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Abstract Machine learning with optical neural networks has featured unique advantages of the information processing including high speed, ultrawide bandwidths and low energy consumption because the optical dimensions (time, space, wavelength, and polarization) could be utilized to increase the degree of freedom. However, due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain, the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here, we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses. The proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction, and deep-learning diffractive layers as a classifier. The resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding, leading to an accuracy as high as 97.2% for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes, as well as a resistance to eavesdropping in point-to-point free-space transmission. Moreover, through extending the target encoded modes into multiplexed OAM states, we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%. Our work provides a deep insight to the mechanism of machine learning with spatial modes basis, which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

Adeno-associated viral transduction allows the introduction of nucleic fragments into cells and is widely used to modulate gene expressions in vitro and in vivo. It enables the study of genetic functions and disease mechanisms and, more recently, serves as a tool for gene repair. To achieve optimal transduction performance for a given cell type, selecting an appropriate serotype and the number of virus particles per cell, also known as the multiplicity of infection, is critical. Fluorescent proteins are one of the common reporter genes to visualize successfully transduced cells and assess transduction efficiencies. Traditional methods of measuring fluorescence-positive cells are endpoint analysis by flow cytometry or manual counting with a fluorescence microscope. However, the flow cytometry analysis does not allow further measurement in a test run, and manual counting by microscopy is time-consuming. Here, we present a method that repeatedly evaluates transduction efficiencies by adding the DNA-stain Hoechst 33342 during the transduction process combined with a microscope or live-cell imager and microplate image analysis software. The method achieves fast, high-throughput, reproducible, and real-time post-transduction analysis and allows for optimizing transduction parameters and screening for a proper approach.

BackgroundDepression is widespread global problem that not only severely impacts individuals’ physical and mental health but also imposes a heavy disease burden on nations and societies. The role of inflammation in the pathogenesis and pathophysiology of depression has received much attention, but the precise relationship between the two remains unclear. This study aims to investigate the correlation between depression and inflammation using a network medicine approach.MethodsWe utilized a degree-preserving approach to identify the large connected component (LCC) of all depression-related proteins in the human interactome. The LCC was deemed as the disease module for depression. To measure the association between depression and other diseases, we calculated the overlap between these disease protein modules using the Sab algorithm. A smaller Sab value indicates a stronger association between diseases. Building on the results of this analysis, we further explored the correlation between inflammation and depression by conducting enrichment and pathway analyses of critical targets. Finally, we used a network proximity approach to calculate drug-disease proximity to predict the efficacy of drugs for the treatment of depression. We calculated and ranked the distances between depression disease modules and 6,100 drugs. The top-ranked drugs were selected to explore their potential for treating depression based on the hypothesis that their antidepressant effects are related to reducing inflammation.ResultsIn the human interactome, all depression-related proteins are clustered into a large connected component (LCC) consisting of 202 proteins and multiple small subgraphs. This indicates that depression-related proteins tend to form clusters within the same network. We used the 202 LCC proteins as the key disease module for depression. Next, we investigated the potential relationships between depression and 299 other diseases. Our analysis identified over 18 diseases that exhibited significant overlap with the depression module. Where SAB = −0.075 for the vascular disease and depressive disorders module, SAB = −0.070 for the gastrointestinal disease and depressive disorders module, and SAB = −0.062 for the endocrine system disease and depressive disorders module. The distance between them SAB

With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation.

PurposeTransforming growth factor-beta (TGFB)-mediated epithelial-mesenchymal transition (EMT) plays a crucial role in the pathogenesis of retinal fibrosis, which is one of the leading causes of impaired vision. Current approaches to treating retinal fibrosis focus, among other things, on inhibiting the TGFB signaling pathway. Transient expression of microRNAs (miRNAs) is one way to inhibit the TGFB pathway post-transcriptionally. Our previous study identified the miRNA miR-302d as a regulator of multiple TGFB-related genes in ARPE-19 cells. To further explore its effect on primary cells, the effect of miR-302d on TGFB-induced EMT in primary human retinal pigment epithelium (hRPE) was investigated in vitro.MethodshRPE cells were extracted from patients receiving enucleation. Transfection of hRPE cells with miR-302d was performed before or after TGFB1 stimulation. Live-cell imaging, immunocytochemistry staining, Western blot, and ELISA assays were utilized to identify the alterations of cellular morphology and EMT-related factors expressions in hRPE cells.ResultshRPE cells underwent EMT by TGFB1 exposure. The transfection of miR-302d inhibited the transition with decreased production of mesenchymal markers and increased epithelial factors. Meanwhile, the phosphorylation of SMAD2 activated by TGFB1 was suppressed. Moreover, miR-302d expression promoted TGFB1-induced fibroblast-like hRPE cells to revert towards an epithelial stage. As confirmed by ELISA, miR-302d reduced TGFB receptor 2 (TGFBR2) and vascular endothelial growth factor A (VEGFA) levels 48 hours after transfection.ConclusionsThe protective effect of miR-302d might be a promising approach for ameliorating retinal fibrosis and neovascularization. MiR-302d suppresses TGFB-induced EMT in hRPE cells via downregulation of TGFBR2, even reversing the process. Furthermore, miR-302d reduces the constitutive secretion of VEGFA from hRPE cells.

Human skin sensing of mechanical stimuli originates from transduction of mechanoreceptors that converts external forces into electrical signals. Although imitating the spatial distribution of those mechanoreceptors can enable developments of electronic skins capable of decoupled sensing of normal/shear forces and strains, it remains elusive. We report a three-dimensionally (3D) architected electronic skin (denoted as 3DAE-Skin) with force and strain sensing components arranged in a 3D layout that mimics that of Merkel cells and Ruffini endings in human skin. This 3DAE-Skin shows excellent decoupled sensing performances of normal force, shear force, and strain and enables development of a tactile system for simultaneous modulus/curvature measurements of an object through touch. Demonstrations include rapid modulus measurements of fruits, bread, and cake with various shapes and degrees of freshness. [ABSTRACT FROM AUTHOR]

Based on the analysis of the importance of professional cluster construction by ecological theory, with the change of social demand for talents, this paper explores the practice of environmental chemical professional cluster construction in Pingdingshan University, including gradually perfecting teaching conditions and reforming teaching mode, breaking through the limitations of resources, integrating the boundaries of colleges and departments, integrating multiple resources, innovating systems and mechanisms, reconstructing professional clusters, deconstructing professional connotations, reorganizing curriculum systems, etc., in order to better build the ecological chain network of education in application-oriented colleges and universities, realize the deep integration of industry and education, train future-oriented interdisciplinary applied talents of new engineering, and realize the construction of characteristic professional cluster in application-oriented colleges. [ABSTRACT FROM AUTHOR]

In this article, we focus on prediction of a target model by transferring the information of source models. To be flexible, we use semiparametric additive frameworks for the target and source models. Inheriting the spirit of parameter-transfer learning, we assume that different models possibly share common knowledge across parametric components that is helpful for the target predictive task. Unlike existing parameter-transfer approaches, which need to construct auxiliary source models by parameter similarity with the target model and then adopt a regularization procedure, we propose a frequentist model averaging strategy with a J-fold cross-validation criterion so that auxiliary parameter information from different models can be adaptively transferred through data-driven weight assignments. The asymptotic optimality and weight convergence of our proposed method are built under some regularity conditions. Extensive numerical results demonstrate the superiority of the proposed method over competitive methods. [ABSTRACT FROM AUTHOR]

The quality and quantity of crop yields in agriculture primarily depend on the timing and precision of various implemented farming behaviors. Basins and hills dominate southwest China. Due to topographical constraints, the rate of agricultural mechanization in the region remains low, and agriculture remains primarily non-mechanized. The acquisition and recognition of information on farming behaviors play an important role in crop production. In this article, transfer learning was used in a current state-of-the-art 3DCNN-based behavior recognition model for farming behavior recognition and classification tasks. The focus was on fine-tuning and evaluating state-of-the-art 3D convolutional neural networks for farming behavior recognition. The evaluated architectures included Res3D, MC3, and R2+1D. The six common farming behaviors recognized include weeding, planting, harvesting, transplanting, fertilizing, and spraying. The accuracy of all models pretrained on Kinetics-400 after fine-tuning exceeded 90%, where MC3 had the best performance, with an accuracy of 0.9628, precision of 0.9647, sensitivity of 0.963, and specificity of 0.9925, which was slightly greater than the other two. MC3 was also the most lightweight of all models; its parameters were only 32.6% of Res3D and 36.7% of R2+1D. The experimental results demonstrated that the fine-tuned MC3 model offers high classification accuracy and effective recognition and classification of farming behaviors, which lays a good foundation for improved crop production. [ABSTRACT FROM AUTHOR]

Abstract Background Staphylococcus aureus and its single or mixed biofilm infections seriously threaten global public health. Phage therapy, which uses active phage particles or phage-derived endolysins, has emerged as a promising alternative strategy to antibiotic treatment. However, high-efficient phage therapeutic regimens have yet to be established. Results In this study, we used an enrichment procedure to isolate phages against methicillin-resistant S. aureus (MRSA) XN108. We characterized phage SYL, a new member of the Kayvirus genus, Herelleviridae family. The phage endolysin LysSYL was expressed. LysSYL demonstrated stability under various conditions and exhibited a broader range of efficacy against staphylococcal strains than its parent phage (100% vs. 41.7%). Moreover, dynamic live/dead bacterial observation demonstrated that LysSYL could completely lyse MRSA USA300 within 10 min. Scan and transmission electron microscopy revealed evident bacterial cell perforation and deformation. In addition, LysSYL displayed strong eradication activity against single- and mixed-species biofilms associated with S. aureus. It also had the ability to kill bacterial persisters, and proved highly effective in eliminating persistent S. aureus when combined with vancomycin. Furthermore, LysSYL protected BALB/c mice from lethal S. aureus infections. A single-dose treatment with 50 mg/kg of LysSYL resulted in a dramatic reduction in bacterial loads in the blood, liver, spleen, lungs, and kidneys of a peritonitis mouse model, which resulted in rescuing 100% of mice challenged with 108 colony forming units of S. aureus USA300. Conclusions Overall, the data provided in this study highlight the strong therapeutic potential of endolysin LysSYL in combating staphylococcal infections, including mono- and mixed-species biofilms related to S. aureus.

Abstract Focal and systemic infections are serious threats to human health. Preclinical models enable the development of new drugs and therapeutic regimens. In vivo, animal bioluminescence (BL) imaging has been used with bacterial reporter strains to evaluate antimicrobial treatment effects. However, high‐sensitivity bioluminescent systems are required because of the limited tissue penetration and low brightness of the BL signals of existing approaches. Here, we report that NanoLuc (Nluc) showed better performance than LuxCDABE in bacteria. However, the retention rate of plasmid constructs in bacteria was low. To construct stable Staphylococcus aureus reporter strains, a partner protein enolase (Eno) was identified by screening of S. aureus strain USA300 for fusion expression of Nluc‐based luciferases, including Nluc, Teluc, and Antares2. Different substrates, such as hydrofurimazine (HFZ), furimazine (FUR), and diphenylterazine (DTZ), were used to optimize a stable reporter strain/substrate pair for BL imaging. S. aureus USA300/Eno‐Antares2/HFZ produced the highest number of photons of orange‐red light in vitro and enabled sensitive BL tracking of S. aureus in vivo, with sensitivities of approximately 10 CFU from mouse skin and 750 CFU from mouse kidneys. USA300/Eno‐Antares2/HFZ was a powerful combination based on the longitudinal evaluation of the therapeutic efficacy of antibiotics. The optimized S. aureus Eno‐Antares2/HFZ pair provides a technological advancement for the in vivo evaluation of antimicrobial treatment.

In this paper, the nonlinear mechanical response of elastic cable structures under mechanical load is studied based on the discrete catenary theory. A cable net is discretized into multiple nodes and edges in our numerical approach, which is followed by an analytical formulation of the elastic energy and the associated Hessian matrix to realize the dynamic simulation. A fully implicit framework is proposed based on the discrete differential geometry (DDG) theory. The equilibrium configuration of a target object is derived by adding damping force into the system, known as the dynamic relaxation method. The mechanical response of a single suspended cable is investigated and compared with the analytical solution for cross-validation. A more intricate scenario is further discussed in detail, where a structure consisting of multiple slender cables is connected through joints. Utilizing the robustness and efficiency of our discrete numerical framework, a systematic parameter sweep is performed to quantify the force displacement relationships of nets with the different number of cables and different directions of fibers. Finally, an empirical scaling law is provided to account for the rigidity of elastic cable net in terms of its geometric properties, material characteristics, component numbers, and cable orientations. Our results would provide new insight in revealing the connections between flexible structures and tensegrity structures, and could motivate innovative designs in both mechanical and civil engineered equipment.

Messenger RNA (mRNA) has emerged as an innovative therapeutic modality, offering promising avenues for the prevention and treatment of a variety of diseases. The tremendous success of mRNA vaccines in effectively combatting coronavirus disease 2019 (COVID-19) evidences the unlimited medical and therapeutic potential of mRNA technology. Overcoming challenges related to mRNA stability, immunogenicity, and precision targeting has been made possible by recent advancements in lipid nanoparticles (LNPs). This review summarizes state-of-the-art LNP-mRNA-based therapeutics, including their structure, material compositions, design guidelines, and screening principles. Additionally, we highlight current preclinical and clinical trends in LNP-mRNA therapeutics in a broad range of treatments in ophthalmological conditions, cancer immunotherapy, gene editing, and rare-disease medicine. Particular attention is given to the translation and evolution of LNP-mRNA vaccines into a broader spectrum of therapeutics. We explore concerns in the aspects of inadequate extrahepatic targeting efficacy, elevated doses, safety concerns, and challenges of large-scale production procedures. This discussion may offer insights and perspectives on near- and long-term clinical development prospects for LNP-mRNA therapeutics.

The flexible and conformal interconnects for electronic systems as a potential signal transmission device have great prospects in body-worn or wearable applications. High-efficiency wave propagation and conformal structure deformation around human body at radio communication are still confronted with huge challenges due to the lack of methods to control the wave propagation and achieve the deformable structure simultaneously. Here, inspired by the kirigami technology, a new paradigm to construct spoof plasmonic interconnects (SPIs) that support radiofrequency (RF) surface plasmonic transmission is proposed, together with high elasticity, strong robustness, and multifunction performance. Leveraging the strong field-confinement characteristic of spoof surface plasmons polaritons, the Type-I SPI opens its high-efficiency transmission band after stretching from a simply connected metallic surface. Meanwhile, the broadband transmission of the kirigami-based SPI exhibits strong robustness and excellent stability undergoing complex deformations, i.e., bending, twisting, and stretching. In addition, the prepared Type-II SPI consisting of 2 different subunit cells can achieve band-stop transmission characteristics, with its center frequency dynamically tunable by stretching the buckled structure. Experimental measurements verify the on-off switching performance in kirigami interconnects triggered by stretching. Overcoming the mechanical limitation of rigid structure with kirigami technology, the designer SPIs exhibit high stretchability through out-of-plane structure deformation. Such kirigami-based interconnects can improve the elastic functionality of wearable RF electronics and offer high compatibility to large body motion in future body network systems.

BackgroundMembrane vesicles (MVs) are nanoscale vesicular structures produced by bacteria during their growth in vitro and in vivo. Some bacterial components can be loaded in bacterial MVs, but the roles of the loaded MV molecules are unclear.MethodsMVs of Staphylococcus aureus RN4220 and its derivatives were prepared. Dynamic light scattering analysis was used to evaluate the size distribution, and 4D-label-free liquid chromatography–tandem mass spectrometry analysis was performed to detect protein composition in the MVs. The site-mutation S. aureus RN4220-Δhld and agrA deletion mutant RN4220-ΔagrA were generated via allelic replacement strategies. A hemolysis assay was performed with rabbit red blood cells. CCK-8 and lactate dehydrogenase release assays were used to determine the cytotoxicity of S. aureus MVs against RAW264.7 macrophages. The serum levels of inflammatory factors such as IL-6, IL-1β, and TNFα in mice treated with S. aureus MVs were detected with an enzyme-linked immunosorbent assay kit.ResultsDelta-hemolysin (Hld) was identified as a major loaded factor in S. aureus MVs. Further study showed that Hld could promote the production of staphylococcal MVs with smaller sizes. Loaded Hld affected the diversity of loaded proteins in MVs of S. aureus RN4220. Hld resulted in decreased protein diversity in MVs of S. aureus. Site-mutation (RN4220-Δhld) and agrA deletion (RN4220-ΔagrA) mutants produced MVs (ΔhldMVs and ΔagrAMVs) with a greater number of bacterial proteins than those derived from wild-type RN4220 (wtMVs). Moreover, Hld contributed to the hemolytic activity of wtMVs. Hld-loaded wtMVs were cytotoxic to macrophage RAW264.7 cells and could stimulate the production of inflammatory factor IL-6 in vivo.ConclusionThis study presented that Hld was a major loaded factor in S. aureus MVs, and the loaded Hld played vital roles in the MV-property modification.

In light of the current situation of STEM education in China, as well as in meeting the need for international education (students studying abroad), Yunnan Key Laboratory of Earth System Science (Yunnan University) and Aufine International carried out a joint scientific expedition on Earth science of Yunnan Province, China. Yunnan is rich in geoscience research and education resources, owing to its unique geographic location to the southeast of the Tibetan Plateau, and its geologic evolutionary history. The expedition was designed under the concept of STEM framework, allowing students to learn the basic geologic knowledge not only in the classrooms, but also in the field, through observing geologic profiles, fossils and rocks, and by using scientific instruments. In addition, students also learned the history of geologic research and experienced multi-ethnic cultures, all of which are elements of STEM education.

Combining Terahertz (THz) and Orbital Angular Momentum (OAM) technologies has great potential in high-speed wireless communication. Theoretically, OAM with different modes has strict orthogonality. The communication capacity of the system will improve significantly if OAM technology is applied to the THz communication system. Thus, the manner to generate a high-quality and dynamically controllable THz-OAM beam has been of significant interest to researchers in related fields. In this study, a double-layer transmissive metasurface that uses 3D printing as the processing method with a low cost and processing difficulty is designed. Note that the height of the unit cell for constructing the metasurface is configurable. As the height changes continuously, the phase of the transmitted wave covers 0～2\begin{document}${\pi }$\end{document} within 90~110 GHz, while the transmittance of the units is always higher than 88%. At 100 GHz, which is fed by a WR-10 standard waveguide horn antenna, OAM beams with different modes are generated by changing the relative rotation angle between the double-layer metasurface. The simulation results show that the metasurface antenna designed in this study can achieve OAM beams of \begin{document}$ l=1, \mathrm{2,3} $\end{document}, and the two-dimensional amplitude and phase results correspond with the characteristics of the corresponding modes. When \begin{document}$ l=1,\mathrm{ }2,\mathrm{ }3 $\end{document}, the OAM beam’s modal purity is 85.4%, 84.9%, and 83.4%, respectively. The measurement results include the results at frequency points of 90, 100, and 110 GHz. The results show that the OAM beam has a high-quality bandwidth of 20 GHz, which indicates that the metasurface antenna designed in this study has a wide working bandwidth at a high frequency and can be applied to high-frequency OAM communication.

Orbital angular momentum (OAM) has become a research hotspot in radar, communication, and other fields because of its spatial spiral phase distribution in azimuthal domain. However, it encounters some problems in practical application scenarios owing to its inherent defects. Recently, several particular OAM waves and novel antenna structures have been proposed to make up for its defects or optimize its characteristics. However, there lack a universal and systematic theoretical analysis for these schemes. In this paper, the non-uniform travelling-wave current source (NTCS) model for OAM generation is proposed. This model is universal and suitable to be applied to many proposed antennas. The far field pattern and OAM spectrum distribution are theoretically derived based on the uniform travelling-wave source model. Bridged by the OAM spectrum analysis, an application-oriented antenna design process is presented. A PSOAM MG antenna optimal for the MIMO system is designed as an example. This work not only unifies the generation model of different OAM waves, but also provides a new view for the OAM based antenna design.

Despite tremendous progress in the development of untethered soft robots in recent years, existing systems lack the mobility, model‐based control, and motion planning capabilities of their piecewise rigid counterparts. As in conventional robotic systems, the development of versatile locomotion of soft robots is aided by the integration of hardware design and control with modeling tools that account for their unique mechanics and environmental interactions. Here, a framework for physics‐based modeling, motion planning, and control of a fully untethered swimming soft robot is introduced. This framework enables offline co‐design in the simulation of robot parameters and gaits to produce effective open‐loop behaviors and enables closed‐loop planning over motion primitives for feedback control of a frog‐inspired soft robot testbed. This pipeline uses a discrete elastic rods (DERs) physics engine that discretizes the soft robot as many stretchable and bendable rods. On hardware, an untethered aquatic soft robot that performs frog‐like rowing behaviors is engineered. Hardware validation verifies that the simulation has sufficient accuracy to find the best candidates for sets of parameters offline. The simulator is then used to generate a trajectory library of the robot's motion in simulation that is used in real‐time closed‐loop path following experiments on hardware.

This work presents a study on users’ attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user’s baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively

The applications of alginate lyase are diverse, but efficient commercial enzymes are still unavailable. In this study, a novel alginate lyase with high activity was obtained from the marine bacteria Vibrio sp. Ni1. The ORF of the algB gene has 1824 bp, encoding 607 amino acids. Homology analysis shows that AlgB belongs to the PL7 family. There are two catalytic domains with the typical region of QIH found in AlgB. The purified recombinant enzyme of AlgB shows highest activity at 35 °C, pH 8.0, and 50 mmol/L Tris-HCl without any metal ions. Only K+ slightly enhances the activity, while Fe2+ and Cu2+ strongly inhibit the activity. The AlgB preferred polyM as substrate. The end products of enzymatic mixture are DP2 and DP3, without any metal ion to assist them. This enzyme has good industrial application prospects.

Understanding the determinants of tree biomass allocation patterns among organs is crucial for both predicting the rate and potential of forest carbon sinks and guiding future multifunctional forest management. However, it is still not clear how the site conditions (e.g., elevation) and stand structure (e.g., tree dominance, stand density) affect the biomass allocation of single trees in forests. This study was implemented in the Liupan Mountains of the Loess Plateau of Northwest China by collecting the related information of biomass data of 110 sample trees with different dominance and influencing factors within 23 sample plots of larch plantations set up along the elevation gradient. Based on these data, the response tendency and functions of biomass allocation of single trees to individual influencing factors of site conditions and forest structure were analyzed. Moreover, the results illustrated that the ratio between root biomass and aboveground biomass decreased significantly with rising stand age and tree density, but increased significantly with rising elevation, and there was no significant relationship with the dominance of individual trees. The results of this study revealed the importance of considering the influencing factors of site conditions and stand structure when developing dynamic models of tree biomass allocation. The results and research methods used in this study provide useful tools for quantifying the biomass allocation and carbon storage partitioning in the study area and other similar regions.