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Propolis is a resinous hive product commonly used in alternative and complementary medicine. With its high potential as natural antimicrobial and antioxidant complex, propolis can be used as a bio preservative. Propolis is regarded as a good alternative to synthetic preservatives. However, its physical properties associated with the lack of knowledge concerning its bioavailability can be considered as limited factors to its use in food industry. In addition, propolis possesses also a strong odour and can affect the quality and sensory of some food products. In the last recent years, the potential use of propolis in active packaging has been largely investigated. Propolis was reported to have better performance in the reduction of oxidation and prevention of microbial contamination. In addition, propolis had gained wide acceptance as food supplement in various countries. This review summarizes current literature on the different potential strategies for incorporating propolis into active packaging and its benefits and challenges. The review also addresses disadvantages and limitations such as chemical variability and its impact on the standardisation of propolis. The potential sensory alteration in packaged food is also discussed. Despite all the limitations, propolis is regarded as a valuable and effective multifunctional material for developing innovative active packaging solutions. Further research to enhance propolis bioavailability, standardise the propolis extract and mask its sensory alteration are in need and can help to better exploit propolis application in food industry.
Samples of 165 bulk wheat, durum wheat, and barley consignments were taken during loading in western Canada and unloading in Japan from September 2018 through September 2022. The 330 paired samples were analyzed for ochratoxin A (OTA). OTA was observed at concentrations ranging from 0.16 to 3.3 µg/kg, predominantly in wheat and durum wheat. Concentrations of OTA were not consistently greater in unloading as compared to loading samples from the same bulk consignments, nor was there correlation between OTA concentration at unloading and the duration of transport, suggesting the storage conditions during transport were not conducive to Penicillium verrucosum proliferation and OTA production during the mean transport time of 29 days. A lack of correlation of OTA concentrations in loading and unloading samples reflected the heterogeneous distribution of this mycotoxin in bulk grain and emphasized the importance of proper sampling to mitigate the impact of the heterogeneous distribution on variance of OTA measurements, and on inspection of compliance.
The aims of this study were to elucidate factors contributing to the expansion of the distributions of sika deer and wild boar in Japan and to predict the expansion of their distributions by 2025, 2050, and 2100. A predictive model was constructed using information on species distribution collected by the Ministry of the Environment in 1978, 2003 and 2014, days of snow cover, forested and road areas, elevation, human population, and distance to the nearest occupied cell as covariates to calculate the probability of distribution change. Factors contributing to distribution expansion were elucidated and distribution expansion was predicted. Distance to the nearest occupied cell had the strongest influence on distribution expansion, followed by the inherent ability of each species to expand its distribution. For sika deer, human population had a negative effect and forest area and number of days of snow cover have positively affected. For wild boar, forest area, snow days and population had high importance. Predictions of future distribution showed that both species will be distributed nationwidely by 2050.
Background/Objectives: The Japanese Veterinary Antimicrobial Resistance Monitoring System (JVARM) conducts longitudinal monitoring of antimicrobial resistance (AMR) in indicator bacteria from food-producing animals. For Escherichia coli from healthy pigs, slaughterhouse-based sampling has been conducted for approximately a decade, yielding a substantial accumulation of MIC data. While JVARM reporting has traditionally focused on annual resistance proportions by drug, the availability of long-term data enables investigation of cross-drug relationships, including MIC similarity and co-resistance patterns. This study aimed to (i) identify the co-resistance structure among antimicrobial agents using MIC- and phenotype-based similarity measures and (ii) identify drug resistances most strongly associated with multidrug resistance (MDR). Methods: We analyzed broth microdilution MIC data obtained annually for E. coli isolates from healthy pigs in the JVARM program in Japan between 2012 and 2023. Antimicrobial resistance was classified from MIC results and annual resistance prevalence was calculated for each antimicrobial. For the co-resistance and MDR analyses, isolate-level data were pooled across the full study period. To identify co-resistance structure, we performed hierarchical clustering using (i) correlation-based similarity of MIC profiles and (ii) Jaccard similarity of binary resistance profiles (resistant/susceptible classification). Multidrug resistance (MDR; ≥3 antimicrobial classes) was further modeled using XGBoost with each drug resistance as a predictive feature, and feature contributions were evaluated using gain, permutation importance, and SHAP values. We also examined how SHAP-based attributions varied when the outcome definition was set to ≥1-, ≥2-, or ≥3-class resistance. Results: Within the study period, resistance remained highest for tetracycline and moderate for streptomycin, ampicillin, sulfamethoxazole-trimethoprim, and chloramphenicol, whereas resistance to other agents was low. MIC-based correlation analysis revealed coordinated variation among ampicillin, sulfamethoxazole-trimethoprim, streptomycin, chloramphenicol, and tetracycline. Separately, Jaccard similarity of binary resistance profiles identified two closely positioned co-resistance groupings (Ampicillin/Streptomycin/Tetracycline and chloramphenicol/sulfamethoxazole-trimethoprim). Ampicillin was identified as the medoid in both MIC-based and resistance-profile similarity spaces, with streptomycin also positioned near the center in both structures. In the XGBoost model for MDR (≥3 classes), ampicillin resistance was consistently the highest-contributing feature when evaluated by gain, permutation importance, and SHAP. When we examined how SHAP-based attributions varied across outcome definitions (≥1-, ≥2-, and ≥3-class resistance), feature importance largely followed resistance prevalence at ≥1-≥2 classes (tetracycline highest) but shifted at ≥3 classes to ampicillin as the top feature. Conclusions: Both MIC-based and phenotype-based analyses revealed co-resistance structures. Under the MDR definition used in this study, explainable machine-learning analyses showed that ampicillin resistance emerged as a leading resistance feature associated with MDR. Because these findings are associative rather than causal, further work will be needed to clarify mechanisms. These findings have important implications for antimicrobial resistance control in the Japanese pig sector, indicating that stewardship strategies may need to be tailored according to antimicrobial class and underlying co-resistance structure.
Monitoring tropical ecosystem services such as carbon stock and biodiversity with satellite remote sensing is essential for addressing climate change and biodiversity loss, but collecting ground truth data is costly. We investigated whether Unmanned Aerial Vehicles (UAVs) can reduce the costs. First, we developed a method to estimate Above-Ground Carbon (AGC) and biodiversity index (mixing ratio of pioneer and late-successional species) based on data derived from UAV-RGB images in four Forest Management Units (FMUs) in Malaysia. Second, we tested whether adding UAV-based ground truth (i.e., estimated carbon and biodiversity index) improves satellite-based models. We built machine learning models to estimate AGC and biodiversity index based on Landsat metrics and inventory data across Malaysia and Indonesia (395 plots). Accuracy was low without local inventory data (287 plots outside the four FMUs; R2 = 0.43 and 0.46 for AGC and biodiversity, respectively). Adding UAV-based data (n = 934) significantly increased the accuracy (R2 = 0.51 and 0.48), which was comparable to the model with the full dataset including local inventory data (R2 = 0.53 and 0.60). These results underscore that integrating UAV and satellite analyses facilitates the monitoring of ecosystem services in tropical forests by reducing costs while maintaining accuracy.
