EFF_D_SVM: a robust multi-type brain tumor classification system

Brain tumors are one of the most threatening diseases to human health. Accurate identification of the type of brain tumor is essential for patients and doctors. An automated brain tumor diagnosis system based on Magnetic Resonance Imaging (MRI) can help doctors to identify the type of tumor and reduce their workload, so it is vital to improve the performance of such systems. Due to the challenge of collecting sufficient data on brain tumors, utilizing pre-trained Convolutional Neural Network (CNN) models for brain tumors classification is a feasible approach. The study proposes a novel brain tumor classification system, called EFF_D_SVM, which is developed on the basic of pre-trained EfficientNetB0 model. Firstly, a new feature extraction module EFF_D was proposed, in which the classification layer of EfficientNetB0 was replaced with two dropout layers and two dense layers. Secondly, the EFF_D model was fine-tuned using Softmax, and then features of brain tumor images were extracted using the fine-tuned EFF_D. Finally, the features were classified using Support Vector Machine (SVM). In order to verify the effectiveness of the proposed brain tumor classification system, a series of comparative experiments were carried out. Moreover, to understand the extracted features of the brain tumor images, Grad-CAM technology was used to visualize the proposed model. Furthermore, cross-validation was conducted to verify the robustness of the proposed model. The evaluation metrics including accuracy, F1-score, recall, and precision were used to evaluate proposed system performance. The experimental results indicate that the proposed model is superior to other state-of-the-art models

CT-based radiomics models predict spontaneous intracerebral hemorrhage expansion and are comparable with CT angiography spot sign

Background and purposeThis study aimed to investigate the efficacy of radiomics, based on non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) images, in predicting early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (SICH). Additionally, the predictive performance of these models was compared with that of the established CTA spot sign.Materials and methodsA retrospective analysis was conducted using CT images from 182 patients with SICH. Data from the patients were divided into a training set (145 cases) and a testing set (37 cases) using random stratified sampling. Two radiomics models were constructed by combining quantitative features extracted from NCCT images (the NCCT model) and CTA images (the CTA model) using a logistic regression (LR) classifier. Additionally, a univariate LR model based on the CTA spot sign (the spot sign model) was established. The predictive performance of the two radiomics models and the spot sign model was compared according to the area under the receiver operating characteristic (ROC) curve (AUC).ResultsFor the training set, the AUCs of the NCCT, CTA, and spot sign models were 0.938, 0.904, and 0.726, respectively. Both the NCCT and CTA models demonstrated superior predictive performance compared to the spot sign model (all P < 0.001), with the performance of the two radiomics models being comparable (P = 0.068). For the testing set, the AUCs of the NCCT, CTA, and spot sign models were 0.925, 0.873, and 0.720, respectively, with only the NCCT model exhibiting significantly greater predictive value than the spot sign model (P = 0.041).ConclusionRadiomics models based on NCCT and CTA images effectively predicted HE in patients with SICH. The predictive performances of the NCCT and CTA models were similar, with the NCCT model outperforming the spot sign model. These findings suggest that this approach has the potential to reduce the need for CTA examinations, thereby reducing radiation exposure and the use of contrast agents in future practice for the purpose of predicting hematoma expansion

Data_Sheet_1_Intercropping enhances microbial community diversity and ecosystem functioning in maize fields.docx

Background and aims:Intercropping, a widely used planting pattern, could affect soil physicochemical properties, microbial community diversity, and further crop yields. However, its impacts on soil microbial diversity and ecosystem functioning and further soil sustainability are poorly understood.Methods:We conducted field experiments by intercropping maize with four important crops (i.e., sesame, peanut, soybean, and sweet potato), and examined soil microbial community diversity and ecosystem functioning such as microbial biomass and enzyme activities under monocropping and intercropping. We quantified their intercropping effects on microbial diversity and ecosystem functions with effect size metric Cohen d by comparing to the monocropping of maize.Results:We found that the four intercropping systems significantly increased soil aggregates in respective of the 2–0.25 mm grain size. Intercropping consistently elevated ecosystem functioning, such as soil enzyme activities of urease, phosphatase, and catalase, soil microbial biomass carbon and soil microbial biomass nitrogen. The Cohen d of bacterial richness also increased from 0.39 to 2.36, the latter of which was significant for maize/peanut intercropping. Notably, these ecosystem functions were strongly associated with the diversity of bacteria and fungi and the relative abundance of their ecological clusters identified with network analysis.Conclusion:Together, our findings indicate that intercropping generally affected soil physicochemical properties, ecosystem functions, and promoted microbial community diversity. More importantly, our findings highlight the important roles of microbial diversity of ecological clusters (that is, network modules) in maintaining ecosystem functioning after intercropping. These results will help to better understand the microbial diversity and ecosystem function in intercropping systems and guide agricultural practice.</p

Nanomagnetic encoding of shape-morphing micromachines

ISSN:0028-0836ISSN:1476-468

Elevated LILRB1 expression predicts poor prognosis and is associated with tumor immune infiltration in patients with glioma

Abstract Background Leukocyte immunoglobulin-like receptor subfamily B1 (LILRB1) is regarded as an inhibitory molecule. However, the importance of LILRB1 expression in glioma has not yet been determined. This investigation examined the immunological signature, clinicopathological importance and prognostic value of LILRB1 expression in glioma. Methods We used data from the UCSC XENA database, the Cancer Genome Atlas (TCGA) database, the Chinese Glioma Genome Atlas (CGGA) database, the STRING database, the MEXPRESS database and our clinical glioma samples to perform bioinformatic analysis and used vitro experiments to examine the predictive value and potential biological roles of LILRB1 in glioma. Results Higher LILRB1 expression was considerably present in the higher WHO grade glioma group and was linked to a poorer prognosis in patients with glioma. Gene set enrichment analysis (GSEA) revealed that LILRB1 was positively correlated with the JAK/STAT signaling pathway. LILRB1 combined with tumor mutational burden (TMB) and microsatellite instability (MSI) may be a promising indicator for the effectiveness of immunotherapy in patients with glioma. Increased LILRB1 expression was positively linked with the hypomethylation, M2 macrophage infiltration, immune checkpoints (ICPs) and M2 macrophage makers. Univariate and multivariate Cox regression analyses determined that increased LILRB1 expression was a standalone causal factor for glioma. Vitro experiments determined that LILRB1 positively enhanced the proliferation, migration and invasion in glioma cells. MRI images demonstrated that higher LILRB1 expression was related with larger tumor volume in patients with glioma. Conclusion Dysregulation of LILRB1 in glioma is correlated with immune infiltration and is a standalone causal factor for glioma

CO₂ Hydrogenation to Methanol over Catalysts Derived from Single Cationic Layer CuZnGa LDH Precursors

Ultrathin (1–3 cationic-layers) (CuZn)_1–<i>x</i>Ga_<i>x</i>-CO₃ layered double hydroxide (LDH) nanosheets were synthesized following the aqueous miscible organic solvent treatment (AMOST) method and applied as catalyst precursors for methanol production from CO₂ hydrogenation. It is found that, upon reduction, the aqueous miscible organic solvent treated LDH (AMO-LDH) samples above a critical Ga³⁺ composition give consistently and significantly higher Cu surface areas and dispersions than the catalysts prepared from conventional hydroxyl-carbonate phases. Owing to the distinctive local steric and electrostatic stabilization of the ultrathin LDH structure, the newly formed active Cu­(Zn) metal atoms can be stably embedded in the cationic layers, exerting an enhancement to the catalytic reaction. The best catalyst in this study displayed methanol productivity with a space-time yield of 0.6 g_MeOH·g_cat^–1 h^–1 under typical reaction conditions, which, as far as we are aware, is higher than most reported Cu-based catalysts in the literature