Hierarchical Modes Exploring in Generative Adversarial Networks

In conditional Generative Adversarial Networks (cGANs), when two different initial noises are concatenated with the same conditional information, the distance between their outputs is relatively smaller, which makes minor modes likely to collapse into large modes. To prevent this happen, we proposed a hierarchical mode exploring method to alleviate mode collapse in cGANs by introducing a diversity measurement into the objective function as the regularization term. We also introduced the Expected Ratios of Expansion (ERE) into the regularization term, by minimizing the sum of differences between the real change of distance and ERE, we can control the diversity of generated images w.r.t specific-level features. We validated the proposed algorithm on four conditional image synthesis tasks including categorical generation, paired and un-paired image translation and text-to-image generation. Both qualitative and quantitative results show that the proposed method is effective in alleviating the mode collapse problem in cGANs, and can control the diversity of output images w.r.t specific-level features

Analysis on the Business Model of Fresh E-commerce------Taking Hema Supermarket as an Example

Enterprises are beginning to involve the fresh produce industry, but most companies have withdrawn from the fresh produce industry due to poor performance. This shows that there are many problems with e-commerce of fresh produce. In particular, the business model of e-commerce for fresh produce is a major factor constraining its development. This article takes Hema Supermarket as an example to analyze its business model. It summarizes the areas that can be used for product control, power distribution system construction, platform operation, etc., and provides reference and reference for the operation of fresh agricultural products

Heterogeneity in macrophages along the cochlear spiral in mice: insights from SEM and functional analyses

The susceptibility of sensory cells to pathological conditions differs between the apical and basal regions of the cochlea, and the cochlear immune system may contribute to this location-dependent variability. Our previous study found morphological differences in basilar membrane macrophages between the apical and basal regions of the cochlea. However, the details of this site-dependent difference and its underlying structural and biological basis are not fully understood. In this study, we utilized scanning electron microscopy to examine the ultrastructure of macrophages and their surrounding supporting structures. Additionally, we examined the phagocytic activities of macrophages and the expression of immune molecules in both apical and basal regions of the cochlea. We employed two mouse strains (C57BL/6J and B6.129P-Cx3cr1tm1Litt/J) and evaluated three experimental conditions: young normal (1–4 months), aging (11–19 months), and noise-induced damage (120 dB SPL for 1 h). Using scanning electron microscopy, we revealed location-specific differences in basilar membrane macrophage morphology and surface texture, architecture in mesothelial cell layers, and spatial correlation between macrophages and mesothelial cells in both young and older mice. Observations of macrophage phagocytic activities demonstrated that basal macrophages exhibited greater phagocytic activities in aging and noise-damaged ears. Furthermore, we identified differences in the expression of immune molecules between the apical and basal cochlear tissues of young mice. Finally, our study demonstrated that as the cochlea ages, macrophages in the apical and basal regions undergo a transformation in their morphologies, with apical macrophages acquiring certain basal macrophage features and vice versa. Overall, our findings demonstrate apical and basal differences in macrophage phenotypes and functionality, which are related to distinct immune and structural differences in the macrophage surrounding tissues

An almond-based low carbohydrate diet improves depression and glycometabolism in patients with Type 2 Diabetes through modulating gut microbiota and GLP-1: A randomized controlled trial

A low carbohydrate diet (LCD) is more beneficial for the glycometabolism in type 2 diabetes (T2DM) and may be effective in reducing depression. Almond, which is a common nut, has been shown to effectively improve hyperglycemia and depression symptoms. This study aimed to determine the effect of an almond-based LCD (a-LCD) on depression and glycometabolism, as well as gut microbiota and fasting glucagon-like peptide 1 (GLP-1) in patients with T2DM. Methods: This was a randomized controlled trial which compared an a-LCD with a low-fat diet (LFD). Forty-five participants with T2DM at a diabetes club and the Endocrine Division of the First and Second Affiliated Hospital of Soochow University between December 2018 to December 2019 completed each dietary intervention for 3 months, including 22 in the a-LCD group and 23 in the LFD group. The indicators for depression and biochemical indicators including glycosylated hemoglobin (HbA1c), gut microbiota, and GLP-1 concentration were assessed at the baseline and third month and compared between the two groups. Results: A-LCD significantly improved depression and HbA1c (p <0.01). Meanwhile, a-LCD significantly increased the short chain fatty acid (SCFAs)-producing bacteria Roseburia, Ruminococcus and Eubacterium. The GLP-1 concentration in the a-LCD group was higher than that in the LFD group (p <0.05). Conclusions: A-LCD could exert a beneficial effect on depression and glycometabolism in patients with T2DM. We speculate that the role of a-LCD in improving depression in patients with T2DM may be associated with it stimulating the growth of SCFAs-producing bacteria, increasing SCFAs production and GPR43 activation, and further maintaining GLP-1 secretion. In future studies, the SCFAs and GPR43 activation should be further examined

Village-level poverty identification using machine learning, high-resolution images, and geospatial data

Tracking progress in poverty alleviation and promptly identifying the distribution of poor areas are critical for strategic policy interventions, especially for regions with poor statistical systems. The massive satellite imagery and geospatial data provide great opportunities for timely and cost-effective socioeconomic evaluations. However, existing research on poverty identification is mostly based on satellite images, and the potential of combined multi-source geospatial data on poverty identification has not been fully explored. Here, we propose an approach that evaluates how village-level poverty can be identified by integrating high-resolution imagery (HRI), point-of-interest (POI), OpenStreetMap (OSM), and digital surface model (DSM) data. The study area included 338 villages from Yunyang County, located in Hubei Province, central China. We extracted the explanatory variables indicating access to facilities and services, agricultural production conditions, village construction, and the spatial distribution of village settlements from the HRI, POI, OSM, and DSM data. The random forest algorithm was then used to model the relationship between village-level poverty and explanatory variables. The results demonstrated a 54% accuracy in the prediction of village-level poverty; the best prediction performance (72%) was observed for the villages categorized as poor. The built-up land proportion and the time cost to the facilities and services contributed the most to the identification of village-level poverty, while the proxy variables of agricultural production conditions contributed the least. This study provides an approach to village-level poverty identification using satellite imagery and geospatial data and proves that the data employed in this study could identify the poorest areas that are highly coupled with natural geographical conditions and backward public services

Highlighting the importance of healthy sleep patterns in the risk of adult asthma under the combined effects of genetic susceptibility: a large-scale prospective cohort study of 455 405 participants

Background Individuals with asthma usually have comorbid sleep disturbances; however, whether sleep quality affects asthma risk is still unclear. We aimed to determine whether poor sleep patterns could increase the risk of asthma and whether healthy sleep patterns could mitigate the adverse effect of genetic susceptibility.Methods A large-scale prospective study was performed in the UK Biobank cohort involving 455 405 participants aged 38–73 years. Polygenic risk scores (PRSs) and comprehensive sleep scores, including five sleep traits, were constructed. A multivariable Cox proportional hazards regression model was used to investigate the independent and combined effects of sleep pattern and genetic susceptibility (PRS) on asthma incidence. Subgroup analysis across sex and sensitivity analysis, including a 5-year lag, different covariate adjustments and repeat measurements were performed.Results A total of 17 836 individuals were diagnosed with asthma during over 10 years of follow-up. Compared with the low-risk group, the HRs and 95% CIs for the highest PRS group and the poor sleep pattern group were 1.47 (95% CI: 1.41 to 1.52) and 1.55 (95% CI: 1.45 to 1.65), respectively. A combination of poor sleep and high genetic susceptibility led to a twofold higher risk compared with the low-risk combination (HR (95% CI): 2.22 (1.97 to 2.49), p&lt;0.001). Further analysis showed that a healthy sleep pattern was associated with a lower risk of asthma in the low, intermediate and high genetic susceptibility groups (HR (95% CI): 0.56 (0.50 to 0.64), 0.59 (0.53 to 0.67) and 0.63 (0.57 to 0.70), respectively). Population-attributable risk analysis indicated that 19% of asthma cases could be prevented when these sleep traits were improved.Conclusions Individuals with poor sleep patterns and higher genetic susceptibility have an additive higher asthma risk. A healthy sleep pattern reflected a lower risk of asthma in adult populations and could be beneficial to asthma prevention regardless of genetic conditions. Early detection and management of sleep disorders could be beneficial to reduce asthma incidence

Measuring the Critical Influence Factors for Predicting Carbon Dioxide Emissions of Expanding Megacities by XGBoost

CO2 is the main greenhouse gas. Urban spatial development, land use, and so on may be affected by CO2 and climate change. The main questions studied in this paper are as follows: What are the drivers of CO2 emissions of expanding megacities? How can they be analyzed from different perspectives? Do the results differ for megacities at different stages of development? Based on the XGBoost model, this paper explored the complex factors affecting CO2 emissions by using data of four Chinese megacities, Beijing, Tianjin, Shanghai, and Chongqing, from 2003 to 2017. The main findings are as follows: The XGBoost model has better applicability and accuracy in predicting carbon emissions of expanding megacities, with root mean square error (RMSE) as low as 0.036. Under the synergistic effect of multiple factors, population, land size, and gross domestic product are still the primary driving forces of CO2 emissions. Population density and population become more important in the single-factor analysis. The key drivers of CO2 emissions in megacities at respective developmental stages are different. This paper provides methods and tools for accurately predicting CO2 emissions and measuring the critical drivers. Furthermore, it could provide decision support for megacities to make targeted carbon-emission-reduction strategies based on their own developmental stages

Synthesis, odor characteristics and thermal behaviors of pyrrole esters

A novel approach for transesterification of methyl pyrrole-carboxylate with alcohols is reported. The transformation is performed with t-BuOK and a series of new pyrrole ester were obtained under the optimized conditions. The odor characteristics of the pyrrolyl esters were evaluated by GC–MS-O (gas chromatography-mass spectrometry-olfactometry). Among them, compounds of 4-isopropylbenzyl 1H-pyrrole-2-carboxylate (3d) and naphthalen-2-ylmethyl 1H-pyrrole-2-carboxylate (3 l) present nuts and almond-like aroma, respectively. The Py-GC/MS (pyrolysis–gas chromatography/mass spectrometry) approach was applied to evaluate the pyrolysis intermediates of the pyrrole esters in oxidative conditions. It clarified that 3d and 3 l occurred different degrees of pyrolysis throughout the pyrolysis temperature from 30 °C to 900 °C. In addition, the TG (thermogravimetry) and DSC (differential scanning calorimeter) approaches were applied to investigate at the thermal degradation process. They have good thermal stability under certain temperature according to the results of TG analysis

Using multiple linear regression and random forests to identify spatial poverty determinants in rural China

Identifying poverty determinants in a region is crucial for taking effective poverty reduction measures. This paper utilizes two variable importance analysis methods to identify the relative importance of different geographic factors to explain the spatial distribution of poverty: the Lindeman, Merenda, and Gold (LMG) method used in multiple linear regression (MLR) and variable importance used in random forest (RF) machine learning. A case study was conducted in Yunyang, a poverty-stricken county in China, to evaluate the performances of the two methods for identifying village-level poverty determinants. The results indicated that: (1) MLR and RF had similar explanation accuracy; (2) LMG and RF were consistent in the three main determinants of poverty; (3) LMG better identified the importance of variables that were highly related to poverty but correlated with other variables, while RF better identified the non-linear relationships between poverty and explanatory variables; (4) accessibility metrics are the most important variables influencing poverty in Yunyang and have a linear relationship with poverty.</p