An Information Bottleneck Approach for Markov Model Construction.

Markov state models (MSMs) are valuable for studying dynamics of protein conformational changes via statistical analysis of molecular dynamics (MD) simulations. In MSMs, the complex configuration space is coarse-grained into conformational states, with the dynamics modeled by a series of Markovian transitions among these states at discrete lag times. Constructing the Markovian model at a specific lag time requires state defined without significant internal energy barriers, enabling internal dynamics relaxation within the lag time. This process coarse grains time and space, integrating out rapid motions within metastable states. This work introduces a continuous embedding approach for molecular conformations using the state predictive information bottleneck (SPIB), which unifies dimensionality reduction and state space partitioning via a continuous, machine learned basis set. Without explicit optimization of VAMP-based scores, SPIB demonstrates state-of-the-art performance in identifying slow dynamical processes and constructing predictive multi-resolution Markovian models. When applied to mini-proteins trajectories, SPIB showcases unique advantages compared to competing methods. It automatically adjusts the number of metastable states based on a specified minimal time resolution, eliminating the need for manual tuning. While maintaining efficacy in dynamical properties, SPIB excels in accurately distinguishing metastable states and capturing numerous well-populated macrostates. Furthermore, SPIB's ability to learn a low-dimensional continuous embedding of the underlying MSMs enhances the interpretation of dynamic pathways. Accordingly, we propose SPIB as an easy-to-implement methodology for end-to-end MSM construction.

A Portable Fluorescent Lateral Flow Immunoassay Platform for Rapid Detection of FluA.

The spread of the FluA virus poses significant public health concerns worldwide. Fluorescent lateral flow immunoassay (LFIA) test strips have emerged as vital tools for the early detection and monitoring of influenza infections. However, existing quantitative virus-detection methods, particularly those utilizing smartphone-based sensing platforms, encounter accessibility challenges in resource-limited areas and among the elderly population. Despite their advantages in speed and portability, these platforms often lack user-friendliness for these demographics, impeding their widespread utilization. To address these challenges, this study proposes leveraging the optical pick-up unit (OPU) sourced from commercial optical drives as a readily available fluorescence excitation module for the quantitative detection of antibodies labeled with quantum-dot fluorescent microspheres. Additionally, we utilize miniaturized and high-performance optical components and 3D-printed parts, along with a customized control system, to develop an affordable point-of-care testing (POCT) device. Within the system, a stepping motor scans the test strip from the T-line to the C-line, enabling the calculation of the fluorescence-intensity ratio between the two lines. This simple yet effective design facilitates rapid and straightforward field or at-home testing for FluA. The proposed prototype platform demonstrates promising performance, achieving a limit of detection (LOD) of 2.91 ng/mL, a total detection time of no more than 15 min, and dimensions of 151 mm × 11.2 mm × 10.8 mm3. We believe that the proposed approach holds great potential for improving access to an accurate influenza diagnosis.

Information Bottleneck Approach for Markov Model Construction.

Markov state models (MSMs) have proven valuable in studying the dynamics of protein conformational changes via statistical analysis of molecular dynamics simulations. In MSMs, the complex configuration space is coarse-grained into conformational states, with dynamics modeled by a series of Markovian transitions among these states at discrete lag times. Constructing the Markovian model at a specific lag time necessitates defining states that circumvent significant internal energy barriers, enabling internal dynamics relaxation within the lag time. This process effectively coarse-grains time and space, integrating out rapid motions within metastable states. Thus, MSMs possess a multiresolution nature, where the granularity of states can be adjusted according to the time-resolution, offering flexibility in capturing system dynamics. This work introduces a continuous embedding approach for molecular conformations using the state predictive information bottleneck (SPIB), a framework that unifies dimensionality reduction and state space partitioning via a continuous, machine learned basis set. Without explicit optimization of the VAMP-based scores, SPIB demonstrates state-of-the-art performance in identifying slow dynamical processes and constructing predictive multiresolution Markovian models. Through applications to well-validated mini-proteins, SPIB showcases unique advantages compared to competing methods. It autonomously and self-consistently adjusts the number of metastable states based on a specified minimal time resolution, eliminating the need for manual tuning. While maintaining efficacy in dynamical properties, SPIB excels in accurately distinguishing metastable states and capturing numerous well-populated macrostates. This contrasts with existing VAMP-based methods, which often emphasize slow dynamics at the expense of incorporating numerous sparsely populated states. Furthermore, SPIB's ability to learn a low-dimensional continuous embedding of the underlying MSMs enhances the interpretation of dynamic pathways. With these benefits, we propose SPIB as an easy-to-implement methodology for end-to-end MSM construction.

Discrimination and evaluation of commercial salmons by low-molecule-weight compounds: Oligopeptides and phosphatides.

In this study, the overall sensory characteristics and low-molecule-weight compounds were analyzed to achieve the discrimination of different commercial salmons and investigate the salmon's sensory and nutritional quality. The results showed that above the overall sensory properties, O. mykiss, S. salar, and O. kisutch were the most satisfied salmons by the panel with the desirable texture and flavor, which displayed a large potential for growth in the consumption market. The alcohols and sulfur compounds were key volatile compounds contributing to typical aroma of O. masou and O. gorbuscha, response higher than others by 147% to 167%. The oligopeptides and phospholipids in salmon could be used as biomarkers for discrimination of these salmon. Oligopeptides were also closely related to the taste quality of salmon. Seventeen oligopeptides showed potential umami activity based on molecular docking results, especially Arg-Val and Ser-Asn, which were the key tastants contributing to the umami of salmon.

Comprehensive insights into the mechanism of flavor formation driven via inoculation with mixed starter cultures in dry-fermented tilapia sausages: Integration of macrogenomics, volatilomics, and lipidomics.

To investigate the mechanisms of flavor formation in dry-fermented tilapia sausages, the volatiles, bacterial community, and lipid composition during fermentation were analyzed using gas chromatography-ion mobility spectrometry, 16S high throughput sequencing, and ultra-performance liquid chromatography-mass spectrometer. Pediococcus pentosaceus, Staphylococcus xylosus, and Staphylococcus carnosus became dominant bacteria during the fermentation. A total of 66 volatiles and 293 lipids (48 differential lipids) were identified. PC and PE content decreased. Aldehyde and 1-octen-3-ol content decreased. Most esters and ketones content increased during fermentation. Six metabolic pathways associated with differential lipids were identified by enrichment analysis. Glycerophospholipid metabolism was the main metabolic pathway. Correlation analysis revealed that PC and PE were precursors for volatiles, including PC 16:0/18:2 and PE 18:0/22:6. The dominant bacteria facilitate the hydrolysis of PC and PE, leading to the formation of esters and ketones. This study provides a theoretical basis for the targeted regulation of fermented sausage flavors.

Sustained and enhanced inhibitory effects of allelochemicals on Microcystis Aeruginosa during its recruitment stage.

Microcystis aeruginosa is the main toxic strain in cyanobacterial blooms, and the recruitment stage in its temperature-dependent seasonal succession is considered as the key to its subsequent growth. In this study, a protocol with specific temperature settings was developed as the simulated recruitment stage in order to investigate and confirm the superior inhibitory effects of allelochemicals on M. aeruginosa at that stage of recruitment. One of the most common allelochemicals, gallic acid (GA) (10 mg/L, 20 mg/L) was employed to treat M. aeruginosa under initially low temperature condition (15 °C), then intermediate (20 °C) and last normal (26 °C), which corresponds to the critical temperatures for cyanobacterial recruitment and growth. Growth, metabolism, photosynthetic activity, extracellular polysaccharides (EPS) and microcystins (MCs) release were analyzed and discussed in this study, and a more sustained and better inhibitory effect over a 20-day period was achieved. Notably, GA (10 mg/L) markedly delayed the recruitment of M. aeruginosa from low temperature, with an inhibition efficiency of 85.71 %, and suppressing Fv/Fm and photosynthetic pigments production. It is also observed that M. aeruginosa at recruitment stage exhibited higher sensitivity and poorer resistance to allelochemical treatment, with variable responses suggesting that optimal dosages may alter. The antioxidant enzyme activities remained high under prolonged stress, and the secretion of EPS was stimulated, indicating that cyanobacteria were more inclined to form colonies. While the laboratory-based inhibitory mechanism appeared to increase the release of microcystins in individual cells, the actual concentration of microcystins in natural aquatic environments requires further investigation.

Tri-layered core-shell structured deferoxamine magnetic particles promote Microcystis aeruginosa growth.

This experiment prepared magnetic composite siderophores (DMPs) with strong magnetism, excellent adsorption capacity, and high specific surface area. Exploring the synergistic effect of magnetic nanoparticles and siderophores on Microcystis aeruginosa growth under iron-deficient condition, by utilizing the characteristics of the three-layer core-shell structure of DMPs. This study elucidated the potential mechanism by which DMPs promote the cyanobacterial growth through physiological indicators and transcriptome analysis. On the experiment's final day, cell density in DMPs treatment group at 2, 4, and 8 mg/L were 1.10, 1.14 and 1.16 times higher than those in the control group (Ct), respectively. Similarly, chlorophyll and photosynthetic efficiency results showed improved algae growth with increasing DMPs dosage. The microcystin content in DMPs experimental groups at low, medium, and high concentration were 0.91, 0.86, and 0.83 times that of Ct, indicating alleviation of iron deficiency stress. Additionally, based on extracellular polymers, intracellular and extracellular siderophores, and visualization techniques, DMPs nanoparticles captured free iron sources in the environment, promoting algae growth by entering algal cells and facilitating the uptake and utilization of free iron ions from the solution. During the experiment, the iron uptake and transport genes (feoA and feoB) were significantly upregulated, whereas the algal siderophore synthesis gene (pchF) and the TonB-dependent transport system gene (TonB_C) were significantly downregulated, suggesting heightened activity in intracellular iron uptake and transport. This indicates an abundance of intracellular iron, eliminating the need for secrete siderophores to overcome iron deficiency. Microcystis aeruginosa increased iron bioavailability by using iron transported through DMPs in the environment while internalizing these DMPs. This study explored the mechanism of this synergistic effect to boost algal growth, and provided new ideas for elucidating the mechanism of cyanobacterial bloom outbreaks as well as the innovative application of biotechnology.

The formation and conversion of characteristic aroma profiles and key harmful substances in different high-temperature processing of hairtail (Trichiurus Haumela).

The balance regulation between characteristic aroma and hazards in high-temperature processed fish is a hot spot. This study was aimed to explore the interactive relationship between the nutritional value, microstructures, aroma, and harmful substances of hairtail under different frying methods including traditional frying (TF), air frying (AF), and vacuum frying (VF) via chemical pattern recognition. The results indicated that VF-prepared hairtail could form a crunchy mouthfeel and retain the highest content of protein (645.53 mg/g) and the lowest content of fat (242.03 mg/g). Vacuum frying reduced lipid oxidation in hairtail, resulting in the POV reaching 0.02 mg/g, significantly lower than that of TF (0.05 mg/g) and AF (0.21 mg/g), and TBARS reached 0.83 mg/g, significantly lower than that of AF (1.96 mg/g) (P < 0.05), respectively. Notable variations were observedin the aroma profileof hairtail preparedfrom different frying methods. Vacuum frying of hairtail resulted in higher levels of pyrazines and alcohols, whereas traditional frying and air frying were associated with the formation of aldehydes and ketones, respectively. Air frying was not a healthy way to cook hairtail which produced the highest concentration of harmful substances (up to 190.63 ng/g), significantly higher than VF (5.72 ng/g) and TF (52.78 ng/g) (P < 0.05), especially norharman (122.57 ng/g), significantly higher than VF (4.50 ng/g) and TF (32.63 ng/g) (P < 0.05). Norharman and acrylamide were the key harmful substances in hairtail treated with traditional frying. The vacuum frying method was an excellent alternative for deep-fried hairtail as a snack food with fewer harmful substances and a fine aroma, providing a theoretic guidance for preparing healthy hairtail food with high nutrition and superior sensory attraction.

Metabolomic insights into the effects of seasonal shifts on the dynamic variation of non-volatile compounds of abalone (Haliotis discus hannai).

Abalone (Haliotis spp.) is a shellfish known for its exceptional nutritional value and significant economic worth. This study investigated the dynamic characteristics of non-volatile compounds over a year, including metabolites, lipids, nucleotides, and free amino acids (FAAs), which determined the nutritional quality and flavor of abalone. 174 metabolites and 371 lipids were identified and characterized, while 20 FAAs and 11 nucleotides were quantitatively assessed. These non-volatile compounds of abalone were fluctuated with months variation, which was consistent with the fluctuations of environmental factors, especially seawater temperature. Compared with seasonal variation, gender had less influence on these non-volatiles. June and July proved to be the optimal harvesting periods for abalone, with the levels of overall metabolites, lipids, FAAs, and nucleotides in abalone exhibiting a higher value in June and July over a year. Intriguingly, taurine covered 60% of the total FAAs and abalone could be used as dietary taurine supplementation.

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms.

Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect. With the organic phase ratio from low to high, most compounds changed by 1.0 log units in log IE. The addition of formic acid enhanced the signal but also promoted the matrix effect, which often occurred in compounds with strong ionization capacity. It was speculated that the matrix effect was mainly in the form of competitive charge and charged droplet' gasification sites during MS detection. Subsequently, we present a log IE prediction method built using the COSMO-RS software and the artificial neural network (ANN) algorithm to address this difficulty and overcome the shortcomings of previous models, which always ignore the matrix effect. This model was developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). Furthermore, we validated this approach by predicting the log IE of 70 compounds, including those not involved in the log IE model development. The results presented demonstrate that the method we put forward has an excellent prediction accuracy for log IE (R2pred = 0.880), which means that it has the potential to predict the log IE of new compounds without authentic standards.

Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes.

Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME_tutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models.

Unraveling the relationship between aroma characteristics and lipid profile of abalone (Haliotis discus hannai) during seasonal fluctuation and thermal processing.

Abalone, a highly sought-after aquatic product, possesses significant nutritional value. In this study, the relationship between aroma characteristics and lipid profile of abalone (Haliotis discus hannai) during seasonal fluctuation and thermal processing were profiled via volatolomics and lipidomics. 46 aroma compounds and 371 lipids were identified by HS-SPME-GC-MS and UPLC-Q-Extractive Orbitrap-MS, respectively. Multivariate statistical analysis indicated that carbonyls (aldehydes and ketones) and alcohols were the characteristic aroma compounds of abalone. The fluctuations in the aroma compound and lipid composition of abalone were consistent with the seasonal variation, especially seawater temperature. In addition, based on the correlation analysis, it was found that carbonyls (aldehydes and ketones) and alcohols had a positive correlation with phospholipids (lysophosphatidylethanolamines and lysophosphatidylcholines), while a negative correlation was observed with fatty acyls. These findings suggested that the effect of seasonal variations on the aroma changes of abalone might achieved by modulating the lipids composition of abalone.

Submillisecond Atomistic Molecular Dynamics Simulations Reveal Hydrogen Bond-Driven Diffusion of a Guest Peptide in Protein-RNA Condensate.

Liquid-liquid phase separation mediated by proteins and/or nucleic acids is believed to underlie the formation of many distinct condensed phases, or membraneless organelles, within living cells. These condensates have been proposed to orchestrate a variety of important processes. Despite recent advances, the interactions that regulate the dynamics of molecules within a condensate remain poorly understood. We performed accumulated 564.7 µs all-atom molecular dynamics (MD) simulations (system size ∼200k atoms) of model condensates formed by a scaffold RNA oligomer and a scaffold peptide rich in arginine (Arg). These model condensates contained one of three possible guest peptides: the scaffold peptide itself or a variant in which six Arg residues were replaced by lysine (Lys) or asymmetric dimethyl arginine (ADMA). We found that the Arg-rich peptide can form the largest number of hydrogen bonds and bind the strongest to the scaffold RNA in the condensate, relative to the Lys- and ADMA-rich peptides. Our MD simulations also showed that the Arg-rich peptide diffused more slowly in the condensate relative to the other two guest peptides, which is consistent with a recent fluorescence microscopy study. There was no significant increase in the number of cation-π interactions between the Arg-rich peptide and the scaffold RNA compared to the Lys-rich and ADMA-rich peptides. Our results indicate that hydrogen bonds between the peptides and the RNA backbone, rather than cation-π interactions, play a major role in regulating peptide diffusion in the condensate.

Goal-Directed, Evidence-Based Care Reduces the Incidence of Perioperative Stress Injury.

Objective: This study aimed to investigate the clinical impact of goal-oriented, evidence-based nursing in preventing perioperative stress injuries. Methods: A total of 380 patients undergoing surgery were allocated into either the control or study group. The study group received goal-oriented, evidence-based nursing, while the control group received routine nursing care. Various perioperative indicators, including operating time, position change time, intraoperative bleeding, and length of hospitalization, were assessed and compared between the two groups. Additionally, the Mini-Nutritional Assessment (MNA) score, Munro score, incidence of stress injuries, and nursing satisfaction rate were compared. Patients with perioperative pressure sores (PS) were further evaluated using the Pressure Ulcer Healing Score (PUSH), Braden Stress Injury Scale (Braden), visual analogue scale of pain (VAS), and wound healing time. Results: The study group exhibited higher MNA levels during and after the operation, while Munro levels were lower compared to the control group (P < .05). The study group demonstrated a shorter length of stay and quicker body position changes than the control group. Incidence of pressure sores (PS) was lower in the study group, accompanied by higher nursing satisfaction. PS patients in the study group had lower VAS and PUSH scores, higher Braden scores, and shorter wound healing times than those in the control group. Conclusion: This study highlights the efficacy of goal-oriented, evidence-based nursing in reducing perioperative stress injuries, advocating its adoption for improved care and patient outcomes. However, the single-center design limits generalizability, necessitating further validation. Ultimately, this approach signifies a step forward in nursing practice, promising better patient recovery and satisfaction.

In Silico-Predicted Dynamic Oxlipidomics MS/MS Library: High-Throughput Discovery and Characterization of Unknown Oxidized Lipids.

Nontargeted lipidomics using liquid chromatography-tandem mass spectrometry can detect thousands of molecules in biological samples. However, the annotation of unknown oxidized lipids is limited to the structures present in libraries, restricting the analysis and interpretation of experimental data. Here, we describe Doxlipid, a computational tool for oxidized lipid annotation that predicts a dynamic MS/MS library for every experiment. Doxlipid integrates three key simulation algorithms to predict libraries and covers 32 subclasses of oxidized lipids from the three main classes. In the evaluation, Doxlipid achieves very high prediction and characterization performance and outperforms the current oxidized lipid annotation methods. Doxlipid, combined with a molecular network, further annotates unknown chemical analogs in the same reaction or pathway. We demonstrate the broad utility of Doxlipid by analyzing oxidized lipids in ferroptosis hepatocellular carcinoma, tissue samples, and other biological samples, substantially advancing the discovery of biological pathways at the trace oxidized lipid level.

Unraveling the Formation Mechanism of Egg's Unique Flavor via Flavoromics and Lipidomics.

Egg products after thermal treatment possess a unique flavor and are favored by consumers. In this study, the key aroma-active compounds of egg yolk products and their formation mechanism during thermal treatment were investigated. The volatile aroma compounds in egg yolks were monitored using an electronic nose, gas chromatography-mass spectrometry (GC-MS) and gas chromatography-olfactometry-mass spectrometry (GC-O-MS), and the lipid molecular species were explored using ultra-high-performance liquid chromatography- mass spectrometry with a Q-Exactive HF-X Orbitrap (UPLC-Q-Exactive HF-X). A total of 68 volatile compounds were identified. Boiled eggs mainly derived their flavor from hexanal, 2-pentyl-furan, 2-butanone, 3-methyl-butanal and heptane. Meanwhile, fried eggs relied mainly on 14 compounds, the most important of which were 2-ethyl-3-methyl-pyrazine, 3-ethyl-2,5-dimethyl-pyrazine, 2-ethyl-3,5-dimethyl-pyrazine, nonanal and 2,3-diethyl-5-methyl-pyrazine, providing a baked and burnt sugar flavor. A total of 201 lipid molecules, belonging to 21 lipid subclasses, were identified in egg yolks, and 13 oxidized lipids were characterized using a molecular network. Phosphoethanolamines (PEs) containing polyunsaturated fatty acids were the primary flavor precursors contributing to the development of egg yolks' flavor, participating in lipid oxidation reactions and the Maillard reaction and regulating the production of aldehydes and pyrazine compounds. This study provides reference and guidance for the development of egg yolk flavor products.

B56δ long-disordered arms form a dynamic PP2A regulation interface coupled with global allostery and Jordan's syndrome mutations.

Intrinsically disordered regions (IDR) and short linear motifs (SLiMs) play pivotal roles in the intricate signaling networks governed by phosphatases and kinases. B56δ (encoded by PPP2R5D) is a regulatory subunit of protein phosphatase 2A (PP2A) with long IDRs that harbor a substrate-mimicking SLiM and multiple phosphorylation sites. De novo missense mutations in PPP2R5D cause intellectual disabilities (ID), macrocephaly, Parkinsonism, and a broad range of neurological symptoms. Our single-particle cryo-EM structures of the PP2A-B56δ holoenzyme reveal that the long, disordered arms at the B56δ termini fold against each other and the holoenzyme core. This architecture suppresses both the phosphatase active site and the substrate-binding protein groove, thereby stabilizing the enzyme in a closed latent form with dual autoinhibition. The resulting interface spans over 190 Šand harbors unfavorable contacts, activation phosphorylation sites, and nearly all residues with ID-associated mutations. Our studies suggest that this dynamic interface is coupled to an allosteric network responsive to phosphorylation and altered globally by mutations. Furthermore, we found that ID mutations increase the holoenzyme activity and perturb the phosphorylation rates, and the severe variants significantly increase the mitotic duration and error rates compared to the normal variant.

Ultrahigh-Throughput Directed Evolution of Polymer-Degrading Enzymes Using Yeast Display.

Enzymes that degrade synthetic polymers have attracted intense interest for eco-friendly plastic recycling. However, because enzymes did not evolve for the cleavage of abiotic polymers, directed evolution strategies are needed to enhance activity for plastic degradation. Previous directed evolution efforts relied on polymer degradation assays that were limited to screening ∼104 mutants. Here, we report a high-throughput yeast surface display platform to rapidly evaluate >107 enzyme mutants for increased activity in cleaving synthetic polymers. In this platform, individual yeast cells display distinct mutants, and enzyme activity is detected by a change in fluorescence upon the cleavage of a synthetic probe resembling a polymer of interest. Highly active mutants are isolated by fluorescence activated cell sorting and identified through DNA sequencing. To demonstrate this platform, we performed directed evolution of a polyethylene terephthalate (PET)-depolymerizing enzyme, leaf and branch compost cutinase (LCC). We identified activity-boosting mutations that substantially increased the kinetics of degradation of solid PET films. Biochemical assays and molecular dynamics (MD) simulations of the most active variants suggest that the H218Y mutation improves the binding of the enzyme to PET. Overall, this evolution platform increases the screening throughput of polymer-degrading enzymes by 3 orders of magnitude and identifies mutations that enhance kinetics for depolymerizing solid substrates.

4D hyperspectral surface topography measurement system based on the Scheimpflug principle and hyperspectral imaging.

A four-dimensional (4D) hyperspectral surface topography measurement (HSTM) system that can acquire uniform inelastic signals [three-dimensional (3D) spatial data] and reflection/fluorescence spectra of an object is proposed. The key components of the system are a light-sheet profilometer based on the Scheimpflug principle and a hyperspectral imager. Based on the mapping relationships among the image coordinate systems of the two imaging subsystems and the coordinate system of the real space, the spectral data can be assigned to the corresponding 3D point cloud, forming a 4D model. The spectral resolution is better than 4 nm. 700 nm, 546 nm, and 436 nm are selected as the three primary colors of red, green, and blue to restore the color. The 4D hyperspectral surface reconstruction experiments of philodendron and chlorophytum have shown the good performance of the proposed HSTM system and the great application potential for plant phenotype and growth analysis in agriculture.

The regulation of key flavor of traditional fermented food by microbial metabolism: A review.

The beneficial microorganisms in food are diverse and complex in structure. These beneficial microorganisms can produce different and unique flavors in the process of food fermentation. The unique flavor of these fermented foods is mainly produced by different raw and auxiliary materials, fermentation technology, and the accumulation of flavor substances by dominant microorganisms during fermentation. The succession and metabolic accumulation of microbial flora significantly impacts the distinctive flavor of fermented foods. The investigation of the role of microbial flora changes in the production of flavor substances during fermentation can reveal the potential connection between microbial flora succession and the formation of key flavor compounds. This paper reviewed the evolution of microbial flora structure as food fermented and the key volatile compounds that contribute to flavor in the food system and their potential relationship. Further, it was a certain guiding significance for food industrial production.