Combination Of Features Research Articles

Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques

Background Inflammation of coronary arterial plaque is considered a key factor in the development of coronary heart disease. Early the plaque detection and timely treatment of the atherosclerosis could effectively reduce the risk of cardiovascular events. However, there is no study combining radiomics with deep learning techniques to predict non-calcified plaques (NCP) in coronary artery at present. Objective To investigate the value of combination of radiomics and deep learning features based on non-contrast computerized tomography (CT) scans of pericoronary adipose tissue (PCAT), integrating with clinical risk factors of patients, in identifying coronary inflammation and predicting the presence of NCP. Methods The clinical and imaging data of 353 patients were analyzed. The region of interest (ROI) of PCAT was manually outlined on non-contrast CT scan images, like coronary CT calcium score sequential images, then the radiomics and deep learning features in ROIs were extracted respectively. In training set (Center 1), after performing feature selection, radiomics and deep learning feature models were established, meanwhile, clinical models were built. Finally, combined models were developed out via integrating clinical, radiomics, and deep learning features. The predictive performance of the four feature model groups (clinical, radiomics, deep learning, and three combination) was assessed by seven different machine learning models through generation of receiver operating characteristic curves (ROC) and the calculation of area under the curve (AUC), sensitivity, specificity, and accuracy. Furthermore, the predictive performance of each model was validated in an external validation set (Center 2). Results For the single model comparation, eXtreme Gradient Boosting (XGBoost) showed the best performance among the clinical model group in the validation set. And Random Forest (RF) exhibited the best indicative performance not only among the radiomics feature group but also in the deep learning feature model group. What's more, among the combined model group, RF still displayed the best predictive performance, with the value of AUC, sensitivity, specificity, and accuracy in the validation set are 0.963, 0.857, 0.929, and 0.905. Conclusion The RF model in the combined model group based on non-contrast CT scan PCAT can predict the presence of NCP more accurately and has the potential for preliminary screening of the NCP.

Research on rock strength prediction model based on machine learning algorithm

The compressive strength of rocks is one of its mechanical characteristics. It has been a difficult problem to predict rock compressive strength conveniently and efficiently, and to solve the limitations of traditional rock compressive strength tests such as high cost, long time consumption, and reliability assurance. In this study, a data set containing 1774 groups of rock compressive strength test data was constructed through file retrieval, including 9 input parameters: rock type, temperature, confining pressure, dimension of specimen, shape of specimen, and experimental method. Eight supervised learning algorithms were used to learn the rock compressive strength test data, and eight rock compressive strength prediction models considering multiple factors were established to obtain a better method of predicting rock compressive strength. By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). Finally, four regression evaluation indicators, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2), were used to evaluate the predictive performance of the established regression models. The results showed that the best-trained model had a MAPE as low as 3.61%, MAE as low as 9.19 MPa, and R2 as high as 0.995. It is noteworthy that AdaBoost was found to be the best model for predicting rock compressive strength. This study presents a significant advancement in the field by demonstrating the effectiveness of machine learning algorithms in this context, which have not been extensively applied to rock compressive strength predictions. The findings suggest that these models can offer substantial improvements over traditional methods, not only in accuracy but also in operational efficiency. This research is important for geotechnical engineering, as accurate rock strength predictions are critical for the design and stability assessments of construction projects, ultimately contributing to safer and more cost-effective engineering solutions.

Multi-Sensor Adaptive Fusion and Convolutional Neural Network-Based Acoustic Emission Diagnosis for Initial Damage of the Engine

Abstract Aiming at the problems of traditional fault diagnosis means that are difficult to identify initial damage, as well as the poor reliability and fault tolerance with a single sensor, an acoustic emission (AE) diagnosis method for initial damage of the engine based on multi-sensor adaptive fusion and convolutional neural network (CNN) is proposed. Firstly, under the premise of utilizing parametric analysis to characterize the multi-sensor AE signals, the feature parameter entropy is used to determine the primary and secondary relationships between multi-sensor signals, and then the AE feature parameter matrix is formed by adaptive fusion. Secondly, CNN is employed to mine and learn the fault feature combinations from the AE feature parameter matrix by multi-layer fusion to realize the identification and diagnosis for initial damage of the engine. Finally, the proposed method is validated on the engine test bench designed for initial damage identification and is compared with conventional methods in terms of diagnostic performance. The results demonstrate that the proposed method can achieve an identification accuracy of 98.83% for initial damage, and has advantages in various aspects such as TAMSE, K, F1mic, and F1mac, which explicitly provides a theoretical and methodological basis for identifying initial faults comprehensively and accurately. This research not only enriches the theory and methods in the field of structural health monitoring, but also provides strong technical support for engine health management, which is expected to play a key role in the maintenance and guarantee of aviation engines in the future.

An Open-Pit Mines Land Use Classification Method Based on Random Forest Using UAV: A Case Study of a Ceramic Clay Mine

Timely and accurate land use information in open-pit mines is essential for environmental monitoring, ecological restoration planning, and promoting sustainable progress in mining regions. This study used high-resolution unmanned aerial vehicle (UAV) imagery, combined with object-oriented methods, optimal segmentation algorithms, and machine learning algorithms, to develop an efficient and practical method for classifying land use in open-pit mines. First, six land use categories were identified: stope, restoration area, building, vegetation area, arterial road, and waters. To achieve optimal scale segmentation, an image segmentation quality evaluation index is developed, emphasizing both high intra-object homogeneity and high inter-object heterogeneity. Second, spectral, index, texture, and spatial features are identified through out-of-bag (OOB) error of random forest and recursive feature elimination (RFE) to create an optimal multi-feature fusion combination. Finally, the classification of open-pit mines was executed by leveraging the optimal feature combination, employing the random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) classifiers in a comparative analysis. The experimental results indicated that classification of appropriate scale image segmentation can extract more accurate land use information. Feature selection effectively reduces model redundancy and improves classification accuracy, with spectral features having the most significant effect. The RF algorithm outperformed SVM and KNN, demonstrating superior handling of high-dimensional feature combinations. It achieves the highest overall accuracy (OA) of 90.77%, with the lowest misclassification and omission errors and the highest classification accuracy. The disaggregated data facilitate effective monitoring of ecological changes in open-pit mining areas, support the development of mining plans, and help predict the quality and heterogeneity of raw clay in some areas.

Search anything: segmentation-based similarity search via region prompts

AbstractSearch Anything is presented, a novel approach to perform similarity search in images. In contrast to other approaches to image similarity search, Search Anything enables users to utilize point, box, and text prompts to search for similar regions in a set of images. The region selected by a prompt is automatically segmented, and a binary feature vector is extracted. This feature vector is then used as a query for an image region index, and the images that contain the corresponding regions are returned. Search Anything is trained in a self-supervised manner on mask features extracted by the FastSAM foundation model and semantic features for masked image regions extracted by the CLIP foundation model to learn binary hash code representations for image regions. By coupling these two foundation models, images can be indexed and searched at a more fine-grained level than finding only entire similar images. Experiments on several datasets from different domains in a zero-shot setting demonstrate the benefits of Search Anything as a versatile region-based similarity search approach for images. The efficacy of the approach is further supported by qualitative results. Ablation studies are performed to evaluate how the proposed combination of semantic features and segmentation features together with masking improves the performance of Search Anything over the baseline using CLIP features alone. For large regions, relative improvements of up to 9.87% in mean average precision are achieved. Furthermore, considering context is beneficial for searching small image regions; a context of 3 times an object’s bounding box gives the best results. Finally, we measure computation time and determine storage requirements.

EEG channel and feature investigation in binary and multiple motor imagery task predictions

IntroductionMotor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.MethodsOur main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.ResultsAmong all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications.DiscussionOur research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation.

A new chelid turtle with an ornamented bony shell (Pleurodira: Chelidae) from the Portezuelo Formation (Upper Cretaceous) of Patagonia, Argentina

ABSTRACT We describe Baalemys mansillai gen. et sp. nov. a chelid turtle, based on an articulated shell from the Portezuelo Formation of the Baal locality, northern shore of Barreales lake, Neuquén Province, Argentina. Baalemys mansillai gen. et sp. nov. is distinguished from other chelids by its oval shape, moderate size, distinctive granulated ornamentation, narrow nuchal plate, first hexagonal neural with short posterolateral margins, a second neural plate that is a regular hexagon, and a posterior lobe that is longer than both the anterior lobe and the bridge. This unique combination of features supports the establishment of a new genus and species. Baalemys mansillai gen. et sp. nov was discovered in the Baal locality with sauropod dinosaurs, theropod dinosaurs, crocodiles, pelomedusoid turtles and other genera of chelids; a similar biota was identified in other localities of the Portezuelo Formation. The identification of Baalemys mansillai gen. et sp. nov. significantly adds to our knowledge of the diversity of pleurodiran turtles from the Portezuelo Formation, the Neuquén Basin, and the broader Patagonian region.

A new species of Macropodus (Teleotsei: Osphronemidae) from Fujian Province, southern China

A new Macropodus species was collected from the river basins of southeastern China. The new species Macropodus minnanensis, from Fujian Province, China can be well distinguished from congeners by the following combination of features: (1) dorsal fin with 13 spines and 7 soft rays; (2) anal fin with 20 spines and modally 10 soft rays; (3) vertical insertion of dorsal fin origin at second to third soft rays of anal fin; (4) longitudinal scale rows in midline modally 28; (5) transverse scale rows 14; (6) vertebrae 27 and (7) specific coloration: body with posteriorly black margin on scale pockets; eye greyish; a large opecular, deep black ocellus spot in male. The brief discussions of its related species are also provided.

Feature mining for thermoelectric materials based on interpretable machine learning.

In previous laboratory preparation processes, the selection and proportioning of specific experimental parameters often stemmed from the empirical experience of predecessors, necessitating the derivation of the optimal scheme for the target experiment through extensive trial and error. This process typically required the consumption of substantial resources and time. Thanks to the advancement of machine learning technologies, these have gradually become a powerful tool for addressing complex functional problems in material optimization. This study is based on a collected database of thermoelectric materials, aiming to clarify the correspondence between physical characteristics and the thermoelectric figure of merit, zT. The key features that can directly affect the experimental results are identified, and the features that indirectly affect the experimental results are also analyzed. By employing interpretable machine learning methods, this research analyzes critical molecular features in the characterized data, utilizing feature engineering techniques to construct and optimize machine learning models for the selected key features. Furthermore, in the subsequent model fitting and analysis phase, by comparing the efficiency of different feature combinations, the optimal feature descriptors for the current experimental system were determined. The adoption of the aforementioned improved methods endowed the related models with the potential for high-throughput screening, thereby further enhancing the efficiency of experimental optimization.

Two new species of Paratanakia (Teleostei: Cyprinidae) from Taiwan and mainland China

Two new species of bitterling of Paratanakia were found and collected from Taiwan island and Fujian Province, mainland China recently. Paratanakia fuvlidorsalis n. sp. which is endemic in Taiwan island can be well distinguished from other congeners by the following unique combination of features: (1) fin ray counts: dorsal fin rays 3, 8; anal fin rays 3, 10; pectoral fin rays modally 1, 12; (2) squamtion: lateral-line scales modally 34; and (3) its own specific dorsal fin color pattern in male. Paratanakia julongjiangensis n. sp. which is endemic in Fujian Province, mainland China can be well distinguished from other congeners by the following unique combination of features: (1) fin ray counts: dorsal fin rays 3, 9; anal fin rays 3, 11; pectoral fin rays modally 1, 12; (2) lateral-line scales modally 34; and (3) its own specific dorsal fin color pattern in male. The brief morphological comparison with close related species would be also addressed.

A new species of marine goby Trimma (Teleostei:Gobiidae) from Taiwan

A new species of marine goby, Trimma tigrinum sp. nov. is discovered and collected from the coastal waters off eastern and southern Taiwan. It can be well distinguished from other congeners by the following unique combination of features: (1) fin rays: D2 I/9, A I/9, P 17–19; second and third spines of D1 equal in length and not elongated; D2 rays branched; first ray of A not branched; fifth ray of V unbranched. (2) Squamation: LR 23–24 (modally 23), TR 6–7 (modally 7), PredS 7–8; no scales on the cheek and opercle. (3) Interorbital width narrow with deep trench and postorbital trench with slightly groove. (4) Specific colorations: cheek with two yellow vertical bands; anterior margin of pectoral-fin base with large yellow bar, two third of pupil diameter-sized; spine of second dorsal-fin with two small, dark reddish spots. dorsum from D1 to dorsal procurrent caudal-fin with seven yellow-orange bands like saddles; ventral from origin A to ventral procurrent caudal-fin with five diffuse yellowish or reddish orange bands. The molecular evidence from mtDNA COI sequence is also revealed that the new species is discrete species from others. A diagnostic key to nominal species of Trimma from Taiwanese waters would be also provided in this paper.

Two new species of Luciogobius Gill (Teleostei: Gobiidae) from the Matsu Islands in Taiwan

Two new gobiid fishes of Luciogobius Gill, 1859 have recently been collected from the intertidal waters of the Matsu Islands in Taiwan. The first new species, Luciogobius matsuensis sp. nov., is characterized by the following unique combination of features: (1) second dorsal fin rays: I/12 and anal fin rays I/12; (2) pectoral fin rays modally 16 and with one upper, short free soft ray; (3) vertebral count modally 17+20–21=37–38; and (4) specific coloration: second dorsal fin translucent with tiny brown spots; caudal fin with many tiny brown spots; anal fins pale yellow and translucent, spotless, and darker in its rays; and pectoral fin creamy yellow with anterior 2/3 region with tiny brownish to black spots. The second new species, Luciogobius dongyinensis sp. nov., is characterized by the following unique combination of features: (1) second dorsal fin rays: I/16 and anal fin rays I/16; (2) pectoral fin rays modally 14 and with 2 upper, short free soft rays; (3) vertebral count modally 18+23=41; and (4) specific coloration: a long infraorbital deep brown stripe below eye; a long deep brown stripe above and along upper jaw; pectoral fin with many small patches of melanophores except posterior, distal 1/5 region; and caudal fin brown with many light rounded spots. A brief discussion of their own related species will also be addressed.

A new species of bagrid catfish genus Tachysurus (Teleostei: Bagridae) from Wu river basin in central Taiwan

A new species of freshwater bagrid catfishes, Tachysurus (Teleostei: Bagridae) were collected from Wu river basin in central Taiwan. The new species, Tachysurus flumendraco n. sp. is most similar to Taiwanese endemic, T. adiposalis (Oshima, 1919) than any other congeneric species in eastern Asia. It can be well distinguished by the following unique combination of features: (1) dorsal fin rays II + 8; (2) anal fin rays: modally 3 + 18 = 21; (3) spine of pectoral fin with posterior serrations modally 18; (4) vertebral count usually 5 + 43–44; and (5) specific coloration pattern: body and head entirely bright creamy yellow to golden yellow, dorsal half of head and trunk with light brown; all 4 pairs of barbels creamy white to snow white; adipose fin creamy yellow to brownish yellow; anal fin creamy yellow with lighter basal skin; and caudal fin creamy yellow with distal black margin. The brief morphological comparison with close related species would be also addressed.

A new Calamiana species (Teleostei: Gobiidae) from brackish waters of northern Taiwan

A new species of brackish water goby, Calamiana taiwanensis sp. nov., from northern Taiwan is described in this paper. This new species is characterized by the following unique combinations of features: (1) second dorsal fin rays I/7–8, anal fin rays I/7–8; (2) longitudinal scale rows 34–36; (3) first dorsal fin triangular, second to third spines filamentous in adult males; and (4) specific head pore system: anterior termination with a pair of head pores σ and λ, median interorbital region with a pair of pores κ1 and κ, postorbital region with a pair of pores ω, lateral termination with a pair of pores α, upper margin of preopercular region with a pair of pores β and ρ. The present study revealed that the head pore pattern is varied among the species of genus Calamiana, therefore this feature can be regarded as an important character for the species identification within Calamiana.

Mapping rapeseed (Brassica napus L.) aboveground biomass in different periods using optical and phenotypic metrics derived from UAV hyperspectral and RGB imagery

Aboveground biomass (AGB) is a key indicator of crop nutrition and growth status. Accurately and timely obtaining biomass information is essential for crop yield prediction in precision management systems. Remote sensing methods play a key role in monitoring crop biomass. However, the saturation effect makes it challenging for spectral indices to accurately reflect crop changes at higher biomass levels. It is well established that rapeseed biomass during different growth stages is closely related to phenotypic traits. This study aims to explore the potential of using optical and phenotypic metrics to estimate rapeseed AGB. Vegetation indices (VI), texture features (TF), and structural features (SF) were extracted from UAV hyperspectral and ultra-high-resolution RGB images to assess their correlation with rapeseed biomass at different growth stages. Deep neural network (DNN), random forest (RF), and support vector regression (SVR) were employed to estimate rapeseed AGB. We compared the accuracy of various feature combinations and evaluated model performance at different growth stages. The results indicated strong correlations between rapeseed AGB at the three growth stages and the corresponding indices. The estimation model incorporating VI, TF, and SF showed higher accuracy in estimating rapeseed AGB compared to models using individual feature sets. Furthermore, the DNN model (R2 = 0.878, RMSE = 447.02 kg/ha) with the combined features outperformed both the RF (R2 = 0.812, RMSE = 530.15 kg/ha) and SVR (R2 = 0.781, RMSE = 563.24 kg/ha) models. Among the growth stages, the bolting stage yielded slightly higher estimation accuracy than the seedling and early blossoming stages. The optimal model combined DNN with VI, TF, and SF features. These findings demonstrate that integrating hyperspectral and RGB data with advanced artificial intelligence models, particularly DNN, provides an effective approach for estimating rapeseed AGB.

Prediction of Major Adverse Limb Events in Females with Peripheral Artery Disease using Blood-Based Biomarkers and Clinical Features.

The objective of this study wasto identify a female-specific prognostic biomarker for peripheral artery disease (PAD) and develop a prediction model for 2-year major adverse limb events (MALE). Patients with/without PAD were recruited (n=461). Plasma concentrations of 68 circulating proteins were measured and patients were followed for 2 years. The primary outcome was MALE (composite of vascular intervention, major amputation, or acute/chronic limb threatening ischemia). We trained a random forest model using: 1) clinical characteristics, 2) female-specific PAD biomarker, and 3) clinical characteristics and female-specific PAD biomarker. Galectin-9 was the only protein to be significantly elevated in females compared to males in the discovery/validation analyses. The random forest model achieved the following AUROC's: 0.72 (clinical features), 0.83 (Galectin-9), and 0.86 (clinical features + Galectin-9). We identified Galectin-9 as a female-specific PAD biomarker and developed an accurate prognostic model for 2-year MALE using a combination of clinical features and plasma Galectin-9 levels.

Enhanced Semantic Natural Scenery Retrieval System Through Novel Dominant Colour and Multi‐Resolution Texture Feature Learning Model

ABSTRACTA conventional content‐based image retrieval system (CBIR) extracts image features from every pixel of the images, and its depiction of the feature is entirely different from human perception. Additionally, it takes a significant amount of time for retrieval. An optimal combination of appropriate image features is necessary to bridge the semantic gap between user queries and retrieval responses. Furthermore, users should require minimal interactions with the CBIR system to obtain accurate responses. Therefore, the proposed work focuses on extracting highly relevant feature information from a set of images in various natural image databases. Subsequently, a feature‐based learning/classification model is introduced before similarity measure calculations, aiming to minimise retrieval time and the number of comparisons. The proposed work analyses the learning models based on the retrieval system's performance separately for the following features: (i) dominant colour, (ii) multi‐resolution radial difference texture patterns, and a combination of both. The developed work is assessed with other techniques, and the results are reported. The results demonstrate that the implemented ensemble learning model‐based CBIR outperforms the recent CBIR techniques.

Molecular phylogeny and morphology reveal a new Eichleriella species (Auriculariales, Basidiomycota) from China

A new wood-inhabiting fungus Eichleriella yunnanensis is proposed based on a combination of the morphological features and molecular evidence. Eichleriella yunnanensis is characterized by the buff to clay-pink hymenial surface, a monomitic hyphal system with thin- to thick walled generative hyphae having the clamp connections, and thin-walled, subglobose to subclavate basidia with longitudinally septate, and thin-walled, curved-cylindrical to allantoid basidiospores. The phylogenetic tree inferred from ITS+nLSU sequences revealed that the new species was nested into the family Auriculariaceae within the order Auriculariales, in which it was closely related to the species E. discolor and E. xinpingensis. On the basis of ITS sequences indicated that the new species was nested into the genus Eichleriella, and also grouped with the clade comprising E. discolor and E. xinpingensis.

Circulating Tumor cells and multiple indicators combined to identify the risk of poorer prognosis in patients with resected non-small cell lung cancer

BackgroundSurgical resection is an important treatment option for patients with non-small cell lung cancer (NSCLC). However, recurrence and survival rates remain a cause of concern. To further improve prognosis, more studies have focused on liquid biopsy, which has significant value as a prognostic factor for defining the risk stratification of postoperative NSCLC patients. This study aimed to identify circulating tumor cells (CTCs) as biomarkers that indicate a poor prognosis, combined with multiple indicators to determine prognostic risks in advance and develop individualized treatment strategies.MethodsBetween November 2015 and August 2018, 65 radical resected patients with NSCLC were analyzed. Preoperative CTCs were collected, and follow-up lasted until August 2023. Overall survival (OS) and disease-free survival (DFS) were the primary outcomes.ResultsWith an 11 CTC unit threshold, the high preoperative CTC level group had worse OS and DFS than the low-level group, suggesting that preoperative CTC levels have prognostic value. Time-dependent receiver operating characteristic (ROC) curves also showed satisfactory predictive efficiency of CTCs. Univariate analysis revealed that preoperative CTC levels were significantly associated with increasing risks for OS and DFS. Moreover, we combined CTCs and multiple indicators to provide a reference for a group at high risk of adverse outcomes.ConclusionsCTCs serve as feasible biomarkers for predicting postoperative prognosis in NSCLC patients. The combination of hematological, radiological, and pathological features could be valuable tools to guide postoperative management and treatment decisions in these patients. A multimodal prognostic approach is important for the clinical evaluation of lung cancer.

Four new species of Phanerochaete (Polyporales, Basidiomycota) from China.

Four new wood-inhabiting fungi viz. Phanerochaetecastanea, P.citrinoalba, P.citrinorhizomorpha, and P.wuyiensis spp. nov. - are proposed based on a combination of morphological features and molecular evidence. Phanerochaetecastanea is characterized by soft coriaceous basidiomata detachable from the substrate, becoming reddish brown in KOH, subulate cystidia with an obtuse apex. Phanerochaetecitrinoalba is characterized by the coriaceous basidiomata with smooth, cracking hymenial surface, sterile margins with yellowish to whitish rhizomorphs, a monomitic hyphal system, generative hyphae mostly with simple septa and occasionally with clamp connections at basal hyphae. Phanerochaetecitrinorhizomorpha is characterized by soft coriaceous basidiomata with a salmon to peach hymenial surface, a sterile margin with yellowish rhizomorphs, simple septate generative hyphae, and clavate to subfusiform or subulate cystidia with an obtuse apex. Phanerochaetewuyiensis is characterized by membranaceous basidiomata with smooth or locally tuberculate hymenial surface and the whitish rhizomorphs, generative hyphae with both simple septa and clamp connections at basal hyphae, cystidia projecting above hymenium. DNA sequences of the ITS and LSU markers of the studied samples were generated, and phylogenetic analyses were performed with Maximum Likelihood and Bayesian Inference methods. The phylogenetic tree inferred from the concatenated ITS+nLSU dataset highlighted the placement of the four new species in the genus Phanerochaete (Phanerochaetaceae, Polyporales). Phylogenetically related and morphologically similar species to these four new species are discussed. Furthermore, an identification key to accepted species of Phanerochaete in China is given.