Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore ...robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clinical success of immune checkpoint blockades in diverse malignancies. However, effective tools for comprehensively interpreting multi-omics data are still warranted to provide increased granularity into the intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Therefore, we developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing a comprehensive investigation of the estimation of reported or user-built signatures, TME deconvolution, and signature construction based on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, and signatures generated from single-cell RNA sequencing (scRNA-seq) data in different cancer settings. Additionally, IOBR integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool for leveraging multi-omics data to facilitate immuno-oncology exploration and to unveil tumor-immune interactions and accelerating precision immunotherapy.
Pattern recognition receptors (PRRs) recognize the conserved molecular structure of pathogens and trigger the signaling pathways that activate immune cells in response to pathogen infection. ...Toll-like receptors (TLRs) are the first and best characterized innate immune receptors. To date, at least 20 TLR types (TLR1, 2, 3, 4, 5M, 5S, 7, 8, 9, 13, 14, 18, 19, 20, 21, 22, 23, 24, 25, and 26) have been found in more than a dozen of fish species. However, of the TLRs identified in fish, direct evidence of ligand specificity has only been shown for TLR2, TLR3, TLR5M, TLR5S, TLR9, TLR21, and TLR22. Some studies have suggested that TLR2, TLR5M, TLR5S, TLR9, and TLR21 could specifically recognize PAMPs from bacteria. In addition, other TLRs including TLR1, TLR4, TLR14, TLR18, and TLR25 may also be sensors of bacteria. TLR signaling pathways in fish exhibit some particular features different from that in mammals. In this review, the ligand specificity and signal pathways of TLRs that recognize bacteria in fish are summarized. References for further studies on the specificity for recognizing bacteria using TLRs and the following reactions triggered are discussed. In-depth studies should be continuously performed to identify the ligand specificity of all TLRs in fish, particularly non-mammalian TLRs, and their signaling pathways. The discovery of TLRs and their functions will contribute to the understanding of disease resistance mechanisms in fish and provide new insights for drug intervention to manipulate immune responses.
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•Toll-like receptors (TLRs) could specifically recognize pathogenic microbes.•Contrary to TLRs in mammals, TLRs have been massively expanded in fish.•TLR2, TLR5M, TLR5S, TLR9 and TLR21 could sense the PAMPs from bacteria.•The differences in TLR signal pathways between fish and mammals have been summarized.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Cases with pituitary adenoma comprise 10-25% of intracranial neoplasm, being the third most common intracranial tumor, most of the adenomas are considered to be benign. About 35% of pituitary ...adenomas are invasive. This review summarized the known molecular basis of the invasiveness of pituitary adenomas. The study pointed out that hypoxia-inducible factor-1α, pituitary tumor transforming gene, vascular endothelial growth factor, fibroblast growth factor-2, and matrix metalloproteinases (MMPs, mainly MMP-2, and MMP-9) are core molecules responsible for the invasiveness of pituitary adenomas. The reason is that these molecules have the ability to directly or indirectly induce cell proliferation, epithelial-to-mesenchymal transition, angiogenesis, degradation, and remodeling of extracellular matrix. HIF-1α induced by hypoxia or apoplexy inside the adenoma might be the initiating factor of invasive transformation, followed with angiogenesis for overexpressed VEGF, EMT for overexpressed PTTG, degradation of ECM for overexpressed MMPs, creating a suitable microenvironment within the tumor. Together, they form a complex interactive network. More investigations are required to further elucidate the mechanisms underlying the invasiveness of pituitary adenomas.
The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation ...particle swarm optimization variational mode decomposition (GMPSO-VMD) algorithm and applies it to rolling bearing vibration signal fault feature extraction. Firstly, the minimum envelope entropy is used as the objective function of the GMPSO to find the optimal parameter combination of the VMD algorithm. Then, the optimized VMD algorithm is used to decompose the vibration signal of the rolling bearing and several intrinsic mode functions (IMFs) are obtained. The envelope spectrum analysis of GMPSO-VMD decomposed rolling bearing fault signal IMF1 was carried out. Moreover, the feature frequency of the four fault states of the rolling bearing are extracted accurately. Finally, the GMPSO-VMD algorithm is utilized to analyze the simulation signal and rolling bearing fault vibration signal. The effectiveness of the GMPSO-VMD algorithm is verified by comparing it with the fixed parameter VMD (FP-VMD) algorithm, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm and empirical mode decomposition (EMD) algorithm.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The decomposition number K and penalty factor \alpha in the variational mode decomposition (VMD) algorithm have a great influence on the decomposition effect and the accuracy of subsequent fault ...diagnosis. Therefore, a gear fault diagnosis method based on genetic mutation particle swarm optimization VMD and probabilistic neural network (GMPSO-VMD-PNN) algorithm is proposed in this paper. Firstly, the GMPSO algorithm is used to optimize the K,\alpha parameter combination in the VMD algorithm, and the optimal K,\alpha parameter combination of each gear fault vibration signal to be decomposed is selected. Then, the gear fault vibration signal is decomposed into several intrinsic mode functions (IMFs) by VMD, and the sample entropy value of each IMFs is extracted to form the feature vector of subsequent fault diagnosis. Finally, the characteristic vector of gear fault vibration signal is input into PNN model, and gear fault is accurately classified. By comparing with fixed parameter VMD algorithm, empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm, the superiority of this method in gear fault diagnosis is verified. Therefore, the GMPSO-VMD-PNN algorithm proposed in this paper has certain application value for gear fault diagnosis.
Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to ...support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking.
This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care.
We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes.
Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia.
CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources.
RR2-10.37766/inplasy2022.9.0061.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Gearbox is an important transmission component in mechanical equipment, which is prone to failure. When diagnosing the faults of gearbox, the recognition ability of support vector machine (SVM) is ...greatly influenced by the kernel function and its parameters. However, the optimal parameters are difficult to find. To solve this problem, the parameter optimization method of support vector machine based on the artificial colony bee algorithm was presented. The vibration acceleration signals of normal gear, chipped tooth gear, and missing tooth gear were collected and the fault features were extracted based on the EEMD and the kernel function. In the experiments, the optimization of Gauss RBF kernel support vector machine parameters was taken as the object, and the optimize performance of the genetic algorithm, the particle swarm optimization algorithm and the artificial bee colony algorithm were compared and analyzed. The results show that the artificial bee colony takes the least time, while the genetic algorithm takes the longest time, the accuracy of the artificial bee colony algorithm is better than the others. The recognition rates of gearbox faults were improved by using the SVM classification model which was optimized by the bee colony algorithm.
•The gearbox fault diagnosis method based on ABC was proposed.•The performance of the GA, the PSO and the ABC were compared and analyzed.•EEMD and kernel function feature extraction method was proposed.•The work improves the accuracy of gear fault diagnosis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Abstract
The transmission paths of vibration signals in the planetary gearboxes are complex. The signals have the characteristics of strong background noise, instability and non-Gaussian. ...Bi-spectrums can suppress Gaussian colored noise and are suitable for vibration signal processing of planetary gearboxes. In the traditional fault diagnosis methods based on bi-spectrums, the amplitudes of fault characteristic frequency, or the other further quantitative calculations values, are generally used as the basis of fault diagnosis processes. It has been found that bi-spectrum images can directly characterize the faults of the planetary gearboxes. Convolutional neural networks (CNNs) have been used in mechanical fault diagnoses in recent years. One-dimensional original signals are converted into two-dimensional images as CNN input, which is an effective method for mechanical fault diagnoses. At the present time, there has not been any relevant research conducted using bi-spectral images as CNN input. In this study, a fault diagnosis method based on local bi-spectrum and CNN was proposed. A bi-spectral analysis of the vibration signals of the planetary gearbox was first carried out in order to reveal the fault information while retaining the non-Gaussian information. Then, according to the bi-spectrum symmetry, local images containing the main information were taken as the input of the CNN, which reduced the redundancy of the fault information. Then, in order to improve the diagnostic accuracy of the CNN, the key parameters of CNN architecture were optimized. Finally, a CNN diagnosis model was built to realize the classification diagnoses of different fault positions and different fault degrees of planetary gearboxes. This study’s comparison of the diagnosis results of the full bi-spectrum + CNN, original vibration signal + CNN, local bi-spectrum + (support vector machines), and local bi-spectrum + (stacked auto-encoder) showed that the proposed method in this study had achieved both accuracy and rapidity in the fault diagnoses of planetary gearboxes.
•TaSTP was identified to be an interaction protein of TaDISI.•TaSTP was degraded by TaDIS1 via the 26S proteasome pathway.•The present study provides information of E3 ubiquitin ligase in plant ...drought tolerance.
Drought stress has a great impact on wheat yields. The ubiquitin/26S proteasome system is one of the most important mechanisms employed by plants for responding to stress. E3 ubiquitin ligase is an important part of the ubiquitin/26S proteasome system. In wheat, the mechanism of E3 ubiquitin ligase TaDIS1 has not been investigated in great detail. In this study, TaSTP was identified as an interacting partner using yeast two-hybrid screening. The results obtained from bimolecular fluorescence complementation, pull-down, and co-immunoprecipitation assays also demonstrated that TaDIS1 interacts with TaSTP. In vitro ubiquitination assays showed that TaDIS1 has an E3 ubiquitin ligase activity and the results based on two TaDIS1 mutants suggested that the RING domain is essential for its E3 ubiquitin ligase activity. In addition, we used MG132 to show that TaSTP can be degraded by TaDIS1 via the 26S proteasome pathway. The transcript levels of TaSTP showed that it can also respond to different abiotic stresses, such as drought, salt, and abscisic acid treatment. RING E3 ubiquitin ligase TaDIS1 may through the posttranslational regulation of TaSTP to play an important role in drought tolerance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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