Dietary salidroside supplementation improves meat quality and antioxidant capacity and regulates lipid metabolism in broilers.

We aimed to explore the effect of salidroside (SAL) on meat quality, antioxidant capacity, and lipid metabolism in broilers. The results demonstrated that SAL significantly reduced the yellowness (b*), shear force, cooking loss, drip loss, MDA, TBARS, and carbonyl content in breast (P < 0.05), while increasing the pH value (P < 0.05), suggesting an improvement in meat quality. SAL lowered the lipid contents in liver and serum (P < 0.05), while increasing the proportion of unsaturated fatty acids in breast (P < 0.05), indicating effective regulation of lipid metabolism by SAL. SAL increased the activity of antioxidant enzymes and the expression of antioxidant genes in both liver and muscle (P < 0.05). Additionally, SAL improved the meat quality and antioxidant capacity of breast subjected to repeated freeze-thaw treatment. SAL may enhance meat quality by improving antioxidative stability and regulating lipid metabolism, potentially serving as a dietary supplement for broilers.

Exploring Imaging Genetic Markers of Alzheimer's Disease Based on a Novel Nonlinear Correlation Analysis Algorithm.

Alzheimer's disease (AD) is an irreversible neurological disorder characterized by insidious onset. Identifying potential markers in its emergence and progression is crucial for early diagnosis and treatment. Imaging genetics typically merges genetic variables with multiple imaging parameters, employing various association analysis algorithms to investigate the links between pathological phenotypes and genetic variations, and to unearth molecular-level insights from brain images. However, most existing imaging genetics algorithms based on sparse learning assume a linear relationship between genetic factors and brain functions, limiting their ability to discern complex nonlinear correlation patterns and resulting in reduced accuracy. To address these issues, we propose a novel nonlinear imaging genetic association analysis method, Deep Self-Reconstruction-based Adaptive Sparse Multi-view Deep Generalized Canonical Correlation Analysis (DSR-AdaSMDGCCA). This approach facilitates joint learning of the nonlinear relationships between pathological phenotypes and genetic variations by integrating three different types of data: structural magnetic resonance imaging (sMRI), single-nucleotide polymorphism (SNP), and gene expression data. By incorporating nonlinear transformations in DGCCA, our model effectively uncovers nonlinear associations across multiple data types. Additionally, the DSR algorithm clusters samples with identical labels, incorporating label information into the nonlinear feature extraction process and thus enhancing the performance of association analysis. The application of the DSR-AdaSMDGCCA algorithm on real data sets identified several AD risk regions (such as the hippocampus, parahippocampus, and fusiform gyrus) and risk genes (including VSIG4, NEDD4L, and PINK1), achieving maximum classification accuracy with the fewest selected features compared to baseline algorithms. Molecular biology enrichment analysis revealed that the pathways enriched by these top genes are intimately linked to AD progression, affirming that our algorithm not only improves correlation analysis performance but also identifies biologically significant markers.

Reendothelialization of Acellular Adipose Flaps under Mimetic Physiological Dynamic Conditions.

The extensive soft-tissue defects resulting from trauma and tumors pose a prevalent challenge in clinical practice, characterized by a high incidence rate. Autologous tissue flap transplantation, considered the gold standard for treatment, is associated with various drawbacks, including the sacrifice of donor sources, postoperative complications, and limitations in surgical techniques, thereby impeding its widespread applicability. The emergence of tissue-engineered skin flaps, notably the acellular adipose flap (AAF), offers potential alternative solutions. However, a critical concern confronting large-scale tissue-engineered skin flaps currently revolves around the reendothelialization of internal vascular networks. In our study, we have developed an AAF utilizing perfusion decellularization, demonstrating excellent physical properties. Cytocompatibility experiments have confirmed its cellular safety, and cell adhesion experiments have revealed spatial specificity in facilitating endothelial cells adhesion within the adipose flap scaffold. Using a novel mimetic physiological fluid shear stress setting, endothelial cells were dynamically inoculated and cultured within the acellular vascular network of the pedicled AAF in our research. Histological and gene expression analyses have shown that the mimetic physiological fluid dynamic model significantly enhanced the reendothelialization of the AAF. This innovative platform of acellular adipose biomaterials combined with hydrodynamics may offer valuable insights for the design and manufacturing of 3D vascularized tissue constructs, which can be applied to the repair of extensive soft-tissue defects.

HIF-1α in cartilage homeostasis, apoptosis, and glycolysis in mice with steroid-induced osteonecrosis of the femoral head.

With the prevalence of coronavirus disease 2019, the administration of glucocorticoids (GCs) has become more widespread. Treatment with high-dose GCs leads to a variety of problems, of which steroid-induced osteonecrosis of the femoral head (SONFH) is the most concerning. Since hypoxia-inducible factor 1α (HIF-1α) is a key factor in cartilage development and homeostasis, it may play an important role in the development of SONFH. In this study, SONFH models were established using methylprednisolone (MPS) in mouse and its proliferating chondrocytes to investigate the role of HIF-1α in cartilage differentiation, extracellular matrix (ECM) homeostasis, apoptosis and glycolysis in SONFH mice. The results showed that MPS successfully induced SONFH in vivo and vitro, and MPS-treated cartilage and chondrocytes demonstrated disturbed ECM homeostasis, significantly increased chondrocyte apoptosis rate and glycolysis level. However, compared with normal mice, not only the expression of genes related to collagens and glycolysis, but also chondrocyte apoptosis did not demonstrate significant differences in mice co-treated with MPS and HIF-1α inhibitor. And the effects observed in HIF-1α activator-treated chondrocytes were similar to those induced by MPS. And HIF-1α degraded collagens in cartilage by upregulating its downstream target genes matrix metalloproteinases. The results of activator/inhibitor of endoplasmic reticulum stress (ERS) pathway revealed that the high apoptosis rate induced by MPS was related to the ERS pathway, which was also affected by HIF-1α. Furthermore, HIF-1α affected glucose metabolism in cartilage by increasing the expression of glycolysis-related genes. In conclusion, HIF-1α plays a vital role in the pathogenesis of SONFH by regulating ECM homeostasis, chondrocyte apoptosis, and glycolysis.

Nontargeted metabolomics and enzyme inhibitory and antioxidant activities for chemical and biological characterization of jujube (Ziziphus jujuba) extracts.

The chemical and biologically active characterization of jujube samples (fruits, cores, and leaves) were carried out by the integrated nontargeted metabolomics and bioassay. Firstly, collision cross-section values of active compounds in jujubes were determined by ultrahigh-performance liquid chromatography coupled with ion mobility quadrupole time-of-flight mass spectrometry. Then, a multidimensional statistical analysis that contained principal component analysis, partial least squares-discriminant analysis and hierarchical clustering analysis was employed to effectively cluster different tissues and types of jujubes, making identification more scientific. Furthermore, angiotensin-converting enzyme (ACE) and 2, 2-diphenyl-1-picrylhydrazyl (DPPH) were used to evaluate the quality of jujubes from a double activity dimension. The analytical results obtained by using ACE and DPPH to evaluate the quality of jujube were different from multivariate statistics, providing a reference for the application of jujube. Therefore, integrating chemical and biological perspectives to evaluate the quality of jujube provided a more comprehensive evaluation and effective reference for clinical needs.

The Effect of Oxalic Acid and Citric Acid on the Modification of Wollastonite Surface.

The modification mechanism of low-molecular-weight organic acids on a single-chain silicate mineral (wollastonite) was investigated through a leaching method. Solid and liquid samples were analyzed using atomic absorption spectrophotometer (AAS), X-ray diffraction (XRD), scanning electron microscope (SEM), and Fourier-transform infrared spectroscopy (FTIR). After 720 h of reaction, the results revealed that the dissolution concentration of Si (2200 µmol/L) in citric acid solution is more than that (1950 µmol/L) in oxalic acid. In the composite acids (citric acid and oxalic acid), the dissolution concentration of Si release from wollastonite reached the maximum value of 3304 µmol/L. The dissolution data of Si in wollastonite were fittingly described by the parabolic equation (Ct = a + bt1/2), with the highest correlation coefficients (R2 > 0.993), in the presence of the low-molecular-weight organic acids. The dissolution data suggested that the dissolution reaction process of Si was consistent with the diffusion-controlled model. Citric acid exhibited a higher affinity for attacking the (200) surface, while oxalic acid was prone to dissolve the (002) crystal face. The synergistic effects of oxalic acid and citric acid led to the weakening of the XRD diffraction peak intensity of wollastonite. When exposed to composite acids, the surface of wollastonite was covered with insoluble reactants that restricted the substance diffusion and hindered the reaction. This study offers valuable theoretical insights into the modification or activation of wollastonite by composite low-molecular-weight organic acids.

Decellularized diseased tissues: current state-of-the-art and future directions.

Decellularized matrices derived from diseased tissues/organs have evolved in the most recent years, providing novel research perspectives for understanding disease occurrence and progression and providing accurate pseudo models for developing new disease treatments. Although decellularized matrix maintaining the native composition, ultrastructure, and biomechanical characteristics of extracellular matrix (ECM), alongside intact and perfusable vascular compartments, facilitates the construction of bioengineered organ explants in vitro and promotes angiogenesis and tissue/organ regeneration in vivo, the availability of healthy tissues and organs for the preparation of decellularized ECM materials is limited. In this paper, we review the research advancements in decellularized diseased matrices. Considering that current research focuses on the matrices derived from cancers and fibrotic organs (mainly fibrotic kidney, lungs, and liver), the pathological characterizations and the applications of these diseased matrices are mainly discussed. Additionally, a contrastive analysis between the decellularized diseased matrices and decellularized healthy matrices, along with the development in vitro 3D models, is discussed in this paper. And last, we have provided the challenges and future directions in this review. Deep and comprehensive research on decellularized diseased tissues and organs will promote in-depth exploration of source materials in tissue engineering field, thus providing new ideas for clinical transformation.

The Dissolution Mechanism of Low-Molecular-Weight Organic Acids on the Sillimanite.

The interaction between low-molecular-weight organic acids (LMWOAs) and minerals in nature has been widely studied; however, limited research has been conducted on the dissolution mechanism of sillimanite in the presence of different organic acids. In this study, the interaction between the sillimanite sample and LMWOAs (citric acid, oxalic acid, and citric/oxalic mixture) at the same pH was investigated. The dissolution rate of Si and Al was high during the initial reaction time, then slowed down in the presence of LMWOAs. The dissolution data for Si and Al from sillimanite in the LMWOAs fit well with the first-order equation (Ct = a(1 - exp(-kt))) (R2 > 0.991). The dissolution process of sillimanite in the organic acids was controlled by the surface chemical reaction step. The dissolution concentration of Si in aqueous citric acid was higher than that in oxalic acid. In contrast, the dissolution concentration of Al in oxalic acid was more than that in citric acid. The maximum concentrations of Si and Al in the presence of composite organic acids were 1754 µmol/L and 3904 µmol/L. The sillimanite before and after treatment with LMWOAs were studied using X-ray diffraction (XRD) and scan electron microscopy (SEM). These results are explained by the characterization of the sillimanite. Under the single acid solution, the (210) crystal plane with a high areal density of Al in sillimanite was easily dissolved by the oxalic acid, while the (120) in sillimanite with a high areal density of Si was more easily dissolved by citric acid. In the composite organic acids, the Si-O bond and Al-O bond in sillimanite were attacked alternately, leading to the formation of some deeper corrosion pits on the surface of sillimanite. The results are of interest in the dissolution mechanisms of sillimanite in the low-molecular-weight organic acids and the environmentally friendly activation of sillimanite.

The Dissolution Behavior of Feldspar Minerals in Various Low-Molecular-Weight Organic Acids.

Feldspar is a high-abundance mineral in the earth's crust, and its natural weathering and dissolution processes are an important phenomenon on the earth's surface. This study focused on the dissolution behavior of silicon (Si) and aluminum (Al) in feldspar minerals (microcline and albite) when exposed to low-molecular-weight organic acids (LMWOAs). Various analytical techniques, including atomic absorption spectrophotometer, X-ray diffraction, scanning electron microscope, and Fourier-transform infrared spectroscopy, were employed to investigate these processes. The results revealed that the concentration of Si and Al released from alkali feldspar increased after treatment with LMWOAs, exhibiting non-stoichiometric dissolution. The Si/Al release ratio from feldspar deviated from the expected value of three. Among the LMWOAs tested, oxalic acid was found to be more effective in dissolving aluminum, while citric acid showed greater efficacy in dissolving silicon. Notably, the composite acid demonstrated the highest capacity for feldspar dissolution, with values of 538 µM (Si) and 287 µM (Al) after treatment for 720 h, respectively. The dissolution data for Si and Al in the organic acid solution was fittingly described by a first-order equation, with high correlation coefficients (R2 ≥ 0.992). The characterization of feldspar powders indicated that the (040) crystal plane of feldspar was particularly susceptible to attack by organic acids. In the presence of these acids, the chemical bonds Si (Al)-O, Si-Si(Al), and O-Si(Al)-O shifted to higher wavenumbers. Additionally, the surface corrosion morphology of feldspar exhibited distinct nanostructures, which became more pronounced with increasing exposure time. It was also observed that the reactivity of feldspar increased over time. These findings provide valuable insights into the natural dissolution process of feldspar and offer a new perspective for the study of this phenomenon.

Biomimetic hydrogel derived from decellularized dermal matrix facilitates skin wounds healing.

Cutaneous wound healing affecting millions of people worldwide represents an unsolvable clinical issue that is frequently challenged by scar formation with dramatical pain, impaired mobility and disfigurement. Herein, we prepared a kind of light-sensitive decellularized dermal extracellular matrix-derived hydrogel with fast gelling performance, biomimetic porous microstructure and abundant bioactive functions. On account of its excellent cell biocompatibility, this ECM-derived hydrogel could induce a marked cellular infiltration and enhance the tube formation of HUVECs. In vivo experiments based upon excisional wound splinting model showed that the hydrogel prominently imparted skin wound healing, as evidenced by notably increased skin appendages and well-organized collagen expression, coupled with significantly enhanced angiogenesis. Moreover, the skin regeneration mediated by this bioactive hydrogel was promoted by an accelerated M1-to-M2 macrophage phenotype transition. Consequently, the decellularized dermal matrix-derived bioactive hydrogel orchestrates the entire skin healing microenvironment to promote wound healing and will be of high value in treatment of cutaneous wound healing. As such, this biomimetic ddECMMA hydrogel provides a promising versatile opinion for the clinical translation.

Application of orthogonal sparse joint non-negative matrix factorization based on connectivity in Alzheimer's disease research.

Based on the mining of micro- and macro-relationships of genetic variation and brain imaging data, imaging genetics has been widely applied in the early diagnosis of Alzheimer's disease (AD). However, effective integration of prior knowledge remains a barrier to determining the biological mechanism of AD. This paper proposes a new connectivity-based orthogonal sparse joint non-negative matrix factorization (OSJNMF-C) method based on integrating the structural magnetic resonance image, single nucleotide polymorphism and gene expression data of AD patients; the correlation information, sparseness, orthogonal constraint and brain connectivity information between the brain image data and genetic data are designed as constraints in the proposed algorithm, which efficiently improved the accuracy and convergence through multiple iterative experiments. Compared with the competitive algorithm, OSJNMF-C has significantly smaller related errors and objective function values than the competitive algorithm, showing its good anti-noise performance. From the biological point of view, we have identified some biomarkers and statistically significant relationship pairs of AD/mild cognitive impairment (MCI), such as rs75277622 and BCL7A, which may affect the function and structure of multiple brain regions. These findings will promote the prediction of AD/MCI.

Effect of different ventilation modalities on the early prognosis of patients with sleep apnea after acute ischemic stroke---protocol for a prospective, open-label and randomised controlled trial.

BACKGROUND: Sleep apnea is highly prevalent after acute ischemic stroke (AIS) and has increased stroke-related mortality and morbidity. The conventional sleep apnea treatment is continuous positive airway pressure (CPAP) ventilation. However, it is poorly tolerated by patients and is not used in all stroke patients. This protocol describes the impact of high-flow nasal cannula (HFNC) oxygen therapy compared to nasal continuous positive airway pressure (nCPAP) ventilation or usual care on the early prognosis of patients with sleep apnea after AIS. METHODS: This randomised controlled study will be conducted in the intensive care unit of the Department of Neurology at the Wuhan Union Hospital. According to the study plan, 150 patients with sleep apnea after AIS will be recruited. All patients are randomly allocated in a 1:1:1 ratio to one of three groups: the nasal catheter group (standard oxygen group), the HFNC group, and the nCPAP group. Patients receive different types of ventilation after admission to the group, and their tolerance while using the different ventilation is recorded. Patients will be followed up by telephone three months after discharge, and stroke recovery is recorded. The primary outcomes were 28-day mortality, the incidence of pulmonary infection and endotracheal intubation. DISCUSSION: This study analyses different ventilation modalities for early interventions in patients with sleep apnea after AIS. We will investigate whether nCPAP and HFNC reduce early mortality and endotracheal intubation rates and improve distant neurological recovery in patients. TRIAL REGISTRATION: This trial was registered at ClinicalTrials.gov (NCT05323266; 25 March 2022).

A spatiotemporal release hydrogel based on an M1-to-M2 immunoenvironment for wound management.

Cutaneous wounds remain a major clinical challenge that urgently requires the development of advanced and functional wound dressings. During the wound healing process, macrophages are well known to exhibit temporal dynamics with a pro-inflammatory phenotype at early stages and a pro-healing phenotype at late stages, thus playing an important role in regulating inflammatory responses and tissue regeneration. Meanwhile, disrupted temporal dynamics of macrophages caused by poor wound local conditions and deficiency of macrophage function always impair the wound-healing progression. Here in this work, we proposed a novel controllable strategy to construct a spatiotemporal dynamical immune-microenvironment for the treatment of cutaneous wounds. To achieve this goal, a concentric decellularized dermal hydrogel was constructed with the combination of type 1 and type 2 macrophage-associated cytokine complexes in the sheath portion and core portion, respectively. The in vitro degradation experiment exhibited a sequential cascade release of pro-inflammatory cytokines and pro-healing cytokines. The enhanced cell biocompatibility and tube formation of HUVECs were confirmed. A full-thickness skin defect model of rats was developed to analyze the effect of the spatiotemporal dynamical bioactive hydrogels on wound healing. Remarkable angiogenesis, rapid wound restoration, moderate extracellular matrix deposition and obvious skin appendage neogenesis were identified at different time points after treatment with the macrophage cytokine-based decellularized hydrogels. Consequently, the concentric decellularized hydrogels with spatiotemporal dynamics of immune cytokines have considerable potential for cell-free therapy for wound healing.

A Novel Longitudinal Phenotype-Genotype Association Study Based on Deep Feature Extraction and Hypergraph Models for Alzheimer's Disease.

Traditional image genetics primarily uses linear models to investigate the relationship between brain image data and genetic data for Alzheimer's disease (AD) and does not take into account the dynamic changes in brain phenotype and connectivity data across time between different brain areas. In this work, we proposed a novel method that combined Deep Subspace reconstruction with Hypergraph-Based Temporally-constrained Group Sparse Canonical Correlation Analysis (DS-HBTGSCCA) to discover the deep association between longitudinal phenotypes and genotypes. The proposed method made full use of dynamic high-order correlation between brain regions. In this method, the deep subspace reconstruction technique was applied to retrieve the nonlinear properties of the original data, and hypergraphs were used to mine the high-order correlation between two types of rebuilt data. The molecular biological analysis of the experimental findings demonstrated that our algorithm was capable of extracting more valuable time series correlation from the real data obtained by the AD neuroimaging program and finding AD biomarkers across multiple time points. Additionally, we used regression analysis to verify the close relationship between the extracted top brain areas and top genes and found the deep subspace reconstruction approach with a multi-layer neural network was helpful in enhancing clustering performance.

Notch-RBPJ Pathway for the Differentiation of Bone Marrow Mesenchymal Stem Cells in Femoral Head Necrosis.

Femoral head necrosis (FHN) is a common leg disease in broilers, resulting in economic losses in the poultry industry. The occurrence of FHN is closely related to the decrease in the number of bone marrow mesenchymal stem cells (BMSCs) and the change in differentiation direction. This study aimed to investigate the function of differentiation of BMSCs in the development of FHN. We isolated and cultured BMSCs from spontaneous FHN-affected broilers and normal broilers, assessed the ability of BMSCs into three lineages by multiple staining methods, and found that BMSCs isolated from FHN-affected broilers demonstrated enhanced lipogenic differentiation, activated Notch-RBPJ signaling pathway, and diminished osteogenic and chondrogenic differentiation. The treatment of BMSCs with methylprednisolone (MP) revealed a significant decrease in the expressions of Runx2, BMP2, Col2a1 and Aggrecan, while the expressions of p-Notch1/Notch1, Notch2 and RBPJ were increased significantly. Jagged-1 (JAG-1, Notch activator)/DAPT (γ-secretase inhibitor) could promote/inhibit the osteogenic or chondrogenic ability of MP-treated BMSCs, respectively, whereas the differentiation ability of BMSCs was restored after transfection with si-RBPJ. The above results suggest that the Notch-RBPJ pathway plays important role in FHN progression by modulating the osteogenic and chondrogenic differentiation of BMSCs.

A risk-predictive model for obstructive sleep apnea in patients with chronic obstructive pulmonary disease.

Background: Obstructive sleep apnea syndrome (OSA) is increasingly reported in patients with chronic obstructive pulmonary disease (COPD). Our research aimed to analyze the clinical characteristics of patients with overlap syndrome (OS) and develop a nomogram for predicting OSA in patients with COPD. Methods: We retroactively collected data on 330 patients with COPD treated at Wuhan Union Hospital (Wuhan, China) from March 2017 to March 2022. Multivariate logistic regression was used to select predictors applied to develop a simple nomogram. The area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) were used to assess the value of the model. Results: A total of 330 consecutive patients with COPD were enrolled in this study, with 96 patients (29.1%) confirmed with OSA. Patients were randomly divided into the training group (70%, n = 230) and the validation group (30%, n = 100). Age [odds ratio (OR): 1.062, 1.003-1.124], type 2 diabetes (OR: 3.166, 1.263-7.939), neck circumference (NC) (OR: 1.370, 1.098-1,709), modified Medical Research Council (mMRC) dyspnea scale (OR: 0.503, 0.325-0.777), Sleep Apnea Clinical Score (SACS) (OR: 1.083, 1.004-1.168), and C-reactive protein (CRP) (OR: 0.977, 0.962-0.993) were identified as valuable predictors used for developing a nomogram. The prediction model performed good discrimination [AUC: 0.928, 95% confidence interval (CI): 0.873-0.984] and calibration in the validation group. The DCA showed excellent clinical practicability. Conclusion: We established a concise and practical nomogram that will benefit the advanced diagnosis of OSA in patients with COPD.

Recent advances in decellularized biomaterials for wound healing.

The skin is one of the most essential organs in the human body, interacting with the external environment and shielding the body from diseases and excessive water loss. Thus, the loss of the integrity of large portions of the skin due to injury and illness may lead to significant disabilities and even death. Decellularized biomaterials derived from the extracellular matrix of tissues and organs are natural biomaterials with large quantities of bioactive macromolecules and peptides, which possess excellent physical structures and sophisticated biomolecules, and thus, promote wound healing and skin regeneration. Here, we highlighted the applications of decellularized materials in wound repair. First, the wound-healing process was reviewed. Second, we elucidated the mechanisms of several extracellular matrix constitutes in facilitating wound healing. Third, the major categories of decellularized materials in the treatment of cutaneous wounds in numerous preclinical models and over decades of clinical practice were elaborated. Finally, we discussed the current hurdles in the field and anticipated the future challenges and novel avenues for research on decellularized biomaterials-based wound treatment.

An improved MOPSO approach with adaptive strategy for identifying biomarkers from gene expression dataset.

Biomarkers plays an important role in the prediction and diagnosis of cancers. Therefore, it is urgent to design effective methods to extract biomarkers. The corresponding pathway information of the microarray gene expression data can be obtained from public database, which makes possible to identify biomarkers based on pathway information and has been attracted extensive attention. In the most existing methods, all the member genes in the same pathway are regarded as equally important for inferring pathway activity. However, the contribution of each gene should be different in the process of inferring pathway activity. In this research, an improved multi-objective particle swarm optimization algorithm with penalty boundary intersection decomposition mechanism (IMOPSO-PBI) has been proposed to quantify the relevance of each gene in pathway activity inference. In the proposed algorithm, two optimization objectives namely t-score and z-score respectively has been introduced. In addition, in order to solve the problem that optimal set with poor diversity in the most multi-objective optimization algorithms, an adaptive mechanism for adjusting penalty parameters based on PBI decomposition has been introduced. The performance of the proposed IMOPSO-PBI approach compared with some existing methods on six gene expression datasets has been given. To verify the effectiveness of the proposed IMOPSO-PBI algorithm, experiments were carried out on six gene datasets and the results has been compared with the existing methods. The comparative experiment results show that the proposed IMOPSO-PBI method has a higher classification accuracy and the extracted feature genes are verified possess biological significance.

Preparation of Anorthite/Mullite In Situ and Phase Transformation in Porcelain.

A high sintering temperature is required to acquire excellent performance in the production of porcelain but results in high fuel consumption. To prepare the porcelain with outstanding performance at a lower temperature, a self-produced additive containing calcium (CaK) was added into a three-component system of kaolinite-feldspar-quartz. XRD and SEM were used to characterize the samples. The toughening mechanism and Gibbs free energy were investigated. After introducing the CaK, the bending strength of the porcelain fired at 1513 K increased from 56.32 ± 0.65 MPa to 95.31 ± 0.63 MPa, which was 21.83% higher than that of the porcelain without CaK at an optimal firing temperature of 1603 K. The main crystal phase of the sample comprised mullite and quartz in the raw materials at 1453~1603 K. The anorthite was observed at 1453 K and interlocked with needle-shaped mullite at 1513 K in the porcelain after adding CaK, which resulted in the higher bending strength. Quantitative analysis indicated that the amount of anorthite decreased at 1513 K and disappeared at 1543 K; the amount of mullite increased with temperature. The Gibbs free energy of the reaction (CaO•Al2O3•2SiO2 + 2(Al2O3•2SiO2) → 3Al2O3•2SiO2 + CaO + 4SiO2) at high temperature was negative, which suggested that the formation of mullite (3Al2O3•2SiO2) from anorthite (CaO•Al2O3•2SiO2) was possible. These findings implied that the addition of CaK contributed to the appropriate phase composition and microstructure, and the excellent performance of the porcelain at a lower temperature. In addition, the transformation between anorthite and mullite was possible in the special raw material system. The results are of interest in producing anorthite/mullite ceramics at reduced sintering temperatures and the conversion between anorthite and mullite.

Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease.

At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.