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Chicken is the main source of protein for humans in most parts of the world. However, excessive fat deposition in chickens has become a serious problem. This adversely affects the growth of chickens and causes economic losses. Fat formation mainly occurs through preadipocyte differentiation, and excessive fat deposition results from the accumulation of preadipocytes after differentiation. Our previous studies have found that the connective tissue growth factor (CTGF) may be an important candidate gene for fat deposition. However, its function and mechanism in preadipocyte differentiation are still unclear. In this study, the RT-qPCR and Western blot results showed that the expression of CTGF mRNA and protein in the abdominal adipose of lean chickens was significantly higher than that of fat chickens. Therefore, we studied the function and mechanism of the CTGF in the differentiation of chicken preadipocytes. Functionally, the CTGF inhibited the differentiation of chicken preadipocytes. Mechanistically, the CTGF mediated the TGFβ1/Smad3 signaling pathway, thereby inhibiting the differentiation of chicken preadipocytes. In addition, we used the unique molecular identifier (UMI) RNA-Seq technology to detect genes that can be regulated by the CTGF in the whole genome. Through transcriptome data analysis, we selected actin gamma 2 (ACTG2) as a candidate gene. Regarding the function of the ACTG2gene, we found that it inhibited the differentiation of chicken preadipocytes. Furthermore, we found that the CTGF can inhibit the differentiation of preadipocytes through the ACTG2gene. In summary, this study found the CTGF as a new negative regulator of chicken preadipocyte differentiation. The results of this study help improve the understanding of the molecular genetic mechanism of chicken adipose tissue growth and development and also have reference significance for the study of human obesity.

Contrastive learning has been successfully leveraged to learn action representations for addressing the problem of semisupervised skeleton-based action recognition. However, most contrastive learning-based methods only contrast global features mixing spatiotemporal information, which confuses the spatial- and temporal-specific information reflecting different semantic at the frame level and joint level. Thus, we propose a novel spatiotemporal decouple-and-squeeze contrastive learning (SDS-CL) framework to comprehensively learn more abundant representations of skeleton-based actions by jointly contrasting spatial-squeezing features, temporal-squeezing features, and global features. In SDS-CL, we design a new spatiotemporal-decoupling intra–inter attention (SIIA) mechanism to obtain the spatiotemporal-decoupling attentive features for capturing spatiotemporal specific information by calculating spatial- and temporal-decoupling intra-attention maps among joint/motion features, as well as spatial- and temporal-decoupling inter-attention maps between joint and motion features. Moreover, we present a new spatial-squeezing temporal-contrasting loss (STL), a new temporal-squeezing spatial-contrasting loss (TSL), and the global-contrasting loss (GL) to contrast the spatial-squeezing joint and motion features at the frame level, temporal-squeezing joint and motion features at the joint level, as well as global joint and motion features at the skeleton level. Extensive experimental results on four public datasets show that the proposed SDS-CL achieves performance gains compared with other competitive methods.

A 1200-V rated CoolSiC Trench MOSFET structure has excellent overall electrical characteristics due to its unique asymmetric structure. However, it is sensitive to space heavy ion radiation. In this study, heavy ion irradiation experiments were conducted on 1.2 kV CoolSiC Trench MOSFET devices to calibrate the simulation structure model and critical failure conditions. The 2-D numerical simulator SILVACO TCAD was used to investigate the single event burnout (SEB) hardening method for the 1.2 kV-rated CoolSiC Trench MOSFET under high linear energy transfer (LET) values. The simulation results show that the improved CoolSiC Trench MOSFET structure can effectively enhance the SEB capability of the device without sacrificing its excellent overall electrical characteristics compared to the traditional CoolSiC Trench MOSFET. At 360 V voltage and 0.5 pC/ $\mu $ m LET value, the highest temperature is reduced from 3097 to 1502 K, and the lowest burnout threshold is increased by 200 V in the improved structure while maintaining almost unchanged characteristics at other locations.

Hybrid halide perovskites are good candidates for a range of functional materials such as optical electronic and photovoltaic devices due to their tunable band gaps, long carrier diffusion lengths, and solution processability. However, the instability in moisture/air, the toxicity of lead, and rigorous reaction setup or complex postprocessing have long been the bottlenecks for practical application. Herein, we present a simultaneous configurational entropy design at A-sites, B-sites, and X-sites in the typical (CHA)2PbBr4two-dimensional (2D) hybrid perovskite. Our results demonstrate that the high-entropy effect favors the stabilization of the hybrid perovskite phase and facilitates a simple crystallization process without precise control of the cooling rate to prepare regular crystals. Moreover, high-entropy 2D perovskite crystals exhibit tunable energy band gaps, broadband emission, and a long carrier lifetime. Meanwhile, the high-entropy composition almost maintains the initial crystal structure in deionized water for 18 h while the original (CHA)2PbBr4crystal mostly decomposes, suggesting obviously improved humidity stability. This work offers a facile approach to synthesize humidity-stable hybrid perovskites under mild conditions, accelerating relevant preparation of optoelectronics and light-emitting devices and facilitating the ultimate commercialization of halide perovskite.

We successfully developed an enantioselective trifluoromethylthiolation of structurally diverse carbonyl compounds. Trichloroisocyanuric acid and AgSCF3were employed to generate active electrophilic trifluoromethylthio species in situ for asymmetric C-SCF3bond formation. A broad variety of chiral SCF3-carbon nucleophiles (pyrazolones, β-keto esters, and β-keto amides) were obtained in excellent yields with high enantioselectivities (up to 92% ee) by Cinchona alkaloid derived squaramide catalysts. The reaction exhibits high efficiency, good enantioselectivity, and high functional group tolerance, which provided a novel and efficient way for asymmetric synthesis of trifluoromethylthiolated carbonyl compounds.

We pay attention to the topological properties and skin effects in the one-dimensional Su-Schrieffer-Heeger (SSH) structure, by considering the presence of different-density non-reciprocal couplings. We find that when only one single non-reciprocal coupling term is introduced, e.g., in the middle of the SSH structure, edge states can be induced around the sites suffering from the non-reciprocal coupling. As one non-reciprocal coupling term is introduced in each two cells, the topological phase transition is modified. Besides, new topological edge states and skin effects appear at the ends of the SSH structure, and the edge states can be differentiated by their localities, localization degrees, and the imaginary part of energy. Furthermore, if two neighboring cells of the SSH structure are coupled by the non-reciprocal coupling, the topological edge states and skin effect more well-defined, accompanied by the further change of the topological phase transition condition. These results suggest that the non-reciprocal coupling plays a unique and important role in modulating the topological properties and the skin effects of the one-dimensional SSH structure.

Few multi-agent reinforcement learning (MARL) researches on Google research football (GRF) focus on the 11-vs-11 multi-agent full-game scenario and to the best of our knowledge, no open benchmark on this scenario has been released to the public. In this work, we fill the gap by providing a population-based MARL training pipeline and hyperparameter settings on multi-agent football scenario that outperforms the bot with difficulty 1.0 from scratch within 2 million steps. Our experiments serve as a reference for the expected performance of independent proximal policy optimization (IPPO), a state-of-the-art multi-agent reinforcement learning algorithm where each agent tries to maximize its own policy independently across various training configurations. Meanwhile, we release our training framework Light-MALib which extends the MALib codebase by distributed and asynchronous implementation with additional analytical tools for football games. Finally, we provide guidance for building strong football AI with population-based training and release diverse pretrained policies for benchmarking. The goal is to provide the community with a head start for whoever experiment their works on GRF and a simple-to-use population-based training framework for further improving their agents through self-play. The implementation is available at https://github.com/Shanghai-Digital-Brain-Laboratory/DB-Football.

The development of sensitive, selective, and rapid methods to detect bacteria in complex media is essential to ensuring human health. Virulence factors, particularly pore-forming toxins (PFTs) secreted by pathogenic bacteria, play a crucial role in bacterial diseases and serve as indicators of disease severity. In this study, a nanochannel-based label-free electrochemical sensing platform was developed for the detection of specific pathogenic bacteria based on their secreted PFTs. In this design, wood substrate channels were functionalized with a Fe-based metal–organic framework (FeMOF) and then protected with a layer of phosphatidylcholine (PC)-based phospholipid membrane (PM) that serves as a peroxidase mimetic and a channel gatekeeper, respectively. Using Staphylococcus aureus(S. aureus) as the model bacteria, the PC-specific PFTs secreted by S. aureusperforate the PM layer. Now exposed to the FeMOF, uncharged 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonate) (ABTS) molecules in the electrolyte undergo oxidation to cationic products (ABTS•+). The measured transmembrane ionic current indicates the presence of S. aureusand methicillin-resistant S. aureus(MRSA) with a low detection limit of 3 cfu mL–1. Besides excellent specificity, this sensing approach exhibits satisfactory performance for the detection of target bacteria in the complex media of food.

Water, covering over two-thirds of the Earth’s surface, holds immense potential for generating clean water, sustainable energy, and metal resources, which are the cornerstones of modern society and future development. It is highly desired to produce these crucial elements through eco-friendly processes with minimal carbon footprints. Interfacial solar evaporation, which utilizes solar energy at the air–liquid interface to facilitate water vaporization and solute separation, offers a promising solution. In this review, we systematically report the recent progress of the cogeneration of clean water and energy/resources including electricity, hydrogen, and metal resources via interfacial solar evaporation. We first gain insight into the energy and mass transport for a typical interfacial solar evaporation system and reveal the residual energy and resources for achieving the cogeneration goal. Then, we summarize the recent advances in materials/device designs for efficient cogeneration. Finally, we discuss the existing challenges and potential opportunities for the further development of this field.

On certain occasions, such as bending, a sheet with gradient microstructure may present higher formability compared to one with uniform microstructure. In this study, the formation of gradient microstructure in AZ31 alloy sheets extruded by the asymmetric die is comprehensively investigated. The results exhibit the microstructure evolution undergoes three stages, namely the initial dynamic recrystallization (DRX), abnormal grain growth (AGG), and the secondary DRX. After the initial DRX, the microstructure is fine and uniform, and the transverse direction (TD) preferred texture is formed due to the initiation of non-basal slip. Owing to the high extrusion temperature, AGG occurs after the initial DRX. The grains with basal texture are engulfed by the grains with TD-preferred texture. During the AGG, the grain size is coarsened from 11.4 μm to 382.1 μm. As the material flows through the die bearing, the secondary DRX occurs near the corner of the die. Ascribed to the gradient strain near the bearing, the grains near the bottom surface of the sheet are refined again, while those near the other side inherit the large sizes from grown grains. Ultimately, the extruded sheet exhibits gradient microstructure along the thickness direction, i.e. normal direction (ND). The gradient-distributed plastic strain and strain rate are the main causes of gradient microstructure.

Although iodine modulates bone metabolism in the treatment of thyroid disease, the effect of iodine intake on bone metabolism remains less known.

An ultrathin polyethylene nanofibrous membrane (UTPENFM) substrate with a large specific surface area was prepared by biaxial stretching, and the modified UTPENFM adsorbent, called the AO–UTPENFM, was successfully prepared by a two-step grafting method. By adjusting the pore structure of the matrix material by biaxial stretching, the area of functional groups can be exposed to the maximum extent on the membrane surface layer so as to realize the rapid adsorption process of uranium. Herein, in the simulated seawater with a uranium concentration of 8 ppm, the adsorption capacity is 172.88 mg/g for 6 h. Meanwhile, the adsorption capacity of uranium is 3.38 mg/g in natural seawater for 7 days, which indicates that the adsorption of uranium by the UTPENFM-g-PHEA-(PAO-co-PAA) (AO–UTPENFM) adsorption material is an ultrafast adsorption process. The AO–UTPENFM, with its higher specific surface area and larger pore structure, provides abundant space for the entry of uranyl ions, which can accelerate the adsorption rate of uranium, indicating that the AO–UTPENFM is a promising adsorbent for uranium extraction from natural seawater. These findings provide insights into uranium extraction from seawater and bode well for the potential of uranium extraction from seawater using ultrathin polymer-based adsorbents.

Fiber‐spinning chemistry (FSC) has emerged as a promising micro‐reaction platform due to its high‐specific surface area, efficient heat and mass transfer, and enhanced reaction rate. The FSC strategy employs spinning fibers as microreactors, lessening the emissions of volatile organic compounds (VOCs), and realizing the design of micro/nanoscale fibers and the synthesis of nanomaterials. In this review, we highlight the latest advancements in FSC in respect of preparation mechanisms and technical advantages. Various FSC strategies, including microfluidic spinning, electro‐microfluidic spinning (EMS), and microfluidic blow spinning (MBS) are emphasized. In particular, the regulation of microfluidic chips in the FSC process is introduced. Additionally, the application of the FSC strategy is summarized in the synthesis of fluorescent nanomaterials, nonweaving for multidimensional fibers, and all‐weather smart textiles. Finally, the advanced progress and future perspectives are discussed. Overall, this review will provide theoretical guidance for the design of well‐defined micro/nanoscale fibers based on the FSC platforms.

Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks. A proportional-integral-observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Then, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control strategy.

Most existing techniques to handle imbalanced data might be invalid in the presence of data missing since they are based on the assumption that the data is complete. To shorten such a gap, a novel synthetic minority oversampling technique (SMOTE), i.e., Non-negative latent factor analysis-incorporated and Switching triple-weight-SMOTE (NSS), is proposed. The main idea of NSS is 4-fold: 1) a Lagrange non-negative matrix factorization (LNMF) method is put forward to impute the missing values with a guaranteed non-negativity according to the original distribution owing to the consideration of the global feature information; 2) by mapping the complete data after imputation into an empirical feature space (EFS), a more separable dataset is achieved, which rigidly maintains the same geometrical structure as the original data while efficiently reducing the redundant features to enhance model generalization and operational efficiency; 3) after fulfilling the fuzzy $c$ -means (FCM) clustering, the inter-cluster distance, the capacity of each minority cluster and its sparsity are comprehensively taken into account to develop a triple-weight assignment strategy, which contributes to allocate the number of synthetic samples to each cluster appropriately; 4) a switching oversampling strategy is provided in response to the clusters with different distributions (i.e., either Gaussian or uniform distribution). Moreover, a posterior is used to check the correctness of the synthetic samples. Finally, experiments on a real dataset and 12 public datasets show that the proposed NSS outperforms other 11 state-of-art methods. Note to Practitioners—Data classification is an important computer task, which has been successfully used in many domains, including but not limited to, the medical domain, finance domain, and manufacturing domain. However, there are two big challenges when a classifier is to handle real-world data in the presence of imbalanced data and missing values. More specifically, the model performance is very likely to be degraded due to the missing information and the inclination of classifiers to the majority class. To surmount this problem, a natural idea is to use the LNMF model to obtain the desired recovery. Then, for the complete data after imputation, the empirical-feature-space-based switching triple-weight-SMOTE is applied to synthesize safe and correct data (i.e., lie solidly on the region of minority class) to achieve the balance. Such a working principle generates a novel NSS strategy. The proposed NSS strategy has the following obvious merits: 1) the imputation guarantees the similarity to the original dataset; and 2) new synthetic data are safely generated by taking adequate consideration of the information and distribution of datasets. Thus, the proposed NSS can greatly help improve the classification accuracy of real-world datasets.

Inflammation is the body's response to defence against infection or injury, and is associated with the progression of many diseases, such as inflammatory bowel disease (IBD) and rheumatoid arthritis (RA). LCA, a dibenzylbutane lignan extracted from the roots of traditional medicinal plant Litsea cubeba(Lour.) Pers., has demonstrated promising anti-inflammatory activity. In this study, a series of novel LCA derivatives were designed, synthesized, and evaluated for anti-inflammatory activity. Lipopolysaccharide (LPS)-induced RAW 264.7 cell model experiments showed that compound 10h(at 20 μM of concentration) had the strongest inhibitory effect on NO release, and inhibited the secretion and gene expression levels of interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α in vitro.In addition, western blot, immunofluorescence, and molecular docking showed that the anti-inflammatory mechanism of compound 10hmay be related to the nuclear factor (NF)-κB signalling pathway. In vivostudies based on a carrageenan-induced mouse paw edema model have shown significant anti-inflammatory activity of compound 10hat 20 mg kg−1. Preliminary in vitroand in vivostudies indicate that compound 10hhas the potential to be developed as a novel anti-inflammatory agent.

Heteroatoms in situ-doped hierarchical porous hollow-activated carbons (HPHACs) have been prepared innovatively by pyrolyzation of setaria viridis combined with alkaline activation for the first time. The micro-morphology, pore structure, chemical compositions, and electrochemical properties are researched in detail. The obtained HPHACs are served as outstanding electrode materials in electrochemical energy storage ascribe to the particular hierarchical porous and hollow structure, and the precursor setaria viridis is advantage of eco-friendly as well as cost-effective. Electrochemical measurement results of the HPHACs electrodes exhibit not only high specific capacitance of 350 F g−1at 0.2 A g−1, and impressive surface specific capacitance (Cs) of 49.9 μF cm−2, but also substantial rate capability of 68% retention (238 F g−1at 10 A g−1) and good cycle stability with 99% retention over 5000 cycles at 5 A g−1in 6 M KOH. Besides, the symmetrical supercapacitor device based on the HPHACs electrodes exhibits excellent energy density of 49.5 Wh kg−1at power density of 175 W kg−1, but still maintains favorable energy density of 32.0 Wh kg−1at current density of 1 A g−1in 1-ethy-3-methylimidazolium tetrafluoroborate (EMIMBF4) ionic liquid electrolyte, and the excellent cycle stability behaviour shows the nearly 97% ratio capacitance retention of the initial capacitance after 10,000 cycles at current density of 2 A g−1. Overall, the results indicate that HPHACs derived from setaria viridis have appealing electrochemical performances thus are promising electrode materials for supercapacitor devices and large-scale applications.

Platooning control is a promising scheme to alleviate traffic congestion while maintaining the safety of road traffic. Vehicles' cooperation deeply depends on the capability of communication infrastructure suffering from bandwidth constraints. This paper focuses on the platooning control problem of a class of nonlinear automated vehicles under constrained bit rate. A neural network (NN)-based observer is constructed to estimate the vehicular state, where a coding-decoding scheme is presented to alleviate the communication burden and an NN with carefully designed tuning laws is adopted to approximate the unknown nonlinearity. By using the Lyapunov stability theory, a sufficient condition is received for realizing the required formation with the given constant inter-vehicle spacing. Based on this condition, the desired gains of NN-based observers and decoder-based controllers are accessible by resolving matrix inequalities insensitive to the number of vehicles. Furthermore, the upper bound of platoon tracking errors dependent on bit rate is analytically derived. Finally, an illustrative example shows the validity of the developed control scheme.

Despite the significant progress made in coalbed methane (CBM) exploration and development in recent years, understanding of CBM enrichment mechanisms remains limited. This study aims to elucidate the CBM enrichment mechanism in the southern Qinshui Basin, China, by analyzing characteristics of global CBM basins and building a geological model of the study area. Field analyses are conducted to predict sweet spots of high CBM abundance and production potential. The findings reveal a high-yield model of CBM accumulation at relatively elevated structural positions within enriched areas. Compared to other global basins, low permeability poses the primary challenge for CBM development in China. Coal seam thickness shows minimal variation in southern Qinshui Basin, exerting negligible impact on CBM productivity. The shallow burial depth of coal seams in this region results in low stress, conferring high permeability conducive to high CBM yields. In situ stress conditions exert a primary control on the development of microfracture systems, which in turn govern reservoir permeability. This work provides new insights into CBM enrichment patterns in the southern Qinshui Basin. The proposed high-yield model enables better understanding of favorable conditions for CBM accumulation. Overall, this study represents a valuable contribution toward unlocking China’s CBM potential through improved geological characterization.

The growing urbanization and the development of new energy automobiles have motivated a significant increase in the number of vehicles on the highway, which has put increasing pressure on existing transportation systems. Platooning control is an effective scheme to enhance road traffic safety and alleviate traffic congestion. In this paper, platooning control under multiple description encoding schemes (MDESs) is investigated for automated vehicles with nonlinear dynamics. A learning-based observer with MDESs is first utilized to acquire the vehicular state, where a neural network with a weight updating rule is created to approximate unknown nonlinearities. In light of estimated states as well as the features of encoding-decoding errors, some sufficient conditions are derived in the framework of the Lyapunov stability to attain the desired platoon with given constant inter-vehicle spacing. In addition, a set of matrix inequalities is used to solve the desired gain parameters based on the maximum and minimum eigenvalues of the communication topology. Finally, an illustrative example demonstrates the validity of the developed control scheme.

The fault diagnosis of bearing in mechanical equipment is critical for ensuring safety and saving costs. The feature fusion technology is an effective way to improve the performance of fault diagnosis. However, how to extract and fuse the complementary fault features from multisensor data is still an important challenge. This study proposes a multiple-level feature tensor fusion network (MLFTFN) for bearing fault diagnosis to address this problem. A modified mutual attention is applied to feature extraction in MLFTFN to perform interactions between different modes of signal (signals in the time domain and frequency domain). Afterward, a feature tensor construction strategy is proposed, in which the feature tensors contain complementary fault information to the original features. The features extracted from feature tensors and the original features are concatenated for fault classification. Paderborn bearing dataset is used to verify the effectiveness of the MLFTFN. Results show that the diagnostic accuracy is greatly improved, which can be up to 99.5%.

Owing to the exterior self-trapped excitons (STEs) with adjustable fluorescence beams, low-dimensional ns2-metal halides have recently received considerable attention in solid-state light-emitting applications. However, the photoluminescence (PL) mechanism in metal halides remains a major challenge in achieving high efficiency and controllable PL properties because the excited-state energy of ns2conformational ions varies inhomogeneously with their coordination environments. Here, a novel zero-dimensional (0D) lead-free bismuth-based Rb3BiCl6·0.5H2O crystal was reported as a pristine crystal to modulate the optical properties. By doping Sb3+ions with 5s2electrons into Rb3BiCl6·0.5H2O crystals, bright orange emission at room temperature was obtained with a photoluminescence quantum yield of 39.7%. Optical characterizations and theoretical studies show that the Sb3+doping can suppress the strong exciton–phonon coupling, optimize the electronic energy band structure, improve the thermal activation energy, soften the structural lattice of the host crystals, deepen the STE states, and ultimately lead to strong photoluminescence. This work manifests a fruitful manipulation in ripening bismuth-based halides with high-efficiency PL properties, and the PL enhancement mechanisms will guide future research in the exploration of emerging luminescent materials.

Spatio-temporal action localization aims to locate the spatial and temporal positions of actors and classify their actions. However, prior research overlooks the fact that human actions often interact with novel objects in real-world scenarios, which neglects the various combinations of action-object, and considerably limits the generalization of the developed models. In this paper, we study the action-object combinations by researching multi-modal vision information of them. To this end, we propose a novel compositional spatio-temporal action localization (Com-STAL) task, which features non-overlapping action-object combinations in their training and test sets. Based on this, we construct a compositional action localization dataset (Com-AD). Beyond that, we propose a simple yet effective framework, Instance-Centric Interaction Network (ICIN), to reduce invalid induction biases within the visual modality and alleviate the combined distribution bias issue by leveraging additional modal information. The extensive experiment results on Com-AD demonstrate superior action localization performance of ICIN.

ABSTRACTCortinariussect. Camphoraticonsists of telamonioid species with violet basidioma, strong odor, and distinct cheilocystidia. In this study, phylogenetic analysis based on nuc rDNA internal transcribed spacer region ITS1-5.8S-ITS2 (ITS) and partial nuc rDNA 28S sequences has revealed three new species of the section from southwestern China, namely, C. longicystidiatus, C. megacystidiatus, and C. paraputorius, and a newly recorded species from southwestern China, viz., C. camphoratus, supplemented by the support from calculations of genetic distances of ITS sequences. Detailed descriptions of the four species are given with photographs, and their micromorphological characteristics are statistically compared. For species delimitation within the section, the size of basidiospores, the morphology of cheilocystidia, and the associated vegetation types and tree species are informative. A key to species in section Camphoratifrom the Northern Hemisphere is provided, and their geographic distributions and ecology are discussed.

•Soil depth is the key to affecting the distribution of soil microorganisms•Microorganisms have different preferences for carbon source at different soil depths•Soil microbe metabolic activity and diversity are regulated by soil nutrients•The microbial metabolism activity in deep soil cannot be ignored