve the development of smart cities with urban data. This paper proposes a Capability&ndash;Performance&ndash;Experience (CPE) evaluation model. An empirical study was conducted with 275 Chinese cities as samples. Principal component analysis and k-means clustering were adopted to classify cities according to their infrastructure readiness level. For each category, multi-linear regression and sensitivity analysis were adopted to analyze the impact of each input factors on each output factors. The results contribute to reasonably design or adjust strategies for smart cities based on their own development stages. Some policy implications are proposed to better prioritize investment in smart cities and to maximize the return on citizens&rsquo; experience

How to Promote a Smart City Effectively? An Evaluation Model and Efficiency Analysis of Smart Cities in China

With the rapid development of smart cities, smart city evaluation is receiving an increasing amount of attention. However, the link between the evaluation results of smart cities and the decision making of urban construction roadmap is still relatively lacking. Therefore, it is necessary to quantitatively analyze the evaluation results, to support cities to formulate specific measures for effectively improving their smartness construction. The era of big data gives us the opportunity to evaluate and improve the development of smart cities with urban data. This paper proposes a Capability–Performance–Experience (CPE) evaluation model. An empirical study was conducted with 275 Chinese cities as samples. Principal component analysis and k-means clustering were adopted to classify cities according to their infrastructure readiness level. For each category, multi-linear regression and sensitivity analysis were adopted to analyze the impact of each input factors on each output factors. The results contribute to reasonably design or adjust strategies for smart cities based on their own development stages. Some policy implications are proposed to better prioritize investment in smart cities and to maximize the return on citizens’ experience