Comprehensive Recognition Research Articles

Synthetic Nanoassemblies for Regulating Organelles: From Molecular Design to Precision Therapeutics.

Each organelle referring to a complex multiorder architecture executes respective biological processes via its distinct spatial organization and internal microenvironment. As the assembly of biomolecules is the structural basis of living cells, creating synthetic nanoassemblies with specific physicochemical and morphological properties in living cells to interfere or couple with the natural organelle architectures has attracted great attention in precision therapeutics of cancers. In this review, we give an overview of the latest advances in the synthetic nanoassemblies for precise organelle regulation, including the formation mechanisms, triggering strategies, and biomedical applications in precision therapeutics. We summarize the emerging material systems, including polymers, peptides, and deoxyribonucleic acids (DNAs), and their respective intermolecular interactions for intercellular synthetic nanoassemblies, and highlight their design principles in constructing precursors that assemble into synthetic nanoassemblies targeting specific organelles in the complex cellular environment. We further showcase the developed intracellular synthetic nanoassemblies targeting specific organelles including mitochondria, the endoplasmic reticulum, lysosome, Golgi apparatus, and nucleus and describe their underlying mechanisms for organelle regulation and precision therapeutics for cancer. Last, the essential challenges in this field and prospects for future precision therapeutics of synthetic nanoassemblies are discussed. This review should facilitate the rational design of organelle-targeting synthetic nanoassemblies and the comprehensive recognition of organelles by materials and contribute to the deep understanding and application of the synthetic nanoassemblies for precision therapeutics.

Research on the Method of Imperfect Wheat Grain Recognition Utilizing Hyperspectral Imaging Technology

As the primary grain crop in China, wheat holds a significant position in the country’s agricultural production, circulation, consumption, and various other aspects. However, the presence of imperfect grains has greatly impacted wheat quality and, subsequently, food security. In order to detect perfect wheat grains and six types of imperfect grains, a method for the fast and non-destructive identification of imperfect wheat grains using hyperspectral images was proposed. The main contents and results are as follows: (1) We collected wheat grain hyperspectral data. Seven types of wheat grain samples, each containing 300 grains, were prepared to construct a hyperspectral imaging system for imperfect wheat grains, and visible near-infrared hyperspectral data from 2100 wheat grains were collected. The Savitzky–Golay algorithm was used to analyze the hyperspectral images of wheat grains, selecting 261 dimensional effective hyperspectral datapoints within the range of 420.61–980.43 nm. (2) The Successive Projections Algorithm was used to reduce the dimensions of the 261 dimensional hyperspectral datapoints, selecting 33 dimensional hyperspectral datapoints. Principal Component Analysis was used to extract the optimal spectral wavelengths, specifically selecting hyperspectral images at 647.57 nm, 591.78 nm, and 568.36 nm to establish the dataset. (3) Particle Swarm Optimization was used to optimize the Support Vector Machines model, Convolutional Neural Network model, and MobileNet V2 model, which were established to recognize seven types of wheat grains. The comprehensive recognition rates were 93.71%, 95.14%, and 97.71%, respectively. The results indicate that a larger model with more parameters may not necessarily yield better performance. The research shows that the MobileNet V2 network model exhibits superior recognition efficiency, and the integration of hyperspectral image technology with the classification model can accurately identify imperfect wheat grains.

THE ESSENCE OF GOODWILL IN DISCLOSING THE INTANGIBLE VALUE OF BUSINESS IN THE CONTEXT OF DIGITAL TRANSFORMATION

The intangible component of business value is increasing, yet remains inadequately disclosed in financial reporting. Current accounting standards have not fully developed the tools and methodologies for its comprehensive recognition, measurement, assessment, and full disclosure in reports. Discussions on the essence of goodwill have long been ongoing, and it's evident that this category extends beyond accounting and financial reporting. Digital transformation enhances the interest of researchers, scholars, and practitioners in the essence of goodwill, emphasizing the necessity to consider it as a means of disclosing the intangible value of business in modern conditions.The methodological basis of the conducted research includes methods of analysis, synthesis, historical conceptual review, comparison, logical generalization, grouping, abstraction, and a systematic approach.Studying the history of the development of the goodwill concept allowed identifying the main stages of its formation as an object of accounting, reporting, and management. An in-depth critical analysis of definitions supplemented economic, legal, and accounting approaches to understanding the essence of goodwill with new ones, such as marketing, branding, value, emotional, quantitative, and systemic. The developed classification allows considering all aspects of goodwill manifestation and the views of various assessment subjects on its essence and role in disclosing the intangible value of the business. It is proposed to define goodwill as the intangible value of a business reflecting its reputation, brand, customer loyalty, intellectual property, innovative and technological capabilities, digital user experience, online presence, and a set of other unique intangible assets, which together contribute to the sustainable growth of the company's market value and create competitive advantages, enabling adaptation to changes in the era of digital transformation.Goodwill is a category that exists not only in consolidated reporting, as it characterizes the intangible components of business value and requires appropriate recognition and management.

Detection of ballastless track interlayer gap based on vehicle’s multivariate dynamic response and deep learning

To adapt to the rapid detection of interlayer damage in ballastless track structures of high-speed railways, a cement asphalt mortar (CAM) gap localization and damage degree classification scheme based on multivariate data fusion and deep learning is proposed. Based on vertical axle box acceleration (VABA) and vertical wheel-rail force (VWRF) data, the variation patterns of multi-dynamic response data of vehicles under the edge type and internal type interlayer gaps are analyzed. An improved ensemble local mean decomposition algorithm (IELMD) is designed to achieve data denoising and preliminary enhancement preprocessing of weak damage features for VABA and VWRF under interlayer gaps. The measured and simulated VABA and VWRF constituted multiple dynamic response data sets for interlayer gap detection. An interlayer gap detection model: DTA-Tcnformer is constructed, integrating a dual temporal convolutional network with an attention mechanism, transformer architecture, and gate control mechanism. Multiple dynamic response data are input into the model to locate and classify 24 cases of the two interlayer gap types. The influence of damage cases and vehicle speeds on the location effect is analyzed. The learning ability of the proposed model on the gap features is visualized. The misidentification and omission of the gap cases are calculated, and the comprehensive recognition accuracy is 92.48 %. The recognition performance of the proposed model is compared and verified.

Research on the Maturity Detection Method of Korla Pears Based on Hyperspectral Technology

In this study, hyperspectral imaging technology with a wavelength range of 450 to 1000 nanometers was used to collect spectral data from 160 Korla pear samples at various maturity stages (immature, semimature, mature, and overripe). To ensure high-quality data, multiple preprocessing techniques such as multiplicative scatter correction (MSC), standard normal variate (SNV), and normalization were employed. Based on these preprocessed data, a custom convolutional neural network model (CNN-S) was constructed and trained to achieve precise classification and identification of the maturity stages of Korla pears. Additionally, a BP neural network model was used to determine the characteristic wavelengths for maturity assessment based on the sugar content feature wavelengths. The results demonstrated that the BP model, based on sugar content feature wavelengths, effectively discriminated the maturity stages of the pears. Specifically, the comprehensive recognition rates for the training, testing, and validation sets were 98.5%, 93.5%, and 90.5%, respectively. Furthermore, the combination of hyperspectral imaging technology and the custom CNN-S model significantly enhanced the detection performance of pear maturity. Compared to traditional CNN models, the CNN-S model improved the accuracy of the test set by nearly 10%. Moreover, the CNN-S model outperformed existing techniques based on partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) in capturing hyperspectral data features, showing superior generalization capability and detection efficiency. The superior performance of this method in practical applications further supports its potential in smart agriculture technology, providing a more efficient and accurate solution for agricultural product quality detection. Additionally, it plays a crucial role in the development of smart agricultural technology.

Deep learning‐based segmentation model for permeable concrete meso‐structures

AbstractThe meso‐structure of pervious concrete significantly influences its overall performance. Accurately identifying the meso‐structure of pervious concrete is imperative for optimizing the design of pervious concrete, considering its mechanical properties and functionality. Therefore, to address the difficulty of recognizing the meso‐structures of pervious concrete, a method utilizing deep learning image semantic segmentation techniques is proposed in this study. First, based on the classical deep learning model, three models, namely, Res‐UNet, ED‐SegNet, and G‐ENet, are proposed for recognizing pervious concrete meso‐structure using deep learning image semantic segmentation techniques. These models introduce a residual module, a hybrid loss function, and a differential recognition branching structure to enhance the ability to recognize detailed information within pervious concrete meso‐structure and small targets. Second, the respective recognition performances of these methods on the meso‐structure of pervious concrete were thoroughly analyzed by experiment. The results indicate that the proposed three recognition methods for recognizing the meso‐structure of permeable concrete outperform conventional techniques not only in terms of efficiency but also in recognition accuracy and the ability to distinguish and identify aggregates, pores, and cement binders. In terms of comprehensive recognition effectiveness, the Res‐UNet model outperforms, followed by ED‐SegNet and G‐ENet. Furthermore, the computational efficiency of these three recognition methods meets the requirements of engineering applications.

Feature Extraction Methods for Underwater Acoustic Target Recognition of Divers.

The extraction of typical features of underwater target signals and excellent recognition algorithms are the keys to achieving underwater acoustic target recognition of divers. This paper proposes a feature extraction method for diver signals: frequency-domain multi-sub-band energy (FMSE), aiming to achieve accurate recognition of diver underwater acoustic targets by passive sonar. The impact of the presence or absence of targets, different numbers of targets, different signal-to-noise ratios, and different detection distances on this method was studied based on experimental data under different conditions, such as water pools and lakes. It was found that the FMSE method has the best robustness and performance compared with two other signal feature extraction methods: mel frequency cepstral coefficient filtering and gammatone frequency cepstral coefficient filtering. Combined with the commonly used recognition algorithm of support vector machines, the FMSE method can achieve a comprehensive recognition accuracy of over 94% for frogman underwater acoustic targets. This indicates that the FMSE method is suitable for underwater acoustic recognition of diver targets.

CRISPR-Cas12a-mediated colorimetric aptasensor of netilmicin based on enzymes-assisted signal amplification and nanozyme employing a rationally truncated aptamer

Netilmicin (NET) is a typical veterinary antibiotic used in aquaculture and animal husbandry, but its excessive accumulation poses a serious threat to the ecological environment and human health. Therefore, a novel and highly sensitive Crispr-Cas12a-mediated colorimetric aptasensor was developed by combining the enzyme-assisted signal amplification strategy and magnetic NMOF-Pt nanozyme composite material (NPSM) for the visual detection of veterinary antibiotic residues in real samples. A high-affinity truncated aptamer (N-20) targeting NET was evolved from a parental aptamer sequence with the help of comprehensive recognition mechanism studies. Subsequently, N-20 was elaborately designed into the isothermal enzyme-assisted strand displacement amplification reaction. The amplified products (Act-DNAs) activated the trans-cleavage activity in the Crispr Cas12a system. The NMOF-Pt nanozyme was released from NPSM after cleaving the single chain of DNA (ssDNA). The quantity of released nanozyme was positively correlated with the amount of NET to quantitatively achieve the detection with the linear range and limits of detection (LOD) of about 0.003–30 ng/mL and 1.15 pg/mL, respectively. This method was successfully applied to detect NET in Lake water, milk and pork samples with satisfactory recoveries ranging from 89.9 % to 104.3 %. The constructed aptasensor demonstrated superior analytical performance and exhibited enormous application prospects in food supervision, environment monitoring, and medical laboratory science.

Accuracy Assessment of Geometric-Distortion Identification Methods for Sentinel-1 Synthetic Aperture Radar Imagery in Highland Mountainous Regions.

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods-LSM, R-Index, and P-NG-against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings.

Diversity pattern of insects from Macao based on an updated species checklist after 25 years.

Insects represent one of the most diverse groups in the organism world with extremely rich species and morphological diversity, playing important roles in natural and city ecosystems. Regional compilation of insect species lists helps to clarify the richness of insect species in a region, enhances our understanding the structure and function of a local ecosystem and promotes the protection and development of insect resources. Moreover, it also serves as a valuable reference for cities with small area, large population and high urbanisation like Macao. Macao (Macau) Special Administrative Region (SAR) is situated at the Pearl River Delta on the southeast coast of mainland China. With urban development accelerating at great rate in a quite restricted area, Macao still has rich fauna, within which the insect diversity is surprisingly high. In this study, we systematically sorted out major references items of manuals or handbooks, monographs, articles, dissertations, official websites and other publicly available information sources about the insects recorded in Macao and, thus, generated a checklist of 15 orders, 166 families, 868 genera, 1,339 species and 118 subspecies. During this process, the preliminarily summarised list was re-examined to eliminate synonyms and invalid species, based on many more extensive literature reviews. Besides, spelling errors of scientific names, authors and years were corrected. Meanwhile, the catalogue revealed a different composition pattern of species diversity between orders from those of the world and China. Even based on the most conservative estimates, the number of insect species in Macao should not be lower than 3,340 species, which hints at the necessity of deeper investigations with adequate collecting in the future to achieve more comprehensive recognition and understanding of Macao's insect biodiversity.

Optoelectronic and thermoelectric properties of Cs2PtX6 (X=Cl, Br, I) for energy conversion applications: DFT calculations

This research employs density functional theory within the WIEN2k framework to simulate Cs2PtX6 (X = Cl, Br, I) perovskites. The study focuses on assessing the structural stability and thermodynamic properties through the computation of tolerance factor and formation energies, affirming the viability of the simulated materials. Notably, this investigation reveals a direct bandgap semiconducting character of these compounds, with specific values of the bandgap of 2.9, 2.15, and 1.08 eV for Cl, Br, and I-based compositions, respectively. The analysis of optical behavior demonstrates a redshift in absorption edge and an increase in optical conductivity, correlating with the enlarging halogen size within the series. The investigation into the thermoelectric characteristics of Cs2PtX6 perovskites, which include σ and κe as well as S, PF, and ZT, underscores the potential of Cs2PtX6 perovskites for deployment in thermal energy harvesting devices. Furthermore, the mechanical behavior assessment indicates the ductile nature of studied compounds, contributing to their overall mechanical stability. This study gives a comprehensive recognition of structural, electronic, mechanical, optical, and transport characteristics of Cs2PtX6 compounds, suggesting their promise for applications in advanced materials for optoelectronic and thermoelectric devices.

Action capture and VR interactive system for online experimental teaching

The development of technology has intensified people’s interest in virtual reality technology. Virtual reality technology is also widely used in fields such as teaching and healthcare. To improve the real-time and operability of virtual reality interaction systems, a motion capture and virtual reality interaction system design for online experimental teaching is proposed. The active apparent model is used for facial feature point localization, and the joint capture method is improved by combining threshold segmentation and forward kinematics algorithms. Experimental data confirms that compared to Kinect method, the improved hand joint detection method has higher joint positioning accuracy, with a loss rate of less than 40 % for hand joints. The average judgment accuracy of the six facial expression tests is 82 %, 81 %, 79 %, 78 %, 80 %, and 81 %, respectively. The comprehensive recognition accuracy of facial expression recognition is 80.17 %. The read, write, and update times for each frame of the virtual reality interaction system are 102.6 ms and 427 ms, respectively. The system memory usage is 920.5 M, and the CPU and GPU usage rates are 25.2 % and 4.4 %, respectively. The system can run smoothly. The virtual reality system can run continuously for 24 h, with strong operability and low difficulty in expanding system functions, achieving design goals.

Long COVID symptoms and associated factors in registered nurses with COVID-19

Purpose: The coronavirus disease 2019 (COVID-19) pandemic has had significant physical and psychological impacts on registered nurses (RNs). This study aimed to identify long COVID symptoms and their associated factors specifically among RNs.Methods: This descriptive correlational study’s sample comprised 189 nurses (31.57±5.98 years, 93.7% female) in Korea. Self-reported long COVID symptoms were assessed using the COVID-19 Yorkshire Rehabilitation Scale. Data were collected from December 31, 2022, to January 13, 2023, using the online survey method and were analyzed using independent t-test, Wilcoxon signed-rank test, one-way ANOVA, Pearson’s correlation, and a multiple linear regression analysis with the IBM SPSS Statistics 26.0 program.Results: A total of 179 participants (94.7%) experienced one or more long COVID symptoms. The most prevalent symptoms were weakness (77.8%), fatigue (68.3%), breathlessness (67.7%), cough/throat sensitivity/voice change (50.3%), and sleep problems (50.3%). The factors related to long COVID symptoms were marital status, type of institution, working time, acute COVID-19 symptoms, and vaccination status. The quarantine period (β=.26, p<.001) and the nursing workforce after COVID-19 (β=-.17, p=.018) were significantly associated with long COVID symptoms (Adjusted R<sup>2</sup>=.33).Conclusion: Providing comprehensive recognition is necessary for the understanding of long COVID symptoms and their associated factors among nurses and could promote a long COVID symptom management education program targeted at nurses. Moreover, it could facilitate effective nursing care and education plans for long COVID patients.

Research on basketball footwork recognition based on a convolutional neural network algorithm

ObjectiveThe purpose of this paper is to utilize a convolutional neural network (CNN) to identify the types of basketball footwork of athletes as a way to assist in the training of basketball players' footwork and to improve their performance in the game. MethodsA traditional CNN algorithm was improved to a dual-model CNN (DMCNN) algorithm, where convolutional feature extraction was performed separately on both the acceleration and angular velocity data of footwork. The two features were then merged and subjected to principle component analysis (PCA) dimensionality reduction for identifying different types of footwork. In subsequent simulation experiments, ten basketball players' footwork data were collected using sensors. The improved CNN algorithm was used for footwork recognition and compared with the support vector machine (SVM) and traditional CNN algorithms. ResultsThe experimental results showed that the acceleration and angular velocity signals of different basketball footwork had distinct differences. The comprehensive recognition precision of DMCNN for footwork types was 98.8 %, and the comprehensive recall rate and overall F value were 97.8 % and 98.2 %, respectively. Its recognition time was 1.23 s. For the traditional CNN algorithm, the comprehensive precision was 87.5 %, the comprehensive recall rate was 85.7 %, and the overall F value was 86.6 %. Its recognition time was 1.99 s. As for the SVM algorithm, the comprehensive precision was 74.2 %, the comprehensive recall rate was 73.2 %, and the overall F value was 73.7 %. The recognition time was 3.68 s. NoveltyThe novelty of this article lies in using two separate CNNs to extract convolutional features from acceleration and angular velocity, respectively. These features are then combined and reduced dimensionality using PCA, thereby improving both recognition accuracy and efficiency.

Analysis of morphological differences in five large yellow croaker (Larimichthys crocea) populations

To explore the morphological and phenotypic characteristics and differences among different populations of Larimichthys crocea, traditional morphological measurements were carried out on three wild populations from Zhoushan, Xiamen and Zhanjiang and two farmed populations from Ningde and Wenzhou. Seven morphological parameters of five L. crocea populations were compared and analyzed. The results of one-way ANOVA showed significant differences in trunk and caudal stalk among the five populations. The contribution rates of the first five principal components to the total difference among different populations were 29.984%, 18.462%, 17.234%, 12.167%, and 9.904%, respectively, and the cumulative contribution rates were 87.751%. Trunk can be used to distinguish different geographic populations best. The cluster analysis results showed that the distance between wild populations was the closest, while the distance between farmed populations was far. The step discriminant method established the classification discriminant function of 5 populations. The discriminant accuracy P1 was 78.3%-92.7%, the discriminant accuracy P2 was 76.4%-96.5%, and the comprehensive recognition rate was 99.3%. The discriminant accuracy of this method was high, and it could provide a reference for the differentiation of different populations of L. crocea. This study provided basic morphological data for identifying a large yellow croaker population, protecting germplasm resources, and breeding improved varieties.

Accounting Management and Optimizing Production Based on Distributed Semantic Recognition

Accounting management and production optimization are vital aspects of enterprise management, serving as indispensable core components in the modern business landscape. However, conventional methods reliant on manual input exhibit drawbacks such as low recognition accuracy and excessive memory consumption. To address these challenges, semantic recognition technology utilizing voice signals has emerged as a pivotal solution across various industries. Building upon this premise, this paper introduces a distributed semantic recognition‐based algorithm for accounting management and production optimization. The proposed algorithm encompasses multiple modules, including a front‐end feature extraction module, a channel transmission module, and a voice quality vector quantization module. Additionally, a semantic recognition module is introduced to process the voice signals and generate prediction results. By leveraging extensive accounting management and production data for learning and analysis, the algorithm automatically uncovers patterns and laws within the data, extracting valuable information. To validate the proposed algorithm, this study utilizes the dataset from the UCI machine learning repository and applies it for analysis and processing. The experimental findings demonstrate that the algorithm introduced in this paper outperforms alternative methods. Specifically, it achieves a notable 9.3% improvement in comprehensive recognition accuracy and reduces memory usage by 34.4%. These results highlight the algorithm’s efficacy in enhancing the understanding and analysis of customer needs, market trends, competitors, and other pertinent information within the realm of commercial applications for companies.

Abnormal Detection of Financial Fraud in Listed Companies Based on Deep Learning

This study aims to improve the accuracy of detecting financial fraud in listed companies by applying various deep learning algorithms. First, we comprehensively reviewed the current state of research on financial fraud theory and identified 67 recognition features to create a new recognition indicator system. Then, we collected data samples of all A-share listed companies from 2010 to 2022, preprocessed them, and generated a basic dataset. To address the unbalanced dataset, we used the Borderline-SMOTE algorithm. Empirical analysis results show that this algorithm can significantly improve the recognition performance of the model. Finally, we conducted experiments on the new dataset using three types of deep learning algorithms. The results show that the model constructed using the Long Short-Term Memory (LSTM) algorithm has the best prediction performance, with an accuracy rate higher than that of the DCRN, autoencoder, and other models. In addition, the classification effects of all deep learning algorithms are better than basic models and ensemble models. This research provides a powerful tool for the regulatory authorities of listed companies, helping them more effectively monitor and prevent financial fraud. We have three innovations in this study: (1) Development of a comprehensive recognition indicator system with 67 features; (2) Utilization of the Borderline-SMOTE algorithm to handle data imbalance; (3) Demonstration of the superior performance of the LSTM algorithm compared to other deep learning, basic, and ensemble models.

针对玉米叶片病斑图像的人工智能识别方法研究

Maize eyespot and maize curvularia leaf spot are two diseases that often occur on maize leaves. Because of the similarity of the shape and structure, it is difficult to identify the two diseases just relying on the observation of the growers. For the harmfulness and prevention methods are different, it would cause great loss if the disease can't be identified accurately. To address this issue, this paper first employs a connected region feature recognition method to design an automated lesion cropping process after acquiring leaf images with several lesions. Subsequently, a lesion recognition model based on the AlexNet architecture is built and subjected to five-fold cross-validation experiments. The results indicate that the model achieves a comprehensive recognition accuracy exceeding 99%. To further comprehend model characteristics, an analysis of the recognition accuracy and its fluctuations is conducted, revealing that the fractal growth and biological characteristics of the lesions may influence the recognition results. Moreover, the distribution of model parameters could be a potential reason for fluctuations in recognition accuracy rates with increasing number of iterations. This paper could offer valuable reference and support for the intelligent identification and diagnosis of maize and other plant diseases.

Preliminary species diversity and community phylogenetics of wood-inhabiting basidiomycetous fungi in the Dabie Mountains, Central China reveal unexpected richness

Wood-inhabiting fungi have important economic values as well as playing a major ecological role in forest ecosystem cycles. The Dabie Mountains, at the junction of Henan, Hubei, and Anhui Provinces, Central China, provide an ideal climate and favorable niches for the speciation and diversification of various forms of life including fungi. We studied the species diversity and community phylogenetics of wood-inhabiting basidiomycetous fungi that revealed 175 wood-inhabiting basidiomycetous species, of which 20 represented unidentified species, based on morphological and phylogenetic analyses of 575 specimens collected from ten sampling sites. These species belonged to two classes, 11 orders, 42 families, and 106 genera of Basidiomycota, and included 12 edible species, 28 medicinal species, four poisonous species, and seven forest pathogens. Four types of fungal distribution pattern at the genus level were recognized for 65 genera, while another 41 genera could not be placed in any known distribution pattern. The five sampling sites in the eastern part of the Dabie Mountains had significantly higher species diversity and phylogenetic diversity of wood-inhabiting basidiomycetous fungi than those in the western part, and thus deserve priority in terms of conservation. The community of wood-inhabiting basidiomycetous fungi in the Dabie Mountains is generally affected by a combination of habitat filtering and competitive exclusion. This study provides a basis on which to build actions for the comprehensive recognition, utilization, and conservation of wood-inhabiting basidiomycetous fungi in the region.

Nanofibers in Organelles: From Structure Design to Biomedical Applications

AbstractNanofibers are one of the most important morphologies of molecular self‐assemblies, the formation of which relies on the diverse intermolecular interactions of fibrous‐forming units. In the past decade, rapid advances have been made in the biomedical application of nanofibers, such as bioimaging and tumor treatment. An important topic to be focused on is not only the nanofiber formation mechanism but also where it forms, because different destinations could have different influences on cells and its formation could be triggered by unique stimuli in organelles. It is therefore necessary and timely to summarize the nanofibers assembled in organelles. This minireview discusses the formation mechanism, triggering strategies, and biomedical applications of nanofibers, which may facilitate the rational design of nanofibers, improve our understanding of the relationship between nanofiber properties and organelle characteristics, allow a comprehensive recognition of organelles affected by materials, and enhance the therapeutic efficiency of nanofibers.