Deciphering the functional assembly of microbial communities driven by heavy metals in the tidal soils of Hangzhou Bay.

Understanding the interaction between heavy metals and soil microbiomes is essential for maintaining ecosystem health and functionality in the face of persistent human-induced challenges. This study investigated the complex relationships between heavy metal contamination and the functional characteristics of soil microbial communities in the tidal soils of Hangzhou Bay, a region experiencing substantial environmental pressure due to its proximity to densely populated and industrialized regions. The north-shore sampling site showed moderate contaminations (mg/kg) of total arsenic (16.61 ± 1.13), cadmium (0.3 ± 0.05), copper (31.28 ± 1.23), nickel (37.44 ± 2.74), lead (34.29 ± 5.99), and zinc (120.8 ± 5.96), which are 1.29-2.94 times higher than the geochemical background values in Hangzhou Bay and adjacent areas. In contrast, the south-shore sampling site showed slightly higher levels of total arsenic (13.76 ± 1.35) and cadmium (0.13 ± 0.02) than the background values. Utilizing metagenomic sequencing, we decoded microbial functional genes essential for nitrogen, phosphorus, sulfur, and methane biogeochemical cycles. Although soil available nickel content was relatively low at 1 mg/kg, it exhibited strong associations with diverse microbial genes and biogeochemical pathways. Four key genes-hxlB, glpX, opd, and phny-emerged as pivotal players in the interactions with available nickel, suggesting the adaptability of microbial metabolic responses to heavy metal. Additionally, microbial genera such as Gemmatimonas and Ilumatobacter, which harbored diverse functional genes, demonstrated potential interactions with soil nickel. These findings highlight the importance of understanding heavy metal-soil microbiome dynamics for effective environmental management strategies in the tidal soils of Hangzhou Bay, with the goal of preserving ecosystem health and functionality amidst ongoing anthropogenic challenges.

Green control for inhibiting Rhizopus oryzae growth by stress factors in forage grass factory.

The forage grass factory could break through the restrictions of land resources, region and climate to achieve efficient production throughout the year by accurate and intelligent management. However, due to its closed environment, mold outbreaks in the forage grass factory were severe, significantly affecting barley production. In this study, 9 contaminated barley tissues were collected and 45 strains were isolated in forage grass factory. After ITS sequencing, 45 strains were all identified as Rhizopus oryzae. Through stress factor assays, R. oryzae growth was seriously hindered by low concentration of sodium nitrate, high pH value and ozone water treatment. High pH and ozone water affected growth mainly by altering membrane integrity of R. oryzae. Sodium nitrate inhibited the growth of R. oryzae mainly by affecting the amount of sporulation. Low concentration of sodium nitrate and ozone water did not affect the growth of barley. High concentrations of sodium nitrate (100 mM) and pH values (8-8.5) inhibited barley growth. Among them, ozone water had the most obvious inhibition effect on R. oryzae. Large-scale ozone water treatment in the forage grass factory had also played a role in restoring barley production. Taken together, the green techonology to control mold disease and maintain the safety of forage through different physicochemical methods was selected, which was of considerable application value in animal husbandry.

Analysis of lettuce transcriptome reveals the mechanism of different light/dark cycle in promoting the growth and quality.

Light/dark (L/D) cycle plays a crucial role in controlling the production and quality of vegetables. However, the mechanism of L/D cycle on vegetable growth and quality is scarce studied. To investigate the impact of L/D cycle on lettuce growth and quality, we designed three diel scenarios, including 16 hours of light and 8 hours of darkness (L16/D8), 12 hours of light and 6 hours of darkness (L12/D6), and 8 hours of light and 4 hours of darkness (L8/D4). By phenotypic analysis, we found that lettuce grew taller under the L8/D4 scenario than under L16/D8 light cycle scenarios. The physiological indexes showed that the lettuce leaves grown in the L8/D4 scenario exhibited greater enhancements in the levels of soluble protein, soluble sugar, and carotenoid content compared to the other scenarios. By comparing the expression levels under different diel scenarios (L16/D8 vs L12/D6, L16/D8 vs L8/D4, and L12/D6 vs L8/D4), we identified 7,209 differentially expressed genes (DEGs). Additionally, 3 gene modules that were closely related to L/D cycle of lettuce were selected by WGCNA analysis. The eigengenes of three gene modules were enriched in plant hormone signal transduction, sphingolipid metabolism, and nucleocytoplasmic transport pathways. Through network analysis, we identified six hub genes (CIP1, SCL34, ROPGEF1, ACD6, CcmB, and Rps4) in the three gene modules, which were dominant in plant circadian rhythms and greatly affected lettuce growth. qRT-PCR analysis confirmed the diurnal response patterns of the 6 hub genes in different treatments were significant. This study intensively enhanced our comprehension of the L/D cycle in the growth morphology, nutritional quality, and metabolic pathways of lettuce.

Synthesis and photocatalytic property of Au-TiO2 nanocomposites with controlled morphologies in microfluidic chips.

We present an efficient approach for the consecutive synthesis of Au-TiO2 nanocomposites with controlled morphologies in a microfluidic chip. The seed-mediated growth method was employed to synthesize Au nanorods as the core, and TiO2 layers of varying thicknesses were deposited on the surface or tip of the Au nanorods. Au-TiO2 nanocomposites with core-shell, dumbbell, and dandelion-like structures can be precisely synthesized in a one-step manner within the microfluidic chip by finely tuning the flow rate of NaHCO3 and the amount of hexadecyl trimethyl ammonium bromide. Furthermore, we have investigated the photocatalytic activity of the synthesized nanocomposites, and it was found that Au NR-TiO2 core-shell nanostructure with a thin TiO2 shell exhibits superior catalytic performance. This work provides an effective and controlled method for the microscale preparation and photocatalytic application of various Au-TiO2 nanocomposite structures.

A hyperspectral deep learning attention model for predicting lettuce chlorophyll content.

BACKGROUND: The phenotypic traits of leaves are the direct reflection of the agronomic traits in the growth process of leafy vegetables, which plays a vital role in the selection of high-quality leafy vegetable varieties. The current image-based phenotypic traits extraction research mainly focuses on the morphological and structural traits of plants or leaves, and there are few studies on the phenotypes of physiological traits of leaves. The current research has developed a deep learning model aimed at predicting the total chlorophyll of greenhouse lettuce directly from the full spectrum of hyperspectral images. RESULTS: A CNN-based one-dimensional deep learning model with spectral attention module was utilized for the estimate of the total chlorophyll of greenhouse lettuce from the full spectrum of hyperspectral images. Experimental results demonstrate that the deep neural network with spectral attention module outperformed the existing standard approaches, including partial least squares regression (PLSR) and random forest (RF), with an average R2 of 0.746 and an average RMSE of 2.018. CONCLUSIONS: This study unveils the capability of leveraging deep attention networks and hyperspectral imaging for estimating lettuce chlorophyll levels. This approach offers a convenient, non-destructive, and effective estimation method for the automatic monitoring and production management of leafy vegetables.

Light Intensity Modulates the Functional Composition of Leaf Metabolite Groups and Phyllosphere Prokaryotic Community in Garden Lettuce (Lactuca sativa L.) Plants at the Vegetative Stage.

Light intensity primarily drives plant growth and morphogenesis, whereas the ecological impact of light intensity on the phyllosphere (leaf surface and endosphere) microbiome is poorly understood. In this study, garden lettuce (Lactuca sativa L.) plants were grown under low, medium, and high light intensities. High light intensity remarkably induced the leaf contents of soluble proteins and chlorophylls, whereas it reduced the contents of leaf nitrate. In comparison, medium light intensity exhibited the highest contents of soluble sugar, cellulose, and free amino acids. Meanwhile, light intensity resulted in significant changes in the composition of functional genes but not in the taxonomic compositions of the prokaryotic community (bacteria and archaea) in the phyllosphere. Notably, garden lettuce plants under high light intensity treatment harbored more sulfur-cycling mdh and carbon-cycling glyA genes than under low light intensity, both of which were among the 20 most abundant prokaryotic genes in the leaf phyllosphere. Furthermore, the correlations between prokaryotic functional genes and lettuce leaf metabolite groups were examined to disclose their interactions under varying light intensities. The relative abundance of the mdh gene was positively correlated with leaf total chlorophyll content but negatively correlated with leaf nitrate content. In comparison, the relative abundance of the glyA gene was positively correlated with leaf total chlorophyll and carotenoids. Overall, this study revealed that the functional composition of the phyllosphere prokaryotic community and leaf metabolite groups were tightly linked in response to changing light intensities. These findings provided novel insights into the interactions between plants and prokaryotic microbes in indoor farming systems, which will help optimize environmental management in indoor farms and harness beneficial plant-microbe relationships for crop production.

Genome-Wide Identification and Characterization of Effector Candidates with Conserved Motif in Falciphora oryzae.

Microbes employ effectors to disrupt immune responses and promote host colonization. Conserved motifs including RXLR, LFLAK-HVLVxxP (CRN), Y/F/WxC, CFEM, LysM, Chitin-bind, DPBB_1 (PNPi), and Cutinase have been discovered to play crucial roles in the functioning of effectors in filamentous fungi. Nevertheless, little is known about effectors with conserved motifs in endophytes. This research aims to discover the effector genes with conserved motifs in the genome of rice endophyte Falciphora oryzae. SignalP identified a total of 622 secreted proteins, out of which 227 were predicted as effector candidates by EffectorP. By utilizing HMM features, we discovered a total of 169 effector candidates with conserved motifs and three novel motifs. Effector candidates containing LysM, CFEM, DPBB_1, Cutinase, and Chitin_bind domains were conserved across species. In the transient expression assay, it was observed that one CFEM and one LysM activated cell death in tobacco leaves. Moreover, two CFEM and one Chitin_bind inhibited cell death induced by Bax protein. At various points during the infection, the genes' expression levels were increased. These results will help to identify functional effector proteins involving omics methods using new bioinformatics tools, thus providing a basis for the study of symbiosis mechanisms.

Effects of Ion Combinations and Their Concentrations on Denitrification Performance and Gene Expressions of an Aerobic Strain Marinobacter Hydrocarbonoclasticus RAD-2.

Salinity is one of the most important factors affecting the nitrogen-removal efficiency of denitrifying bacteria. A series of different ion combinations and salinity gradients were carried out to clarify the effects of ion types and concentrations on nitrogen removal by halophilic aerobic denitrifying bacteria RAD-2. Nitrate concentrations, nitrite concentrations, TAN concentrations, and OD600 were monitored to investigate their effects on denitrification in each group. The results showed that Na+, K+, and Cl- accelerated the denitrification process and improved nitrogen-removal efficiency at moderate additions, while Ca2+ and Mg2+ showed no significant effect. Na+ was effective alone, while K+ or Cl- needed to be combined with at least one of Na+, K+, or Cl- to achieve similar efficiency. The batch tests of salinity confirmed that the addition of a moderate concentration of NaCl/Na2SO4 could effectively improve nitrogen-removal efficiency, while excessive salinity might hinder denitrification metabolism. In the salinity range of 5~40‱, a 5‱ dosage might be the most economical method for strain RAD-2. Real-time PCR experiments on 17 key nitrogen metabolism-related genes revealed that chloride was widely involved in the nitrogen and carbon metabolism of microorganisms by altering cell osmotic pressure and opening ion channel proteins, thereby affecting the efficiency of denitrification. The results of this study may contribute to a better understanding of the different roles of various ions in aerobic denitrification and highlight the importance of salinity control in highly salted wastewater treatment.

Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework.

In recent years, rice seedling raising factories have gradually been promoted in China. The seedlings bred in the factory need to be selected manually and then transplanted to the field. Growth-related traits such as height and biomass are important indicators for quantifying the growth of rice seedlings. Nowadays, the development of image-based plant phenotyping has received increasing attention, however, there is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images in environmentally-controlled plant factories. In this study, a method based on convolutional neural networks (CNNs) and digital images was applied to estimate the growth of rice seedlings in a controlled environment. Specifically, an end-to-end framework consisting of hybrid CNNs took color images, scaling factor and image acquisition distance as input and directly predicted the shoot height (SH) and shoot fresh weight (SFW) after image segmentation. The results on the rice seedlings dataset collected by different optical sensors demonstrated that the proposed model outperformed compared random forest (RF) and regression CNN models (RCNN). The model achieved R2 values of 0.980 and 0.717, and normalized root mean square error (NRMSE) values of 2.64% and 17.23%, respectively. The hybrid CNNs method can learn the relationship between digital images and seedling growth traits, promising to provide a convenient and flexible estimation tool for the non-destructive monitoring of seedling growth in controlled environments.

Thermal Annealing Effect on Surface-Enhanced Raman Scattering of Gold Films Deposited on Liquid Substrates.

We prepare metal films with various thicknesses on liquid substrates by thermal evaporation and investigate the annealing effect on these films. Gold films deposited on a silicone oil surface consist of a large number of branched aggregates, which contains plenty of gold nanoparticles. This characteristic morphology is mainly attributed to the isotropic and free-sustained liquid substrate. Thermal annealing results in the reintegration of nanoparticles; thus, the surface morphology and microstructure of gold films change significantly. The dependence of annealing conditions on the surface-enhanced Raman scattering performance of gold films is studied, in which gold films show favorable Raman activity when annealed at certain annealing temperature and the experimental results are verified by simulation analysis. The study on the optimal annealing temperature of surface-enhanced Raman scattering substrate will pave the way for the potential application of films deposited on liquid surfaces in microfluidics and enhanced Raman detection.

Cu-Sn Aerogels for Electrochemical CO2 Reduction with High CO Selectivity.

This work reports the synthesis of CuxSny alloy aerogels for electrochemical CO2 reduction catalysts. An in situ reduction and the subsequent freeze-drying process can successfully give CnxSny aerogels with tuneable Sn contents, and such aerogels are composed of three-dimensional architectures made from inter-connected fine nanoparticles with pores as the channels. Density functional theory (DFT) calculations show that the introduction of Sn in Cu aerogels inhibits H2 evolution reaction (HER) activity, while the accelerated CO desorption on the catalyst surface is found at the same time. The porous structure of aerogel also favors exposing more active sites. Counting these together, with the optimized composition of Cu95Sn5 aerogel, the high selectivity of CO can be achieved with a faradaic efficiency of over 90% in a wide potential range (-0.7 V to -1.0 V vs. RHE).

An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China.

Accurate estimation of daily spatially-continuous PM2.5 (fine particulate matter) concentration is a prerequisite to address environmental public health issues, and satellite-based aerosol optical depth (AOD) products have been widely used to estimate PM2.5 concentrations using statistical-based or machine learning-based models. However, statistical-based models oversimplify the AOD-PM2.5 relationships, whereas complex machine learning technologies ignore the spatiotemporal heterogeneity of the predictors and demonstrate shortage in interpretation. Besides, large AOD data gaps resulting in PM2.5 estimation biases have been seldom imputed in previous studies, especially at national scales. To fill the above research gaps, this study attempts to present a feasible methodology to estimate daily spatially-continuous PM2.5 concentrations in China. The AOD data gaps across China were first imputed via a random forest (RF) model. Then, an interpretable self-adaptive deep neural network (SADNN) model, incorporating AOD, meteorological and other auxiliary predictors, was developed to estimate daily spatially-continuous PM2.5 concentrations from 2017 to 2018. Five-fold sample (site)-based cross-validation results showed a high accuracy of the SADNN model, with coefficient of determination and root mean square error values equal to 0.86 (0.84) and 13.07 (14.30) µg/m3, respectively, outperforming the standard DNN and the RF model. Furthermore, the SADNN model identified the spatiotemporal patterns of predictor importance, and demonstrated that the boundary layer height, elevation and AOD were the most important predictors both spatially and temporally. And the predictor importance in the Qinghai-Tibet Plateau was different from that in the rest of China. These results enhance our understanding of AOD-PM2.5 relationships and elucidate the estimated PM2.5 datasets with complete coverage are applicable for related air pollution studies and epidemiological cohort studies. Moreover, considering the effective nonlinear model capability and interpretability, the SADNN model is beneficial for not only PM2.5 estimation but also other earth data and scenarios.

Mapping and Discriminating Rural Settlements Using Gaofen-2 Images and a Fully Convolutional Network.

New ongoing rural construction has resulted in an extensive mixture of new settlements with old ones in the rural areas of China. Understanding the spatial characteristic of these rural settlements is of crucial importance as it provides essential information for land management and decision-making. Despite a great advance in High Spatial Resolution (HSR) satellite images and deep learning techniques, it remains a challenging task for mapping rural settlements accurately because of their irregular morphology and distribution pattern. In this study, we proposed a novel framework to map rural settlements by leveraging the merits of Gaofen-2 HSR images and representation learning of deep learning. We combined a dilated residual convolutional network (Dilated-ResNet) and a multi-scale context subnetwork into an end-to-end architecture in order to learn high resolution feature representations from HSR images and to aggregate and refine the multi-scale features extracted by the aforementioned network. Our experiment in Tongxiang city showed that the proposed framework effectively mapped and discriminated rural settlements with an overall accuracy of 98% and Kappa coefficient of 85%, achieving comparable and improved performance compared to other existing methods. Our results bring tangible benefits to support other convolutional neural network (CNN)-based methods in accurate and timely rural settlement mapping, particularly when up-to-date ground truth is absent. The proposed method does not only offer an effective way to extract rural settlement from HSR images but open a new opportunity to obtain spatial-explicit understanding of rural settlements.

Deposition of Ag Films on Liquid Substrates via Thermal Evaporation for Surface-Enhanced Raman Scattering.

Surface-enhanced Raman scattering (SERS) substrates were prepared by depositing Ag atoms on liquid surfaces via thermal evaporation at room temperature. These free-sustained substrates result in the formation of uniform Ag films, in which ramified Ag aggregates consist of substantial Ag nanoclusters with narrow gaps of several nanometers in between. SERS spectra of rhodamine 6G were investigated for this substrate to evaluate the SERS performance of this characteristic film morphology, and the results indicated that the SERS intensity from the closely-packed Ag nanostructures and small intervals were significantly enhanced. The dependence of SERS enhancement on the film thickness, nanoparticle size, and gap width was studied. An analytical model was proposed to simulate the electric field distribution during SERS detection, and the results validated the experimental observations.

Bioinspired Brochosomes as Broadband and Omnidirectional Surface-Enhanced Raman Scattering Substrates.

Surface-enhanced Raman scattering (SERS) substrates capable of working under laser excitation in a broad wavelength range are highly desirable in diverse application fields. Here, we demonstrate that the bioinspired Ag brochosomes, hollow microscale particles with submicroscale pits, have broadband and omnidirectional SERS performance. The SERS performance of the Ag brochosomes under near-infrared laser excitation makes them promising for applications in biosensing fields, such as the sensitive detection of Staphylococcus aureus bacteria and bovine hemoglobin protein. Additionally, the SERS intensity was insensitive to the incident angle of the laser beam, resulting from the spherical structure of the Ag brochosomes. The omnidirectional SERS performance makes the Ag brochosomes have application potential for in-the-field analysis using a hand-held Raman spectrometer for which it is difficult to accurately control the laser beam normal to the SERS substrates. Overall, the broadband and omnidirectional brochosome SERS substrates will find applications in diverse fields, particularly in biomedicine and in-the-field analysis.

The study on size dependent dipole-dipole interaction in the self-assembly of twisting nanoribbons with circular polarization activation.

The mechanism of self-assembling process of inorganic nanoparticle (NPs) is still an open question due to the various and non-additive interactions between NPs. Kotov et al reported that the semiconductor NPs can be self-assembled by external activation such as irradiation. In this paper, the twisted CdTe nanoribbons were successfully assembled with circular polar light activation based on the chiral selective resonance absorption. The effect of NP size on the morphology of assemblies under circular polar light irradiation is discussed by introducing a new mechanism of photooxidation induced dipole moment which decreases with increasing sizes of the NPs because of the change of band offsets at the CdS/CdTe interface. Moreover, we find that the competition between the dipole-dipole interaction and electrostatic repulsion can be modulated by the size of the NPs and the concentration of dispersion, which are the key points to produce the chiral twisted nanoribbons.

Chirality-dependent electromagnetically induced transparency based on a double semi-periodic helix metastructure.

A chiral metastructure composed of spatially separated double semi-periodic helices is proposed and investigated theoretically and experimentally in this Letter. Chirality-dependent electromagnetically induced transparency (EIT) and a slow light effect in the microwave region are observed from a numerical parameter study, while experimental results from the 3D printing sample yield good agreement with the theoretical findings. The studied EIT phenomenon arises as a result of destructive interference by coupled resonances, and the proposed chiral metastructure can be applied in areas such as polarization communication, pump-probe characterization, and quantum computing areas.

Surface enhanced Raman scattering substrates prepared by thermal evaporation on liquid surfaces.

We present an effective surface-enhancement Raman scattering (SERS) substrate enabled by depositing metallic film on a liquid surface at room temperature. Thermal evaporation is used to deposit Au atoms on silicone oil surface and then form quasi-continuous films. Due to the isotropic characteristics of the liquid surface, this film consists of substantial nanoparticles with uniform diameter, which is different from films fabricated on solid substrates and can be served as an applicable substrate for SERS detection. With the assistance of this substrate, SERS signals of rhodamine 6G were significantly enhanced, the dependence between SERS spectra and film thickness was investigated. Analytical simulation results confirm the experimental observations and the superiorities of our proposed method for preparation of SERS substrate. This work provides a potential application of metallic film deposition on free-sustained surface and holds promise as an efficient sensor in rapid trace detection of small molecule analytes.

Cobalt sulfide aerogel prepared by anion exchange method with enhanced pseudocapacitive and water oxidation performances.

This work introduces the anion exchange method into the sol-gel process for the first time to prepare a metal sulfide aerogel. A porous Co9S8 aerogel with a high surface area (274.2 m2 g-1) and large pore volume (0.87 cm3 g-1) has been successfully prepared by exchanging cobalt citrate wet gel in thioacetamide and subsequently drying in supercritical ethanol. Such a Co9S8 aerogel shows enhanced supercapacitive performance and catalytic activity toward oxygen evolution reaction (OER) compared to its oxide aerogel counterpart. High specific capacitance (950 F g-1 at 1 A g-1), good rate capability (74.3% capacitance retention from 1 to 20 A g-1) and low onset overpotential for OER (220 mV) were observed. The results demonstrated here have implications in preparing various sulfide chalcogels.

Co9S8 nanoparticle-decorated carbon nanofibers as high-performance supercapacitor electrodes.

This work reported Co9S8 nanoparticle-decorated carbon nanofibers (CNF) as a supercapacitor electrode. By using a mild ion-exchange method, the cobalt oxide-based precursor nanoparticles were transformed to Co9S8 nanoparticles in a microwave hydrothermal process, and these nanoparticles were decorated onto a carbon nanofiber backbone. The composition of the nanofibers can be readily tuned by varying the Co acetate content in the precursor. The porous carbon nanofibers offered a fast electron transfer pathway while the well dispersed Co9S8 nanoparticles acted as the redox center for energy storage. As a result, high specific capacitance of 718 F g-1 at 1 A g-1 can be achieved with optimized Co9S8 loading. The assembled asymmetric supercapacitor with Co9S8/CNF as the cathode showed a high energy density of 23.8 W h kg-1 at a power density of 0.75 kW kg-1 and good cycling stability (16.9% loss over 10 000 cycles).