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Cats are increasingly favored as companion animals; their health has drawn widespread attention. Given the continuous improvements in the required living standards of both humans and animals, inflammatory bowel disease, allergies, diarrhea, constipation, periodontal disease, obesity, diabetes, and other health issues have become recognized as valid pet problems. Antibiotics are commonly used to treat pet diseases, greatly improving animal health. However, antibiotic abuse is common, especially when seeking to treat bacterial infections. Probiotics are beneficial microorganisms that may be directly ingested in food or as feed additives; they improve the intestinal microflora balance, enhance immunity, and ensure healthy growth. However, cat data are usually inferred from reports on dogs or humans; cat research remains preliminary in nature. Therefore, we here describe the current understanding of how probiotics improve cat health, facilitating the further development and application of probiotics for cats.

Lactiplantibacillus plantarum is a candidate probiotic that has been included in the list of recommended biological agents for certification by the European Food Safety Authority. It has been found to be widely present in acidic-gruel, yogurt, cheese, kefir, kimchi, and so on. In this article, we have reviewed both preclinical and human studies related to the health promoting effects of L. plantarum that have been published for the past decade. We found that L. plantarum could significantly improve intestinal function, oral as well as skin health, promote neuro as well as immune regulation, and be effective against metabolic diseases, etc. L. plantarum primarily enters the body through the oral cavity and acts on the gastrointestinal tract to effectively improve the intestinal flora. It can affect the female reproductive endocrine system through interaction with estrogen, androgen, insulin, and other hormones, as well as improve the body’s allergic reaction and immunity by regulating Th1/Th2 response. Several prior reports also suggest that this Gram-positive bacterium can promote production and secretion of key neurotransmitters and neural activators in the intestinal tract by regulating the intestinal flora by directly or indirectly affecting the gut–brain axis through modulation of vagus nerve, cytokines, and microbial metabolites, thus relieving stress and anxiety symptoms in adults. This review is the first report describing the health promoting effects of L. plantarum, with the aim of providing a theoretical basis for the development of various beneficial applications of L. plantarum.

China’s population is showing the characteristics of “fewer children” and “aging”, which will have a long-term and far-reaching impact on the food and nutritional needs of China and the world. In this paper, adult equivalent scale (AES) variables representing the household population structure were introduced into the energy intake model to quantify the impact of population structure changes on energy intake and reveal the characteristics of energy intake changes in the life cycle of Chinese residents. It is found that the change in the population structure has a significant impact on the energy intake of urban households in China, and the elasticity is 0.446. The energy intake of Chinese elderly over the age of 60 shows a trend of first increasing and then decreasing, especially after the age of 65, which indicates that the aging will promote a decline in food consumption in China. The energy intake of 1–10-year-old children, 22–30 year old, and 40–45 year old women all showed a change of first a decrease and then an increase, which may be related to parents’ care for young children and women with children’s management of body size. The average household size expressed by the adult equivalent scales of energy consumption is 2.341, which is smaller than the 3.052 calculated by the population number. The conclusion indicates that the prediction of the food and nutritional demand should be adjusted according to the changes and differences in accordance with the intensification of “fewer children” and “aging”, as well as the life cycle changes in residents’ energy intake, which is conducive to the formulation of food and nutrition security policies.

Culinary circles have experienced a recent trend towards low-salt hotpot sauces. Here, changes in the physicochemical quality, flavour, and bacterial diversity of hotpot sauces with different salt concentrations were studied during storage. The results indicated that the peroxide and acid values of hotpot sauce increased gradually and that the quality began to deteriorate with storage. A storage temperature of 4 °C and salt concentration above 4.4% significantly reduced spoilage. The salt concentration had no significant effect on the flavour but extended storage resulted in significant differences in flavour reflected in the changes of sweet, sour, bitter, umami, aftertaste-A, abundance, organic sulphide, and alkanes. Significant differences were found in the bacterial composition between samples stored at different temperatures. Norank-f-o-Chloroplast was the main bacterium in the samples stored at low temperatures, which was beneficial for preservation. Bacillus was detected in 4.1% NaCl samples stored at 25 °C, directly promoting sauce spoilage and an unpleasant flavour. This bacterium signalled the spoilage of low-salt hotpot sauce stored at room temperature.

Sea buckthorn wine (SW) and distilled liquor (DL) are fruit wines with beneficial health effects. However, their unpleasant flavour limits their development and widespread acceptance. Therefore, it is necessary to analyse their flavour composition and changes. In this study, differential metabolites of sea buckthorn DL during processing were analysed, and the relationships between E–nose sensor values and key volatile organic compounds (VOCs) were established. The results show that 133 VOCs were identified, with 22 aroma–contributing components. Fermentation significantly increased the content of VOCs, especially esters. A total of seven and 51 VOCs were significantly upregulated after fermentation and distillation, respectively. Meanwhile, seven sensors were positively correlated with the increased level of alcohols and esters, and reflected the increasing trends of 10 key VOCs.

China is undergoing a rapid dietary transition as well as a changing income distribution. In this paper, we examine the impacts of income heterogeneity on the prediction of food consumption using a dataset that covered 22,210 urban households in China’s 6 provinces. The two-stage Exact Affine Stone Index Implicit Marshallian Demand System (EASI demand system) model, which deals with the problem of censoring and endogeneity, is applied to estimate demand elasticity across income strata. Additionally, a dynamic simulation method considering income heterogeneity is conducted to predict future food consumption trends. The results reveal that income elasticity follows a decreasing trend with income growth. Furthermore, the results show that the consumption of major food items in the 15th period will increase by 7.9% to 42.0% over the base period. The growth potential of low-income groups is significantly higher than that of middle- and high-income groups. However, the prediction results may be overestimated if the differences in consumer behavior across income groups and the dynamic simulation procedure are not taken into account. Our study indicates that the consumption features of different income groups need to be included in food consumption forecasts. Moreover, the government should formulate food policies for different income groups to promote a sustainable food system transformation.

Tropical forest degradation is a major contributor to greenhouse gas emissions. Tree height can be used as an important predictor of forest growth, and yield models can provide basic data for forest degradation assessments. As an important parameter of unmanned aerial vehicle-light detection and ranging (UAV-LiDAR), it is not clear how the point cloud density affects the extraction accuracy of tree height in degraded tropical rain forests. To solve this problem, we collected UAV-LiDAR data at a flight altitude of 150 m, and then resampled the UAV-LiDAR data obtained according to the point cloud density percentage resampling method and obtained UAV-LiDAR data for five different point cloud densities, namely, 12, 17, 28, 64, and 108 points/m2. On the basis of the resampled LiDAR data, we generated a canopy height model (CHM) to extract the height of Dacrydium pierrei (D. pierrei). The results show that (1) With the increase in the point cloud density, the accuracy of tree height extraction gradually increased, with a maximum accuracy at 108 points/m2 (root mean squared error (RMSE)% = 22.78%, bias% = 14.86%). The accuracy (RMSE%) increased by 6.92% as the point cloud density increased from 12 points/m2 to 17 points/m2, but only increased by 0.99% as the point cloud density increased from 17 points/m2 to 108 points/m2, indicating that 17 points/m2 is a critical point for tree height extraction of D. pierrei. (2) Compared with the results from broad-leaved forests, the accuracy of D. pierrei height extraction from coniferous forest was higher. With the increase in point cloud density, the difference in the accuracy of D. pierrei height between two stands gradually increased. When the point cloud density was 108 points/m2, the differences in RMSE% and bas% were 3.55% and 6.22%, respectively. When the point cloud density was 12 points/m2, the differences in RMSE% and bias% were 2.71% and 4.69%, respectively. Our research identified the lowest LiDAR data point cloud density required to ensure a certain accuracy in tree height extraction, which will help scholars formulate UAV-LiDAR forest resource survey plans.

Knowledge of forest structure is vital for sustainable forest management decisions. Terrestrial laser scanning cannot describe the canopy trees in a large area, and it is unclear whether unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the ability to capture the forest canopy structural parameters in tropical forests. In this study, we estimated five forest canopy structures (stand density (N), basic area (G), above-ground biomass (AGB), Lorey’s mean height (HL), and under-crown height (hT)) with four modeling algorithms (linear regression (LR), bagged tree (BT), support vector regression (SVR), and random forest (RF)) based on UAV-LiDAR data and 60 sample plot data from tropical forests in Hainan and determined the optimal algorithms for the five canopy structures by comparing the performance of the four algorithms. First, we defined the canopy tree as a tree with a height ≥70% HL. Then, UAV-LiDAR metrics were calculated, and the LiDAR metrics were screened by recursive feature elimination (RFE). Finally, a prediction model of the five forest canopy structural parameters was established by the four algorithms, and the results were compared. The metrics’ screening results show that the most important LiDAR indexes for estimating HL, AGB, and hT are the leaf area index and some height metrics, while the most important indexes for estimating N and G are the kurtosis of heights and the coefficient of variation of height. The relative root mean squared error (rRMSE) of five structure parameters showed the following: when modeling HL, the rRMSEs (10.60%–12.05%) obtained by the four algorithms showed little difference; when N was modeled, BT, RF, and SVR had lower rRMSEs (26.76%–27.44%); when G was modeled, the rRMSEs of RF and SVR (15.37%–15.87%) were lower; when hT was modeled, BT, RF, and SVR had lower rRMSEs (10.24%–11.07%); when AGB was modeled, RF had the lowest rRMSE (26.75%). Our results will help facilitate choosing LiDAR indexes and modeling algorithms for tropical forest resource inventories.