Characteristics Of Different Types Research Articles

Description and Characterization of Different Types of Lymphoma in Cats in Hong Kong.

This study aimed to characterize and describe the different lymphoma types and anatomical forms in cats in Hong Kong. The clinical and histopathological data of cats diagnosed with lymphoma by cytology and/or histopathology were collected from a large diagnostic laboratory in Hong Kong. In total, 444 cats were diagnosed with lymphoma over four years (2019-2022). Like other countries where there is a low prevalence of FeLV infection, the predominant form of lymphoma was gastrointestinal (abdominal). Nasopharyngeal and peripheral nodal lymphoma were the second and third most common forms of lymphoma. The large cell/high-grade lymphoma type was much more common than the low-grade/small cell lymphoma in the study population. Domestic short hair was the most commonly affected breed in our study (n = 259/444). Among the cats with identified T/B-cell status, B-cell lymphoma (n = 61/81) prevailed as the most common phenotype. This study describes and characterizes the different types of feline lymphoma in cats in Hong Kong, adding valuable information to the body of knowledge.

DCNeT: A disease comorbidity network-based temporal deep learning framework to predict cardiovascular risk in patients with mental disorders

Patients with mental disorders (MDs) are at higher subsequent risk of developing cardiovascular diseases (CVDs) than the general population. Early identification of cardiovascular risk in patients with MDs is beneficial for timely intervention and reducing disease burden. Recently, deep learning approaches have been increasingly applied in CVDs risk prediction. However, these methods have three major issues: 1) mostly relying on multiple types of clinical data, 2) not sufficiently mining and utilizing comorbidity patterns hidden in complex correlations among various diseases, and 3) not fully leveraging the time information, including the irregular intervals. To address these issues, we propose a disease comorbidity network-based temporal deep learning framework (DCNeT) to predict the subsequent CVDs risk for patients with MDs based on routinely collected administrative health data. Firstly, to identify the comorbidity patterns of MDs, we construct a disease comorbidity network (DCN) for MDs and apply graph embedding method to generate disease embeddings for each disease in the DCN. Then, a code attention mechanism is proposed to obtain the weight of each disease which is embedded into dense vectors based on the structure of DCN. We present a view attention mechanism to compute the attention weights of different types of features including disease embeddings, basic features, and disease indicators for generating the final representations of patients’ hospitalizations. Furthermore, to fully utilize the information on the irregular time intervals between hospitalizations, a time encoding module is designed, and the time-aware LSTM is adopted to model the irregular time intervals and capture the temporal patterns of patients’ hospitalizations. The experimental results show that DCNeT outperforms the state-of-the-art methods, with the area under the receiver operating characteristic curve of 0.7658, 0.8143, 0.8110, and 0.7839 on four datasets, respectively. The ablation experiments further demonstrate that each module of DCNeT, including the code attention, view attention, and time encoding module, contributes to its superior performance, with average improvements of 1.20 %, 1.65 %, and 1.13 % in accuracy, respectively. Our DCNeT could be utilized as an efficient framework for identifying high-risk groups of CVDs among patients with MDs that may benefit from screening and preventive strategies.

SelfMixed: Self-supervised Mixed Noise Attenuation for Distributed Acoustic Sensing Data

Distributed acoustic sensing (DAS) is an emerging data acquisition technique known for its high sensing density, cost-effectiveness, and environmental friendliness, making it a technology with significant future application potential in many fields. However, DAS signals are often contaminated by various types of noise, such as high-frequency, high-amplitude erratic, and horizontal noise, making their processing challenging. Therefore, it is crucial to leverage the physical characteristics of these diverse types of noise in DAS data and effectively attenuate them. In this work, we propose SelfMixed, a novel self-supervised learning method for mixed noise suppression of DAS data. We fully exploit the physical characteristics of different types of noise in DAS data and introduce a physical characteristic-based training strategy. Specifically, we utilize the ℓ2-norm to characterize {random noise}, the ℓ1-norm for erratic noise, and horizontal smoothness and vertical non-smoothness for horizontal noise. Additionally, we employ a blind-spot-based training strategy for DAS denoising, relying solely on observed noisy data. To more effectively attenuate horizontal noise, we also introduce a Fourier transform-based parameterization method. By combining self-supervised deep priors with the physical characteristics of mixed DAS noise, our method effectively {attenuates} complex mixed noise in field DAS data. Extensive experiments on synthetic and field data from various geographic scenarios validate the superiority of SelfMixed over seven state-of-the-art DAS denoising approaches.

Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming

Background:: In the research and practice of medical sciences, accurate classification of biomedical images with computer programs may provide an important basis for the study and diagnosis of many diseases. Methods:: This paper proposes a new statistical approach that can accurately classify biomedical images based on their statistical features. In the first step of the proposed approach, a number of SIFT features of different types are computed for each pixel in a biomedical image and a statistical feature that describes the distribution of each type of SIFT features is obtained for the image. In the second step, a dynamic programming approach is used to efficiently analyze the dependence among different statistical features associated with an image and compute the probability for an image to belong to each possible class; the class with the largest probability is determined as the result of classification. Results:: Experimental results show that the proposed approach can lead to classification results with accuracy higher than that of a few state-of-the-art approaches for the classification of biomedical images Conclusion:: The proposed approach can achieve classification accuracy comparable to that of several state-of-the-art classification approaches. It is thus potentially useful for applications where large models are not appropriate for classification tasks due to limitations in computational or communication resources

Investigation of key morphological parameters of pores in different grades of asphalt mixture based on CT scanning technology

The internal pore structure of asphalt mixtures directly influences their permeability characteristics. Current research has not yet clarified the quantitative characterization of different types of meso-pore structures and their impact mechanisms on permeability characteristics. Therefore, this study, based on the acquisition of the internal pore structure of asphalt mixtures through CT non-destructive scanning technology, classifies pores according to their characteristics. It conducts a multi-dimensional and multi-scale qualitative and quantitative analysis of the meso-pore structures of asphalt mixtures in terms of pore distribution, equivalent radius of pore throats, inclination angle of pore throats, tortuosity of pore throats, and pore connectivity. The results indicate that the internal pore distributions of AC-16, SMA-16, OGFC-13, and OGFC-16 gradation asphalt mixtures are uniform, without noticeable segregation. The results of porosity obtained through non-destructive CT scanning technology demonstrate good consistency with those of macroscopic experiments. This demonstrates that analyzing CT images can accurately acquire pores and quantify pore characteristics. The number of pores in AC-16 gradation specimens is approximately 3–5 times that of other groups of gradation specimens, but the average volume of the pores is only 0.98 mm3. The average pore volume for SMA-16 gradation is 5.1 mm3, while for OGFC-13 and OGFC-16 gradation, the average pore volumes are 28.3 mm3 and 29.6 mm3, respectively. In SMA-16 grade specimens, the volume of open pores accounts for 95% of the total pore volume, despite the fact that open pores make up only 1% of the total number of pores. In the OGFC-13 and OGFC-16 graded asphalt mixtures, the volumes of interconnected pores account for 88.7% and 88.9% of the total pore volume, respectively. The number and volume proportion of pore throats decrease with the increase in pore throats angle, indicating that there are more pore throats with smaller angles, which are conducive to longitudinal drainage, and fewer pore throats with larger angles, which are conducive to lateral drainage. This suggests that the vertical permeability of samples formed by traditional Marshall compaction is usually greater than their horizontal permeability.

Analysis of Two Kinds of MV Trailer Creation Content

MV trailer is the MV inside the more eye-catching or eye-catching, to attract everyone's attention, it is a very effective way to promote the MV. This paper focuses on two different types of MV trailer type creation features, and analyzes the visual effect of MV trailer to attract the public with theoretical and practical significance. Using two representative female groups thumb amount of the first five MV forecast data in this paper found: (1) dance type MV forecast play characteristic is the music of chorus accompaniment with a short dance and different perspective scene to catch the public eye; (2) Story type MV forecast is characterized by some specific scene and perspective transformation suggested that the subsequent MV story. This paper aims to analyze the theoretical and practical significance of the visual effects of MV trailers in attracting the public by studying the creative characteristics of different types of MV trailers, and provide reference for the subsequent MV releases for better publicity.

Different types of tumor microvessels in stage I-IIIA squamous cell lung cancer and their clinical significance.

Lung cancer (LC) is the leading cause of morbidity and mortality among malignant neoplasms. Improving the diagnosis and treatment of LC remains an urgent task of modern oncology. Previously, we established that in gastric, breast and cervical cancer, tumor microvessels (MVs) differ in morphology and have different prognostic significance. The connection between different types of tumor MVs and the progression of LC is not well understood. To evaluate the morphological features and clinical significance of tumor MVs in lung squamous cell carcinoma (LUSC). A single-center retrospective cohort study examined medical records and archival paraffin blocks of 62 and 180 patients with stage I-IIIA LUSC in the training and main cohorts, respectively. All patients underwent radical surgery (R0) at the Orenburg Regional Cancer Clinic from May/20/2009 to December/14/2021. Tumor sections were routinely processed, and routine Mayer's hematoxylin and eosin staining and immunohistochemical staining for cluster of differentiation 34 (CD34), podoplanin, Snail and hypoxia-inducible factor-1 alpha were performed. The morphological features of different types of tumor MVs, tumor parenchyma and stroma were studied according to clinicopathological characteristics and LUSC prognosis. Statistical analysis was performed using Statistica 10.0 software. Univariate and multivariate logistic regression analyses were performed to identify potential risk factors for LUSC metastasis to regional lymph nodes (RLNs) and disease recurrence. Receiver operating characteristic curves were constructed to discriminate between patients with and without metastases in RLNs and those with and without disease recurrence. The effectiveness of the predictive models was assessed by the area under the curve. Survival was analyzed using the Kaplan-Meier method. The log-rank test was used to compare survival curves between patient subgroups. A value of P < 0.05 was considered to indicate statistical significance. Depending on the morphology, we classified tumor vessels into the following types: normal MVs, dilated capillaries (DCs), atypical DCs, DCs with weak expression of CD34, "contact-type" DCs, structures with partial endothelial linings, capillaries in the tumor solid component and lymphatic vessels in lymphoid and polymorphocellular infiltrates. We also evaluated the presence of loose, fine fibrous connective tissue (LFFCT) and retraction clefts in the tumor stroma, tumor spread into the alveolar air spaces (AASs) and fragmentation of the tumor solid component. According to multivariate analysis, the independent predictors of LUSC metastasis in RLNs were central tumor location (P < 0.00001), the presence of retraction clefts (P = 0.003), capillaries in the tumor solid component (P = 0.023) and fragmentation in the tumor solid component (P = 0.009), whereas the independent predictors of LUSC recurrence were tumor grade 3 (G3) (P = 0.001), stage N2 (P = 0.016), the presence of LFFCT in the tumor stroma (P < 0.00001), fragmentation of the tumor solid component (P = 0.0001), and the absence of tumor spread through the AASs (P = 0.0083). The results obtained confirm the correctness of our previously proposed classification of different types of tumor vessels and may contribute to improving the diagnosis and treatment of LUSC.

Characteristics of fibres based on secondary raw materials and their use in concrete technology

Different types of fibres in cementitious composites, particularly in concrete, are currently used for a number of reasons. Fibres are being added to improve mechanical properties (especially steel and glass fibres), to increase the durability – to reduce occurrence of microcracks during the concrete aging (mainly synthetic and cellulose fibres), or to increase the fire resistance (polypropylene fibres). Within the study, characterization of different types of alternative fibres (fibres generated during waste recycling that would otherwise end in incinerators or landfills) with possible use in cementitious composites. These were fibres from recycled PET bottles, paper, and mineral wool, whose properties were compared to the traditionally used cellulose and polypropylene fibres. In the experimental part, the thickness, length, shape, and surface of individual fibres were monitored by an optical microscope. Furthermore, the amount of heat of combustion was determined by the calorimetric method, and the differential thermal analysis (DTA) was carried out for determination of the impact of high temperature on monitored fibres. The microstructure of fibres was monitored using a scanning electron microscope. The focus of the experimental study was on fibres usable in concrete and capable of enduring high temperature stress.

Identification and morphological characterization of different types of plastic microparticles

The knowledge of the polymeric composition of microplastics (MPs) is interesting because offers useful information on the resistance, durability, and degradability of these materials, also allowing progress in the control of this contamination. However, there is currently a lack of reliable standardized methods for the identification, and characterization of the plastic microparticles. This work uses different techniques in a complementary manner for the identification, and characterization of MPs that more frequently are found in the environment. A total of 10 types of plastics were collected (polystyrene (PS), polyethylene terephthalate (PETE), polyethylene (PE), high- and low-density polyethylene (HDPE and LDPE, respectively), polyvinyl chloride (PVC), polypropylene (PP), polytetrafluoroethylene (PTFE), Polyamide (PA, Nylon 6,6) and poly-carbonate (PC)) and their chemical identification were analyzed by reflectance-attenuated infrared (FTIR-ATR). Furthermore, the samples were observed using light microscopy, and scan-ning electron microscopy (SEM). Also, staining with 12 different dyes was performed to improve the identification of microplastics. The results of this study revealed that PETE, PE, HDPE and LDPE, whose SEM images exhibited smoothness and flat uniformity of their surface, were not (or less) susceptible to adsorb staining solutions while PP, PA, PVC, and PTFE, were capable of adsorbing the dye solutions.

MulTFBS: A Spatial-Temporal Network with Multichannels for Predicting Transcription Factor Binding Sites.

Revealing the mechanisms that influence transcription factor binding specificity is the key to understanding gene regulation. In previous studies, DNA double helix structure and one-hot embedding have been used successfully to design computational methods for predicting transcription factor binding sites (TFBSs). However, DNA sequence as a kind of biological language, the method of word embedding representation in natural language processing, has not been considered properly in TFBS prediction models. In our work, we integrate different types of features of DNA sequence to design a multichanneled deep learning framework, namely MulTFBS, in which independent one-hot encoding, word embedding encoding, which can incorporate contextual information and extract the global features of the sequences, and double helix three-dimensional structural features have been trained in different channels. To extract sequence high-level information effectively, in our deep learning framework, we select the spatial-temporal network by combining convolutional neural networks and bidirectional long short-term memory networks with attention mechanism. Compared with six state-of-the-art methods on 66 universal protein-binding microarray data sets of different transcription factors, MulTFBS performs best on all data sets in the regression tasks, with the average R2 of 0.698 and the average PCC of 0.833, which are 5.4% and 3.2% higher, respectively, than the suboptimal method CRPTS. In addition, we evaluate the classification performance of MulTFBS for distinguishing bound or unbound regions on TF ChIP-seq data. The results show that our framework also performs well in the TFBS classification tasks.

Experimental study on the damage characteristics of cyclic disturbance and acoustic emission characteristics of different types of sandstones under high stress in deep mines

AbstractThree sandstone specimens common in rock engineering were selected to study the differences in the mechanical properties of rocks with different lithologies. The development and expansion of the internal cracks in the specimens were observed by combining the simulation system with the acoustic emission system. Through the combination of dynamic and static stresses, the deformation and damage of rocks under deep rock excavation and blasting were simulated. As the results show, the acoustic emission events of specimens with different lithologies under combined static and dynamic cyclic loading can be roughly divided into three phases: weakening, stabilizing, and surging periods. In addition, the acoustic emission characteristics of specimens with different lithologies show general consistency in different compression phases. The degree of fragmentation of specimens increases with the applied stress level; therefore, the stress level is one of the important factors influencing the damage pattern of specimens. The acoustic emission system was used to simulate the deformation and damage of rocks subjected to deep rock body excavation and engineering blasting. Cyclic dynamic perturbations under sinusoidal waves with a frequency of 5 Hz, a loading rate of 0.1 mm/min, a cyclic amplitude of 5 MPa, and a loading rate of 0.1 mm/min were applied to the three rock samples during the experiments. Among them, the fine‐grained sandstones are the most sensitive to the sinusoidal cyclic perturbation, followed by the muddy siltstone and the medium‐grained sandstones. On this basis, the acoustic emission energy release characteristics were analyzed, and the waveform characteristics in the damage evolution of the specimen under dynamic perturbation were studied by extracting the key points and searching for the main frequency eigenvalues.

DSFE: Decoding EEG-based finger motor imagery using feature-dependent frequency, feature fusion and ensemble learning.

Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of featuredependent frequency band selection, feature fusion, and ensemble learning (DSFE) for finger motor imagery. First, a feature-dependent frequency band selection method based on correlation coefficient (FDCC) was proposed to select feature-specific effective bands. Second, a feature fusion method was proposed to fuse different types of candidate features to produce multiple refined sets of decoding features. Finally, an ensemble model using the weighted voting strategy was proposed to make full use of these diverse sets of final features. The results on a public EEG dataset of five fingers motor imagery showed that the DSFE method is effective and achieves the highest decoding accuracy of 50.64%, which is 7.64% higher than existing studies using exactly the same data. The experiments further revealed that both the effective frequency bands of different subjects and the effective frequency bands of different types of features are different in finger motor imagery. Furthermore, compared with two-hand motor imagery, the effective decoding information of finger motor imagery is transferred to the lower frequency. The idea and findings in this paper provide a valuable perspective for understanding fine motor imagery in-depth.

Analysis of the prenatal ultrasound diagnostic value and prognostic factors of fetal mediastinal cysts.

A prenatal fetal mediastinal cyst is a benign disease. However, if a cyst enlargement grows, it may compress the adjacent organs and affect the fetal cardiopulmonary function. This study aimed to compare and analyze the prenatal ultrasound characteristics of different mediastinal cysts, and to evaluate the pregnancy outcome of the fetus and the factors affecting the prognostic of the fetus. To compare and analyze the prenatal ultrasound characteristics of different types of mediastinal cysts, and to evaluate the fetal pregnancy outcome and the influencing factors of fetal prognosis. A retrospective analysis of patients with prenatal diagnoses of mediastinal cysts was conducted to evaluate the ultrasound characteristics and to monitor the pregnancy outcomes to identify prognostic influences and provide a reliable basis for patient prognosis. In total, 30 patients were diagnosed with mediastinal cysts [including bronchogenic cysts (n=12), esophageal cysts (n=9), pericardial cysts (n=5), and thymic cysts (n=4)] on prenatal ultrasonography. The diagnostic accuracy rate was 93.33%; two cases of esophageal cysts were misdiagnosed as bronchial cysts. In total, 4 (44.44%) of 9 esophageal cysts and 4 thymic cysts were located in the anterior mediastinum, 10 (83.33%) of 12 bronchogenic cysts and 5 pericardial cysts were located in the middle mediastinum, and 2 (16.67%) of 12 bronchogenic cysts and 5 (55.56%) of 9 esophageal cysts were located in the posterior mediastinum. There were significant differences in the distribution of the cyst location, morphology, and cyst wall thickness (P<0.05). After delivery, 17 patients had clinical symptoms. There was a significant difference in the clinical symptoms between patients with a maximum diameter of postpartum cysts <5 and ≥5 cm (P<0.05), and children with a low gestational age and birth weight were more likely to have clinical symptoms. The prenatal ultrasound features of fetal mediastinal cysts were similar. However, the ultrasound characteristics related to the cyst location, morphology, and cyst wall thickness were helpful in providing an accurate diagnosis. In addition, the postpartum cyst size, location, adjacent relationship with the surrounding tissues, volume, gestational age, and weight were related to patient prognosis.

Pseudo-Helmholtz decomposition for an elastic VTI wavefield based on wavefront phase direction

P and S waves are coupled when propagating in anisotropic elastic media. The separation of P and S waves helps to study the characteristics of different types of seismic waves as well as mitigating crosstalk artifacts in elastic reverse time migration and elastic full-waveform inversion. At present, the methods of seismic wave mode separation in anisotropic media are mainly built on divergence- and curl-like operations, pseudo-Helmholtz decomposition, and low-rank approximation. We develop a new pseudo-Helmholtz decomposition operator based on eigenform analysis and the wavefront phase direction to decompose vertically transversely isotropic elastic wavefields. The corresponding P-/S-wave decoupling formulas are also derived in detail. Compared with the divergence- and curl-like methods, the new method does not change the phase of P and S waves. Compared with existing pseudo-Helmholtz decomposition methods based on eigenform analysis, our method achieves more accurate wavefield separation than the zero-order pseudo-Helmholtz decomposition operator. Our method requires solving one vector Poisson equation only, resulting in much less computational cost than the existing first-order pseudo-Helmholtz decomposition methods. In addition, the accuracy of our method is analyzed by providing homogeneous media with different parameter settings. Finally, the numerical examples demonstrate that the new pseudo-Helmholtz decomposition method is effective, efficient, and robust against random noise.

Feature Importance Analysis and Machine Learning for Alzheimer’s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio

Introduction: Alzheimer’s disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15–20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods: To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results: The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion: Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer’s detection.

Characterisation of different types of power plant ashes as potential lunar regolith simulants

Characterisation of different types of power plant ashes as potential lunar regolith simulants

A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

The covariant structural and functional neuro-correlates of cognitive impairments in patients with end-stage renal diseases.

Cognitive impairment (CI) is a common complication of end-stage renal disease (ESRD) that is associated with structural and functional changes in the brain. However, whether a joint structural and functional alteration pattern exists that is related to CI in ESRD is unclear. In this study, instead of looking at brain structure and function separately, we aim to investigate the covariant characteristics of both functional and structural aspects. Specifically, we took the fusion analysis approach, namely, multimodal canonical correlation analysis and joint independent component analysis (mCCA+jICA), to jointly study the discriminative features in gray matter volume (GMV) measured by T1-weighted (T1w) MRI, fractional anisotropy (FA) in white matter measured by diffusion MRI, and the amplitude of low-frequency fluctuation (ALFF) measured by blood oxygenation-level-dependent (BOLD) MRI in 78 ESRD patients versus 64 healthy controls (HCs), followed by a mediation effect analysis to explore the relationship between neuroimaging findings, cognitive impairments and uremic toxins. Two joint group-discriminative independent components (ICs) were found to show covariant abnormalities across FA, GMV, and ALFF (all p < 0.05). The most dominant joint IC revealed associative patterns of alterations of GMV (in the precentral gyrus, occipital lobe, temporal lobe, parahippocampal gyrus, and hippocampus), alterations of ALFF (in the precuneus, superior parietal gyrus, and superior occipital gyrus), and of white matter FA (in the corticospinal tract and inferior frontal occipital fasciculus). Another significant IC revealed associative alterations of GMV (in the dorsolateral prefrontal and orbitofrontal cortex) and FA (in the forceps minor). Moreover, the brain changes identified by FA and GMV in the above-mentioned brain regions were found to mediate the negative correlation between serum phosphate and mini-mental state examination (MMSE) scores (all p < 0.05). The mCCA+jICA method was demonstrated to be capable of revealing covariant abnormalities across neuronal features of different types in ESRD patients as contrasted to HCs, and joint brain changes may play an important role in mediating the relationship between serum toxins and CIs in ESRD. Our results show the mCCA+jICA fusion analysis approach may provide new insights into similar neurobiological studies.

Research on the Classification of Rail Transit Stations and Passenger Flow Patterns—A Case from Xi’an, China

Transit-oriented development (TOD) has been promoted and implemented worldwide through the efficient integration of rail transit stations and land use. However, the interactions between stations and the surrounding catchment areas (CAs) are characterized by different features of the built environment and regional development. Therefore, it is necessary to develop a quantitative classification method for rail transit stations based on the built environment within a CA and to identify the passenger flow characteristics of different types of stations to develop targeted planning and design policies. In this study, Line 1 and Line 2 within the third ring road of Xi’an City were taken as the objects, and a station classification system was constructed by taking station traffic levels and different building functions within the CA as the classification factors. Secondly, indicators of the built environment, such as six different types of functions, were calculated through refined vector modeling, and 30 typical stations were typologically analyzed. Furthermore, 10 typical types—totaling 11 stations—were selected for passenger flow monitoring, and the passenger flow characteristics of the different types of stations were summarized in terms of the dimensions of stations and exits. Finally, the correlations between the indicators of the built environment and passenger traffic for different functions were quantified. This study provides a basis for the future optimization of stations and the built environment, as well as station design and management.

Morphometric Features of Different Types of Bifurcations in the Splenic Intraorgan Arterial System in Individuals of Different Gender and Age

INTRODUCTION: A promising direction that has recently emerged in morphology is investigation of the arterial bed of various human organs as fractal or quasi-fractal systems. Conceptual models have been developed permitting a quantitative description of the vascular bed features. This approach will help to create a morphometric standard of the intraorgan blood flow, which will be useful in objective diagnosis of probable deviations from the normal structure.&#x0D; AIM: To identify the morphometric features of various types of bifurcations of the splenic intraorgan arterial bed in individuals of different gender and age.&#x0D; MATERIALS AND METHODS: The characteristics of the splenic intraorgan arterial bed have been studied in 67 individuals who died suddenly and from accidental causes at the age of 21 to 60 years. The arterial bed was represented as connected graphs with vertices corresponding to the bifurcation points of arteries, and edges to the arterial segments. The diameter and length of an arterial segment were measured on corrosion preparations. Based on the morphometry data, the following parameters were determined: generation number, division level, form factor FF1, branching factor ƞ and asymmetry factor γ. Statistical analysis was carried out using the R language.&#x0D; RESULTS: The total number of examined arterial bifurcations was 6,840. The examined bifurcations were located at 20 division levels and presented 8 generations. In the structure of the vascular bed, bifurcations of neutral kind (0) predominated with the relative quantity 51%. The least numerous was type 2 bifurcations — 9%. The intermediate position was taken by open (1) bifurcations accounting for about 40% in the vascular bed structure.&#x0D; CONCLUSIONS: The intraorgan arterial bed of the spleen is a quasi-fractal system consisting of three types of bifurcations — open, neutral and closed. A relative number of different types of bifurcations differs depending on gender and age and is also related to the generation number and division level.