Revealing the intrinsic relationship between microbial communities and physicochemical properties during ripening of Xuanwei ham.

To clarify the relationship between microorganisms and physicochemical indicators of Xuanwei ham. Six ham samples for the first, second and third year were selected, respectively. The changes of physicochemical properties, the free fatty acids and microbial communities of Xuanwei ham were investigated by GC-MS and high-throughput sequencing technology. Results showed that scores of colour, overall acceptability, texture, taste and aroma were the highest in the third year sample. With increasing ripening time, moisture content, water activity (Aw), lightness (L*), springiness, and resilience decreased continuously, and yellowness (b*) was the highest in the second year sample. 31 free fatty acids were detected, and unsaturated fatty acids such as palmitoleic acid, oleic acid, and linoleic acid were the major fatty acids. The content of palmitoleic acid, oleic acid and eicosenoic acid increased significantly during processing. At the phylum level, the dominant bacteria were Proteobacteria and Firmicutes, and fungi were Ascomycota. At the genus level, the dominant bacteria were Staphylococcus and Psychrobacter, and fungi were Aspergillus. Correlation analysis showed that water content and Aw were closely related to microorganisms, and most unsaturated fatty acids were significantly correlated with microorganisms. These findings showed that microorganisms played an important role in the quality of Xuanwei ham, and provided a scientific basis for the quality control of Xuanwei ham.

Evaluation of the effects of compound curing agents on the lipid profiles and volatile flavors in Nuodeng ham based on lipidomics and GC-IMS analysis.

Eighteen raw legs were evenly divided into two groups and salted with 100% NaCl and compound curing agent (60% NaCl + 40% KCl + 90 mg/kg NaNO2) to investigate the effect of compound curing agent on lipid metabolites and volatile flavor compounds in Nuodeng ham. The results of UHPLC-QE-MS and GC-IMS combined with multivariate statistical analysis showed that 27 lipid metabolites and 30 characteristic volatile flavor compounds were identified as characteristic markers in different treatment groups. The compound curing agent promoted the release of TG, SQDG, Hex1Cer, and LPC in Nuodeng ham, and accelerated the formation of volatile compounds such as 2-propanone, nonanal-d, gamma-butyrolactone, ethhyl acetate and benzeneacetaldehyde et al. Through correlation analysis, ketones were positively correlated with some PUFAs and negatively correlated with most MUFAs. Processing Nuodeng ham with compound curing agents has a positive effect on improving its quality. These findings provide a scientific theoretical basis for the development and utilization of compound curing agent.

Identification of characteristic flavor compounds and small molecule metabolites during the ripening process of Nuodeng ham by GC-IMS, GC-MS combined with metabolomics.

To investigate effects of metabolites and volatile compounds on the quality of Nuodeng ham, gas chromatography-mass spectrometry (GC-MS), ultra-high performance liquid chromatography-Q exactive orbitrap-mass spectrometry (UHPLC-QE-MS), and gas chromatography-ion transfer spectroscopy (GC-IMS) were used to analyze the differences of free fatty acids, small molecule metabolites and volatile compounds of Nuodeng ham at different ripening stages (the first, second and third year sample). 40 free fatty acids were detected. 757 and 300 metabolites were detected in positive and negative ion modes, respectively. 48 differential metabolites (VIP ≥ 1.5, P < 0.05) might important components affecting flavor differences of Nuodeng ham. Metabolic pathways revealed that fermenting-ripening of ham was associated with 31 metabolic pathways, among, 19 pathways were significant (Impact > 0.01, P < 0.05). 58 volatile compounds were identified, combined with PCA and PLS-DA, 15 flavor markers were screened out. These findings provide a scientific basis for further research on the flavor formation mechanism of Nuodeng ham.

UHPLC-MS/MS-based lipidomics for the evaluation of the relationship between lipid changes and Zn-protoporphyrin formation during Nuodeng ham processing.

The effects of changes in lipids on the formation of Zn-protoporphyrin (ZnPP) during the processing of Nuodeng ham were analyzed using a lipidomics approach based on UHPLC-MS/MS. The Nuodeng ham samples had a strong fluorescence emission peak at 590 nm, and the fluorescence intensity increased with the processing time. Lipid profiles were mainly affected by processing time. A total of 5 lipid classes were detected, of which glycerophospholipids (GP) and glycerolipids (GL) were the most abundant lipids. Fifty differential lipid compounds were screened, which were mainly GP and GL. Correlation analysis showed that 13 differential lipid compounds were significantly positively correlated with ZnPP content, and they contained more linoleic acid and oleic acid branch chains. Meanwhile, all triglycerides (TG) were negatively correlated with heme content, and they were enriched in stearic acid and palmitic acid branch chains. These findings can deepen the understanding of the relationship between ZnPP and lipids.

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models.

Internet of Things (IoT) devices for the home have made a lot of people's lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study's findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models' generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

Fosthiazate exposure induces oxidative stress, nerve damage, and reproductive disorders in nontarget nematodes.

As a forceful nematicide, fosthiazate has been largely applied in the management of root-knot nematodes and other herbivorous nematodes. However, the toxicity of fosthiazate to nontarget nematodes is unclear. To explore the toxicity and the mechanisms of fosthiazate in nontarget nematodes, Caenorhabditis elegans was exposed to 0.01-10 mg/L fosthiazate. The results implied that treatment with fosthiazate at doses above 0.01 mg/L could cause injury to the growth, locomotion behavior, and reproduction of the nematodes. Moreover, L1 larvae were more vulnerable to fosthiazate exposure than L4 larvae. Reactive oxygen species (ROS) production and lipofuscin accumulation were fairly increased in 1 mg/L fosthiazate-exposed nematodes. Treatment with 0.1 mg/L fosthiazate significantly inhibited the activity of acetylcholinesterase (p < 0.01). Furthermore, subacute exposure to 10 mg/L fosthiazate strongly influenced the expression of genes related to oxidative stress, reproduction, and nerve function (e.g., gst-1, sod-1, puf-8, wee-1.3, and ace-1 genes). These findings suggested that oxidative stress, reproduction and nerve disorders could serve as key endpoints of toxicity induced by fosthiazate. The cyp-35a family gene was the main metabolic fosthiazate in C. elegans, and the cyp-35a5 subtype was the most sensitive, with a change in expression level of 2.11-fold compared with the control. These results indicate that oxidative stress and neurological and reproductive disorders played fundamental roles in the toxicity of fosthiazate in C. elegans and may affect the abundance and function of soil nematodes.

Enantioselective Bioactivity, Toxicity, and Degradation in Vegetables and Soil of Chiral Fungicide Mandipropamid.

Mandipropamid (MDP) is a widely used chiral fungicide to control oomycete pathogens with two enantiomers. In this study, the enantioselective bioactivity, toxicity, and degradation of MDP were investigated for the first time. The bioactivity of S-MDP was 118-592 times higher than that of R-MDP and 1.14-1.67 times higher than that of Rac-MDP against six phytopathogens. Molecular docking found that S-MDP formed a strong halogen bond with HIS 693 of cellulose synthase and possessed a lower binding energy, which validated the results of the bioactivity assay. S-MDP showed lower toxicity toward Spirodela polyrhiza, while it exhibited higher toxicity in Danio rerio embryo and larva. S-MDP preferentially degraded in cowpea and pepper, while R-MDP preferentially degraded in soil. There is no significant difference between the two enantiomers in the toxicity of adult D. rerio and in cucumber degradation. Therefore, the development of the S-enantiomer was considered as a better option to exhibit high efficiency, which could reduce the residual risk of the pesticide and ensure environmental safety.

Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers for multiple attribute group decision making.

In this paper, a set of Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers (qROFNs) are presented, and a multiple attribute group decision making (MAGDM) method based on these operators is proposed. First, the operational rules of qROFNs based on the Dombi t-conorm and t-norm are introduced. A q-rung orthopair fuzzy Dombi partitioned Heronian mean (qROFDPHM) operator and its weighted form are then established in accordance with these rules. To reduce the negative effect of unreasonable attribute values on the aggregation results of these operators, a q-rung orthopair fuzzy Dombi power partitioned Heronian mean operator and its weighted form are constructed by combining qROFDPHM operator with the power average operator. A method to solve MAGDM problems based on qROFNs and the constructed operators is designed. Finally, a practical example is described, and experiments and comparisons are performed to demonstrate the feasibility and effectiveness of the proposed method. The demonstration results show that the method is feasible, effective, and flexible; has satisfying expressiveness; and can consider all the interrelationships among different attributes and reduce the negative influence of biased attribute values.

A Hilbert-type integral inequality in the whole plane related to the kernel of exponent function.

By using real analysis and weight functions, we obtain a few equivalent statements of a Hilbert-type integral inequality in the whole plane related to the kernel of exponent function with intermediate variables. The constant factor related to the gamma function is proved to be the best possible. We also consider some particular cases and the operator expressions.