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Background: Air pollution in China has raised great concerns due to its adverse effects on air quality, human health, and climate. Emissions of air pollutants (APs) are inherently linked with CO2 emissions through fossil-energy consumption. Knowledge of the characteristics of APs and CO2 emissions and their relationships is fundamentally important in the pursuit of co-benefits in addressing air quality and climate issues in China. However, the linkages and interactions between APs and CO2 in China are not well understood. Results: Here, we conducted an ensemble study of six bottom-up inventories to identify the underlying drivers of APs and CO2 emissions growth and to explore their linkages in China. The results showed that, during 1980–2015, the power and industry sectors contributed 61–79% to China's overall emissions of CO2, NOx, and SO2. In addition, the residential and industrial sectors were large emitters (77–85%) of PM10, PM2.5, CO, BC, and OC. The emissions of CH4, N2O and NH3 were dominated by the agriculture sector (46–82%) during 1980–2015, while the share of CH4 emissions in the energy sector increased since 2010. During 1980–2015, APs and greenhouse gases (GHGs) emissions from residential sources generally decreased over time, while the transportation sector increased its impact on recent emissions, particularly for NOx and NMVOC. Since implementation of stringent pollution control measures and accompanying technological improvements in 2013, China has effectively limited pollution emissions (e.g., growth rates of –10% per year for PM and –20% for SO2) and slowed down the increasing trend of carbon emissions from the power and industrial sectors. We also found that areas with high emissions of CO, NOx, NMVOC, and SO2 also emitted large amounts of CO2, which demonstrates the possible common sources of APs and GHGs. Moreover, we found significant correlations between CO2 and APs (e.g., NOx, CO, SO2, and PM) emissions in the top 5% high-emitting grid cells, with more than 60% common grid cells during 2010–2015. Conclusions: We found significant correlation in spatial and temporal aspects for CO2, and NOx, CO, SO2, and PM emissions in China. We targeted sectorial and spatial APs and GHGs emission hot-spots, which help for management and policy-making of collaborative reductions of them. This comprehensive analysis over 6 datasets improves our understanding of APs and GHGs emissions in China during the period of rapid industrialization from 1980 to 2015. This study helps elucidate the linkages between APs and CO2 from an integrated perspective, and provides insights for future synergistic emissions reduction. [ABSTRACT FROM AUTHOR]

Background: High breast density is a strong risk factor for breast cancer. As such, high consistency and accuracy in breast density assessment is necessary. Purpose: To validate our proposed deep learning (DL) model and explore its impact on radiologists on density assessments. Material and Methods: A total of 3732 mammographic cases were collected as a validated set: 1686 cases before the implementation of the DL model and 2046 cases after the DL model. Five radiologists were divided into two groups (junior and senior groups) to assess all mammograms using either two- or four-category evaluation. Linear-weighted kappa (K) and intraclass correlation coefficient (ICC) statistics were used to analyze the consistency between radiologists before and after implementation of the DL model. Results: The accuracy and clinical acceptance of the DL model for the junior group were 96.3% and 96.8% for two-category evaluation, and 85.6% and 89.6% for four-category evaluation, respectively. For the senior group, the accuracy and clinical acceptance were 95.5% and 98.0% for two-category evaluation, and 84.3% and 95.3% for four-category evaluation, respectively. The consistency within the junior group, the senior group, and among all radiologists improved with the help of the DL model. For two-category, their K and ICC values improved to 0.81, 0.81, and 0.80 from 0.73, 0.75, and 0.76. And for four-category, their K and ICC values improved to 0.81, 0.82, and 0.82 from 0.73, 0.79, and 0.78, respectively. Conclusion: The DL model showed high accuracy and clinical acceptance in breast density categories. It is helpful to improve radiologists' consistency. [ABSTRACT FROM AUTHOR]

Violence risk prediction in forensic mental health care settings is important in facilitating its management. Although not specifically developed for risk assessment, previous studies have demonstrated that the Health of the Nation Outcome Scales-Secure (HoNOS-secure) and its predecessor may have some predictive validity for violence. This retrospective cohort study examines the predictive validity of HoNOS-secure scores rated on admission in a medium-secure forensic psychiatric ward in New Zealand for inpatient violence in the short- to medium-term at 30 and 90 days respectively. A sample size of 33 eligible admissions was obtained within the study period (2005–2017), and incident reports were rated by two independent clinicians for the occurrence and level of inpatient violence. Poisson, negative binomial and Cox regression analyses were used to investigate HoNOS-secure predictivity for the occurrence of inpatient violence, number of violent events, and time to the first violent event respectively. Although the HoNOS-secure total and subscale scores were not found to be significant predictors, post hoc analysis at the item level demonstrated association of various items with violence. Future studies could consider pooling data from multiple study sites, extending follow-up periods and adopting a prospective design. [ABSTRACT FROM AUTHOR]

Single crystal metal halide perovskites thin films are considered to be a promising optical, optoelectronic materials with extraordinary performance due to their low defect densities. However, it is still difficult to achieve large‐scale perovskite single‐crystal thin films (SCTFs) with tunable bandgap by vapor‐phase deposition method. Herein, the synthesis of CsPbCl3(1–x)Br3x SCTFs with centimeter size (1 cm × 1 cm) via vapor‐phase deposition is reported. The Br composition of CsPbCl3(1–x)Br3x SCTFs can be gradually tuned from x = 0 to x = 1, leading the corresponding bandgap to change from 2.29 to 2.91 eV. Additionally, an low‐threshold (≈23.9 µJ cm−2) amplified spontaneous emission is achieved based on CsPbCl3(1–x)Br3x SCTFs at room temperature, and the wavelength is tuned from 432 to 547 nm by varying the Cl/Br ratio. Importantly, the high‐quality CsPbCl3(1–x)Br3x SCTFs are ideal optical gain medium with high gain up to 1369.8 ± 101.2 cm−1. This study not only provides a versatile method to fabricate high quality CsPbCl3(1–x)Br3x SCTFs with different Cl/Br ratio, but also paves the way for further research of color‐tunable perovskite lasing. [ABSTRACT FROM AUTHOR]

A Lagrangian mesh-less model is proposed to simulate fluid–solid flows with multiple-sized solids, i.e., millimeter-sized particle and larger-sized debris. Considering the difference in the size of solid phases, a hybrid resolved and unresolved model is established based on the coupling method of smoothed particle hydrodynamics (SPH) and discrete element method (DEM). SPH is used to model fluid, and the locally averaged Navier–Stokes equations are adopted as governing equations. DEM is used to model the particle–particle interactions, and the unresolved description of hydrodynamic forces including drag and buoyancy is established. The large-sized debris is modeled as the rigid body, which is discretized by particle elements having both SPH and DEM characteristics, where SPH particle elements are involved in the closure of the SPH fluids, and DEM particle elements interact with the solid particles following the contact law. The numerical model is validated and verified by several examples, including single-particle sedimentation, collapse of cylinder columns, and debris dam break. Results show that the present model reproduces general features of the complex fluid–solid flow with free surfaces. The advantage of the hybrid model is that it can deal with the fluid–solid flow problem with both small particles and large objects at a suitable resolution, and it is especially good at dealing with the free surface flow problem. A discretization for the modeling of debris flows is proposed based on the coupled SPH-DEM method. The novelty of the work is a coupled resolved–unresolved scheme for the free surface flow with multi-sized solids. The present scheme allows using a uniform resolution by bridging the size difference between small-scale solid particles and large-scale debris. The unresolved model of fluid-particle flow is efficient because the fluid resolution can be configured comparably to the particle size. The unified nature of the model allows the combination of resolved and unresolved simulations in the same computational domain. [ABSTRACT FROM AUTHOR]

The rapid and sensitive detection of pathogenic bacteria is highly demanded for early warning of infectious disease epidemics and protection of human health. Herein, a reusable and universal impedimetric sensing platform based on a bacteria-imprinted polythiophene film (BIF) is proposed for the rapid and sensitive detection of pathogenic bacteria using Staphylococcus aureus (S. aureus) as a model analyte. Monomer screening among four 3-substituted thiophenes was first performed based on the imprinting factor, and 3-thiopheneethanol (TE) was eventually selected. The BIF as a recognition layer was quickly deposited in an environmentally friendly process on a glassy carbon electrode via electro-copolymerization of the S. aureus template and TE monomer followed by in situ template removal. Upon rebinding of S. aureus on the BIF, the impedance increased. Under optimal conditions, the BIF-based sensor can quantitatively detect S. aureus in a wide linear range of 10 to 107 CFU mL−1 with a low detection limit of 4 CFU mL−1. Additionally, the sensor exhibits excellent selectivity, capable of identifying S. aureus from multi-bacterial strain mixtures. It also demonstrates applicability in the analysis of real lettuce and shrimp samples with good recoveries. Most significantly, the BIF sensing interface can be reused up to five times with good signal retention. Compared with most reported methods, this sensor is more rapid with a much shorter total assay time of 30 min, including the BIF preparation, bacterial rebinding, and impedance detection. This assay may hold great potential to help in the rapid, sensitive, and label-free detection of pathogenic bacteria in fields of food safety and public health. [ABSTRACT FROM AUTHOR]

Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review. [ABSTRACT FROM AUTHOR]

A hyperspectral imaging system was employed to develop a general model for monitoring moisture distribution of four types of vegetables undergoing microwave-vacuum drying (MVD). Partial least squares regression and least squares support vector machines were developed based on full and optimal wavelengths, while the multiple linear regression (MLR) was built from optimal spectral data. Genetic algorithm-MLR (GA-MLR) model with a determination coefficient in prediction of 0.974 and a root mean square error in prediction of 4.70% was used for the moisture content visualization of four MVD vegetables. Moisture distribution maps indicated a similar center drying pattern among different vegetables, while the conner effects occurred in celery stalks and spinach leaves. Additionally, evaluations of color changes of the vegetables using the computer vision system indicated a decrease of the browning index in dried carrot slices and celery stalks but an increase in potato slices and spinach leaves. [ABSTRACT FROM AUTHOR]

Anomaly detection in time series data is crucial for many fields such as healthcare, meteorology, and industrial fault detection. However, traditional unsupervised time series anomaly detection methods suffer from biased anomaly measurement under contaminated training data. Most of existing methods employ hard strategies for contamination calibration by assigning pseudo-label to training data. These hard strategies rely on threshold selection and result in suboptimal performance. To address this problem, in this paper, we propose a novel unsupervised anomaly detection framework for contaminated time series (NegCo), which builds an effective soft contamination calibration strategy by exploiting the observed negative correlation between semantic representation and anomaly detection inherent within the autoencoder framework. We innovatively redefine anomaly detection in data contamination scenarios as an optimization problem rooted in this negative correlation. To model this negative correlation, we introduce a dual construct: morphological similarity captures semantic distinctions relevant to normality, while reconstruction consistency quantifies deviations indicative of anomalies. Firstly, the morphological similarity is effectively measured based on the representative normal samples generated from the center of the learned Gaussian distribution. Then, an anomaly measurement calibration loss function is designed based on negative correlation between morphological similarity and reconstruction consistency, to calibrate the biased anomaly measurement caused by contaminated samples. Extensive experiments on various time series datasets show that the proposed NegCo outperforms state-of-the-art baselines, achieving an improvement of 6.2% to 26.8% in A rea U nder the R eceiver O perating C haracteristics (AUROC) scores, particularly in scenarios with heavily contaminated training data. • Unsupervised time series anomaly detection under data contamination. • Calibrating the biased anomaly measurement by exploiting the negative correlation. • Normal samples from a learned Gaussian distribution to model negative correlation. • Single forward propagation enables anomaly detection using the trained autoencoder. [ABSTRACT FROM AUTHOR]

Herein, ultraviolet B (UVB) persistent luminescence phosphors containing SrAl 12 O 19 : Ce3+, Sc3+ nanoparticles were reported. Thermoluminescence (TL) spectrum analysis reveals that the shallow trap induced by Sc3+ co-doping plays an important role in photoluminescence persistent luminescence (PersL) development, while the deep trap dominates the generation of optical stimulated luminescence (OSL). Owing the appearance of deep trap, the OSL is observed under light (700 nm - 900 nm) excitation. UVB luminescence exerts good bactericidal effects on pathogenic bacteria involved in the process of food spoilage. Thus, the smart window with SrAl 12 O 19 : Ce3+, Sc3+/PDMS produces UVB PersL to efficiently inactivate Escherichia coli and Staphylococcus aureus. In addition, the presence of the smart window delays the critical point of pork decay, and greatly reduces the time of pork spoilage. It maximizes the convenience of eradicating bacteria and preserving food, thus offering a fresh perspective on the use of UV light for food sterilization and preservation. • PL, PersL and OSL in the UVB range are demonstrated in SrAl 12 O 19 doped with Ce3+. • The shallow trap induced by the Sc3+ co-doping plays an important role in PersL. • The deep trap dominates the generation of OSL. • Smart window can inactivate Escherichia coli and Staphylococcus aureus. • This smart windows dramatically delay pork spoilage. [ABSTRACT FROM AUTHOR]

Bubble evolution and shear thinning effect are main factors affecting lubrication behavior of bubbly oil. In this study, a thermohydrodynamic lubrication model for the bubbly oil considering the bubble evolution and the shear thinning effect was established based on the multiphase mixtures theory. The influence of bubble evolution and shear thinning effect on the static behavior of bubbly oil lubricated bearing was analyzed, and an experimental study was conducted to verify the model. The result shows that the bubble radius is mainly dependent on the liquid pressure; the bearing static behavior is affected by the bubble evolution through the interface effect; the shear thinning effect of the pure oil can be enhanced with regarding the bubble evolution effect. • A thermohydrodynamic lubrication model for the bubbly oil was established based on the multiphase mixtures theory. • Bubble evolution and shear thinning effect on the lubrication performance of bubbly oil is discovered. • The bubble evolution process can be calculated with using the thermohydrodynamic lubrication model established. • An in-depth physical research on the bubbly oil lubrication is conducted through a case study with using journal bearing. [ABSTRACT FROM AUTHOR]

Hearing is one of the five sensory organs that allows us to interact with society and our environment. However, one in eight Americans suffers from sensorineural hearing loss that is great enough to adversely impact their daily life. There is an urgent need to identify what part/degree of the auditory pathway (sensory or neural) is compromised so that appropriate treatment/intervention can be implemented. Single- or two-tone evoked potentials, the electrocochleography (eCochG), were measured along the auditory pathway, i.e., at the round window and remotely at the vertex, with simultaneous recordings of ear canal distortion product otoacoustic emissions. Sensory (cochlear) and neural components in the (remote-) eCochG responses showed distinct level- and frequency-dependent features allowing to be differentiated from each other. Specifically, the distortion products in the (remote-)eCochGs can precisely localize the sensory damage showing that they are effective to determine the sensory or neural damage along the auditory pathway. [ABSTRACT FROM AUTHOR]

Dendrobium mixture (DMix) is an effective treatment for diabetic nephropathy (DN), but the molecular mechanism underlying its action remains unclear. In this study, we investigated whether DMix regulates the transforming growth factor-β1 (TGF-β1)/Smads signal transduction pathway. Twenty-four db/db mice were randomly divided into three groups: the model, DMix, and gliquidone groups, while eight db/m mice were selected as the normal control group. The drug was administered by continuous gavage for 8 weeks. Body weight (BW), kidney weight (KW), kidney index, fasting blood glucose (FBG), blood lipid, 24-hour urinary albumin excretion rate, blood urea nitrogen, and serum creatinine levels were measured. Pathological changes in the renal tissue were observed under a light microscope. Real-time quantitative PCR and immunohistochemical staining were used to detect the mRNA and protein expression levels of TGF-β1 and alpha-smooth muscle actin (α-SMA), respectively, in renal tissues. TGF-β1, Smad2, p-Smad2, Smad3, p-Smad3, and α-SMA expression levels were measured using western blotting. The results showed that DMix significantly reduced the FBG level, BW, KW, and blood lipid level and improved renal function in db/db mice. Histopathology showed that DMix alleviated glomerular mesangial cell proliferation and renal interstitial fibrosis in db/db mice. Additionally, DMix reduced the protein and mRNA expression levels of TGF-β1 and α-SMA and inhibited Smad2 and Smad3 phosphorylation. We conclude that DMix may inhibit renal fibrosis and delay the progression of DN by regulating the TGF-β1/Smads signaling pathway. [ABSTRACT FROM AUTHOR]

Atmospheric methane (CH 4) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, contributes considerably to the global anthropogenic CH 4 emissions by rice cultivation, ruminant feeding, and coal production. Understanding the characteristics of China's CH 4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH 4 emissions. This study provides a comprehensive comparison of China's anthropogenic CH 4 emissions by synthesizing the most current and publicly available datasets (13 inventories). The results show that anthropogenic CH 4 emissions differ widely among inventories, with values ranging from 44.4–57.5 Tg CH 4 yr -1 in 2010. The discrepancy primarily resulted from the energy sector (27.3 %–60.0 % of total emissions), followed by the agricultural (26.9 %–50.8 %) and waste treatment (8.1 %–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s but increased significantly thereafter, with annual average growth rates (AAGRs) of 2.6 %–4.0 % during 2000–2010 but slower AAGRs of 0.5 %–2.2 % during 2011–2015, and the emissions became relatively stable, with AAGRs of 0.3 %–0.8 %, during 2015–2019 because of the stable emissions from the energy sector (mainly coal production). Spatially, there are large differences in emissions hotspot identification among inventories, and incomplete information on emission patterns may mislead or bias mitigation efforts for CH 4 emission reductions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions and reducing the uncertainty in bottom-up inventories. Data used in this article are available at 10.6084/m9.figshare.12720989 (Lin et al., 2021). [ABSTRACT FROM AUTHOR]

Sensory analysis is the evaluation of the signals received through senses of sight, sound, taste, smell, and touch. The traditional methods for the analysis of sensory properties including trained sensory panel, colourimeters and texture analysers are invasive, laborious, and small-scale procedures. Hyperspectral imaging systems (HSI) has emerged as a less time-consuming and non-destructive method to determine the sensory properties of a diverse range of food products. This review provides a comprehensive overview of recent application developments since 2010 for identifying sensory properties including colour, defects, texture, flavour, freshness, and maturity in various food products by HSI. The Vis-NIR (400–1000 nm) hyperspectral imaging is most used for the assessment of sensory properties. Moreover, the commonly applied multivariate analysis in the sensory evaluation by HSI is linear regression algorithms (PLSR and MLR), but non-linear analysis achieves better performance for the prediction of sensory features. Regression models are applied for determining the texture, colour, flavour, and freshness of food products while classification models are used to detect the defects and maturity by which the most accurate results are obtained. Furthermore, the combination of algorithms and the integration of spectral and spatial information yield more accurate results in the assessment of sensory features. Although there are still some limitations to overcome for the improvement of the HSI system, studies on the application of HSI in the real-time monitoring of sensory properties have proved a great potential for industrial applications. • Vis-NIR (400–1000 nm) hyperspectral imaging is most used in sensory evaluation. • The most used chemometric is linear regression algorithms in sensory evaluation. • The most accurate results are obtained in the detection of bruise and maturity. • Regression models are applied to predict texture, color, flavour and freshness. • Classification models are used to detect the defect and maturity. [ABSTRACT FROM AUTHOR]

This study aimed to compare the moisture content (MC) uniformity of ginger slices subjected to microwave-vacuum drying (MVD) and hot-air drying (HAD) and discriminate MVD ginger slices from HAD ones (MC < 20 g/100 g) by NIR hyperspectral imaging (HSI). MC maps were visualized by a simplified partial least squares regression (PLSR) model with the determination coefficient in the prediction of 0.971. The hole effect of the semivariogram in MC maps indicated the moisture difference between the center and the edge. Furthermore, principal component analysis (PCA) of the semivariogram indicated a similar degree of moisture dissimilarity between MVD and HAD ginger slices with MC lower than 60 g/100 g. Partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) classifiers were respectively applied to the datasets of spectral reflectance at full wavelengths and nine optimum wavelengths selected by PCA, and variances in E-W and N-S directions to build classification models. The highest correct classification rate (CCR) of 95.1% was achieved by applying PLS-DA or SVM to the dataset of full wavelengths, while CCR of 90.3% was obtained for SVM model based on the dataset of nine selected wavelengths, confirming the potential of HSI spectra for MVD and HAD ginger slices discrimination. [ABSTRACT FROM AUTHOR]

A number of 344 samples were collected from Tianjin, China and were analysed for the occurrence of emerging Fusarium mycotoxins including enniatin A (ENN A), enniatin A1 (ENN A1), enniatin B (ENN B), enniatin B1 (ENN B1) and beauvericin (BEA) by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The frequencies of mycotoxins studied were 69.0% (40/58), 69.8% (37/53), 85.9% (67/78), 78.9% (75/95), and 30.0% (18/60) for rice, wheat, corn, wheat flour, and corn flour, respectively. BEA was the predominant toxin in rice (average = 37.2 μg/kg) and wheat (average = 58.4 μg/kg), followed by ENN B and ENN B1, while less ENN A and ENN A1 were detected. ENN A was most common in corns with an average level of 28.1 μg/kg, while BEA had a higher average of 62.8 μg/kg. The levels of ENNs and BEA significantly decreased in wheat flours and corn flours, presumably due to the production process. The co-occurrences of ENNs and BEA in cereal samples were common in the combination of two and three mycotoxins and the significant positive correlations in concentrations were also obtained among them. Besides, agroclimate was considered as an important factor for Fusarium production and the mycotoxin contamination was found more serious in Jizhou district, which had more rainfall and less sunshine, than the other agriculture regions. These results suggested the necessity of carrying out in-depth and large-scale monitoring of mycotoxins in cereals and their products nationwide. [ABSTRACT FROM AUTHOR]

Although the ingestion of microplastics by fish has been investigated extensively over the last 50 years, the quality of these ingestion studies is questionable due to the lack of standard methods for sampling, extraction, and identification of microplastics. This review aims to provide reliable results of microplastic occurrence in fish sourced from 104 published articles through a rigorous assessment of the data quality. A scoring system was performed to assess ten quality assurance/quality control criteria including sampling methods, sample size, sample processing and storage, laboratory preparation, clean air conditions, negative controls, positive controls, target component, sample treatment and polymer identification. We found that 59 out of 104 articles had scores ≤10, and only two studies are identified with scores higher than 15, demonstrating the urgent need to improve the quality and reliability of fish digestion studies. Particularly, five criteria, namely, the positive controls, sampling method, clean air conditions, sample size, and polymer identification with an average score of less than 1 need attention and improvement in future studies. This review also revealed that the 104 studies considered here originated mainly from five countries, i.e., China (23%), Iran (8%), Thailand (5%), Turkey (4%), and Indonesia (4%). Among 104 studies, the major polymer type found was polyethylene, with a size range of 0–1 mm. The most frequently reported colour and shape of microplastics found in the fish were blue and fibre, respectively. [ABSTRACT FROM AUTHOR]

Tea is one of the most popular beverages around the world for its health benefit. The availability of rapid and reliable techniques for evaluating tea quality and safety is important for the tea industry. However, tea components and pesticide residues detection, sensory analysis, grading, and pricing are carried out by laborious, time-consuming, and destructive methods. Therefore, vibrational spectroscopy has emerged as an increasingly popular technology in tea quality and safety evaluation owing to its simple, nondestructive and rapid detection procedures. The current review compares the employment of different vibrational spectroscopic techniques including near-infrared (NIR), mid-infrared (MIR), Raman, Terahertz (THz) spectroscopies and hyperspectral imaging (HSI) in tandem with regression and classification models in the quantitative and qualitative analyses of tea quality and safety. Furthermore, band assignments of tea components based on these spectroscopies are summarized. NIR spectroscopy dominates the applications of vibrational spectroscopy in the evaluation of tea quality, while only one study is related to THz spectroscopy. Raman technology is mainly applied to detect pesticide residues. HSI providing both spectral and spatial information has a potential in the optimization of the tea production process. Additionally, regression models find widespread use in the quantitative analysis of tea, such as phenolics, flavonoids, pigments, sensory evaluation, and pesticide residues, while classification models are useful for discriminating geographical origins, varieties, and fermentation degrees. The online detection of tea quality and safety by vibrational spectroscopy on the industrial scale is still an important and challenging task for future studies. • NIR spectroscopy is the most used spectroscopy in tea quality evaluation. • Raman technology is mainly applied for detecting pesticide residues in tea. • Quantitative investigation is more extensive than qualitative evaluation of tea. [ABSTRACT FROM AUTHOR]

Atmospheric methane (CH4) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, e.g., rice cultivation, ruminant feeding and coal production, contributes considerably to the global anthropogenic CH4 emissions. Understanding the characteristics of China's CH4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH4 emissions. This study provides a comprehensive evaluation of China's anthropogenic CH4 emissions by synthesizing most of the currently available data (12 inventories). The results show that anthropogenic CH4 emissions differ widely among inventories, with values ranging from 41.9-57.5 Tg CH4 yr-1 in 2010. The discrepancy primarily resulted from the energy sector (27.3-60.0% of total emissions), followed by the agricultural (26.9-50.8%), and waste treatment (8.1-21.2%) sectors. Temporally, emissions among inventories stabilized in the 1990s, but increased significantly thereafter, with annual average growth rates (AAGRs) of 1.8-3.9% during 2000-2010, but slower AAGRs of 0.5-2.2% during 2011-2015. Spatially, the growth of CH4 emissions could be attributed mostly to an increase in emissions from the energy sector (mainly from coal mining) in the northern and central inland regions, followed by waste treatment in the southern and eastern regions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions, and reducing the uncertainty of bottom-up inventories. [ABSTRACT FROM AUTHOR]

Summary: A sense field effect transistor (FET)–based inductor‐current sensor for buck converters is introduced in this paper. The proposed current sensor adopts and realizes piecewise‐linear adaptive bias to maintain consistent bandwidth and phase margin in the sensor's control loop throughout a wide range of load currents. Moreover, the specially designed turn‐on/off process of switches is introduced together with the sole gain–boosted amplifier to avoid the sharp transitions in the sensed peak and valley currents during the deadtime zone of synchronous buck converters. Simulation results of the proposed sensor show an 11.6‐dB rise in direct current (DC) gain at light load (approximately 200 mA) and 21° improvement in phase margin at heavy load (approximately 2 A), compared with the traditional structure with fixed‐bias current. Furthermore, the variation in the unity gain bandwidth of the proposed structure across the whole load range is 12.8 times lower than that of the traditional designs. [ABSTRACT FROM AUTHOR]

Since its emergence, the application of CRISPR-associated nuclease 9 (Cas9) technology in cancer research has accelerated studies to investigate many aspects of treatment approaches for lung cancer, including the identification of target genes, construction of animal tumor models, and identification of drug resistance-related genes. Moreover, CRISPR/Cas9 can be used in gene therapy for lung cancer, specifically involving molecular targeted drugs and inhibitors. This article reviews the current landscape of CRISPR/Cas9 applications for lung cancer treatment as a basis for further studies. Given its promising performance, in-depth and systematic research on the application of CRISPR/Cas9 in lung cancer treatment will be necessary in future studies for its successful implementation in clinical practice. CRISPR/Cas9-mediated gene editing of proto-oncogenes or tumor-suppressor genes offers great potential as a treatment for lung cancer. Drug resistance to chemotherapy is the main reason for the poor efficacy of current treatments for patients with lung cancer. CRISPR/Cas9 gene editing technology can be used to evaluate genes targeted by chemotherapeutic drugs and to identify new pathways to reduce or eliminate resistance to chemotherapy in lung cancer. Targeted drug therapy, which involves several genes such as EGFR , ALK , and KRAS , has become an important approach in lung cancer treatment. However, drug resistance is a growing concern. CRISPR/Cas9 can lower the resistance of tumors to these molecular targeted drugs or inhibitors by editing related genes, which will benefit patients with lung cancer. [ABSTRACT FROM AUTHOR]

Button mushroom (Agaricus bisporus) is widely used on the world market. Browning of mushroom surface is the most intuitive factor affecting consumer purchase. Developments of browning during postharvest are related to the flush number, mechanical damage, polyphenol oxidase, phenolic compounds, pathogens and postharvest environment. Many techniques such as cooling, modified atmosphere packaging (MAP) and coating are developed to control button mushroom browning. In this review, the main factors involved in mushroom browning including mushroom tyrosinase, postharvest factors and pathogen are discussed, and some detection methods for mushroom surface color are introduced. Furthermore, different methods for controlling browning including MAP, polyphenol oxidase (PPO) inhibitors, edible coating, irradiation, cooling and other emerging techniques are reviewed and compared. Internal factors of button mushroom browning include subtracts, enzyme and mushroom cellular integrity, which are affected by postharvest factors such as mechanical damage, temperature, relative humidity, and atmosphere gas. Imaging or computer vision and hyperspectral imaging are emerging methods for the detection of mushroom color and can be used to analyze each pixel of the entire surface of mushrooms. In order to control mushroom browning, preservation methods including MAP, coating, PPO inhibitors and irradiation are extensively studied. In addition, thermally buffered package, commercial film materials and gas composition of MAP, natural coating and PPO inhibitor materials should be investigated in future. • Button mushroom browning is affected by postharvest factors. • Modified atmosphere packaging is often used among all the control methods. • Tendency of inhibitory rate for L* during storage varies with control methods. • Computer vision is a rapid and reliable method for detecting mushroom color. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau is an important component of the global carbon cycle due to the large permafrost carbon pool and its vulnerability to climate warming. The Tibetan Plateau has experienced a noticeable warming over the past few decades and is projected to continue warming in the future. However, the direction and magnitude of carbon fluxes responses to climate change and elevated CO2 concentration under Representative Concentration Pathways (RCP) scenarios in the Tibetan Plateau grassland are poorly known. Here, we used a calibrated and validated biogeochemistry model, CENTURY, to quantify the contributions of climate change and elevated CO2 on the future carbon budget in the alpine grassland under three RCP scenarios. Though the Tibetan Plateau grassland was projected a net carbon sink of 16 ~ 25 Tg C yr-1 in the 21st century, the capacity of carbon sequestration was predicted to decrease gradually because climate-driven increases in heterotrophic respiration (Rh) (with linear slopes 0.49 ~ 1.62 g C m-2 yr-1) was greater than the net primary production (NPP) (0.35 ~ 1.52 g C m-2 yr-1). However, the elevated CO2 contributed more to plant growth (1.9% ~ 7.3%) than decomposition (1.7% ~ 6.1%), which could offset the warming-induced carbon loss. The interannual and decadal-scale dynamics of the carbon fluxes in the alpine grassland were primarily controlled by temperature, while the role of precipitation became increasingly important in modulating carbon cycle. The strengthened correlation between precipitation and carbon budget suggested that further research should consider the performance of precipitation in evaluating carbon dynamics in a warmer climate scenario. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of microwave-vacuum drying (MVD) on moisture uniformity in mushroom slices and whole mushrooms (Agaricus bisporus) by near infrared (NIR) hyperspectral imaging in combination with multivariate analysis. The partial least squares regression (PLSR) models based on standard normal variate (SNV) preprocessing spectra obtained good results for predicting moisture contents (MC) with the coefficient of determination in prediction ( R p 2 ) of 0.985 for mushroom slices and 0.963 for whole mushrooms. The MC of caps was 5–11 g/100 g higher than that of stipes for the first 12 min of drying and after 18 min of drying the MC of stipes was 6–22 g/100 g higher than that of caps. The MC of peripheral caps of whole mushrooms was 2–12 g/100 g higher than that of centers for the first 30 min of drying, but the MC of peripheral caps was 4–5 g/100 g lower than that of centers in the last 20 min of drying. In addition, the difference of MC between caps and stipes (6–22 g/100 g) in mushroom slices was more significant than that between the center and periphery (4–5 g/100 g) in whole mushrooms during the middle and later periods of drying procedure. • Rates of shrinkages, color changes and evaporation vary during drying. • Caps of mushroom slices tend to lose moisture faster than stipes. • Stipes under caps of whole mushrooms can cause uneven moisture distribution. • Moisture of sliced and whole mushrooms shows different patterns during drying. [ABSTRACT FROM AUTHOR]

• LED cooling by thermoelectric cooler integrated microchannel heat sink is proposed. • The relative importance of the factors was revealed by the Taguchi method. • The proposed LED + TEC + WMHS device showed excellent cooling performance. • The LED headlight can be cooled to temperature lower than the coolant temperature. • Cooling system and factor levels for different applications were recommended. Effective thermal management is crucial for light-emitting diodes (LED). Thermoelectric cooler (TEC) and microchannel heat sink (MHS) have been demonstrated to be effective for thermal management of LED. However, neither the combination of these two methods, nor the dominant factors which could be effectively adjusted to control the LED temperature, have been reported. In this study, a cooling device integrating TEC and water-cooled microchannel heat sink (WMHS) is proposed to improve the thermal management of high-power LED headlights. The start-up performance of the proposed device, LED + TEC + WMHS, was evaluated and compared with three different systems, namely LED + WMHS, LED + AHS (air-cooled heat sink), and LED + TEC + AHS. Experimental results have demonstrated the best performance of the LED + TEC + WMHS device. Orthogonal experiments based on the Taguchi method were conducted to reveal dominant factors among four variables, including TEC current, water inlet temperature, water flow velocity, and ambient temperature. The results show that effects of all the factors were significant while the TEC current has most important influence on the performance. The variation of the thermal management performance with the levels of the factors were investigated. The LED temperature was only 60.0 °C even at severe working conditions (T a = 80 °C, T i = 55 °C) and moderate cooling input (I TEC = 2 A, u = 0.49 m/s), indicating great thermal management performance of the proposed method. Choosing a cooling system and adjusting the factor levels for different applications were recommended. [ABSTRACT FROM AUTHOR]

Accurate identification of road network traffic status is the key to improve the efficiency of urban traffic control and management. Both data mining method and MFD-based methods can divide the traffic state of road network, but each has its own advantages and disadvantages. The data mining method is oriented to traffic data with high efficiency, but it can only discriminate traffic status from microlevel, while the MFD of road network can discriminate traffic status from macrolevel, but there are still some problems, such as the fact that the discriminant method of equivalence points based on MFD lacks theoretical support or that traffic status could not be subdivided. If data mining methods and road network's MFD are combined, the accuracy of road network traffic state identification will be greatly improved. In addition, the research shows that the combination of unsupervised learning clustering analysis method (such as spectral clustering algorithm) and supervised learning machine algorithm (such as support vector machine algorithm (SVM)) is more accurate in traffic state identification. Therefore, a traffic state identification method based on MFD and spectral clustering and SVM is proposed, combining the advantages of spectral clustering algorithm and SVM algorithm. Firstly, spectral clustering algorithm is used to classify the traffic state of road network's MFD. Secondly, SVM multiclassifier is trained with the partitioned road network's MFD parameters, and the accuracy evaluation method of classification results based on obfuscation matrix is given. Finally, the connected-vehicle network simulation platform is built for empirical analysis. The results show that the classification results of spectral clustering algorithm are closer to the theoretical values, compared with K-means algorithm, and the accuracy of SVM multiclassifier is 96.3%. It can be seen that our algorithm can identify the road network traffic state more effectively from the macrolevel. [ABSTRACT FROM AUTHOR]

A novel template repairing-PCR technology based on miRNA-primed bypass synthesis at the abasic sites on the PCR template is developed as a sensitive and selective platform for miRNA detection. The assay is expected to show great promise for reverse transcription-free RNA PCR amplification and target-based coding analysis. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Enrichment of zinc(II) from and into ammoniacal media was achieved with NDSX. • TOPO has an evident synergistic role on zinc transport. • Zinc transport shows a decreasing trend as the order of octane > toluene > octanol. • The low pH and total ammonia concentration of feed phase favors for zinc transport. • Non-disperse extraction process has a good stability. Abstract The enrichment of zinc(II) in ammoniacal media with non-dispersive synergistic extraction (NDSX) was achieved in a sandwich-supported liquid membrane module, in which a mixed extractant containing 4-ethyl-1-phenyl-1,3-octadione (HA) and trioctylphosphine oxide (TOPO) was used as the carrier. Several parameters of Zn(II) transport, including carrier concentration, diluent type, feed pH, total ammonia concentration, and temperature, were systematically investigated. The results showed that the mixing of TOPO with HA can effectively improve the Zn(II) enrichment from and into ammoniacal solutions. The transport efficiency of zinc increases with increasing carrier concentration and temperature. Moreover, Zn(II) transport is dependent on the total ammonia concentration and solution pH of the feed phase. The nonpolar n-octane is more beneficial to zinc transport than the polar octanol. Five successive transports exhibit excellent membrane stability and the effective uphill transport of zinc from and into ammoniacal media with NDSX. [ABSTRACT FROM AUTHOR]

Feature relationships are complex and may contain important information. k top scoring pairs ( k -TSP) studies feature relationships by the horizontal comparison. This study examines feature relationships and proposes vertical and horizontal k -TSP (VH- k -TSP) to identify the discriminative feature pairs by evaluating feature pairs based on the vertical and horizontal comparisons. Complexity is introduced to compute the discriminative abilities of feature pairs by means of these two comparisons. VH- k -TSP was compared with support vector machine-recursive feature elimination, relative simplicity-support vector machine, k -TSP and M- k -TSP on nine public genomics datasets. For multi-class problems, one-to-one method was used. The experiments showed that VH- k -TSP outperformed the four methods in most cases. Then, VH- k -TSP was applied to a metabolomics data of liver disease. An accuracy rate of 88.11 ± 3.30% in discrimination between cirrhosis and hepatocellular carcinoma was obtained by VH- k -TSP, better than 77.39 ± 4.10% and 79.28 ± 3.73% obtained by k -TSP and M- k -TSP, respectively. Hence combining the vertical and horizontal comparisons could define more discriminative feature pairs. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA–disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases. [ABSTRACT FROM AUTHOR]

Honey has been used not only as a food source but also for medicinal purposes. Recent studies have indicated that honey exhibits antioxidant, hepatoprotective, hypolipidemic, hypoglycemic and anti‐obesity properties, as well as anticancer, anti‐atherosclerotic, hypotensive, neuroprotective and immunomodulatory activities. These health benefits of honey could be attributed to its wide range of nutritional components, including polysaccharides and polyphenols, which have been proven to possess various beneficial properties. It is notable that the composition of honey can also be affected by nectar, season, geography and storage condition. Moreover, the safety of honey requires caution to avoid any potential safety incidents. Therefore, this review aims to provide recent research regarding the chemical composition, biological activities and safety of honey, which might be attributed to comprehensive utilization of honey. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Abstract: An abnormal trap distribution and coordination geometry‐dependent multi‐band emission are discovered in chromium‐doped aluminates. The multi‐band and broadband persistent luminescence from 650–1100 nm peaking at 688 and 793 nm from Cr3+‐doped SrAl12O19 is systematically studied via structural and spectroscopic analysis. Solid state nuclear magnetic resonance allows the visualization of various coordination configurations in SrAl12O19, thus offering the possibility of tailoring the local geometry of the emission center to trigger the control of the spectral parameter. Deep tissue ex vivo and in vivo imaging in mice both demonstrate that multi‐band‐emissive SrAl12O19:Cr3+ shows superior, high‐quality near‐infrared (NIR) bio‐imaging in the biological transparency window compared to single band‐emissive (with emission only at 688 nm) SrAl2O4:Cr3+, although SrAl2O4:Cr3+ has a higher luminescent intensity and longer duration at 688 nm. Moreover, by measuring the thermoluminescence spectra the driving force of carrier release is discovered to be only from the deep trap (the depth is > 1 eV), which is different from the generally accepted shallow‐dependent afterglow‐emitting process. These findings pave the way for opening a vista of possible avenues for the enhancement of signal‐to‐noise ratio, the improvement of imaging quality, as well as the understanding of the trapping and de‐trapping process in long‐persistent phosphors. [ABSTRACT FROM AUTHOR]

A thermohydrodynamic cavitating flow lubrication model including effects of cavitation, inertia and turbulence for high speed water-lubricated spiral groove thrust bearing was established. The static characteristics of the water-lubricated spiral groove thrust bearing at high speed condition were investigated using this model. The numerical calculation results have shown that load carrying capacity and friction torque reduce due to inertia and cavitation in high speed condition, while that load carrying capacity and friction torque increase due to turbulence. The maximum temperature rise increases due to inertia effect, and reduces due to cavitation effect. In addition, the influence of inertia effect on the static characteristics of water-lubricated spiral groove thrust bearing was greater than the cavitation effect under high speed condition. [ABSTRACT FROM AUTHOR]

The C2H2 type zinc-finger transcription factor Nerfin-1 expresses dominantly in Drosophila nervous system and plays an important role in early axon guidance decisions and preventing neurons dedifferentiation. Recently, increasing reports indicated that INSM1 (homologue to nerfin-1 in mammals) is a useful marker for prognosis of neuroendocrine tumors. The dynamic expression of Nerfin-1 is regulated post-transcriptionally by multiple microRNAs; however, its post-translational regulation is still unclear. Here we showed that the protein turnover of Nerfin-1 is regulated by Slimb, the substrate adaptor of SCF Slimb ubiquitin ligase complex. Mechanistically, Slimb associates with Nerfin-1 and promotes it ubiquitination and degradation in Drosophila S2R + cells. Furthermore, we determined that the C-terminal half of Nerfin-1 (Nerfin-1 CT ) is required for its binding to Slimb. Genetic epistasis assays showed that Slimb misexpression antagonizes, while knock-down enhances the activity of Nerfin-1 CT in Drosophila eyes. Our data revealed a new link to understand the underlying mechanism for Nerfin-1 turnover in post-translational level, and provided useful insights in animal development and disease treatment by manipulating the activity of Slimb and Nerfin-1. [ABSTRACT FROM AUTHOR]

Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data. [ABSTRACT FROM AUTHOR]

Connected-vehicles network provides opportunities and conditions for improving traffic signal control, and macroscopic fundamental diagrams (MFD) can control the road network at the macrolevel effectively. This paper integrated proposed real-time access to the number of mobile vehicles and the maximum road queuing length in the Connected-vehicles network. Moreover, when implementing a simple control strategy to limit the boundary flow of a road network based on MFD, we determined whether the maximum queuing length of each boundary section exceeds the road-safety queuing length in real-time calculations and timely adjusted the road-network influx rate to avoid the overflow phenomenon in the boundary section. We established a road-network microtraffic simulation model in VISSIM software taking a district as the experimental area, determined MFD of the region based on the number of mobile vehicles, and weighted traffic volume of the road network. When the road network was tending to saturate, we implemented a simple control strategy and our algorithm limits the boundary flow. Finally, we compared the traffic signal control indicators with three strategies: (1) no control strategy, (2) boundary control, and (3) boundary control with limiting queue strategy. The results show that our proposed algorithm is better than the other two. [ABSTRACT FROM AUTHOR]

Defining meaningful feature (molecule) combinations can enhance the study of disease diagnosis and prognosis. However, feature combinations are complex and various in biosystems, and the existing methods examine the feature cooperation in a single, fixed pattern for all feature pairs, such as linear combination. To identify the appropriate combination between two features and evaluate feature combination more comprehensively, this paper adopts kernel functions to study feature relationships and proposes a new omics data analysis method KF- k -TSP. Besides linear combination, KF- k -TSP also explores the nonlinear combination of features, and allows hybridizing multiple kernel functions to evaluate feature interaction from multiple views. KF- k -TSP selects k > 0 top-scoring pairs to build an ensemble classifier. Experimental results show that KF- k -TSP with multiple kernel functions which evaluates feature combinations from multiple views is better than that with only one kernel function. Meanwhile, KF- k -TSP performs better than TSP family algorithms and the previous methods based on conversion strategy in most cases. It performs similarly to the popular machine learning methods in omics data analysis, but involves fewer feature pairs. In the procedure of physiological and pathological changes, molecular interactions can be both linear and nonlinear. Hence, KF- k -TSP, which can measure molecular combination from multiple perspectives, can help to mine information closely related to physiological and pathological changes and study disease mechanism. [ABSTRACT FROM AUTHOR]

As the Earth's third pole, the Tibetan Plateau ecosystems are extremely sensitive to climate change. However, the interannual climate sensitivity of the carbon balance of the Tibetan Plateau alpine grassland has not been well quantified under changes in the climate and atmospheric CO 2 concentration. Here, we used a process-based biogeochemistry model, CENTURY, to evaluate the sensitivity of the carbon balance to climate change and rising atmospheric CO 2 concentration on the Tibetan Plateau grassland during the period 1901–2010. We show that the magnitude of the increase in net primary production (NPP) (0.31 g C m − 2 yr − 1 ) was larger than that in heterotrophic respiration (Rh) (0.26 g C m − 2 yr − 1 ), and thus indicate that the Tibetan Plateau grassland acted as a net carbon sink of 7.45 Tg C yr − 1 from 1901 to 2010. The spatiotemporal dynamics of carbon fluxes in the Tibetan Plateau grassland were primarily controlled by temperature, and positively correlated with precipitation and elevated CO 2 concentration. The temperature sensitivities of NPP ( γ NPP Temp ), Rh ( γ Rh Temp ), and net ecosystem production (NEP, γ NEP Temp ) during the period 1961–2010 weakened by 16%, 17%, and 15%, respectively, compared with the period 1901–1960. By contrast, the precipitation sensitivities of these variables, i.e., γ NPP Prec , γ Rh Prec , and γ NEP Prec , strengthened by 46%, 67%, and 23%, respectively, from 1961 to 2010 compared with the 1901–1960 period. The continuing increase in atmospheric CO 2 concentration tended to enhance the climate sensitivity of the carbon fluxes, by ~ 3% for γ Temp and 2%–4% for γ Prec , as a result of CO 2 fertilization and water use efficiency improvement. The climate sensitivity heterogeneity revealed that interannual variation in Rh is more likely to be amplified than NPP or NEP. The findings imply that climate change exerts a strong influence on the carbon dynamics of the alpine ecosystem in the Tibetan Plateau, and this could further modulate the carbon balance depending on the magnitude of different carbon component fluxes. Our study suggests that changes in the climate sensitivity of carbon dynamics should be considered to further quantify the carbon dynamics in this climate-sensitive region. [ABSTRACT FROM AUTHOR]

Sesame (Sesamum indicum L.) seeds are popular nutritional food but with limited knowledge about their antioxidant and antiproliferative activities of various varieties. Phytochemical profiles and antioxidant and antiproliferative activities of six varieties of sesame (Sesamum indicum L.) seeds were studied. Fenheizhi3 (black) cultivar exhibited the maximum contents of total phenolics and lignans and values of total oxygen radical absorbance capacity (ORAC) and antiproliferative activity (EC50) against HepG2 cells. Bound ORAC values showed strong associations with bound phenolics contents (r=0.976, p<0.01); in bound phenolic extracts, EC50 values showed strong negative associations with phenolic contents (r=-0.869, p<0.05) and ORAC values (r=-0.918, p<0.01). Moreover, the contents of free phenolics were higher than that of the bound phenolics, and the three black sesame seeds generally depicted higher total phenolics compared to the three white varieties. The antioxidant (ORAC values) and antiproliferation activities of six sesame seeds were both associated with contents of bound phenolics (r>0.8, p<0.05). Interestingly, nonlignan components in bound phenolics contributed to the antioxidant and antiproliferative activities. This study suggested that Fenheizhi3 variety is superior to the other five varieties as antioxidant supplements. [ABSTRACT FROM AUTHOR]

Mo-12Si-8.5B alloy with layered Mo 2 TiAlC 2 (a new MAX phase) additions, in order to improve its fracture toughness, were prepared by hot pressing. The composition analysis combined with microstructure features revealed that after hot pressing sintering, the Mo 2 TiAlC 2 does not decompose but still maintains the layered structure and exhibits a good interface bonding with the matrix. The grain size of Mo-12Si-8.5B alloys was apparently refined by Mo 2 TiAlC 2. The mechanical properties suggested that the Mo 2 TiAlC 2 phase affects the hardness of Mo-12Si-8.5B alloy mainly through grain refinement. As the amount of Mo 2 TiAlC 2 increased, the compressive strength and flexure strength of alloys improved nonlinearly which is caused by the relatively low strength of Mo 2 TiAlC 2 and stress release due to its basal plane sliding, the enhanced strength was attributed to the particle strengthening and fine grain strengthening. The Mo 2 TiAlC 2 phase exhibits a remarkably toughening effect on Mo-12Si-8.5B alloy. The fracture toughness of Mo-12Si-8.5B-2%Mo 2 TiAlC 2 alloy increased by about 52% compared with the Mo 2 TiAlC 2 free alloy. Except for the fine-grain toughening caused by Mo 2 TiAlC 2 particles, the improvement of fracture toughness of Mo-12Si-8.5B alloy by Mo 2 TiAlC 2 is mainly caused by the crack deflection, the steps formed by Mo 2 TiAlC 2 fracture and basal plane (0001) sliding of Mo 2 TiAlC 2. • The layered Mo 2 TiAlC 2 MAX phase was firstly added to Mo-12Si-8.5B alloy. • Mo 2 TiAlC 2 shows a good interface bonding with matrix and refine the grain size. • Compressive and flexure strength improved nonlinearly with Mo 2 TiAlC 2 increasing. • The Mo 2 TiAlC 2 exhibits a remarkably toughening effect on Mo-12Si-8.5B alloy. [ABSTRACT FROM AUTHOR]

Analyzing the biological data by considering the molecule interactions may induce a more accurate identification of disease-related biomarkers. In this study, a novel feature selection method based on molecule (feature) interactive effect network is proposed, denoted as Distance Correlation Gain-Network (DCG-Net). In DCG-Net, DCG is defined to measure the interactive effects between pairwise features with respect to the process of physiological and pathological changes and infer the molecule interactive effect network. DCG index is suitable for discrete random variables and continuous random variables. Then a greedy searching strategy is developed to search the informational modules of the interactive features with high statistical dependence on disease outcome. To evaluate the performance of DCG-Net, it was compared with eight representative feature selection techniques including t -test, ReliefF, SVM-RFE, mRMR, IG-RFE, INDEED, MN-PCC and Dcor-SFS on ten public datasets. The experiment results showed the superior performance of DCG-Net in classification accuracy rate, sensitivity, and specificity for three different classifiers. Subsequently, DCG-Net was employed to analyze a lung adenocarcinoma metabolomics dataset, and the metabolites selected involved in the important pathway and had a better discrimination ability. The experiments demonstrate that DCG can effectively detect the molecular interactions, and incorporation of the molecule interactions is helpful to identify informational biomarkers reflecting the occurrence and development of complex diseases. [Display omitted] • A new method is proposed to extract important information based on feature interactions. • The distance correlation gain suitable for continuous and discrete random variables is defined. • A greedy searching strategy is developed to search the informational modules from the interactive effect network. • Experiments on the public datasets and the application in the metabolomics data showed the validity of the method. • The distance correlation gain is used to explore the interactions between features and construct the molecular network. [ABSTRACT FROM AUTHOR]

A theoretical model for the translocation process of biomacromolecule is developed based on the self-consistent field theory (SCFT), where the biomacromolecule is regarded as a self-avoiding polymer chain actuated by the external potential. In this theoretical model, the external potential, the Coulomb electrostatic potential of the charged ions (the electrolyte effect), and the attractive interaction between the polymer and the nanopore (the excluded volume effect) are all considered, which have effects on the free energy landscape and conformation entropy during the translocation stage. The result shows that the entropy barrier of the polymer in the solution with high valence electrolyte is much larger than that with low valence electrolyte under the same condition, leading to that the translocation time of the DNA molecules in the solution increases when the valence electrolyte increases. In addition, the attractive interaction between the polymer and the nanopore increases the free energy of the polymer, which means that the probability of the translocation through the nanopore increases. The average translocation time decreases when the excluded volume effect parameter increases. The electrolyte effect can prolong the average translocation time. The simulation results agree well with the available experimental results. [ABSTRACT FROM AUTHOR]

• A new graph convolutional network model for molecular distance matrix prediction (DMGCN) is proposed. • Compared with the other two methods, DMGCN obtains better mean absolute error (MAE) on QM9 dataset. • The prediction results of DMGCN can be used for the downstream task of molecular property prediction. Molecular structure has important applications in many fields. For example, some studies show that molecular spatial information can be used to achieve better prediction results when predicting molecular properties. However, traditional molecular geometry calculations, such as density functional theory (DFT), are time-consuming. In view of this, we propose a model based on graph convolutional networks to predict the pairwise distance between atoms, also called distance matrix prediction of the molecule (DMGCN). In order to indicate the effect of DMGCN model, the model is compared with the model DeeperGCN-DAGNN and the method of calculating molecular conformation in RDKit. Results show that the MAE of DMGCN is smaller than DeeperGCN-DAGNN and RDKit. In addition, the distances predicted by the DMGCN model and the distances calculated by the QM9 dataset are used to predict the molecular properties, thus showing the effectiveness of the distance predicted by the DMGCN model. Our code is available at https://github.com/Lin12138xh/DMGCN. [Display omitted] [ABSTRACT FROM AUTHOR]

With the gradual depletion of traditional zinc resources, the full use of various non‐traditional zinc‐containing resources has received intensive attention. However, the efficient recovery of zinc ions with low concentrations remains challenging. Here efficient and continuous recovery of zinc ions in ammoniacal chloride media by a flat supported liquid membrane system is achieved, using Cyanex923 and TBP mixed extractant as the membrane phase. This article discusses the synergistic effect between Aliquat336, Cyanex923 and TBP, the effects of feed pH, total ammonia concentration, Cl− concentration and temperature on Zn(II) transport. [ABSTRACT FROM AUTHOR]

• MVD mushroom stipes showed higher moisture contents, ε ' and ε ' ' values than caps. • MVD mushroom caps displayed higher water evaporation and rehydration rates. • The centre area of the MVD mushroom slice had the highest temperature. • The more porous structure in the MVD cap led to its higher mass transfer rates. This research work aimed to investigate the effects of microstructures, dielectric property and temperature distributions on drying feature difference between the mushroom cap and stipe during the microwave-vacuum drying (MVD) process. Near-infrared hyperspectral imaging (NIR HSI) was employed to visualize distribution maps for moisture content (MC), dielectric constant ε ' and dielectric loss factor ε ' ' of mushroom slices during the MVD process. Infrared (IR) thermal imaging was used to evaluate the temperature distribution of the mushroom slices. Results demonstrated higher MC, ε ' and ε ' ' values in MVD mushroom stipes. Nevertheless, the centre area of the mushroom slice showed the highest temperature, while the MVD mushroom cap displayed a more porous structure. The effect of microstructure could encounter effects of dielectric properties and temperature to cause higher water evaporation in the MVD cap. This work highlights the novelty to combine different detection techniques to investigate the effects of microstructures, dielectric property and temperature distributions on drying patterns of mushroom slices. [ABSTRACT FROM AUTHOR]

Abstract: Filtering the discriminative metabolites from high dimension metabolome data is very important in metabolomics study. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique and has shown promising applications in the analysis of the metabolome data. SVM-RFE measures the weights of the features according to the support vectors, noise and non-informative variables in the high dimension data may affect the hyper-plane of the SVM learning model. Hence we proposed a mutual information (MI)-SVM-RFE method which filters out noise and non-informative variables by means of artificial variables and MI, then conducts SVM-RFE to select the most discriminative features. A serum metabolomics data set from patients with chronic hepatitis B, cirrhosis and hepatocellular carcinoma analyzed by liquid chromatography–mass spectrometry (LC–MS) was used to demonstrate the validation of our method. An accuracy of 74.33±2.98% to distinguish among three liver diseases was obtained, better than 72.00±4.15% from the original SVM-RFE. Thirty-four ion features were defined to distinguish among the control and 3 liver diseases, 17 of them were identified. [Copyright &y& Elsevier]

Abstract: Magnetic Co–Cu alloy nanowires with low Cu content were prepared by SC electrodeposition in pores of anodic aluminum oxide templates. The as-deposited Co–Cu nanowires, with a diameter of 15nm, show distinctive magnetic anisotropy as an applied magnetic field parallel to the axis of nanowires. With increase in the molar ratio of Co and Cu, the coercivity along nanowire axis increases and reaches a maximum value of 1977.5Oe at the Co/Cu molar ratio of 60:1, but the maximum value of coercivity increases to 1743.6Oe with the decrease of frequency to 2Hz. [Copyright &y& Elsevier]