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The effects of various fermentation conditions (fermentation temperature, amount of added Osmanthus, and timing of flower addition) on the quality of Osmanthus ‘Qingcui’ plum complex flavor wine were investigated using fresh ‘Qingcui’ plum juice and dried Osmanthus as raw materials, with volatile flavor substances and senses serving as the main indexes. Through orthogonal and single component experiments, the best brewing technique was discovered. The volatile fragrance components of Osmanthus ‘Qingcui’ plum wine with various fermenting techniques were identified using solid-phase microextraction (SPME) in conjunction with gas chromatography-mass spectrometry (GC-MS). Together with the aroma thresholds, the odor activity values (OAV) of the various volatile components were calculated, and the OAV was then utilized to assess the aroma contribution. The ranking technique was used to evaluate the organoleptic quality of the wine samples, and the Kramer test was used to statistically assess the ranking findings. Results showed that, according to the scent contributions of the sample and the findings of the organoleptic assessment, the following process variables were found to be the most effective. Fermentation temperature of 18 ℃, addition of 0.5 g/100 mL, and addition of flowers on the first day of fermentation. The Osmanthus ‘Qingcui’ plum wine had the greatest total odor activity levels (47470.04 μg/L) and the lowest sensory rating (39), which was the ideal sensory outcome, under these circumstances. A novel theoretical foundation for enhancing the quality of ‘Qingcui’ plum wine was supplied by this study.

The paocai industry faces challenges related to the production of large volumes of high-salinity and acidic brine by-products. Maintaining paocai quality while reducing brine production is crucial. This study utilized high-throughput sequencing technology to analyze microbial changes throughout the fermentation process, along with the non-volatile flavor compounds and physicochemical properties, to assess the impact of hot-air and salt-pressing pre-dehydration treatments on paocai quality. The findings indicate that pre-dehydration of raw material slowed the fermentation process but enhanced the concentration of non-volatile flavor substances, including free amino acids and organic acids. Hot-air pre-dehydration effectively reduced initial salinity to levels comparable to those in high-salinity fermentation of fresh vegetables. Furthermore, pre-dehydration altered microbial community structures and simplified inter-microbial relationships during fermentation. However, the key microorganisms such as Lactobacillus, Weissella, Enterobacter, Wallemia, Aspergillus, and Kazachstania remained consistent across all groups. Additionally, this study found that biomarkers influenced non-volatile flavor formation differently depending on the treatment, but these substances had minimal impact on the biomarkers and showed no clear correlation with high-abundance microorganisms. Overall, fermenting pre-dehydrated raw materials presents an environmentally friendly alternative to traditional paocai production.

A transient ischemic attack (TIA) affects millions of people worldwide. Although TIA risk factors have been identified individually, a systemic quantitative analysis of all health factors relevant to TIA using electronic medical records (EMR) remains lacking. This study employed a data-driven approach, leveraging hospital EMR data to create a TIA patient health factor graph. This graph consisted of 737 TIA and 737 control patient nodes, 740 health factor nodes, and over 33,000 relations between patients and factors. For all health factors in the graph, the connection delta ratios (CDRs) were determined and ranked, generating a quantitative distribution of TIA health factors. A literature review confirmed 56 risk factors in the distribution and unveiled a potential new risk factor “rhinosinusitis” for future validation. Moreover, the patient graph was visualized together with the TIA knowledge graph in the Unified Medical Language System. This integration enables clinicians to access and visualize patient data and international standard knowledge within a unified graph. In conclusion, graph CDR analysis can effectively quantify the distribution of TIA risk factors. The resulting TIA risk factor distribution might be instrumental in developing new risk prediction machine learning models for screening and early detection of TIA.

Ultrasound has been widely used for physical modifications of starch because of its effectiveness and environment friendliness; however, only a few reports have focused on the effect of varying ultrasonic treatments on the physicochemical properties of potato flour. In the present study, ultrasound at varying power levels (200, 300, 400, 500, and 600 W) and time intervals (20, 40, 60, 80, and 100 min) were used to obtain sonicated flour. Sonicated potato flour exhibited a significant (P

Abstract When enabled by machine learning (ML), Learning Health Systems (LHS) hold promise for improving the effectiveness of healthcare delivery to patients. One major barrier to LHS research and development is the lack of access to EHR patient data. To overcome this challenge, this study demonstrated the feasibility of developing a simulated ML-enabled LHS using synthetic patient data. The ML-enabled LHS was initialized using a dataset of 30,000 synthetic Synthea patients and a risk prediction XGBoost base model for lung cancer. 4 additional datasets of 30,000 patients were generated and added to the previous updated dataset sequentially to simulate addition of new patients, resulting in datasets of 60,000, 90,000, 120,000 and 150,000 patients. New XGBoost models were built in each instance, and performance improved with data size increase, attaining 0.936 recall and 0.962 AUC (area under curve) in the 150,000 patients dataset. The effectiveness of the new ML-enabled LHS process was verified by implementing XGBoost models for stroke risk prediction on the same Synthea patient populations. By making the ML code and synthetic patient data publicly available for testing and training, this first synthetic LHS process paves the way for more researchers to start developing LHS with real patient data.

BackgroundElectronic medical records (EMRs) of patients with lung cancer (LC) capture a variety of health factors. Understanding the distribution of these factors will help identify key factors for risk prediction in preventive screening for LC. ObjectiveWe aimed to generate an integrated biomedical graph from EMR data and Unified Medical Language System (UMLS) ontology for LC, and to generate an LC health factor distribution from a hospital EMR of approximately 1 million patients. MethodsThe data were collected from 2 sets of 1397 patients with and those without LC. A patient-centered health factor graph was plotted with 108,000 standardized data, and a graph database was generated to integrate the graphs of patient health factors and the UMLS ontology. With the patient graph, we calculated the connection delta ratio (CDR) for each of the health factors to measure the relative strength of the factor’s relationship to LC. ResultsThe patient graph had 93,000 relations between the 2794 patient nodes and 650 factor nodes. An LC graph with 187 related biomedical concepts and 188 horizontal biomedical relations was plotted and linked to the patient graph. Searching the integrated biomedical graph with any number or category of health factors resulted in graphical representations of relationships between patients and factors, while searches using any patient presented the patient’s health factors from the EMR and the LC knowledge graph (KG) from the UMLS in the same graph. Sorting the health factors by CDR in descending order generated a distribution of health factors for LC. The top 70 CDR-ranked factors of disease, symptom, medical history, observation, and laboratory test categories were verified to be concordant with those found in the literature. ConclusionsBy collecting standardized data of thousands of patients with and those without LC from the EMR, it was possible to generate a hospital-wide patient-centered health factor graph for graph search and presentation. The patient graph could be integrated with the UMLS KG for LC and thus enable hospitals to bring continuously updated international standard biomedical KGs from the UMLS for clinical use in hospitals. CDR analysis of the graph of patients with LC generated a CDR-sorted distribution of health factors, in which the top CDR-ranked health factors were concordant with the literature. The resulting distribution of LC health factors can be used to help personalize risk evaluation and preventive screening recommendations.

Understanding the spectrum of risk factors affecting a patient’s specific disease can help personalize disease screening recommendations. Is it possible to use synthetic patient data to develop a novel graph methodology for visualizing and identifying potential risk factors for any given disease? This study simulated an electronic medical record (EMR) database with 150K synthetic patients generated by the Synthea tool; it constructed the patient health factor graph in the Neo4j graph database for lung cancer as an example target disease. The graph contained over 990,000 relations between the factor nodes and the patient nodes, and it was connected to Unified Medical Language System (UMLS) lung cancer subgraph. Such an integrated biomedical graph was able to view and compare patient health factors and biomedical knowledge in the same graph. Through graph search, the connection delta ratio (CDR) was calculated for each factor node as a simple measure of factor–disease relationship strength. Ranking health factors by CDR produced a distribution of potential risk factors. The top-ranked factors were largely verified by and consistent with reports in literature, demonstrating the validity of this graph method. Once validated in real patient data, the new graph method may have significant implications in identifying potential risk factors for personalized disease screening recommendations and personalized medicine.

Pellicle biofilm-forming bacteria Bacillus amyloliquefaciens are the major spoilage microorganisms of soy products. Due to their inherent resistance to antibiotics and disinfectants, pellicle biofilms formed are difficult to eliminate and represent a threat to food safety. Here, we assessed linalool’s ability to prevent the pellicle of two spoilage B. amyloliquefaciens strains. The minimum biofilm inhibitory concentration (MBIC) of linalool against B. amyloliquefaciens DY1a and DY1b was 4 μL/mL and 8 μL/mL, respectively. The MBIC of linalool had a considerable eradication rate of 77.15% and 83.21% on the biofilm of the two strains, respectively. Scanning electron microscopy observations revealed that less wrinkly and thinner pellicle biofilms formed on a medium supplemented with 1/2 MBIC and 1/4 MBIC linalool. Also, linalool inhibited cell motility and the production of extracellular polysaccharides and proteins of the biofilm matrix. Furthermore, linalool exposure reduced the cell surface hydrophobicity, zeta potential, and cell auto-aggregation of B. amyloliquefaciens. Molecular docking analysis demonstrated that linalool interacted strongly with quorum-sensing ComP receptor and biofilm matrix assembly TasA through intermolecular hydrogen bonds, hydrophobic contacts, and van der Waals forces interacting with site residues. Overall, our findings suggest that linalool may be employed as a potential antibiofilm agent to control food spoilage B. amyloliquefaciens.

Pre-harvest bagging or post-harvest ethylene treatments on lemons are commonly applied to change the surface color from green to favorable yellow. In this study, the differential mechanisms of the pigment metabolism by the two treatments were investigated by pigments contents and related genetic expression. The results showed that both treatments reduced the number of chloroplasts and the content of chlorophyll. The differential expression of PSY1 and PSY2 were observed, causing the different accumulation of the main carotenoid phytoene content. The differential expression of NYC resulted in altered contents of chlorophyll a and chlorophyll b, and further led to the difference in a* value. More interestingly, the degradation of chlorophyll uncovered the color of carotenoids, leading to the color changed from green to yellow.

Our previous study extracted and identified an antibacterial peptide that was named NP-6. Herein, we investigated the physicochemical properties of NP-6, and elucidated the mechanisms underlying its antimicrobial activity against Staphylococcus aureus. The results showed that the hemolysis activity of NP-6 was 2.39 ± 0.13%, lower than Nisin A (3.91 ± 0.43%) at the same concentration (512 µg/mL). Negligible cytotoxicity towards RAW264.7 cells was found when the concentration of NP-6 was lower than 512 µg/mL. In addition, it could keep most of its activity in fetal bovine serum. Moreover, transmission electron microscopy, confocal laser scanning microscopy, and flow cytometry results showed that NP-6 can destroy the integrity of the bacterial cell membrane and increase the membrane permeability. Meanwhile, NP-6 had binding activity with bacterial DNA and RNA in vitro and strongly inhibited the intracellular β-galactosidase activity of S. aureus. Our findings suggest that NP-6 could be a promising candidate against S. aureus.

“Chinese paocai” is typically made by fermenting red radish or cabbage with aged brine (6–8 w/w). This study aimed to reveal the effects of paocai raw materials on fermentation microorganisms by metagenomics sequencing technology, and on volatile organic compounds (VOCs) by gas chromatography–mass spectroscopy, using red radish or cabbage fermented for six rounds with aged brine. The results showed that in the same fermentation period, the microbial diversity in cabbage was higher than that in red radish. Secundilactobacillus paracollinoides and Furfurilactobacillus siliginis were the characteristic bacteria in red radish paocai, whereas 15 species of characteristic microbes were found in cabbage. Thirteen kinds of VOCs were different between the two raw materials and the correlation between the microorganisms and VOCs showed that cabbage paocai had stronger correlations than radish paocai for the most significant relationship between 4-isopropylbenzyl alcohol, α-cadinol, terpinolene and isobutyl phenylacetate. The results of this study provide a theoretical basis for understanding the microbiota and their relation to the characteristic flavors of the fermented paocai.

This work evaluated the improvement effects of lipids incorporation on water resistance of composite biodegradable film prepared with wheat bran cellulose/wheat gluten (WBC/WG) using an alkaline–ethanol film forming system. Four types of lipids, paraffin wax (PW), beeswax (BW), paraffin oil (PO), and oleic acid (OA), were tested. We found that PW, BW, and PO incorporation at 5–20% improved water vapor permeability (WVP) and surface hydrophobicity of prepared films. Particularly, incorporation of 15% BW could best improve the water resistance properties of the film, with the lowest WVP of 0.76 × 10−12 g/cm·s·Pa and largest water contact angle (WCA) of 86.18°. Incorporation of OA led to the decline in moisture barrier properties. SEM images revealed that different lipids incorporation changed the morphology and of the composite film, and cross-sectional morphology indicated BW-incorporated film obtained more uniform and compact structures compared to other films. Moreover, Fourier transform infrared spectra indicated that the incorporation of PW or BW enhanced the molecular interactions between the film components, confirmed by the chemical shift of characteristic peaks at 3277 and 1026 cm−1. Differential scanning calorimetry results revealed that incorporation of PW, BW, and PO increased films’ melting point, decomposition temperatures, and enthalpy values. Furthermore, the presence of most lipids decreased tensile strength and elongation at the break of the film. Overall, the composite film containing 15% BW obtained the most promising water resistance performance and acceptable mechanical properties, and it thus most suitable as a hydrophobic biodegradable material for food packaging.

In order to evaluate the antioxidant and α-glucosidase activities of polysaccharides from Chrysanthemum morifolium cv. Hangju (CMPs), the response surface methodology was applied to optimize the parameters for extraction progress of CMPs by ultrasound, with heat reflex extraction (HRE) performed as the control. The difference in the physicochemical properties of polysaccharides obtained by the two methods were also investigated. The maximum yields (8.29 ± 0.18%) of polysaccharides extracted by ultrasonic assisted extraction (UAE) were obtained under the optimized conditions of ultrasonic power 501 W, extraction time 19 min, and ratio of liquid-to-raw material 41 mL/g. Polysaccharides extracted by UAE possessed lower protein contents (2.56%) and higher uronic acids contents (7.08%) and low molecular weight fractions than that by HRE. No significant differences were found in monosaccharide composition and Fourier transform infrared (FT-IR) spectra of polysaccharides extracted by UAE and HRE, while polysaccharides by UAE possessed stronger antioxidant and α-glucosidase inhibitory activities. Therefore, UAE was an efficient way to obtain CMPs.

The purpose of this study was to analyze the volatile compounds in baby ginger paocai and the fresh baby ginger and identify the key aroma components that contribute to the flavor of baby ginger paocai. A total of 86 volatile compounds from the two baby ginger samples were quantified; these compounds were extracted by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography–mass spectrometry (GC-MS). The aroma composition of baby ginger paocai was different from that of fresh baby ginger. Baby ginger paocai was characterized by the presence of aroma-active compounds which varied in concentration from 0.03 to 28.14%. Geranyl acetate was the aroma component with the highest relative content in baby ginger paocai. β-myrcene, eucalyptol, trans-β-ocimene, Z-ocimene, linalool, decanal, cis-citral, geraniol, geranyl acetate, curcumene, and β-bisabolene contributed to the overall aroma of the product of baby ginger paocai which had gone through a moderate fermentation process.

Dimensionless dropping shock dynamic equations of suspension nonlinear packaging system with critical components were obtained. The numerical results of shock response were gained using Runge-Kutta method. To evaluate the dropping shock characteristics of critical components, the dropping damage boundary curve was established, where the system parameter and the dimensionless shock velocity were selected as two coordinate parameters. Then, the frequency ratio and the system damping ratio were taken as third basic parameters of the dropping damage boundary surface, respectively. To study dynamic properties of the suspension system with critical components, the shock response acceleration, shock response displacements, and dropping damage boundary were analyzed. Based on the numerical results, the effects of the relevant parameters on dropping shock response and damage boundary of critical component were investigated. It is demonstrated that both a higher frequency ratio and a system damping ratio in the specific range can exert a positive effect on the product protection and should be selected in design process of the suspension system. Furthermore, with the decrease of suspension angle, the acceleration response peak decreases, the displacement response peak increases, and the safety zone enlarges.

We investigated the permselectivity and interfacial electron transfers of an amphiphilic branch-tailed fluorosurfactant self-assembled monolayer (FS-SAM) on a gold electrode by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The FS-SAM was prepared by a self-assembly technique and a “click„ reaction. The barrier property and interfacial electron transfers of the FS-SAM were also evaluated using various probes with different features. The FS-SAM allowed a higher degree of permeation by small hydrophilic (Cl− and F−) electrolyte ions than large hydrophobic (ClO4− and PF6−) ones. Meanwhile, the redox reaction of the Fe(CN)63− couple was nearly completely blocked by the FS-SAM, whereas the electron transfer of Ru(NH3)63+ was easier than that of Fe(CN)63−, which may be due to the underlying tunneling mechanism. For hydrophobic dopamine, the hydrophobic bonding between the FS-SAM exterior fluoroalkyl moieties and the hydrophobic probes, as well as the hydration resistance from the interior hydration shell around the oligo (ethylene glycol) moieties, hindered the transport of hydrophobic probes into the FS-SAM. These results may have profound implications for understanding the permselectivity and electron transfers of amphiphilic surfaces consisting of molecules containing aromatic groups and branch-tailed fluorosurfactants in their structures.

This paper presents the fabrication of a molecularly imprinted, polymer-based disposable electrochemical sensor for paraoxon (PO) determination. The sensor was based on a screen-printed carbon electrode (SPCE) modified with a surface molecularly imprinted poly (p-aminothiophenol) (PATP)/gold nanoparticles (AuNPs) composite film, which consisted of a PATP outer layer and an AuNPs inner layer. We report a novel strategy, combining surface molecularly imprinting and self-assembly directed electro-polymerization with high densely imprinting PO molecules in the PATP/AuNPs film. Firstly, AuNPs were in situ electrodeposited at the electrode surface, and then assembled with electropolmerizable functional monomer p-aminothiophenol (ATP). Subsequently, PO molecules were assembled onto the ATP monolayer-modified AuNPs, forming a basis of surface molecular imprinting. After that, replenished PO molecules were embedded in the PATP/AuNPs film by PO and the ATP molecular self-assembly directed electro-polymerization in the polymerization precursor mixture. The resulting imprinted PATP/AuNPs/SPCE possesses high sensitivity, affinity, and selectivity toward PO, with a low detection limit of 1 × 10−9 M. The proposed sensor was successfully applied for the determination of PO in fruit and vegetables, giving satisfactory recoveries. The strategy reported herein can be further expected to fabricate various molecular imprinted sensors for the determination of other pesticide residuals.

Background: To expand nasopharyngeal carcinoma (NPC) screening to larger populations, more practical NPC risk prediction models independent of Epstein-Barr virus (EBV) and other lab tests are necessary. Methods: Patient data before diagnosis of NPC were collected from hospital electronic medical records (EMR) and used to develop machine learning (ML) models for NPC risk prediction using XGBoost. NPC risk factor distributions were generated through connection delta ratio (CDR) analysis of patient graphs. By combining EMR-wide ML with patient graph analysis, the number of variables in these risk models was reduced, allowing for more practical NPC risk prediction ML models. Results: Using data collected from 1,357 patients with NPC and 1,448 patients with control, an optimal set of 100 variables (ov100) was determined for building NPC risk prediction ML models that had, the following performance metrics: 0.93-0.96 recall, 0.80-0.92 precision, and 0.83-0.94 AUC. Aided by the analysis of top CDR-ranked risk factors, the models were further refined to contain only 20 practical variables (pv20), excluding EBV. The pv20 NPC risk XGBoost model achieved 0.79 recall, 0.94 precision, 0.96 specificity, and 0.87 AUC. Conclusions: This study demonstrated the feasibility of developing practical NPC risk prediction models using EMR-wide ML and patient graph CDR analysis, without requiring EBV data. These models could enable broader implementation of NPC risk evaluation and screening recommendations for larger populations in urban community health centers and rural clinics. Impact: These more practical NPC risk models could help increase NPC screening rate and identify more patients with early-stage NPC. [ABSTRACT FROM AUTHOR]

Pre-harvest bagging or post-harvest ethylene treatments on lemons are commonly applied to change the surface color from green to favorable yellow. In this study, the differential mechanisms of the pigment metabolism by the two treatments were investigated by pigments contents and related genetic expression. The results showed that both treatments reduced the number of chloroplasts and the content of chlorophyll. The differential expression of PSY1 and PSY2 were observed, causing the different accumulation of the main carotenoid phytoene content. The differential expression of NYC resulted in altered contents of chlorophyll a and chlorophyll b, and further led to the difference in a* value. More interestingly, the degradation of chlorophyll uncovered the color of carotenoids, leading to the color changed from green to yellow. [ABSTRACT FROM AUTHOR]