Depth Separation with Multilayer Mean-Field Networks

Depth separation -- why a deeper network is more powerful than a shallower one -- has been a major problem in deep learning theory. Previous results often focus on representation power. For example, arXiv:1904.06984 constructed a function that is easy to approximate using a 3-layer network but not approximable by any 2-layer network. In this paper, we show that this separation is in fact algorithmic: one can learn the function constructed by arXiv:1904.06984 using an overparameterized network with polynomially many neurons efficiently. Our result relies on a new way of extending the mean-field limit to multilayer networks, and a decomposition of loss that factors out the error introduced by the discretization of infinite-width mean-field networks.Comment: ICLR 202

Comparative Study and Simulation of Soil Infiltration Performance in Open Green Space

Soil infiltration is important for urban open space to exert sponge benefits, and its permeability characteristics are influenced by physical and chemical properties. To determine the characteristics and differences of soil permeability in different open spaces, we used the cutting-ring method to measure the soil infiltration process in four types of open space. The effects of physicochemical properties on soil infiltration were analyzed through comparison. The infiltration process of the four types of green spaces was fitted on the basis of Kostiakov and Philip infiltration models, and the suitability of the models was discussed. The water infiltration process shows that the law of initial infiltration rate > average infiltration rate > stable infiltration rate. The stable infiltration rate of each green space ranges from 2.46 mm/min to 3.60 mm/min, and the ranking is as follows: park > square > block > other shared space. The determination coefficient of the Kostiakov model for the soil infiltration process of the four types of green space is higher than 0.94, which is suitable to describe the soil infiltration characteristics of green space in the study area. The soil infiltration performance of green space shows a negative correlation with soil bulk density and moisture content but a positive correlation with non-capillary porosity. This study provides a reference for the construction of sponge cities and ecological hydrological observation

Functional characterization of PETIOLULE-LIKE PULVINUS (PLP) gene in abscission zone development in Medicago truncatula and its application to genetic improvement of alfalfa

Alfalfa (Medicago sativa L.) is one of the most important forage crops throughout the world. Maximizing leaf retention during the haymaking process is critical for achieving superior hay quality and maintaining biomass yield. Leaf abscission process affects leaf retention. Previous studies have largely focused on the molecular mechanisms of floral organ, pedicel and seed abscission but scarcely touched on leaf and petiole abscission. This study focuses on leaf and petiole abscission in the model legume Medicago truncatula and its closely related commercial species alfalfa. By analysing the petiolule-like pulvinus (plp) mutant in M. truncatula at phenotypic level (breakstrength and shaking assays), microscopic level (scanning electron microscopy and cross-sectional analyses) and molecular level (expression level and expression pattern analyses), we discovered that the loss of function of PLP leads to an absence of abscission zone (AZ) formation and PLP plays an important role in leaflet and petiole AZ differentiation. Microarray analysis indicated that PLP affects abscission process through modulating genes involved in hormonal homeostasis, cell wall remodelling and degradation. Detailed analyses led us to propose a functional model of PLP in regulating leaflet and petiole abscission. Furthermore, we cloned the PLP gene (MsPLP) from alfalfa and produced RNAi transgenic alfalfa plants to down-regulate the endogenous MsPLP. Down-regulation of MsPLP results in altered pulvinus structure with increased leaflet breakstrength, thus offering a new approach to decrease leaf loss during alfalfa haymaking process

In-Hospital Formula Feeding Hindered Exclusive Breastfeeding: Breastfeeding Self-Efficacy as a Mediating Factor

Breastfeeding self-efficacy (BSE), defined as a mother’s confidence in her ability to breastfeed, has been confirmed to predict the uptake of exclusive breastfeeding (EBF). Early experiences during the birth hospital stay, especially in-hospital formula feeding (IHFF), can impact both EBF and maternal breastfeeding confidence. Therefore, our objective was to examine the association between IHFF and EBF outcomes and investigate whether this association is influenced by BSE. The study included 778 infants from a larger cohort study conducted in 2021, with a one-year follow-up in rural areas of Sichuan Province, China. We used a causal mediation analysis to estimate the total effect (TE), natural direct (NDE), and nature indirect effects (NIE) using the paramed command in Stata. Causal mediation analyses revealed that IHFF was negatively associated with EBF (TE odds ratio = 0.47; 95% CI, 0.29 to 0.76); 28% of this association was mediated by BSE. In the subgroup analysis, there were no significant differences in the effects between parity subgroups, as well as between infant delivery subgroups. Our study found that IHFF hindered later EBF and that BSE mediated this association. Limiting the occurrence of in-hospital formula feeding or improving maternal breastfeeding self-efficacy is likely to improve exclusive breastfeeding outcomes

The know-do gap in quality of health for chronic non-communicable diseases in rural China

Proper management of non-communicable diseases (NCDs) is a severe challenge to China's rural health system. This study investigates what influences the poor medical treatment of NCDs (diabetes and angina) by evaluating the “know-do gap” between provider knowledge and practice. To determine whether low levels of provider knowledge low quality of patient care is the primary constraint on the quality of NCDs diagnosis and treatment in rural China. Providers from Village Clinics (VC) and Township Health Centers (THC), and Standardized Patients (SP) were selected by a multi-stage random sampling method. Clinical vignettes were administered to 306 providers from 103 VCs and 50 THCs in rural Sichuan Province. SPs presented diabetes symptoms completed 97 interactions with providers in 46 VCs and 51 THCs; SPs presented angina symptoms completed 100 interactions with providers in 50 VCs and 50 THCs. Process quality, diagnosis quality, and treatment quality were assessed against national standards for diabetes and angina. Two-tailed T-tests and tests of proportions for continuous outcomes and tests of proportions for binary dependent variables were used to compare vignette and SP results. Differences between vignette and SP data calculated the know-do gap. Regression analyses were used to examine the providers/facility characteristics and knowledge/practice associations. THC providers demonstrated significantly more knowledge in vignettes and better practices in SP visits than VC providers. However, levels of knowledge were low overall: 48.2% of THC providers and 28.2% of VC providers properly diagnosed type 2 diabetes, while 23.8% of THC providers and 14.7% of VC providers properly diagnosed angina. With SPs, 2.1% of THC providers and 6.8% of VC providers correctly diagnosed type 2 diabetes; 25.5% of THC providers and 12.8% of VC providers correctly diagnosed angina. There were significant know-do gaps in diagnosis process quality, diagnosis quality, and treatment quality for diabetes (p < 0.01), and in diagnosis process quality (p < 0.05) and treatment quality for angina (p < 0.01). Providers in rural China display low levels of knowledge when treating diabetes and angina. Despite low knowledge, evidence of the know-do gap indicates that low-quality healthcare is the primary constraint on the quality of NCD diagnosis and treatment in rural China. Our research findings provide a new perspective for the evaluation of the medical quality and a technical basis for the development of new standardized cases in the future

Waist-to-height ratio and body roundness index: superior predictors of insulin resistance in Chinese adults and take gender and age into consideration

Background and objectivesMetabolic disease has become a global health concern, and insulin resistance (IR) is a crucial underlying mechanism in various metabolic diseases. This study aims to compare the ability of seven anthropometric indicators in predicting IR in the Chinese population, and to find more sensitive and simple anthropometric indicator for early identification of IR.MethodsThis prospective cross-sectional study obtained participants’ medical history, anthropometric indicators, and serum samples from three hospitals in China. Various anthropometric indicators were calculated, including body mass index (BMI), Waist-to-hip ratio (WHR), waist-to-height ratio (WtHR), conicity index (CI), A Body Shape Index (ABSI), body roundness index (BRI), abdominal volume index (AVI). The evaluation of IR is performed using the homeostasis model assessment-insulin resistance (HOMA-IR). Logistic regression analysis examined the relationship between indicators and HOMA-IR. The ability of the anthropometric indicators to predict IR was analyzed using the receiver operating characteristic (ROC) curve. Additionally, a stratified analysis was performed to evaluate the ability of the indicators in different age and gender groups.ResultsThe study included 1,592 adult subjects, with 531 in the non-IR group and 1,061 in the IR group. After adjusting for confounding factors, the anthropometric indicators showed a positive correlation with IR in the general population and across different genders and age groups (OR &gt; 1, p &lt; 0.05), except for ABSI. In the ROC curve analysis, WtHR and BRI had the highest AUC values of 0.711 for detecting IR. The optimal cut-off value for WtHR to diagnose IR was 0.53, while for BRI, it was 4.00. In the gender-stratified and age-stratified analysis, BMI, WtHR, BRI, and AVI all had AUC values &gt;0.700 in females and individuals below 60.ConclusionWtHR and BRI demonstrated a better ability to predict IR in the overall study population, making them preferred indicators for screening IR, and gender and age are important considerations. In the stratified analysis of different genders or age, BMI, WtHR, BRI, and AVI are also suitable for detecting IR in women or individuals under 60 years old in this study.Clinical trial registrationwww.chictr.org.cn, ChiCTR2100054654