Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration

This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images

Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis

Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the fault signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method

Construction of the metabolic reprogramming-associated gene signature for clear cell renal cell carcinoma prognosis prediction

Abstract Background Metabolism reprogramming is a hallmark that associates tumor growth, metastasis, progressive, and poor prognosis. However, the metabolism-related molecular patterns and mechanism in clear cell renal cell carcinoma (ccRCC) remain unclear. Herein, the purpose of this study was to identify metabolism-related molecular pattern and to investigate the characteristics and prognostic values of the metabolism-related clustering. Methods We comprehensively analyzed the differentially expressed genes (DEGs), and metabolism-related genes (MAGs) in ccRCC based on the TCGA database. Consensus clustering was used to construct a metabolism-related molecular pattern. Then, the biological function, molecular characteristics, Estimate/immune/stomal scores, immune cell infiltration, response to immunotherapy, and chemotherapy were analyzed. We also identified the DEGs between subclusters and constructed a poor signature and risk model based on LASSO regression cox analysis and univariable and multivariable cox regression analyses. Then, a predictive nomogram was constructed and validated by calibration curves. Results A total of 1942 DEGs (1004 upregulated and 838 downregulated) between ccRCC tumor and normal samples were identified, and 254 MRGs were screened out from those DEGs. Then, 526 ccRCC patients were divided into two subclusters. The 7 metabolism-related pathways enriched in cluster 2. And cluster 2 with high Estimate/immune/stomal scores and poor survival. While, cluster 1 with higher immune cell infiltrating, expression of the immune checkpoint, IFN, HLA, immune activation-related genes, response to anti-CTLA4 treatment, and chemotherapy. Moreover, we identified 295 DEGs between two metabolism-related subclusters and constructed a 15-gene signature and 9 risk factors. Then, a risk score was calculated and the patients into high- and low-risk groups in TCGA-KIRC and E-MTAB-1980 datasets. And the prediction viability of the risk score was validated by ROC curves. Finally, the clinicopathological characteristics (age and stage), risk score, and molecular clustering, were identified as independent prognostic variables, and were used to construct a nomogram for 1-, 3-, 5-year overall survival predicting. The calibration curves were used to verify the performance of the predicted ability of the nomogram. Conclusion Our finding identified two metabolism-related molecular subclusters for ccRCC, which facilitates the estimation of response to immunotherapy and chemotherapy, and prognosis after treatment

Additional file 11: Table S7. of Phosphoproteomics reveals network rewiring to a pro-adhesion state in annexin-1-deficient mammary epithelial cells

Enrichment of cellular component terms among the different categories. (XLSX 12 kb

Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database

AbstractBackground Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP.Methods Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation.Results We analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM’s performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903).Conclusions The GBM model’s precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care

Menin Reduces Parvalbumin Expression and is Required for the Anti‐Depressant Function of Ketamine

Abstract Dysfunction of parvalbumin (PV) neurons is closely involved in depression, however, the detailed mechanism remains unclear. Based on the previous finding that multiple endocrine neoplasia type 1 (Protein: Menin; Gene: Men1) mutation (G503D) is associated with a higher risk of depression, a Menin‐G503D mouse model is generated that exhibits heritable depressive‐like phenotypes and increases PV expression in brain. This study generates and screens a serial of neuronal specific Men1 deletion mice, and found that PV interneuron Men1 deletion mice (PcKO) exhibit increased cortical PV levels and depressive‐like behaviors. Restoration of Menin, knockdown PV expression or inhibition of PV neuronal activity in PV neurons all can ameliorate the depressive‐like behaviors of PcKO mice. This study next found that ketamine stabilizes Menin by inhibiting protein kinase A (PKA) activity, which mediates the anti‐depressant function of ketamine. These results demonstrate a critical role for Menin in depression, and prove that Menin is key to the antidepressant function of ketamine

Cu(I)/Cu(II) Creutz-Taube Mixed-Valence 2D Coordination Polymers

Graphene-like two-dimensional (2D) coordination polymers (GCPs) have been of central research interest in recent decades with significant impact in many fields. According to classical coordination chemistry, Cu(II) can adopt the dsp2 hybridization to form square planar coordination geometry, but not Cu(I); this is why so far, there has been no 2D layered structures synthesized from Cu(I) precursors. Herein we report a pair of isostructural GCPs synthesized by the coordination of benzenehexathiol (BHT) ligands with Cu(I) and Cu(II) ions, respectively. Various spectroscopic characterizations indicate that Cu(I) and Cu(II) coexist with a near 1:1 ratio in both GCPs but remain indistinguishable with a fractional oxidation state of +1.5 on average, making these two GCPs a unique pair of Creutz-Taube mixed-valence 2D structures. Based on DFT calculations, we further uncovered an intramolecular pseudo-redox mechanism whereby the radicals on BHT ligands can oxidize Cu(I) or reduce Cu(II) ions upon coordination, thus producing isostructures yet with distinct electron configurations. For the first time, we demonstrate that using Cu(I) or Cu(II), one can achieve atomically isostructural 2D structures, indicating that a neutral periodic structure can host a different number of total electrons as ground states, which may open a new chapter for 2D materials