Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development.

The food flavor science, traditionally reliant on experimental methods, is now entering a promising era with the help of artificial intelligence (AI). By integrating existing technologies with AI, researchers can explore and develop new flavor substances in a digital environment, saving time and resources. More and more research will use AI and big data to enhance product flavor, improve product quality, meet consumer needs, and drive the industry toward a smarter and more sustainable future.

Preparation of Microelectrolysis Filler Using Coal Slag for the Removal of Hazardous Substances in Contaminated Water.

Presently, the utilization of gasification slag in the context of wastewater treatment is constrained. Furthermore, the presence of dye wastewater and wastewater containing hazardous metals represents a significant threat to human health. Accordingly, the present study prepared a microelectrolytic filler (MF) from gasification slag via a high-temperature roasting method and evaluated its degradation performance in water containing organic matter and harmful metal ions.

Real-time accident anticipation for autonomous driving through monocular depth-enhanced 3D modeling.

The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies. In this study, we introduce an innovative framework, AccNet, which significantly advances the prediction capabilities beyond the current state-of-the-art 2D-based methods by incorporating monocular depth cues for sophisticated 3D scene modeling. Addressing the prevalent challenge of skewed data distribution in traffic accident datasets, we propose the Binary Adaptive Loss for Early Anticipation (BA-LEA).

[Advances in synthesis of succinic acid using yeast cell factories].

Succinic acid is an important C4 platform compound that serves as a raw material for the production of 1,4-butanediol, tetrahydrofuran, and biodegradable plastics such as polybutylene succinate (PBS). Compared to the traditional petrochemical-based route that uses maleic anhydride as a raw material, the microbial fermentation method for producing succinic acid offers more sustainable economic value and environmental friendliness. Yeasts with good acid tolerance can achieve low-pH fermentation of succinic acid, significantly reducing the cost of product extraction.

Sustainable succinic acid production from lignocellulosic hydrolysates by engineered strains of Yarrowia lipolytica at low pH.

Succinic acid (SA) is a valuable C4 platform chemical with diverse applications. Lignocellulosic biomass represents an abundant and renewable carbon resource for microbial production of SA. However, the presence of toxic compounds in pretreated lignocellulosic hydrolysates poses challenges to cell metabolism, leading to inefficient SA production.

Novel Umami Peptides from Mushroom () and Their Umami Enhancing Effect via Virtual Screening and Molecular Simulation.

This study aimed to identify novel umami peptides in and investigate their umami enhancing effect. We virtually screened 155 potential umami peptides from the ultrasound-assisted hydrolysate according to values, iUmami-SCM, Umami_YYDS, and Tastepeptides_DM models, and molecular docking. Five peptides (AGKNTNGSQF, DEAVARGATF, REESDFQSSF, SEETTTGVHH, and WNNDAFQSSTN) were synthesized for sensory evaluation and kinetic analysis.

A β-Primeverosidase-like Enzyme in Soybean [ (L.) Merr] Hypocotyls: Specificity toward 1-Octen-3-yl and 3-Octanyl β-Primeverosides.

A novel β-primeverosidase-like enzyme, originating from the hypocotyl of soybeans, was isolated and characterized. This enzyme, with an estimated molecular weight of 44 kDa, was identified as a monomer and exhibited peak activity at 55 °C and pH 5.5.

From Molecular Dynamics to Taste Sensory Perception: A Comprehensive Study on the Interaction of Umami Peptides with the T1R1/T1R3-VFT Receptor.

The research on the umami receptor-ligand interaction is crucial for understanding umami perception. This study integrated molecular simulations, sensory evaluation, and biosensor technology to analyze the interaction between umami peptides and the umami receptor T1R1/T1R3-VFT. Molecular dynamics simulations were used to investigate the dissociation process of seven umami peptides with the umami receptor T1R1/T1R3-VFT, and by calculating the potential mean force curve using the Jarzynski equation, it was found that the binding free energy of umami peptide is between -58.

Genome-scale transcriptional activation by non-homologous end joining-mediated integration in Yarrowia lipolytica.

Background: Genome-scale screening can be applied to efficiently mine for unknown genes with phenotypes of interest or special functions. It is also useful to identify new targets for engineering desirable properties of cell factories.

Results: Here, we designed a new approach for genome-scale transcription activation using non-homologous end joining (NHEJ)-mediated integration in Yarrowia lipolytica.

Noteworthy Consensus Effects of D/E Residues in Umami Peptides Used for Designing the Novel Umami Peptides.

Aspartic acid (D) and glutamic acid (E) play vital roles in the umami peptides. To understand their exact mechanism of action, umami peptides were collected and cut into 1/2/3/4 fragments. Connecting D/E to the N/C-termini of the fragments formed D/E consensus effect groups (DEEGs), and all fragments containing DEEG were summarized according to the ratio and ranking obtained in the above four situations.

Reconfiguration of the reductive TCA cycle enables high-level succinic acid production by Yarrowia lipolytica.

Succinic acid (SA) is an important C4-dicarboxylic acid. Microbial production of SA at low pH results in low purification costs and hence good overall process economics. However, redox imbalances limited SA biosynthesis from glucose via the reductive tricarboxylic acid (TCA) cycle in yeast.

[Clinical Features and Prognostic Factors of Patients with Primary Cutaneous Lymphoma].

Objective: To retrospectively analyze the clinical characteristics and prognostic factors of patients with primary cutaneous lymphoma.

Methods: The clinical data of 22 patients with primary cutaneous lymphoma admitted to Xinjiang Hotan District People's Hospital, Heji Hospital affiliated to Changzhi Medical College and the Fifth Medical Center of PLA General Hospital from January 2013 to June 2021 were retrospectively analyzed.

Results: The incidence of primary cutaneous T cell and NK/T cell lymphoma was about 91.

TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning.

In the field of food, the sensory evaluation of food still relies on the results of manual sensory evaluation, but the results of human sensory evaluation are not universal, and there is a problem of speech fraud. This work proposed an electroencephalography (EEG)-based analysis method that effectively enables the identification of umami/non-umami substances. First, the key features were extracted using percentage conversion, standardization, and significance screening, and based on these features, the top four models were selected from 19 common binary classification algorithms as submodels.

Identifying Umami Peptides Specific to the T1R1/T1R3 Receptor via Phage Display.

Umami peptides are small molecular weight oligopeptides that play a role in umami taste attributes. However, the identification of umami peptides is easily limited by environmental conditions, and the abundant source and high chromatographic separation efficiency remain difficult. Herein, we report a robust strategy based on a phage random linear heptapeptide library that targets the T1R1-Venus flytrap domain (T1R1-VFT).

Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning.

Taste peptides, as an important component of protein-rich foodstuffs, potentiate the nutrition and taste of food. Thereinto, umami- and bitter-taste peptides have been ex tensively reported, while their taste mechanisms remain unclear. Meanwhile, the identification of taste peptides is still a time-consuming and costly task.

FAPD: An Astringency Threshold and Astringency Type Prediction Database for Flavonoid Compounds Based on Machine Learning.

Astringency is a puckering or velvety sensation mainly derived from flavonoid compounds in food. The traditional experimental approach for astringent compound discovery was labor-intensive and cost-consuming, while machine learning (ML) can greatly accelerate this procedure. Herein, we propose the Flavonoid Astringency Prediction Database (FAPD) based on ML.

A TastePeptides-Meta system including an umami/bitter classification model Umami_YYDS, a TastePeptidesDB database and an open-source package Auto_Taste_ML.

Taste peptides with umami/bitterness play a role in food attributes. However, the taste mechanisms of peptides are not fully understood, and the identification of these peptides is time-consuming. Here, we created a taste peptide database by collecting the reported taste peptide information.

Engineering Yarrowia lipolytica for the sustainable production of β-farnesene from waste oil feedstock.

Background: β-Farnesene is a sesquiterpene with versatile industrial applications. The production of β-farnesene from waste lipid feedstock is an attractive method for sustainable production and recycling waste oil. Yarrowia lipolytica is an unconventional oleaginous yeast, which can use lipid feedstock and has great potential to synthesize acetyl-CoA-derived chemicals.

Quercetin targets VCAM1 to prevent diabetic cerebrovascular endothelial cell injury.

Introduction: Endothelial cells play important roles in neurodegenerative diseases caused by diabetes, therefore, we aimed at investigating the mechanisms through which endothelial cells are involved in diabetes development.

Methods: Single cell analysis was performed to identify the major endothelial cell subtypes in cardiovascular tissues that are involved in diabetes development. A cell-cell communication approach was then used to identify ligand-receptor interaction pairs between these cell types.

Typical Umami Ligand-Induced Binding Interaction and Conformational Change of T1R1-VFT.

Umami taste receptor type 1 member 1/3 (T1R1/T1R3) heterodimer has multiple ligand-binding sites, most of which are located in T1R1- flytrap domain (T1R1-VFT). However, the critical binding process of T1R1-VFT/umami ligands remains largely unknown. Herein, T1R1-VFT was prepared with a sufficient amount and functional activity, and its binding characteristics with typical umami molecules (monosodium l-glutamate, disodium succinate, beefy meaty peptide, and inosine-5'-monophosphate) were explored multispectroscopic techniques and molecular dynamics simulation.

Cancer incidence and spectrum among Uygurs in Hotan District in China.

Cancer is the leading cause of death in China and a significant public health problem with increasing incidence and fatality rates. The Han nationality is the main ethnic group in China, and many reports on the epidemiology of cancers in Han nationality are published. However no studies report the cancer spectrum of Uygurs which are one of the minority nationalities in China.