Search

Blueberry wine is popular with consumers and has great market potential due to its rich nutrients and unique flavor. Aroma is an important indicator to measure the sensory quality of blueberry wine. However, there are some problems restricting the quality improvement of blueberry wine, such as insufficient studies on aroma components, unclear aroma characteristics and limited ways of aroma quality management. This article summarizes the composition, proportion and sources of volatile aroma components of blueberry wine, analyzes its key aroma substances and characteristic aroma based on odor activity value (OAV), and discusses the influence of cultivars, yeast, and process conditions on blueberry wine aroma. The purpose of this article is to offer a theoretical foundation for research on the aroma quality of blueberry wine aroma and for improving its processing technology.

Abstract Background Accurate prediction of cerebral amyloidosis with easily available indicators is urgently needed for diagnosis and treatment of Alzheimer’s disease (AD). Methods We examined plasma Aβ42, Aβ40, T-tau, P-tau181, and NfL, with APOE genotypes, cognitive test scores and key demographics in a large Chinese cohort (N = 609, aged 40 to 84 years) covering full AD spectrum. Data-driven integrated computational models were developed to predict brain β-amyloid (Aβ) pathology. Results Our computational models accurately predict brain Aβ positivity (area under the ROC curves (AUC) = 0.94). The results are validated in Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Particularly, the models have the highest prediction power (AUC = 0.97) in mild cognitive impairment (MCI) participants. Three levels of models are designed with different accuracies and complexities. The model which only consists of plasma biomarkers can predict Aβ positivity in amnestic MCI (aMCI) patients with AUC = 0.89. Generally the models perform better in participants without comorbidities or family histories. Conclusions The innovative integrated models provide opportunity to assess Aβ pathology in a non-invasive and cost-effective way, which might facilitate AD-drug development, early screening, clinical diagnosis and prognosis evaluation.

Objective: Comprehensive utilization of Dianhong rose resources to improve the added value of products. Methods: The single-factor experiment and response surface experiment were used to optimize the extraction process of supercritical CO2 with essential oil yield as the measurement index. The composition and relative content of different rose oils were analyzed by GC-MS. The components and relative contents of different rose oils were analyzed by GC-MS, and the antioxidant activities of different rose oils were evaluated. Results: The results showed that the optimal extraction parameters were as followed: particle size of 40 mesh, extraction pressure of 25.5 MPa, extraction temperature of 45.5 ℃, extraction time of 123 min, CO2 flow rate of 20 L/h. Under these conditions, the essential oil yield of rose was 1.185%. A total of 74 compounds were identified from different Dianhong rose oil, and the total volatile substances of Anning Dianhong rose essential oil were the highest. The essential oil of Yunnan Dianhong rose had good ability to scavenge free radicals. The antioxidant activities of essential oil from different producing areas were obviously different. Conclusion: The essential oil extracted by supercritical CO2 has good quality and can be used as a natural antioxidant.

PurposeSerum matrix metalloproteinase-7 (MMP-7) is significant in differentiating biliary atresia (BA). This study aims to develop a new peripheral blood quantitative collection device to detect MMP-7 levels via dried blood spot (DBS).MethodsThis is a diagnostic accuracy test. Serum and DBS MMP-7 concentrations were measured using an ELISA kit. Intraoperative cholangiography and subsequent histological examinations were used to confirm BA diagnoses.ResultsA total of 241 infants with obstructive jaundice were enrolled, among whom 168 were BA. Linear regression showed DBS MMP-7 correlated well with serum MMP-7 (R = 0.93, P

A three-dimensional numerical model was established based on ANSYS-AQWA (R19.0) software for the purpose of analyzing the hydrodynamic characteristics of a floating breakwater. This study examines three distinct floating breakwaters with different cross-sectional designs in order to evaluate their respective wave dissipation capabilities. It is suggested that the horizontal multi-cylinder floating breakwater exhibits a superior ability to dissipate waves when compared to both the single-cylinder and square pontoon configurations and can be deemed the most advantageous shielding strategy for potential engineering applications. Subsequently, this study examines the effects of influential parameters, including a large cylinder diameter, a small cylinder diameter, the angular position of the small cylinder, and the height and period of the incident wave, on the wave transmission coefficient. An empirical formula for the wave transmission coefficient was derived based on the numerical results. Additionally, the effects of influential parameters, including wind speed, current velocity, incident wave height and period, and water depth, on the maximum total mooring force were investigated. Furthermore, an empirical formula for the maximum total mooring force is proposed for practical implementation in engineering.

Spindles are often temporally coupled to slow waves (SW). These SW-spindle complexes have been implicated in memory consolidation that involves transfer of information from the hippocampus to the neocortex. However, spindles and SW, which are characteristic of NREM sleep, can occur as part of this complex, or in isolation. It is not clear whether dissociable parts of the brain are recruited when coupled to SW vs. when spindles or SW occur in isolation. Here, we tested differences in cerebral activation time-locked to uncoupled spindles, uncoupled SW and coupled SW-spindle complexes using simultaneous EEG-fMRI. Consistent with the “active system model,” we hypothesized that brain activations time-locked to coupled SW-spindles would preferentially occur in brain areas known to be critical for sleep-dependent memory consolidation. Our results show that coupled spindles and uncoupled spindles recruit distinct parts of the brain. Specifically, we found that hippocampal activation during sleep is not uniquely related to spindles. Rather, this process is primarily driven by SWs and SW-spindle coupling. In addition, we show that SW-spindle coupling is critical in the activation of the putamen. Importantly, SW-spindle coupling specifically recruited frontal areas in comparison to uncoupled spindles, which may be critical for the hippocampal-neocortical dialogue that preferentially occurs during sleep.

Au nanowires have been promoted in flexible electronics, micro-nano bioelectrodes, and micro-electrochemical detection benefit from their inherent size effect, unique chemical stability, and biocompatibility. Nanoskiving methodology has been confirmed as a feasible approach to preparing multidimensional nanostructures simply and efficiently utilizing ultramicrotome. However, the morphology, dimension, and microstructure of the nanowires will be altered by the tool geometry under extrusion and shearing during the nanoskiving process. Herein, a molecular dynamics simulation and experiments of cutting polycrystalline Au utilizing nanoskiving were performed, and the nanowire formation behavior caused by the variation of the tool geometry was analyzed. Smaller rake angle and larger tool cutting edge radius favor thicker chip thickness, larger high-stress areas, increased machining forces, as well as a shift in cutting formation mechanism from shear to extrusion shear. The reduction in the clearance angle only increases the high-stress areas and machining forces. The stress state and dislocation density within the chip and plastic deformation zone were closely related to the tool topography. The conclusions provide a thorough technical analysis of the mechanism of polycrystalline Au nanowire formation as well as theoretical guidance for the design and selection of tools for nanoskiving processes.

Due to galvanic isolation, bidirectional power transmission and extensive voltage adjustment, dual active bridge (DAB) exerts significant interface between renewable sources and electric vehicle (EV) storage port. However, for DAB operations, under light load or high voltage gain conditions, minimization of root‐mean‐square (RMS) current (MRMS) causes a restricted soft‐switching range. With variations of output power and operation modes, maintenance for all switches ZVS operation is difficult to maintain. Therefore, here, a segmented‐region optimum modulation (SROM) scheme based on extended‐phase‐shift (EPS) control is proposed to solve the aforementioned problems and solve complicated coupled relationship among different optimization methods. Firstly, based on voltage gain and transmission power, all EPS modes are divided clearly. Then, for switching loss reduction, fully‐power‐range ZVS (FZVS) is realized through transitions among EPS modes. Thirdly, either all semiconductors ZVS or not, most suitable optimization method is selected for different EPS regions by SROM. Further, to establish accurate boundaries among these optimization methods, relationship between inner phase‐shift ratio D1 and outer phase‐shift ratio D2 are quantified by unified parameter algorithm with unified parameter m. Therefore, for any EPS modes, efficiency is promoted comprehensively. A DAB‐based prototype is established and experimental results verify correctness and effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

The harmonic compensation of SAPF (shunt active power filter) may trigger harmonic oscillation due to the interaction with grid impedance and non‐linear loads, which distorts the power quality. In previous research, the relevant mechanism has been studied. However, the SAPF generally employs complex harmonic detection algorithm, which significantly increases the computational demands. The SAPF in full compensation mode can effectively solve this problem. But the modification of harmonic detection link may change the oscillation mechanism and previous stabilization methods need to be re‐evaluated. Therefore, this paper establishes the small signal model of the SAPF system in full compensation mode and investigates its oscillation mechanism. It is revealed that harmonic compensation and passive resonance collectively induce the harmonic oscillation. Based on the model, the working mechanism and application performance of the main stabilization methods proposed in the past (harmonic virtual admittance and harmonic compensation canceling) are analyzed and compared. However, neither of them can achieve effective stabilization without sacrificing the performance of compensation. Consequently, the harmonic phase compensation method is proposed to achieve the stabilization of the SAPF system in full compensation mode without affecting normal compensation. Finally, simulations and experiments are performed to verify the mechanism analysis and proposed methods. [ABSTRACT FROM AUTHOR]

A comb-type floating breakwater is a new wave dissipation structure with particular force and dissipation performance advantages due to the two wave-reflecting surfaces. In this article, physical model experiments are used to study the hydrodynamic characteristics of a fixed floating comb breakwater and two structural optimization-based measures under the combined action of regular waves, irregular waves, and wave currents. The effects of factors such as the relative width, relative wave height, water flow velocity, and irregular waves on the transmission coefficient of the breakwater are analyzed. In addition, the characteristics of the transmission wave waveform are analyzed based on the time and frequency domains. The results show that (1) the wave transmission coefficient of a comb-type floating breakwater is lower than that of a rectangular floating box for long-period waves, while the transmission coefficient is larger than that of a rectangular floating box for short-period waves. (2) Under combined current and waves, the superimposition of bidirectional currents can increase the transmission coefficient, and the transmission coefficient increases with increasing current speed. The superimposition of the anti-directional current can decrease the transmission coefficient. (3) Moreover, with the same wave parameters, the transmission coefficient for irregular waves is larger than that of regular waves. (4) Finally, extending the bottom plate and adding lower baffles can effectively enhance the wave dissipation effect of the comb-type floating breakwater while also stabilizing the transmitted wave waveform.

IntroductionThe accumulation of endogenous formaldehyde is considered a pathogenic factor in Alzheimer’s disease (AD). The purpose of this study was to investigate the relationship between urinary formic acid and plasma biomarkers in AD.Materials and methodsFive hundred and seventy-four participants were divided into five groups according to their diagnosis: 71 with normal cognitive (NC), 101 with subjective cognitive decline (SCD), 131 with cognitive impairment without mild cognitive impairment (CINM), 158 with mild cognitive impairment (MCI), and 113 with AD.ResultsWith the progression of the disease, urinary formic acid levels showed an overall upward trend. Urinary formic acid was significantly correlated with Mini-Mental State Examination (MMSE) scores, the Chinese version of Addenbrooke’s Cognitive Examination III (ACE-III) scores, and Montreal Cognitive Assessment-Basic (MoCA-B) time. The areas under the receiver operating characteristic curves (AUC) of urinary formic acid in distinguishing NC from AD was 0.797, which was similar to that of plasma neurofilament light chain (NfL; AUC = 0.768) and better than other plasma biomarkers (Aβ40, Aβ42, Aβ42/Aβ40, T-tau, P-tau181, and P-tau181/T-tau). We also found that using urinary formic acid and formaldehyde levels could improve the accuracy of using plasma biomarkers to determine AD disease stage.DiscussionOur study revealed the possibility of urinary formic acid as a potential novel biomarker for the early diagnosis of AD.

Objective: The aim of this study was to identify the important factors affecting cyclosporine (CsA) blood concentration and estimate CsA concentration using seven different machine learning (ML) algorithms. We also assessed the predictability of established ML models and previously built population pharmacokinetic (popPK) model. Finally, the most suitable ML model and popPK model to guide precision dosing were determined.Methods: In total, 3,407 whole-blood trough and peak concentrations of CsA were obtained from 183 patients who underwent initial renal transplantation. These samples were divided into model-building and evaluation sets. The model-building set was analyzed using seven different ML algorithms. The effects of potential covariates were evaluated using the least absolute shrinkage and selection operator algorithms. A separate evaluation set was used to assess the ability of all models to predict CsA blood concentration. R squared (R2) scores, median prediction error (MDPE), median absolute prediction error (MAPE), and the percentages of PE within 20% (F20) and 30% (F30) were calculated to assess the predictive performance of these models. In addition, previously built popPK model was included for comparison.Results: Sixteen variables were selected as important covariates. Among ML models, the predictive performance of nonlinear-based ML models was superior to that of linear regression (MDPE: 3.27%, MAPE: 34.21%, F20: 30.63%, F30: 45.03%, R2 score: 0.68). The ML model built with the artificial neural network algorithm was considered the most suitable (MDPE: −0.039%, MAPE: 25.60%, F20: 39.35%, F30: 56.46%, R2 score: 0.75). Its performance was superior to that of the previously built popPK model (MDPE: 5.26%, MAPE: 29.22%, F20: 33.94%, F30: 51.22%, R2 score: 0.68). Furthermore, the application of the most suitable model and the popPK model in clinic showed that most dose regimen recommendations were reasonable.Conclusion: The performance of these ML models indicate that a nonlinear relationship for covariates may help to improve model predictability. These results might facilitate the application of ML models in clinic, especially for patients with unstable status or during initial dose optimization.

The purpose is to cut the costs of Supply Chain enterprises in Ice-Snow Tourism (IST) and improve the intelligence and automation of Supply Chain Management (SCM). First, the spatial-temporal characteristics of economic data of the IST Supply Chain are analyzed based on the Internet of Things (IoT). Second, the annual Online Public Attention (OPA) data to IST in domestic cities and regions are collected. The quarterly concentration index and Gini coefficient are used to analyze their spatial and temporal characteristics. Then, the weighted fusion algorithm used for the Supply Chain scenario modeling is improved to solve data redundancy and improve information accuracy. Finally, the framework of the IST-oriented Supply Chain scenario ontology model is proposed. The experimental results show that Internet users give much attention to IST from 2011 to 2021. OPA to IST increased first and decreased and peaked in 2016. The final fusion value of the proposed data fusion algorithm is 20.0221, and that of the adaptive Weighted Average Method (WAM) is 20.0724. Thus, the proposed algorithm outperforms the adaptive WAM. The traditional scenario-based ontology model takes people as the center. In contrast, the Supply Chain scenario-based ontology model centers around product state and scenario. Therefore, the proposed Supply Chain scenario-based ontology model is entirely new. The proposed scenariobased ontology model using polymorphic IoT lays the foundation for developing an intelligent and automatic SCM. It has great practical significance in realizing efficient tourism industry management and SCM. First published online 10 August 2022

Human risk tolerance is highly idiosyncratic and individuals often show distinctive preferences when faced with similar risky situations. However, the neural underpinnings of individual differences in risk-taking remain unclear. Here we combined structural and perfusion MRI and examined the associations between brain anatomy and individual risk-taking behavior/risk tolerance in a sample of 115 healthy participants during the Balloon Analogue Risk Task, a well-established sequential risky decision paradigm. Both whole brain and region-of-interest analyses showed that the left cerebellum gray matter volume (GMV) has a strong association with individual risk-taking behavior and risk tolerance, outperforming the previously reported associations with the amygdala and right posterior parietal cortex (PPC) GMV. Left cerebellum GMV also accounted for risk tolerance and risk-taking behavior changes with aging. However, regional cerebral blood flow (CBF) provided no additional predictive power. These findings suggest a novel cerebellar anatomical contribution to individual differences in risk tolerance. Further studies are necessary to elucidate the underestimated important role of cerebellum in risk-taking.

Abstract Sleep resting state network (RSN) functional connectivity (FC) is poorly understood, particularly for rapid eye movement (REM), and in non-sleep deprived subjects. REM and non-REM (NREM) sleep involve competing drives; towards hypersynchronous cortical oscillations in NREM; and towards wake-like desynchronized oscillations in REM. This study employed simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) to explore whether sleep RSN FC reflects these opposing drives. As hypothesized, this was confirmed for the majority of functional connections modulated by sleep. Further, changes were directional: e.g., positive wake correlations trended towards negative correlations in NREM and back towards positive correlations in REM. Moreover, the majority did not merely reduce magnitude, but actually either reversed and strengthened in the opposite direction, or increased in magnitude during NREM. This finding supports the notion that NREM is best expressed as having altered, rather than reduced FC. Further, as many of these functional connections comprised “higher-order” RSNs (which have been previously linked to cognition and consciousness), such as the default mode network, this finding is suggestive of possibly concomitant alterations to cognition and consciousness.

Concussion is a mild traumatic brain injury (mTBI) with increasing prevalence among children and adolescents. Functional connectivity (FC) within and between the default mode network (DMN), central executive network (CEN) and salience network (SN) has been shown to be altered post-concussion. Few studies have investigated connectivity within and between these 3 networks following a pediatric concussion. The present study explored whether within and between-network FC differs between a pediatric concussion and orthopedic injury (OI) group aged 10–18. Participants underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan at 4 weeks post-injury. One-way ANCOVA analyses were conducted between groups with the seed-based FC of the 3 networks. A total of 55 concussion and 27 OI participants were included in the analyses. Increased within-network FC of the CEN and decreased between-network FC of the DMN-CEN was found in the concussion group when compared to the OI group. Secondary analyses using spherical SN regions of interest revealed increased within-network FC of the SN and increased between-network FC of the DMN-SN and CEN-SN in the concussion group when compared to the OI group. This study identified differential connectivity patterns following a pediatric concussion as compared to an OI 4 weeks post-injury. These differences indicate potential adaptive brain mechanisms that may provide insight into recovery trajectories and appropriate timing of treatment within the first month following a concussion.

In older adults, motor sequence learning (MSL) is largely intact. However, consolidation of newly learned motor sequences is impaired compared to younger adults, and there is evidence that brain areas supporting enhanced consolidation via sleep degrade with age. It is known that brain activity in hippocampal–cortical–striatal areas is important for sleep-dependent, off-line consolidation of motor-sequences. Yet, the intricacies of how both age and sleep alter communication within this network of brain areas, which facilitate consolidation, are not known. In this study, 37 young (age 20–35) and 49 older individuals (age 55–75) underwent resting state functional magnetic resonance imaging (fMRI) before and after training on a MSL task as well as after either a nap or a period of awake rest. Young participants who napped showed strengthening of functional connectivity (FC) between motor, striatal, and hippocampal areas, compared to older subjects regardless of sleep condition. Follow-up analyses revealed this effect was driven by younger participants who showed an increase in FC between striatum and motor cortices, as well as older participants who showed decreased FC between the hippocampus, striatum, and precuneus. Therefore, different effects of sleep were observed in younger vs. older participants, where young participants primarily showed increased communication in the striatal-motor areas, while older participants showed decreases in key nodes of the default mode network and striatum. Performance gains correlated with FC changes in young adults, and this association was much greater in participants who napped compared to those who stayed awake. Performance gains also correlated with FC changes in older adults, but only in those who napped. This study reveals that, while there is no evidence of time-dependent forgetting/deterioration of performance, older adults exhibit a completely different pattern of FC changes during consolidation compared to younger adults, and lose the benefit that sleep affords to memory consolidation.

Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection.Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model.Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860–0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.

The purpose is to cut the costs of Supply Chain enterprises in Ice-Snow Tourism (IST) and improve the intelligence and automation of Supply Chain Management (SCM). First, the spatial-temporal characteristics of economic data of the IST Supply Chain are analyzed based on the Internet of Things (IoT). Second, the annual Online Public Attention (OPA) data to IST in domestic cities and regions are collected. The quarterly concentration index and Gini coefficient are used to analyze their spatial and temporal characteristics. Then, the weighted fusion algorithm used for the Supply Chain scenario modeling is improved to solve data redundancy and improve information accuracy. Finally, the framework of the IST-oriented Supply Chain scenario ontology model is proposed. The experimental results show that Internet users give much attention to IST from 2011 to 2021. OPA to IST increased first and decreased and peaked in 2016. The final fusion value of the proposed data fusion algorithm is 20.0221, and that of the adaptive Weighted Average Method (WAM) is 20.0724. Thus, the proposed algorithm outperforms the adaptive WAM. The traditional scenario-based ontology model takes people as the center. In contrast, the Supply Chain scenario-based ontology model centers around product state and scenario. Therefore, the proposed Supply Chain scenario-based ontology model is entirely new. The proposed scenario-based ontology model using polymorphic IoT lays the foundation for developing an intelligent and automatic SCM. It has great practical significance in realizing efficient tourism industry management and SCM. [ABSTRACT FROM AUTHOR]

Shunt active power filter (SAPF) has emerged as a promising solution for mitigating harmonic distortions. Due to the interaction between the current inner‐loop of SAPF, source impedance and non‐linear loads, harmonic oscillation issue arises and adversely affects the stability of the system, thus in turn exacerbating the harmonic distortion. However, previous studies paid less attention to the value of phase adjustment in improving the dynamic behaviour of the loop and thus stabilizing harmonic oscillation. Taking the dynamic characteristics between source impedance and non‐linear load into consideration, this paper models and analyzes the SAPF system with source current direct control to reveal the law of full harmonic compensation oscillation. Based on the analysis, an enhanced phase adjustment strategy using load current feedforward is proposed and designed to stabilize harmonic oscillation, while maintaining a favourable trade‐off between stability and harmonic compensation performance. The small‐signal model validated for stability analysis and the effectiveness of proposed stabilization strategy are verified by simulation and experiment. [ABSTRACT FROM AUTHOR]