Haplotype-resolved chromosomal-level genome assembly reveals regulatory variations in mulberry fruit anthocyanin content.

Understanding the intricate regulatory mechanisms underlying the anthocyanin content (AC) in fruits and vegetables is crucial for advanced biotechnological customization. In this study, we generated high-quality haplotype-resolved genome assemblies for two mulberry cultivars: the high-AC 'Zhongsang5801' (ZS5801) and the low-AC 'Zhenzhubai' (ZZB). Additionally, we conducted a comprehensive analysis of genes associated with AC production. Through genome-wide association studies (GWAS) on 112 mulberry fruits, we identified MaVHAG3, which encodes a vacuolar-type H+-ATPase G3 subunit, as a key gene linked to purple pigmentation. To gain deeper insights into the genetic and molecular processes underlying high AC, we compared the genomes of ZS5801 and ZZB, along with fruit transcriptome data across five developmental stages, and quantified the accumulation of metabolic substances. Compared to ZZB, ZS5801 exhibited significantly more differentially expressed genes (DEGs) related to anthocyanin metabolism and higher levels of anthocyanins and flavonoids. Comparative analyses revealed expansions and contractions in the flavonol synthase (FLS) and dihydroflavonol 4-reductase (DFR) genes, resulting in altered carbon flow. Co-expression analysis demonstrated that ZS5801 displayed more significant alterations in genes involved in late-stage AC regulation compared to ZZB, particularly during the phase stage. In summary, our findings provide valuable insights into the regulation of mulberry fruit AC, offering genetic resources to enhance cultivars with higher AC traits.

Spectrum-Dependent Image Convolutional Processing via a Two-Dimensional Polarization-Sensitive Photodetector.

Currently, the improvement in the processing capacity of traditional processors considerably lags behind the demands of real-time image processing caused by the advancement of photodetectors and the widespread deployment of high-definition image sensors. Therefore, achieving real-time image processing at the sensor level has become a prominent research domain in the field of photodetector technology. This goal underscores the need for photodetectors with enhanced multifunctional integration capabilities than can perform real-time computations using optical or electrical signals. In this study, we employ an innovative p-type semiconductor GaTe0.5Se0.5 to construct a polarization-sensitive wide-spectral photodetector. Leveraging the wide-spectral photoresponse, we realize three-band imaging within a wavelength range of 390-810 nm. Furthermore, real-time image convolutional processing is enabled by configuring appropriate convolution kernels based on the polarization-sensitive photocurrents. The innovative design of the polarization-sensitive wide-spectral GaTe0.5Se0.5-based photodetector represents a notable contribution to the domain of real-time image perception and processing.

Spectrometer-Less Remote Sensing Image Classification Based on Gate-Tunable van der Waals Heterostructures.

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m-phase GaTe0.5Se0.5 with wide-spectral photoresponses (250-1064 nm) and stack it with WSe2 are utilizes to construct a two-dimensional van der Waals heterojunction (2D-vdWH), enabling the design of a gate-tunable wide-spectral photodetector. By utilizing the multi-photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof-of-concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250-1000 nm hyperspectral data (88.72%) and far superior to the accuracy of non-tunable photoresponse (71.17%). Artificially designed gate-tunable wide-spectral 2D-vdWHs GaTe0.5Se0.5/WSe2-based photodetector present a promising pathway for the development of miniaturized and cost-effective remote sensing classification technology.

Dinuclear Gold-Catalyzed para-Selective C-H Arylation of Undirected Arenes by Noncovalent Interactions.

The regioselectivity of C-H functionalization is commonly achieved by directing groups, electronic factors, or steric hindrance, which facilitate the identification of reaction sites. However, such strategies are less effective for reactants such as simple monofluoroarenes due to their relatively low reactivity and the modest steric demands of the fluorine atom. Herein, we present an undirected gold-catalyzed para-C-H arylation of a wide array of monofluoroarenes using air-stable aryl silanes and germanes at room temperature. A high para-regioselectivity (up to 98 : 2) can be realized by utilizing a dinuclear dppm(AuOTs)2 (dppm=bis(diphenylphosphino)methane) as the catalyst and hexafluorobenzene as the solvent. This provides a general and practical protocol for the concise construction of structurally diverse para-arylated monofluoroarenes through C-H activation manner. It features excellent functional group tolerance and a broad substrate scope (>80 examples). Besides, this strategy is also robust for other simple monosubstituted arenes and heteroarenes. Our mechanistic studies and theoretical calculations suggest that para-C-H selectivity arises from highly electrophilic and structurally flexible dinuclear Ar-Au(III)-Au(I) species, coupled with noncovalent interaction induced by hexafluorobenzene.

Fog scavenging of particulate matters in air pollution events: Observation and simulation in the Yangtze River Delta, China.

Stable atmospheric boundary layer is conducive to the accumulation of atmospheric pollution and the occurrence of fog, and fog has a removal effect on air pollution. In this study, we use the observation and WRF-Chem (Weather Research and Forecasting Model with Chemistry) simulation to analyze the factors affecting the removal efficiency in a continuous fog and haze episode from November 26 to 28, 2018 in Jiangsu Province, such as fog thickness and duration. The results show that the WRF-Chem simulation well reproduces the boundary layer characteristics in the stages of fog formation, development and dissipation. The atmospheric boundary layer provides favorable conditions for the maintenance of fog and air pollution. The inversion layer, with the maximum intensity of 3 °C per 100 m, creates favorable thermal conditions, and the water vapor advection is also conducive to the fog maintenance. The ground observation verifies the wet scavenging of PM2.5 during dense fog events. The scavenging effect is related to the fog duration, and the correlation is positive when the fog is just formed and negative when the fog is dissipating. The PM2.5 concentration decreases from 159 µg m-3 to 38 µg m-3 after the fog lasts for 11 h. The fog has a remarkable scavenging effect on PM2.5 in the vertical direction, due to the deposition effect of fog droplets on the pollutant particles. The PM2.5 concentration on the ground is lower than the vertical average in the fog area, and the scavenging effects during the dense fog periods on November 27 and 28 are 47.7 µg m-3 and 36.1 µg m-3, respectively. The fog duration is mostly concentrated in 3-17 h. When the duration of fog is 4-8 h, the scavenging effect on PM2.5 reaches the strongest, with an average PM2.5 concentration decrease of >70 µg m-3.

The interaction between black carbon and planetary boundary layer in the Yangtze River Delta from 2015 to 2020: Why O3 didn't decline so significantly as PM2.5.

Since the Air Pollution Prevention and Control Action Plan (air clean plan) issued in 2013, air quality has been in continuous improvement. The second stage of air clean plan since 2018 was focused on O3 controlling, but it still didn't decline so significantly as PM2.5. This study conducted a long-term observation on black carbon (BC) and utilized the observational data of other air pollutants (PM2.5, PM10, NO2, SO2, CO and O3), the meteorological elements and the vertical sounding data of PBL in Nanjing. In the daytime (08:00-20:00), PM2.5 kept decreasing from 2015 to 2020 at the rate of 4.8 µg⋅m-3⋅a-1, however, BC increased at the rate of 0.6 µg⋅m-3⋅a-1, which has led to the continuous growth of BC/PM2.5 (0.9%⋅a-1). However, during this period, O3 was relatively stable and, in 2020, it returned below its value in 2015 after slight increases in 2017 and 2018. Meanwhile, the average surface temperature had increased by around 1.0 °C during 2015-2019 at the rate of 0.3 °C⋅a-1. Also, the average height of the inversion layer had increased significantly by 494.0 and 176.7 m at 20:00 and 08:00, whose growth ratio was up to 57% and 25%, respectively. The above observation results have formed a set of chain reactions as follows. The growth of the surface BC caused the surface temperature to rise due to the increasing heating effect of BC. The continuous growth of the surface temperature made it easier for the PBL height to develop, which led to the lift of the inversion layer in the PBL and the larger atmospheric environment capacity. Ultimately, it is conducive to the diffusion of the near surface pollutants, thus helping reduce their concentrations, which offsets the increasing tendency of O3 and add to the decreasing trend of PM2.5. This phenomenon is the most remarkable in summer, with the fastest increasing rate of temperature (0.8 °C⋅a-1) and O3 (3.9 µg⋅m-3⋅a-1) during 2015-2019 (excluding 2020 to erase the great effect of COVID-19 lockdown on emissions).

Comparative observation of aerosol vertical profiles in urban and suburban areas: Impacts of local and regional transport.

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments were used to carry out observation of aerosol in the urban and suburban areas of Shanghai from October 17 to November 21, 2019. Fudan University (FDU) site is a typical urban environment, surrounded by residential areas, commercial areas and arterial roads, while Dianshan Lake (DSL) site is a suburban environment with high vegetation coverage and no pollutant emission sources. The aerosol retrieved by MAX-DOAS was in good correlation with the observation of sun photometer and the PM2.5 concentration of the corresponding site, which demonstrates that the aerosol retrieved by MAX-DOAS is reliable and feasible. Comparing the mean aerosol extinction coefficient (AEC) profiles during the observation period between urban and suburban areas, it was found that the occurrence of high aerosol concentration at FDU was nearly 3 h later than that of DSL at suburban site. And the aerosol at DSL was concentrated at an altitude of 0.3- 0.5 km, with a mean peak value of 0.486 km-1, which was slightly higher than the peak AEC of 0.453 km-1 at FDU of 0.2- 0.4 km. The difference in aerosol characteristics between the two sites may be due to the fact that the influences of aerosol transport and boundary layer dynamics are different between the two sites. The backward trajectories analysis also presents that there were mutual transports of aerosol between urban and suburban areas, which affect the optical properties of the aerosol in these two sites. In a case of aerosol pollution, we visualized the transport pathway of aerosol from the western part of the North China Plain to Shanghai using AEC profiles and backward trajectories, providing the evidence that the local aerosol pollution in Shanghai was affected by long-distance transport.

Graph Convolutional Network: Traffic Speed Prediction Fused with Traffic Flow Data.

Traffic speed prediction plays an important role in intelligent transportation systems, and many approaches have been proposed over recent decades. In recent years, methods using graph convolutional networks (GCNs) have been more promising, which can extract the spatiality of traffic networks and achieve a better prediction performance than others. However, these methods only use inaccurate historical data of traffic speed to forecast, which decreases the prediction accuracy to a certain degree. Moreover, they ignore the influence of dynamic traffic on spatial relationships and merely consider the static spatial dependency. In this paper, we present a novel graph convolutional network model called FSTGCN to solve these problems, where the model adopts the full convolutional structure and avoids repeated iterations. Specifically, because traffic flow has a mapping relationship with traffic speed and its values are more exact, we fused historical traffic flow data into the forecasting model in order to reduce the prediction error. Meanwhile, we analyzed the covariance relationship of the traffic flow between road segments and designed the dynamic adjacency matrix, which can capture the dynamic spatial correlation of the traffic network. Lastly, we conducted experiments on two real-world datasets and prove that our model can outperform state-of-the-art traffic speed prediction.

Atmospheric Ammonia in Beijing during the COVID-19 Outbreak: Concentrations, Sources, and Implications.

This study investigates the concentrations and δ15N values of NH3 in Beijing during and after the 2020 COVID-19 lockdown. Higher NH3 concentrations and lower δ15N-NH3(measured) were observed at most sites in 2020 compared to 2017. Except for a site inside a tunnel, NH3 concentrations did not increase significantly after the lockdown had ended compared to those during the lockdown, while δ15N-NH3(measured) increased by 2.1-9.9‰. Nonagricultural sources (fossil fuel and urban waste) overall contributed 81% and 62% of NH3 at on-road (tunnel interior) and nonroad (CAU) sites in 2020, respectively, comparable to those in 2017 (without significant difference). The contribution of nonagricultural sources slightly increased after the lockdown compared to the contribution during the lockdown at the nonroad site and hardly changed at the tunnel interior site. Our results suggest that (1) unfavorable meteorological conditions, especially lower boundary layer heights and changes in regional transport patterns, might play a more important role than reduced anthropogenic emissions in the temporal variations of Beijing NH3 and (2) the effect of reduced anthropogenic emissions, during the COVID-19 outbreak or with the future implementation of emission control strategies, on atmospheric NH3 can be better demonstrated by isotope-based source apportionment of NH3, rather than only by changes in NH3 concentrations.

Estimation of radiative forcing and heating rate based on vertical observation of black carbon in Nanjing, China.

Owing to a lack of vertical observations, the impacts of black carbon (BC) on radiative forcing (RF) have typically been analyzed using ground observations and assumed profiles. In this study, a UAV platform was used to measure high-resolution in-situ vertical profiles of BC, fine particles (PM2.5), and relevant meteorological parameters in the boundary layer (BL). Further, a series of calculations using actual vertical profiles of BC were conducted to determine its impact on RF and heating rate (HR). The results show that the vertical distributions of BC were strongly affected by atmospheric thermodynamics and transport. Moreover. Three main types of profiles were revealed: Type I, Type II, Type III, which correspond to homogenous profiles (HO), negative gradient profiles (NG), and positive gradient profiles (PG), respectively. Types I and II were related to the diurnal evolution of the BL, and Type III was caused by surrounding emissions from high stacks and regional transport. There were no obvious differences in RF calculated for HO profiles and corresponding surface BC concentrations, unlike for NG and PG profiles. RF values calculated using surface BC concentrations led to an overestimate of 13.2 W m-2 (27.5%, surface) and 18.2 W m-2 (33.4%, atmosphere) compared to those calculated using actual NG profiles, and an underestimate of approximately 15.4 W m-2 (35.0%, surface) and 16.1 W m-2 (29.9%, atmosphere) compared to those calculated using actual PG profiles. In addition, the vertical distributions of BC HR exhibited clear sensitivity to BC profile types. Daytime PG profiles resulted in a positive vertical gradient of HR, which may strengthen temperature inversion at high altitudes. These findings indicate that calculations that use BC surface concentrations and ignore the vertical distribution of BC will lead to substantial uncertainties in the effects of BC on RF and HR.

Decarboxylative C-H Alkylation of Heteroarene N-Oxides by Visible Light/Copper Catalysis.

This paper reports a highly site-selective alkylation of heteroarene N-oxides using hypervalent iodine(III) carboxylates to serve as an alkylating agent in the presence of a cheap copper catalyst under visible light conditions. This mild method proceeds at room temperature in an air atmosphere and can withstand various heteroarene N-oxides as well as various primary, secondary, and tertiary alkyl carboxylic acids. It also provides a practical method for enabling the rapid conversion of commercially available raw materials into medically relevant "drug-like" molecules.

An efficient method for the synthesis of 2-pyridones via C-H bond functionalization.

A simple and practical method to access N-substituted 2-pyridones via a formal [3+3] annulation of enaminones with acrylates based on RhIII-catalyzed C-H functionalization was developed. Control and deuterated experiments led to a plausible mechanism involving C-H bond cross-coupling and aminolysis cyclization. This strategy provides a short synthesis of structural motifs of N-substituted 2-pyridones.

Meteorological Variables and Synoptic Patterns Associated with Air Pollutions in Eastern China during 2013-2018.

Steady meteorological conditions are important external factors affecting air pollution. In order to analyze how adverse meteorological variables affect air pollution, surface synoptic situation patterns and meteorological conditions during heavy pollution episodes are discussed. The results showed that there were 78 RPHPDs (regional PM2.5 pollution days) in Jiangsu, with a decreasing trend year by year. Winter had the most stable meteorological conditions, thus most RPHPDs appeared in winter, followed by autumn and summer, with the least days in spring. RPHPDs were classified into three patterns, respectively, as equalized pressure (EQP), advancing edge of a cold front (ACF) and inverted trough of low pressure (INT) according to the SLP (sea level pressure). RPHPDs under EQP were the most (51%), followed by ACF (37%); INT was the minimum (12%). Using statistical methods and meteorological condition data on RPHPDs from 2013 to 2017 to deduce the thresholds and 2018 as an independent dataset to validate the proposed thresholds, the threshold values of meteorological elements are summarized as follows. The probability of RPHPDs without rain was above 92% with the daily and hourly precipitation of all RPHPDs below 2.1 mm and 0.8 mm. Wind speed, RHs, inversion intensity(ITI), height difference in the temperature inversion(ITK), the lower height of temperature inversion (LHTI) and mixed-layer height (MLH) in terms of 25%-75% high probability range were respectively within 0.5-3.6 m s-1, 55%-92%, 0.7-4.0 °C 100 m -1, 42-576 m, 3-570 m, 200-1200 m. Two conditions should be considered: whether the pattern was EQP, ACF or INT and whether the eight meteorological elements are within the thresholds. If both criteria are met, PM2.5 particles tend to accumulate and air pollution diffusion conditions are poor. Unfavorable meteorological conditions are the necessary, but not sufficient condition for RPHPDs.

FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data.

The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate.

Traffic Speed Prediction: An Attention-Based Method.

Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the spatial or some environmental factors, especially the different correlations between the target road and the surrounding roads. This paper proposes a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) to address the above issues. We apply temporal clustering to the target road to distinguish the traffic environment. Traffic data in each cluster have a similar distribution, which can help improve the prediction accuracy. A hierarchical attention-based mechanism is then used to extract the features at each time step. The encoder measures the importance of spatial features, and the decoder measures the temporal ones. The proposed method is evaluated over the data of a certain area in Hangzhou, and experiments have shown that this method can outperform the state of the art for traffic speed prediction.

α-Amino Acetal: A Synthetic Intermediate for the Construction of Aza-Polycycles.

One-step construction of aza-polycyclic skeletons was realized through double cyclization of the oxocarbenium and carbocation intermediates generated from α-amino acetals, which were designed and synthesized by employing our α-amination protocol of aldehydes. A simple process, a broad substrate range, commercialized materials, and extensive applications demonstrated the generality and effectiveness of this method.

Chemical compositions of fog and precipitation at Sejila Mountain in the southeast Tibetan Plateau, China.

Chemical compositions of fog and rain water were measured between July 2017 and September 2018 at Sejila Mountain, southeast Tibet, where fog events frequently occurred in original fir forests at altitude 3950 m. Fog water samples were collected using a Caltech Active Strand Cloud Collector (CASCC), and rain samples were collected using a precipitation gauge. Differences were observed between fog water and rain composition for most analyzed ions. Ion abundance in fog water was Ca2+ > Cl- > Na+ > SO42- > Mg2+ > NH4+ >K+ > NO3- whereas an order of Ca2+ > Na+ > Cl- > Mg2+ > SO42- > NO3- > K+ > NH4+ was observed for rain water. All ion concentrations were higher in fog water than in rain water. Additionally, Ca2+ was the dominant cation in both fog and rain samples, accounting for more than half of all measured cations. NH4+ and SO42- concentrations were notable for being higher in fog than rain water when compared with other ions. For trace elements, Al, As, Mn and Se were the most abundant elements in fog water; only Al and As were detected in rain water. Seventy-two hour back-trajectory analysis showed that air masses during fog and/or rain events mainly came from the south of Sejila Mountain. Spearman correlation analysis and source contribution calculations indicated that both marine and terrestrial sources contributed to the observed ion concentrations. Considering the higher concentrations of NH4+ and higher ratio of NH4+/NO3- measured in fog than in rain, we suggest that quantification of fog nitrogen deposition and its ecological effect in this area should be given more attention.

Eps15 homology domain 1 promotes the evolution of papillary thyroid cancer by regulating endocytotic recycling of epidermal growth factor receptor.

Papillary thyroid cancer (PTC) is the most common type of thyroid malignancy, and it is often observed to overexpress epidermal growth factor receptor (EGFR). Previous research has indicated that EH domain-containing 1 (EHD1) is associated with EGFR-mediated endocytotic recycling in multiple tumor types. The objective of the present study was to determine the protein expression levels and clinical significance of EHD1, EGFR, caveolin-1 (CAV-1) and RAB11 family interacting protein 3 (RAB11FIP3) in PTC. PTC specimens were analyzed for EHD1, EGFR, CAV-1 and RAB11FIP3 expression via immunohistochemistry and western blotting. The associations between protein expression and clinicopathological features were assessed. EHD1, EGFR, CAV-1 and RAB11FIP3 expression levels were increased in human PTC. Additionally, the expression level of EHD1 protein was significantly associated with tumor size, lymph node metastasis and EGFR expression (P<0.05). CAV-1 was associated with tumor size and EGFR expression (P<0.05). EGFR was only associated with lymph node metastasis (P=0.027) and RAB11FIP3 was not associated with any clinicopathological characteristics. The correlations between EHD1 and EGFR (r=0.564, P<0.05), CAV-1 (r=0.865, P<0.01) and RAB11FIP3 (r=0.504, P<0.05) were statistically significant. Overall, EHD1, CAV-1 and RAB11FIP3, which are key proteins in endocytotic recycling, promote PTC tumorigenesis through the regulation of the transport of EGFR.

HE4 level in ascites may assess the ovarian cancer chemotherapeutic effect.

BACKGROUND: The clinical treatment of ovarian cancer with ascites is problematic. The main reasons for treatment failure are the susceptibility to intraperitoneal metastasis and chemotherapeutic drug resistance. The purpose and significance of this study is to evaluate which marker might evaluate treatment efficacy and improve the cure rate. RESULTS: This study compared a no chemotherapy group with a chemotherapy group regarding the determination of carbohydrate antigen 125 and human epididymis protein 4 in ovarian cancer ascitic supernatants and cross-analyzed routine serum carbohydrate antigen 125 levels. The level of human epididymis protein 4 in the ascites of the chemotherapy group was significantly lower than that of the no chemotherapy group (p < 0.001). Moreover, the expression of ascitic human epididymis protein 4 correlated positively with serum carbohydrate antigen 125 levels (p < 0.001). MDR was positive in 13 of the 30 samples (43.33%) in the chemotherapy group with highly expressed CA125. CONCLUSION: The level of human epididymis protein 4 in ovarian cancer ascites may reflect the therapeutic effect of ovarian cancer patients, and a high level of human epididymis protein 4 might predict chemoresistance and the possibility of ascites formation. The determination of the expression of human epididymis protein 4 alone or combined with carbohydrate antigen 125 levels in both serum and ascites in ovarian cancer patients with ascites may have important significance for guiding and improving the treatment regimen.