The flavors of edible mushrooms: A comprehensive review of volatile organic compounds and their analytical methods.

Due to their distinctive flavors, edible mushrooms have gained attention in flavor-related research, and the quality of their flavors determines their consumption. The odor is a vital element of food flavor that significantly impacts consumers' perceptions and purchase decisions. The volatile organic compounds (VOCs) of the odorant ingredient is the primary factors affecting scent characteristics. VOCs analysis and identification require technical assistance. The production and use of edible mushrooms can be aided by a broader examination of their volatile constituents. This review discusses the composition of VOCs in edible mushrooms and how they affect flavors. The principles, advantages, and disadvantages of various methods for extraction, isolation, and characterization of the VOCs of edible mushrooms are also highlighted. The numerous VOCs found in edible mushrooms such as primarily C-8 compounds, organic sulfur compounds, aldehydes, ketones, alcohols, and esters are summarized along with their effects on the various characteristics of scent. Combining multiple extraction, isolation, identification, and quantification technologies will facilitate rapid and accurate analysis of VOCs in edible mushrooms as proof of sensory attributes and quality.

Target profiling of flavonol glycosides in the extract of Ginkgo biloba leaf and their pharmacokinetics in rat plasma by ultra-high-performance liquid chromatography with tandem mass spectrometry.

The extract of Ginkgo biloba leaf is a popular herbal product or dietary supplement in the world to treat various diseases, and flavonol glycosides are considered as the main bioactive constituents. In this study, 37 flavonol glycosides were rapidly screened out by precursor ion scanning in positive ion mode with production ions at m/z 287.05, 303.05, and 317.06. Subsequently, a reliable and sensitive ultra-high-performance liquid chromatography coupled with triple quadrupole-linear ion trap mass spectrometry approach was established and validated to quantify the 20 prototype flavonol glycosides in rat plasma. Calibration curves showed good linearity (R2 ≥ 0.9894) over the corresponding concentration range. The precision, accuracy, extraction recovery, matrix effect, and stability were also satisfactory. The validated method was successfully applied to a pharmacokinetic study of prototype flavonol glycosides in rat after oral administration of the extract of G. biloba leaf. As a result, the Tmax of flavonol glycosides was short at 0.11-0.60 h. Quercetin-3-O-(2",6″-di-O-rhamnosyl)-glucoside, kaempferol-3-O-(2'',6''-di-O-rhamnosyl)-glucoside, quercetin-3-O-rutinoside, quercetin- 3-O-glucosyl-(1-2)-O-rhamnoside, and kaempferol-3-O-glucoside presented relatively high systemic exposure levels with AUC0-∞  > 500 µg h/L and Cmax  > 100 µg/L. This study would provide the valuable information for further scientific research and clinical application of the extract of G. biloba leaf.

A label-free protamine-assisted colorimetric sensor for highly sensitive detection of S1 nuclease activity.

A label-free, sensitive, simple and general colorimetric method was reported to monitor S1 nuclease activity based on protamine-assisted aggregation of gold nanoparticles (AuNPs). Here, protamine, a linear polycation, was used as a medium for causing the aggregation of negatively charged AuNPs by electrostatic interactions, resulting in changes in the surface plasmon resonance (SPR) absorption bands as well as the color of AuNPs. Here, the AuNPs were employed as an indicator to detect the level of S1 nuclease in the solution. Substrate DNA could be cleaved into small fragments by the specific S1 nuclease, which effectively prevents the electrostatic interaction between DNA and protamine and thus facilitates the interaction between protamine and AuNPs. The quantitative analysis of S1 nuclease activity can be performed via directly measuring the changes in the absorption spectra of the AuNPs. Using S1 nuclease as a model analyte, the limit of detection was estimated to be 1.0 × 10-4 U mL-1. Furthermore, the proposed concept has been successfully applied in S1 nuclease analysis of serum samples, offering an ultrasensitive strategy for the speedy detection of the nuclease activity and providing a new avenue for high-throughput screening of nucleases and drugs with potential inhibition properties.

A graphene oxide/gold nanoparticle-based amplification method for SERS immunoassay of cardiac troponin I.

Cardiac troponin I (cTnI) was considered as the "gold standard" for acute myocardial infarction (AMI) diagnosis owing to its superior cardiac specificity for cardiac damage and showing little or no changes in patients with a skeletal muscle disease or trauma. Herein, a new signal amplification surface-enhanced Raman scattering (SERS) platform was developed for recognition and detection of cTnI by using gold nanoparticles (AuNPs), graphene oxide (GO) and magnetic beads (MB). Here, antibody/Raman reporter labeled AuNP-functionalized GO were employed as both SERS nanotags and signal amplification carriers. Monoclonal antibody modified MB were applied as the capture probe and separation agents. In the presence of cTnI, sandwich type immunocomplexes, "capture probe/target/SERS nanotags", were formed through antibody-antigen-antibody interactions. Due to the strong SERS enhancement ability of the designed GO/AuNP complexes and a high binding chance between cTnI and the GO/AuNP complexes, the proposed SERS-based immunoassay could selectively detect cTnI with a high sensitivity (detection limit of 5 pg mL-1) and a good linearity was obtained in a range of 0.01-1000 ng mL-1. In addition, this method was also successfully applied for detecting cTnI in serum substitute media with a similar linear range. Furthermore, this strategy can be constructed with different kinds of antibodies and Raman reporters, and thus can be easily used for simultaneous detection of multiple biomarkers. Therefore, this proposed SERS-based signal amplification technique shows strong potential for the clinical diagnosis of AMI disease.

Tunable reflectance of an inverse opal-chiral nematic liquid crystal multilayer device by electric- or thermal-control.

A new type of electric- or thermal-responsive multilayer device composed of SiO2 bilayer inverse opal (IOP) and chiral nematic liquid crystals (N*LCs) was developed. Bilayer IOP was fabricated by layer-by-layer assembly of polystyrene (PS) spheres with two different sizes and showed a reflectance in an extended range of the near-infrared region. Furthermore, the electrically or thermally tunable reflectance of the bilayer-IOP-N*LC device was investigated. The device exhibited the photonic bandgap (PBG) of the N*LC-IOP composite structure with the application of an electric field (voltage-on), while it presented the reflectance of N*LCs without an electric field (voltage-off) and the electrically-responsive behaviour could be reversibly switched. Besides, the device exhibited a gradient redshift of reflectance as temperature increased below the clearing point (TC) while it showed the PBG of the N*LC-IOP composite structure when the temperature was above TC.

Reliability analysis using Limit equilibrium and Smoothed Particle Hydrodynamics-based method for homogeneous soil slopes.

This paper develops a combined method to predict the volume of sliding mass for homogeneous slopes in an efficient manner. Firstly, the failure surface with minimum factor of safety (FS) in Limit Equilibrium Method is equated to that one determined by Smoothed Particle Hydrodynamics algorithm to obtain the threshold displacement value for unstable and stable particles. Secondly, the threshold displacement value is used to identify the volume of sliding mass using SPH. Finally, a regression model is developed based on a finite number of SPH simulations for homogeneous soil slopes. The proposed LEM-SPH based method is illustrated through a cohesive soil slope. It is concluded that the use of failure surface with minimum FS in LEM tends to underestimate the volume of sliding mass and to give an unconservative risk value. The Coefficient of Variation (Cov) of volume of sliding mass are 0.14, 0.28, 0.4, 0.48, 0.53 for Cov of soil properties = 0.2, 0.3, 0.4, 0.5, and 0.6, respectively. The uncertainty of soil properties has a significant effect on the mean value of volume of sliding mass and therefore the landslide risk value. The proposed method is necessitated for cases where large uncertainties in soil properties exist.

The effectiveness of self: A meta-analysis of using self-referential encoding techniques in education.

BACKGROUND: Self-related information is difficult to ignore and forget, which brings valuable implications for educational practice. Self-referential encoding techniques involve integrating self-referencing cues during the processing of learning material. However, the evidence base and effective implementation boundaries for these techniques in teaching and learning remain uncertain due to research variability. AIMS: The present meta-analysis aims to quantitatively synthesize the results from studies applying self-referential encoding techniques in education. METHODS: The analysis was based on data from 20 independent samples, including 1082 students from 13 primary studies identified through a systematic literature search. RESULTS: Results from random effect models show that incorporating self-referential encoding techniques improved learning (g = .40, 95% CI [.18, .62]). Subgroup analysis showed that the valence of learning material serves as a significant boundary condition for this strategy. The students' cohorts, types of learning materials, and research context did not moderate the effect sizes. CONCLUSIONS: Our results suggest that incorporating self-referential encoding techniques on negative materials shows an aversive effect. Overall, there is a universal benefit to using self-referential encoding techniques as an appropriate design guideline in educational contexts. Implications for teaching practice and future directions are discussed. Further studies are needed to investigate the effectiveness in more diverse educational and teaching situations.

The mechanisms in the gut microbiota regulation and type 2 diabetes therapeutic activity of resistant starches.

Resistant starch (RS) can potentially prevent type 2 diabetes through the modulation of intestinal microbiota and microbial metabolites. Currently, it has been wildly noted that altering the intestinal microbial composition and short-chain fatty acids levels can achieve therapeutic effects, although the specific mechanisms were rarely elucidated. This review systematically explores the structural characteristics of different RS, analyzes the cross-feeding mechanism utilized by intestinal microbiota, and outlines the pathways and targets of butyrate, a primary microbial metabolite, for treating diabetes. Different RS types may have a unique impact on microbiota composition and their cross-feeding, thus exploring regulatory mechanisms of RS on diabetes through intestinal flora interaction and their metabolites could pave the way for more effective treatment outcomes for host health. Furthermore, by understanding the mechanisms of strain-level cross-feeding and metabolites of RS, precise dietary supplementation methods targeted at intestinal composition and metabolites can be achieved to improve T2DM.

A structural similarity networking assisted collision cross-section prediction interval filtering strategy for multi-compound identification of complex matrix by ion-mobility mass spectrometry.

Ion mobility coupled with mass spectrometry (IM-MS), an emerging technology for analysis of complex matrix, has been facing challenges due to the complexities of chemical structures and original data, as well as low-efficiency and error-proneness of manual operations. In this study, we developed a structural similarity networking assisted collision cross-section prediction interval filtering (SSN-CCSPIF) strategy. We first carried out a structural similarity networking (SSN) based on Tanimoto similarities among Morgan fingerprints to classify the authentic compounds potentially existing in complex matrix. By performing automatic regressive prediction statistics on mass-to-charge ratios (m/z) and collision cross-sections (CCS) with a self-built Python software, we explored the IM-MS feature trendlines, established filtering intervals and filtered potential compounds for each SSN classification. Chemical structures of all filtered compounds were further characterized by interpreting their multidimensional IM-MS data. To evaluate the applicability of SSN-CCSPIF, we selected Ginkgo biloba extract and dripping pills. The SSN-CCSPIF subtracted more background interferences (43.24%∼43.92%) than other similar strategies with conventional ClassyFire criteria (10.71%∼12.13%) or without compound classification (35.73%∼36.63%). Totally, 229 compounds, including eight potential new compounds, were characterized. Among them, seven isomeric pairs were discriminated with the integration of IM-separation. Using SSN-CCSPIF, we can achieve high-efficient analysis of complex IM-MS data and comprehensive chemical profiling of complex matrix to reveal their material basis.

Mutual-DTI: A mutual interaction feature-based neural network for drug-target protein interaction prediction.

The prediction of drug-target protein interaction (DTI) is a crucial task in the development of new drugs in modern medicine. Accurately identifying DTI through computer simulations can significantly reduce development time and costs. In recent years, many sequence-based DTI prediction methods have been proposed, and introducing attention mechanisms has improved their forecasting performance. However, these methods have some shortcomings. For example, inappropriate dataset partitioning during data preprocessing can lead to overly optimistic prediction results. Additionally, only single non-covalent intermolecular interactions are considered in the DTI simulation, ignoring the complex interactions between their internal atoms and amino acids. In this paper, we propose a network model called Mutual-DTI that predicts DTI based on the interaction properties of sequences and a Transformer model. We use multi-head attention to extract the long-distance interdependent features of the sequence and introduce a module to extract the sequence's mutual interaction features in mining complex reaction processes of atoms and amino acids. We evaluate the experiments on two benchmark datasets, and the results show that Mutual-DTI outperforms the latest baseline significantly. In addition, we conduct ablation experiments on a label-inversion dataset that is split more rigorously. The results show that there is a significant improvement in the evaluation metrics after introducing the extracted sequence interaction feature module. This suggests that Mutual-DTI may contribute to modern medical drug development research. The experimental results show the effectiveness of our approach. The code for Mutual-DTI can be downloaded from https://github.com/a610lab/Mutual-DTI.

A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study.

The Sine Cosine Algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA's fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine's (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy.

Modulation of gut microbiota and lipid metabolism in rats fed high-fat diets by Ganoderma lucidum triterpenoids.

Ganoderma lucidum triterpenoids (GP) have been reported to help prevent and improve hyperlipidemia. Modulation of the gut microbiota was proposed as underlying factor as well as a novel measure to prevent and treat hyperlipidemia. The effects of GP on high-fat diet (HFD)-induced hyperlipidemia and gut microbiota modulation were determined in rats. Ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS-MS) indicated that GP were enriched with ganoderic acids G, B, H, A, and F. After feeding with GP supplementation, serum lipid levels including total triglyceride, total cholesterol, and low-density-lipoprotein cholesterol were significantly decreased in hyperlipidemic rats. Furthermore, administration of GP also has reversed the HFD-induced gut microbiota dysbiosis, including a significant increase in Alloprevotella and reduced proportion of Blautia. The result above suggests that GP would be developed as a functional food to ameliorate lipid metabolic disorders and hyperlipidemia.

Colorimetric detection of biothiols and Hg2+ based on the peroxidase-like activity of GTP.

Guanosine-5'-triphosphate (GTP) not only plays a key role in a majority of cellular processes but also be proposed as a peroxidase-like mimic. Compared with nanozymes, GTP shows good tolerance under harsh conditions, which can be used to construct an easy colorimetric analysis for the detection of biomolecules. Here, on the basis of the peroxidase-like activity of GTP which can catalyze the oxidation of 3,3',5,5'-tetramethyl benzidine dihydrochloride (TMB), colorimetric sensing was established for biothiols and Hg2+. Biothiols reduced the oxTMB back to colorless TMB, and Hg2+ restored the formation of oxTMB, leading to the recovery of color. This method not only provides a platform for the detection of metal ions and biothiols, but also shows that GTP has great potential for analytical detection.

Detecting uric acid base on the dual inner filter effect using BSA@Au nanoclusters as both peroxidase mimics and fluorescent reporters.

Fluorescent bovine serum albumin-protected gold nanoclusters (BSA@Au NCs) can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to produce blue oxTMB for its peroxidase-like activity. The two absorption peaks of oxTMB overlapped with the excitation and emission peaks of BSA@Au NCs, respectively, causing efficient quenching on the fluorescence of BSA@Au NCs. The quenching mechanism can be attributed to the dual inner filter effect (IFE). Based on the dual IFE, BSA@Au NCs were utilized as both peroxidase mimics and fluorescent reporters for H2O2 detection and further for uric acid detection with uricase. Under optimal detection conditions, the method can be used to detect H2O2 ranging 0.50-50 µM with a detection limit of 0.44 µM and UA ranging 0.50-50 µM with a detection limit of 0.39 µM. The established method had been successfully utilized for the determination of UA in human urine, with massive potential in biomedical applications.

Suppression of hepatocellular carcinoma by Ulva lactuca ulvan via gut microbiota and metabolite interactions.

INTRODUCTION: Ulva lactuca polysaccharide (ULP) is green algae extract with numerous biological activities, including anticoagulant, anti-inflammatory, and antiviral effects. However, the inhibitory ability of ULP in the development of hepatocellular carcinoma warrants further studies. OBJECTIVES: To elucidate the anti-tumor mechanism of ULP action and evaluate its regulatory effect on gut microbiota and metabolism in H22 hepatocellular carcinoma tumor-bearing mice. METHODS: An H22 tumor-bearing mouse model was established by subcutaneously injecting H22 hepatoma cells. The gut microbiota composition in cecal feces was assessed and subjected to untargeted metabolomic sequencing. The antitumor activity of ULP was verified further by western blot, RT-qPCR, and reactive oxygen species (ROS) assays. RESULTS: Administration of ULP alleviated tumor growth by modulating the compositions of the gut microbial communities (Tenericutes, Agathobacter, Ruminiclostridium, Parabacteroides, Lactobacillus, and Holdemania) and metabolites (docosahexaenoic acid, uric acid, N-Oleoyl Dopamine, and L-Kynurenine). Mechanistically, ULP promoted ROS production by inhibiting the protein levels of JNK, c-JUN, PI3K, Akt, and Bcl-6, thereby delaying the growth of HepG2 cells. CONCLUSION: ULP attenuates tumor growth in H22 tumor-bearing mice by modulating gut microbial composition and metabolism. ULP inhibits tumor growth mainly by promoting ROS generation.

Regulatory mechanisms of the edible alga Ulva lactuca polysaccharide via modulation of gut microbiota in diabetic mice.

In this study, Ulva lactuca polysaccharide (ULP) antihyperglycemic effect was assessed by monitoring changes in the gut microbiota of aging diabetic mice. The results showed that ULP alleviated type 2 diabetes by improving insulin tolerance, increasing SOD and CAT activities, and thus lowering blood glucose level. Moreover, ULP regulated the expressions of INSR and AMPK concurrent with inhibition the expression of JNK, JAK, STAT3, p16 and p38 to improve glucose metabolism dysfunction. Interestingly, the abundance of Alloprevotella and Pediococcus change might the key factor for ULP antihyperglycemic effectiveness in aging-related diabetes. These results suggest that ULP can exert a mechanism of blood glucose regulation by improving intestinal diversity composition asides from direct insulin mimetic actions.

Ru(bpy)32+ as a photoinduced oxidase mimic for colorimetric detection of biothiols.

We have found that tris (2,2'-bipyridyl) ruthenium (II) (Ru(bpy)32+) possesses a high photo-induced oxidase-like activity and is capable of catalyzing the color reaction of 3,3',5,5'-tetramethylbenzidine (TMB) with dissolved oxygen. Ru(bpy)32+ has a catalytic constant (Kcat) that is twice as high as that of fluorescein, 170 and 275-fold higher than that of 9-mesityl-10-methyl acridine and Eosin Y, respectively. Electron spin resonance spectroscopy (ESR) and radical scavenging experiments have verified the major active radicals involved in the color reaction are •OH. A colorimetric biothiol assay has been successfully developed for the oxidase-like activity of Ru(bpy)32+ can be suppressed by sulfhydryl compounds. A linear dependence between the decrease in absorbance and the logarithm of thiol concentrations can be found ranging from 5.0 to 50 µM, with a detection limit of 1.0 µM. This work reveals a new oxidase mimic with high catalytic activity and will facilitate the utilization of this oxidase mimic in biochemical analysis.

Halogen-regulating induced reversible high-temperature dielectric and thermal transitions in novel layered organic-inorganic hybrid semiconducting crystals.

Organic-inorganic hybrid metal halides for high-temperature phase transition have become increasingly popular owing to their wide operating temperature range in practical applications, e.g., energy storage, permittivity switches and opto-electronic devices. This paper describes the subtle assembly of two new hybrid perovskite crystals, [Cl-C6H4-(CH2)2NH3]2CdX4 (X = Br 1; Cl 2), undergoing high-T reversible phase transformations around 335 K/356 K. Differential scanning calorimetry (DSC), differential thermal analysis (DTA) and VT PXRD tests uncover their reversible first-order phase transition behaviors. Furthermore, the compounds exhibit switchable dielectricity near T, making them potential dielectric switching materials. Hirshfeld surface analysis well discloses a distinct difference in hydrogen-bonding interaction between 1 and 2. UV spectra and computational analysis demonstrate that the compounds are a type of direct-band-gap semiconductor. This research will contribute an effective approach to the structure and development of multifunctional molecular hybrid crystals.

Money priming enhances sensitivity to the outcome feedback of decision-making under uncertainty: Evidence from an ERP study.

Money is the most common medium of exchange and plays an important role in our daily life. However, current literature has not yet specifically touched on the influence of money priming on decision-making behaviour under uncertainty and related neural mechanisms. In this study, we used event-related potentials with an adapted version of the Balloon Analogue Risk Task (BART) paradigm to examine brain activity related to the effects of money priming on outcome evaluation in decision-making under uncertainty. Reward positivity (RewP) and P300 components were analysed with respect to feedback valence (win vs. loss) and priming condition (money vs. neutral). The ERP results demonstrated that when individuals made decisions after having been primed with the monetary concept, the positive outcome feedback evoked a larger RewP component than after they had been primed with neutral stimuli. Conversely, there was no significant money priming effect when the outcome feedback was negative. In contrast, when individuals made decisions after having been primed with the monetary concept, the negative outcome feedback evoked a larger P300 than after they had been primed with neutral stimuli, whereas there was no significant money priming effect when the outcome feedback was positive. Our findings, thus, indicate that the brain response to money priming effects on the outcome evaluation in the BART occurs at both an early semi-automatic processing stage and a later cognitive appraisal stage. They further suggest that individuals prefer achieving financial gains at first and then focus on preventing financial losses in the money priming condition relative to the neutral priming condition.

Metal-free colorimetric detection of pyrophosphate ions by the peroxidase-like activity of ATP.

Pyrophosphate (P2O74-, PPi) plays a vital role in ecological environment. Its elevated levels in water bodies can lead to eutrophication. Hence, its detection is extremely significant. Whereas most of the existing methods for the actual detection of PPi may cause environmental pollution or suffer from operational complexity. In this study, we introduced a sensitive and selective method for detecting PPi based on the fact that PPi can inhibit the peroxidase-like activity of adenosine 5'-triphosphate (ATP). This strategy not only eliminated the complexity of material preparation (ATP is commercialized), but also addressed the general need for metal ions in detecting PPi. The dynamic range of PPi detection was 1.0-200 µM and the detection limit was 74 nM. In addition, this strategy had been successfully applied to the determination of PPi in tap water and lake water. This work extends the application of natural biological small molecule ATP in the analysis and provides an innovative thought for the metal-free detection of PPi.