Electroluminescence as a Probe of Strong Exciton-Plasmon Coupling in Few-Layer WSe2.

The manipulation of coupled quantum excitations is of fundamental importance in realizing novel photonic and optoelectronic devices. We use electroluminescence to probe plasmon-exciton coupling in hybrid structures consisting of a nanoscale plasmonic tunnel junction and few-layer two-dimensional transition-metal dichalcogenide transferred onto the junction. The resulting hybrid states act as a novel dielectric environment that affects the radiative recombination of hot carriers in the plasmonic nanostructure. We determine the plexcitonic spectrum from the electroluminescence and find Rabi splittings exceeding 50 meV in the strong coupling regime. Our experimental findings are supported by electromagnetic simulations that enable us to explore systematically and in detail the emergence of plexciton polaritons as well as the polarization characteristics of their far-field emission. Electroluminescence modulated by plexciton coupling provides potential applications for engineering compact photonic devices with tunable optical and electrical properties.

Direct Observation of HOON Intermediate in the Photochemistry of HONO.

The photochemistry of nitrous acid (HONO), encompassing dissociation into OH and NO as well as the reverse association reaction, plays a pivotal role in atmospheric chemistry. Here, we report the direct observation of nitrosyl-O-hydroxide (HOON) in the photochemistry of HONO, employing matrix-isolation IR and UV-vis spectroscopy. Despite a barrier of approximately 30 kJ/mol, HOON undergoes spontaneous rearrangement to the more stable HONO isomer through quantum mechanical tunneling, with a half-life of 28 min at 4 K. Kinetic isotope effects and instanton theory calculations reveal that the tunneling process involves the concerted motion of the NO moiety (65.2%) and the hydrogen atom (32.3%). Our findings underscore the significance of HOON as a key intermediate in the photolytic dissociation-association cycle of HONO at low temperatures.

High-beam-quality low-resistance vertical-cavity surface-emitting laser array with graphene electrode.

Thermal crosstalk and current crowding effects are pressing issues that significantly impact the beam quality and efficiency of vertical-cavity surface-emitting laser (VCSEL) arrays. In this paper, by taking advantage of the excellent current transmission characteristics of graphene, what we believe to be a novel VCSEL array based on graphene electrode is designed to realize vertical current injections. The series resistance and self-heating of arrays are reduced by controlling the transport direction of the current, effectively suppressing the thermal crosstalk effect. Furthermore, high array beam quality is obtained by optimizing the current density distribution in active regions. Ultimately, the high-power quasi-single mode emission of VCSEL arrays is achieved by introducing graphene electrodes (Gr-VCSEL array) designs. Compared to traditional VCSEL arrays, the 10 × 10 Gr-VCSEL array demonstrates a 41% reduction in series resistance, a side mode suppression ratio of 32 dB, and a divergence angle around 12 °. This structure simultaneously achieves quasi-single mode emission and effectively suppresses the thermal crosstalk effect, providing a new method for the development of high-beam quality VCSEL arrays.

Synthesis of Chiral Sulfoxides by A Cyclic Oxidation-Reduction Multi-Enzymatic Cascade Biocatalysis.

Optically pure sulfoxides are valuable organosulfur compounds extensively employed in medicinal and organic synthesis. In this study, we present a biocatalytic oxidation-reduction cascade system designed for the preparation of enantiopure sulfoxides. The system involves the cooperation of a low-enantioselective chimeric oxidase SMO (styrene monooxygenase) with a high-enantioselective reductase MsrA (methionine sulfoxide reductase A), facilitating "non-selective oxidation and selective reduction" cycles for prochiral sulfide oxidation. The regeneration of requisite cofactors for MsrA and SMO was achieved via a cascade catalysis process involving three auxiliary enzymes, sustained by cost-effective D-glucose. Under the optimal reaction conditions, a series of heteroaryl alkyl, aryl alkyl and dialkyl sulfoxides in R configuration were synthesized through this "one-pot, one step" cascade reaction. The obtained compounds exhibited high yields of >90 % and demonstrated enantiomeric excess (ee) values exceeding 90 %. This study represents an unconventional and efficient biocatalytic way in utilizing the low-enantioselective oxidase for the synthesis of enantiopure sulfoxides.

Global Potential Energy Surfaces by Compressed-State Multistate Pair-Density Functional Theory for Hyperthermal Collisions in the O2+O2 System.

Interactions between oxygen molecules play an important role in atmospheric chemistry and hypersonic flow chemistry in atmospheric entries. Recently, high-quality ab initio potential energy surface (PES) of the quintet O4 was reported by Paukku et al. [J. Chem. Phys. 147, 034301 (2017)]. 10543 configurations were sampled and calculated at the level of MS-CASPT2/maug-cc-pVTZ with scaled external correlation. The PES was fitted to a many-body (MB) form with the many-body part described by the permutationally invariant polynomial approach (MB-PIP). In this work, the PIP-Neural Network (PIP-NN) and MB-PIP-NN methods were used to refit the PES based on the same data by Paukku et al. Three PESs were compared. It was found that the performances differ significantly in the O+O3 region as well as in the long-range region. Therefore, additional 1300 points were sampled, and the efficient compressed-state multistate pair-density functional theory (CMS-PDFT) was used to calculate the electronic structure of these 1300 points and 10543 points by Paukku et al. Then, a completely new quintet PES was fitted using the MB-PIP-NN method. Based on this PES, the quasi-classical trajectory (QCT) approach was used to reveal all possible reaction channels for hyperthermal O2-O2 collisions.

PFOS Elicits Cytotoxicity in Neuron Through Astrocyte-Derived CaMKII-DLG1 Signaling In Vitro Rat Hippocampal Model.

Both epidemiological investigation and animal experiments demonstrated that pre-/postnatal exposure to perfluorooctane sulfonic acid (PFOS) could induce neurodevelopmental disorders. Previous studies showed that astrocyte was involved in PFOS-induced neurotoxicity, while little information is available. In the present study, the role of astrocyte-derived calmodulin-dependent protein kinase II (CaMKII)-phosphorylated discs large homolog 1 (DLG1) signaling in PFOS eliciting cytotoxicity in neuron was explored with primary cultured hippocampal astrocyte and neuron. The application of PFOS showed a decreased cell viability, synapse length and glutamate transporter 1 (GLT-1) expression, but an increased CaMKII, DLG1 and cyclic AMP response element binding protein (CREB) expression in primary cultured astrocyte. With 2-(2-hydroxyethylamino)-6-aminohexylcarbamic acid tert-butyl ester-9-isopropylpurine (CK59), the CaMKII inhibitor, the disturbed cell viability and molecules induced by PFOS could be alleviated (CREB expression was excluded) in astrocytes. The cytotoxic effect of neuron exposed to astrocyte conditional medium collected from PFOS (PFOS-ACM) pretreated with CK59 was also decreased. These results indicated that PFOS mediated GLT-1 expression through astrocyte-derived CaMKII-DLG signaling, which might be associated with injuries on neurons. The present study gave an insight in further exploration of mechanism in PFOS-induced neurotoxicity.

Manipulating the charge transport via incorporating a cobalt bridge into a single-molecule junction.

Cobalt-bridged organometallic molecular wires (p-Co-p, p-Co-m and m-Co-m) are synthesized, and their charge transport properties are studied. The experimental results show that the quantum interference (QI) effects of cobalt-bridged organometallic wires are determined by the anchoring group. Interestingly, the cobalt-bridge reduces the conductance of the junctions and tunes the QI effect of the wires. These results demonstrate the unique property of metal-bridged organometallic molecular wires and their potential applications in molecular electronics.

How Do the Position and Number of Methyl Substituents Affect the Photochemical Process of Criegee Intermediate? Trajectory Surface-Hopping Dynamics of Four-Carbon CIs.

Electronic-structure calculations combined with nonadiabatic trajectory surface-hopping (TSH) dynamic simulations were carried out on two alkenyl-substituted Criegee intermediates (CIs), i.e., propenyl-substituted CI (PCI) and 1-methyl-propenyl substituted CI (MPCI), in order to investigate the influence of the position and number of substituents on the photochemical process of CI in S1 states. It is found that they play critical roles in the reactivity, dominant product channel, and mechanism of the CIs. More specifically, introducing a methyl group on either C1 (α-C) or C3 (γ-C) position of a vinyl-substituted CI (VCI) skeleton facilitates the rotation of the C1═O1 bond and leads to the formation of a three-membered dioxirane ring; meanwhile, it evidently enhances the reactively of the S1-state molecule. Meanwhile, methyl substitution on the vinyl moiety [i.e., C2 (ß-C) and C3 (γ-C) positions] is beneficial for the rotation of the C2═C3 bond and thus facilitates the formation of the five-membered 1,2-dioxole ring, and the substitution on C2 site decreases the reactivity. The cosubstitution of C2 and C3 atoms by methyl groups well balances the features of VCI in the sense of high reactivity, consistently predominant channel, and possible dioxole side-product. The findings here not only deepen the knowledge on the photochemical processes of the CI but also inspire the rethinking of the "old" concept of substitution effect.

Gaussian Process Regression for State-to-State Integral Cross Sections: The Case of the O + O2 Collision Dissociation Reactions.

Research on hypersonic vehicles has become increasingly important worldwide in recent years. However, accurately simulating the dynamics of the nonequilibrium high-temperature reactions that are in the hypersonic flow around the vehicles presents a significant challenge as a large number of states and transitions are accessible even for the smallest atom-diatom reaction systems. It is quite difficult, sometimes even impossible, to exhaustively investigate all relevant combinations or determine high-dimensional analytical representations for the state-to-state reaction probabilities. In this study, we used Gaussian process regression (GPR) to fit a model based on only 807 QCT data for training. The confidence interval of the GPR prediction and the Kullback-Leibler (KL) divergence were used to help minimize the sampling amount of data for fitting the converged GPR model. The model aims to predict the state-to-state integral cross section (ICS) of the O + O2 → 3O dissociation reaction under random initial conditions (Et, v, j). In total, it took almost a month to obtain this converged GPR model, but it took only a few seconds to predict the ICS value for any initial condition. For 330 initial conditions not included in the training set, the mean-square error (MSE) between the QCT-calculated ICSs and the GPR-predicted ones is only 0.08 Å2 and the R2 is 0.9986, indicating that the GPR model can replace the direct expensive QCT calculation with high accuracy. Finally, we calculated the equilibrium dissociation rate coefficients based on the StS ICS values predicted by the GPR model, and the results were in good agreement with available experimental and theoretical results. Thus, this study provides an effective and accurate approach to the extensive direct state-to-state reaction dynamic calculations.

Meta-analyses of motivational factors of response to natural disaster.

Response behavior of individuals is of critical importance to decrease chances of injury and death as well as ameliorate costs in property and infrastructure damage in natural disasters. Plenty of studies have examined which factors motivate individuals to respond to natural disasters. However, a systematic overview of the key motivating factors of various response behaviors is lacking. This study conducts a series of meta-analyses using data of 53,713 samples from 87 studies (77 papers) conducted in 27 different countries and regions to examine how 17 motivational factors were associated with individuals' response to natural disasters. The results indicate self-efficacy, outcome efficacy, attitudes, subjective norms, and information acquisition show the strongest effects on response behavior. Contrarily, the impact of negative affects like fear, depression, and anxiety on victims is minimal, despite the common assumption that they are significant related to response behaviour. In addition, current studies have disproportionally focused on studying risk perception, experience and information acquisition, earthquake and hurricane, and evacuation and preparation, while attention given to other types of motivational factors, disasters and response behaviors is lacking.

Enhancing soil texture classification with multivariate scattering correction and residual neural networks using visible near-infrared spectra.

Soil texture is one of the most important indicators of soil physical properties, which has traditionally been measured through laborious procedures. Approaches utilizing visible near-infrared spectroscopy, with their advantages in efficiency, eco-friendliness and non-destruction, are emerging as potent alternatives. Nevertheless, these approaches often suffer from limitations in classification accuracy, and the substantial impact of spectral preprocessing, model integration, and sample matrix effect is commonly disregarded. Here a novel 11-class soil texture classification strategy that address this challenge by combining Multiplicative Scatter Correction (MSC) with Residual Network (ResNet) models was presented, resulting in exceptional classification accuracy. Utilizing the LUCAS dataset, collected by the Land Use and Cover Area frame Statistical Survey project, we thoroughly evaluated eight spectral preprocessing methods. Our findings underscored the superior performance of MSC in reducing spatial complexity within spectral data, showcasing its crucial role in enhancing model precision. Through comparisons of three 1D CNN models and two ResNet models integrated with MSC, we established the superior performance of the MSC-incorporated ResNet model, achieving an overall accuracy of 98.97 % and five soil textures even reached 100.00 %. The ResNet model demonstrated a marked superiority in classifying datasets with similar features, as observed by the confusion matrix analysis. Moreover, we investigated the potential benefit of pre-categorization based on land cover type of the soil samples in enhancing the accuracy of soil texture classification models, achieving overall classification accuracies exceeding 99.39 % for woodland, grassland, and farmland with the 2-layer ResNet model. The proposed work provides a pioneering and efficient strategy for rapid and precise soil texture identification via visible near-infrared spectroscopy, demonstrating unparalleled accuracy compared to existing methods, thus significantly enhancing the practical application prospects in soil, agricultural and environmental science.

Cadaverine and putrescine exposure influence carbon and nitrogen cycling genes in water and sediment of the Yellow River.

The response patterns of microbial functional genes involved in biogeochemical cycles to cadaver decay is a central topic of recent environmental sciences. However, the response mechanisms and pathways of the functional genes associated with the carbon (C) and nitrogen (N) cycling to cadaveric substances such as cadaverine and putrescine remain unclear. This study explored the variation of functional genes associated with C fixation, C degradation and N cycling and their influencing factors under cadaverine, putrescine and mixed treatments. Our results showed only putrescine significantly increased the alpha diversity of C fixation genes, while reducing the alpha diversity of N cycling genes in sediment. For the C cycling, the mixed treatment significantly decreased the total abundance of reductive acetyl-CoA pathway genes (i.e., acsB and acsE) and lig gene linked to lignin degradation in water, while only significantly increasing the hydroxypropionate-hydroxybutylate cycle (i.e., accA) gene abundance in sediment. For the N cycling, mixed treatment significantly decreased the abundance of the nitrification (i.e., amoB), denitrification (i.e., nirS3) genes in water and the assimilation pathway gene (i.e., gdhA) in sediment. Environmental factors (i.e., total carbon and total nitrogen) were all negatively associated with the genes of C and N cycling. Therefore, cadaverine and putrescine exposure may inhibit the pathway in C fixation and N cycling, while promoting C degradation. These findings can offer some new insight for the management of amine pollution caused by animal cadavers.

Kinetic resolution of sulfoxides with high enantioselectivity using a new homologue of methionine sulfoxide reductase B.

Optically pure sulfoxides are noteworthy compounds that find wide applications in various industrial fields. Here, we report a methionine sulfoxide reductase B (MsrB) homologue that exhibits high enantioselectivity and broad substrate scope for the kinetic resolution of racemic (rac) sulfoxides. This MsrB homologue, named liMsrB, was identified from Limnohabitans sp. 103DPR2 and showed good activity together with enantioselectivity towards a series of aromatic, heteroaromatic, alkyl and thioalkyl sulfoxides. Chiral sulfoxides in the S configuration were prepared in approximately 50% yield and 92-99% enantiomeric excess through kinetic resolution at an initial substrate concentration of up to 90 mM (11.2 g L-1). This study presents an efficient route for the enzymatic preparation of (S)-sulfoxides through kinetic resolution.

Global and Full-Dimensional Potential Energy Surfaces of the N2 + O2 Reaction for Hyperthermal Collisions.

The energy transfer, dissociations, and chemical reactions between O2 and N2 play an important role in the re-entry process of aircraft and many atmospheric, combustion, and plasma processes. Recently, Varga et al. (J. Chem. Phys., 2016, 144, 024310) developed a full-dimensional high-precision potential energy surface (PES) of the ground triplet electronic state for the O2 and N2 system based on ca. 55,000 data points, whose energies were calculated by multi-state complete-active-space second-order perturbation theory/minimally augmented correlation-consistent polarized valence triple-zeta electronic structure calculations plus dynamically scaled external correlation. The fitting function adopted the many-body expansion form with the four-body interactions fitted by the permutationally invariant polynomial in terms of bond-order functions of the six interatomic distances (MB-PIP). In this work, we refit the PES of the N2O2 system by two methods based on the same data set that was used by Varga et al. The first uses the permutation invariant polynomial-neural network (PIP-NN) method to fit the entire energy of the 55,000 data points. In the second approach, the PIP-NN method is used to fit only the four-body interaction component, a similar treatment in the MB-PIP method, and the resulting PES is named MB-PIP-NN. Then, the performances of these new PESs and the MB-PIP PES are comprehensively and systematically compared, such as comparisons of various scans, properties of stationary points, and dynamics simulations. Possible improvements for the PES of N2O2 are suggested. A more reliable PES of the system can be constructed in terms of data sampling range, electronic structure calculation level, and fitting method for high-temperature calculation and simulation in the future.

Multi-omics methods reveal that putrescine and cadaverine cause different degrees of enrichment of high-risk resistomes and opportunistic pathogens in the water and sediment of the Yellow River.

Contamination of antibiotic resistomes due to animal carcass decay has become a serious environmental concern. However, the relationship between main metabolite compounds of corpse decomposition (i.e., putrescine and cadaverine) and antibiotic resistomes remains unclear. To tackle this issue, the response of antibiotic resistance genes (ARGs) and microbiome in aquatic environment to excess putrescine, cadaverine and a mixture of both based on laboratory simulation experiment was investigated by high-throughput quantitative PCR and amplicon sequencing methods. Our results showed putrescine and cadaverine led to the increasing of TC (total carbon) and TN (total nitrogen) both in water and sediment. Under the exposure of putrescine and cadaverine, the total abundance of mobile genetic elements (MGEs) and most ARGs in water was higher than in sediment. In particular, putrescine and cadaverine caused significantly different decreases in alpha diversity of microbial community in water and sediment compared with the control group. Microbial community structures both in water and sediment were also significantly affected by cadaverine and putrescine. Furthermore, putrescine and cadaverine led to different degrees of increases of high-risk ARGs (like mecA) and opportunistic pathogens (like Delftia) in sediment, promoting the prevalence of antibiotic resistant bacteria. In conclusion, our findings revealed the influences of main metabolites of carcass decay on microbiome and resistomes, providing references for risk assessment and pollution management.

Promoting dewatering efficiency of sludge by bioleaching coupling chemical flocculation.

In recent years, bioleaching has emerged as a cost-effective technology for enhancing the dewaterability of sludge. However, the lengthy treatment time involved in sludge bioleaching processes limits daily treatment capacity for sludge. Here, a novel approach was developed through a short time of sludge bioleaching with A. ferrooxidans LX5 (A. f) and A. thiooxidans TS6 (A. t) followed by polyferric sulfate (PFS) flocculation (A. f + A. t + PFS). After 12.5 h of the A. f + A. t + PFS treatment (30% A. f, 10% A. t, 40 mg/g DS S0, 60 mg/g DS FeSO4•7H2O, and 120 mg/g DS PFS), the reduction efficiency of specific resistance to filtration (SRF) and sludge cake moisture content reached 94.0% and 11.6%, respectively, which were comparable to the results achieved through 24 h of completed bioleaching treatment. In pilot-scale applications, the mechanical dewatering performance was notably improved following A. f + A. t + PFS treatment, with the low moisture content of the treated sludge cake (∼59.2%). This study provides new insights into the A. f + A. t + PFS process and holds potential for developing efficient and promising sludge dewatering strategies in engineering application.

Mammalian carcass decay increases carbon storage and temporal turnover of carbon-fixing microbes in alpine meadow soil.

Corpse decomposition is of great significance to the carbon cycle of natural ecosystem. Carbon fixation is a carbon conversion process that converts carbon dioxide into organic carbon, which greatly contributes to carbon emission reduction. However, the effects of wild animal carcass decay on carbon-fixing microbes in grassland soil environment are still unknown. In this research, thirty wild mammal (Ochotona curzoniae) corpses were placed on alpine meadow soil to study the carbon storage and carbon-fixing microbiota succession for a 94-day decomposition using next-generation sequencing. Our results revealed that 1) the concentration of total carbon increased approximately 2.24-11.22% in the corpse group. 2) Several carbon-fixing bacterial species (Calothrix parietina, Ancylobacter rudongensis, Rhodopseudomonas palustris) may predict the concentration of total carbon. 3) Animal cadaver degradation caused the differentiation of carbon-fixing microbiota structures during succession and made the medium-stage networks of carbon-fixing microbes more complicated. 4) The temporal turnover rate in the experimental groups was higher than that in the control groups, indicating a quick change of gravesoil carbon-fixing microbiota. 5) The deterministic process dominates the assembly mechanism of experimental groups (ranging from 53.42% to 94.94%), which reflects that the carbon-fixing microbial community in gravesoil can be regulated. Under global climate change, this study provides a new perspective for understanding the effects of wild animal carcass decay on soil carbon storage and carbon-fixing microbes.

Deterministic Processes Shape Abundant and Rare Bacterial Communities in Drinking Water.

The deep mechanisms shaping bacterial assembly are a crucial challenge in drinking water ecosystem. However, much less is known about seasonal diversity distributions and assembly mechanisms of abundant and rare bacteria in drinking water. The combination of environmental variables and high-throughput 16S rRNA gene sequencing was conducted to examine the composition, assembly, co-occurrence patterns of abundant and rare bacteria from five drinking water sites across four seasons in one year in China. The results indicated that abundant taxa were mainly composed of Rhizobiales_UG1, Sphingomonadales_UG1, and Comamonadaceae, while rare taxa were Sphingomonadales_UG1, Rhizobiales_UG2, and Rhizobiales_UG1. The richness of rare bacteria was greater than that of abundant ones, and the richness had no differences among seasons. The beta diversity was significantly discrepant in abundant and rare communities and among seasons. Deterministic mechanism accounted for a larger contribution to abundant taxa than rare taxa. Furthermore, water temperature had higher effects on abundant microbiome than rare ones. Co-occurrence network analysis indicated that abundant taxa that occupied frequently in central positions had stronger effect on co-occurrence network. In our study, these results collectively suggested that rare bacteria respond to environmental variables with an analogical pattern to abundant counterparts (similar community assembly), but their ecological diversities, driving forces, and co-occurrence patterns were not equivalent in drinking water.

Spatiotemporal changes of ecological environment quality and climate drivers in Zoige Plateau.

Ecological environment is the essential material basis of human survival and connects regional economy with socially sustainable development. However, climate changes characterized by global climate warming have caused a series of ecological environmental problems in recent years. Few studies have discussed various climate factors affecting the ecological environment, and the spatial non-stationary effects of different climate factors on the ecological environment are still unclear. Dynamically monitoring ecological environment changes in fragile areas and identifying its climate-driving mechanism are essential for ecological protection and environmental repair. Taking Zoige Plateau as a case, this paper simulated the eco-environmental quality during 1987-2020 using remote sensing data, utilized Geodetector method to identify the contributions of various climate drivers to ecological environment quality, and then adopted the Geographically Weighted Regression model to explore the spatial non-stationary impacts of climate factors on ecological environment quality. The results showed that the ecological quality in the middle regions of the Zoige Plateau was slightly better than in the surrounding marginal areas. For the whole area of Zoige Plateau, the average ecological environment quality index was 54.92, 53.99, 56.17, 57.88, 63.44, 56.93, 59.43, and 59.76 in 1987, 1992, 1997, 2001, 2006, 2013, 2016 and 2020, respectively, which indicated that eco-environmental quality witnessed several fluctuations during the study period but showed a generally increasing trend. Among five climate factors, the temperature was the dominant climate factor affecting the ecological environment quality (q value: 0.11-0.19), sunshine duration (0.03-0.17), wind speed (0.03-0.11), and precipitation (0.03-0.08) were the main climate drivers, while the explanatory power of relative humidity to ecological environment quality was relatively small. Such various climate factors impacting the ecological environment quality demonstrated distinct spatial non-stationary and the range of driving impact varied with time. Temperature, sunshine duration, wind speed, and relative humidity promoted ecological environment quality in most regions (regression coefficients > 0), while precipitation mainly had a negative inhibitory impact (regression coefficients < 0). Meanwhile, the greater impacts of these five climate factors were concentrated in high-elevation regions of the south and west or the northern areas. The appropriate enhancement of climate warming and air humidity was beneficial to the improvement of the ecological environment, but the excessive precipitation would result in landslides and exhibit inhibition of vegetation growth. Therefore, selecting cold-tolerant herbs and shrubs, and strengthening climate monitoring and early warning systems (such as drought and excessive precipitation) are essential for ecological restoration.

A new federated learning-based wireless communication and client scheduling solution for combating COVID-19.

Federated learning is a machine learning method that can break the data island. Its inherent privacy-preserving property has an important role in training medical image models. However, federated learning requires frequent communication, which incur high communication costs. Moreover, the data is heterogeneous due to different users' preferences, which may degrade the performance of models. To address the problem of statistical heterogeneity, we propose FedUC, an algorithm to control the uploaded updates for federated learning, where a client scheduling method is made on the basis of weight divergence, update increment, and loss. We also balance the local data of the clients by image augmentation to mitigate the impact of the non-independently identically distribution. The server assigns compression thresholds to the clients based on the weight divergence and update increment of the models for gradient compression to reduce the wireless communication costs. Finally, based on the weight divergence, update increment and accuracy, the server dynamically assigns weights to the model parameters for the aggregation. Simulation and analysis utilizing a publicly available chest disease dataset containing COVID-19 are compared with existing federated learning methods. Experimental results show that our proposed strategy has better training performance in improving model accuracy and reducing wireless communication costs.