SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method

Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learner's performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrates the superiority of the proposed method.Comment: 11 pages, 5 figures, ICAIS 202

Evaluating the value of 18F-PSMA-1007 PET/CT in the detection and identification of prostate cancer using histopathology as the standard

Abstract Background Prostate-specific membrane antigen (PSMA) PET/CT is a highly regarded radionuclide imaging modality for prostate cancer (PCa). This study aimed to evaluate the diagnostic performance of 18F-PSMA-1007 PET/CT in detecting intraprostatic lesions of PCa using radical prostatectomy (RP) specimens as a reference standard and to establish an optimal maximum standardized uptake value (SUVmax) cutoff for distinguishing between PCa and non-PCa lesions. Methods We retrospectively collected 117 patients who underwent 18F-PSMA-1007 PET/CT before RP. The uptake of the index tumor and contralateral non-PCa lesion was assessed. Histopathology of RP specimens was used as the gold standard. Kappa test was used to evaluate the consistency of preoperative PSMA PET/CT staging and postoperative pathological staging. Finally, an SUVmax cutoff value was identified by receiver operating characteristic (ROC) curve analysis to distinguish PCa lesions from non-PCa lesions. A prospective cohort including 76 patients was used to validate the results. Results The detection rate of 18F-PSMA-1007 PET/CT for prostate cancer was 96.6% (113/117). 18F-PSMA-1007 had a sensitivity of 91.2% and a positive predictive value (PPV) of 89.8% for the identification of intraprostatic lesions. The consistency test (Kappa = 0.305) indicated poor agreement between the pathologic T-stage and PSMA PET/CT T-stage. Based on ROC curve analysis, the appropriate SUVmax to diagnose PCa lesions was 8.3 (sensitivity of 71.3% and specificity 96.8%) with an area under the curve (AUC) of 0.93 (P < 0.001). This SUVmax cutoff discriminated PCa lesions from non-PCa lesions with a sensitivity of 74.4%, a specificity of 95.8% in the prospective validation group. Conclusions 18F-PSMA-1007 PET/CT demonstrated excellent performance in detecting PCa. An optimal SUVmax threshold (8.3) could be utilized to identify lesions of PCa by 18F-PSMA-1007 PET/CT. Trial registration ClinicalTrials.gov Identifier: NCT04521894, Registered: August 17, 2020

Molecular Layer Deposition-Modified 5A Zeolite for Highly Efficient CO₂ Capture

Effective pore mouth size of 5A zeolite was engineered by depositing an ultrathin layer of microporous TiO₂ on its external surface and appropriate pore misalignment at the interface. As a result, a slightly bigger N₂ molecule (kinetic diameter: 0.364 nm) was effectively excluded, whereas CO₂ (kinetic diameter: 0.33 nm) adsorption was only influenced slightly. The prepared composite zeolite sorbents showed an ideal CO₂/N₂ adsorption selectivity as high as ∼70, a 4-fold increase over uncoated zeolite sorbents, while maintaining a high CO₂ adsorption capacity (1.62 mmol/g at 0.5 bar and 25 °C) and a fast CO₂ adsorption rate

Molecular Layer Deposition-Modified 5A Zeolite for Highly Efficient CO₂ Capture

Effective pore mouth size of 5A zeolite was engineered by depositing an ultrathin layer of microporous TiO₂ on its external surface and appropriate pore misalignment at the interface. As a result, a slightly bigger N₂ molecule (kinetic diameter: 0.364 nm) was effectively excluded, whereas CO₂ (kinetic diameter: 0.33 nm) adsorption was only influenced slightly. The prepared composite zeolite sorbents showed an ideal CO₂/N₂ adsorption selectivity as high as ∼70, a 4-fold increase over uncoated zeolite sorbents, while maintaining a high CO₂ adsorption capacity (1.62 mmol/g at 0.5 bar and 25 °C) and a fast CO₂ adsorption rate

Cardiovascular magnetic resonance black-blood thrombus imaging for the diagnosis of acute deep vein thrombosis at 1.5 Tesla

Abstract Background The aim was to investigate the feasibility of a cardiovascular magnetic resonance (CMR) black-blood thrombus imaging (BBTI) technique, based on delay alternating with nutation for tailored excitation black-blood preparation and a variable flip angle turbo-spin-echo readout, for the diagnosis of acute deep vein thrombosis (DVT) at 1.5 T. Methods BBTI was conducted in 15 healthy subjects and 30 acute DVT patients. Contrast-enhanced CMR venography (CE-CMRV) was conducted for comparison and only performed in the patients. Apparent contrast-to-noise ratios between the thrombus and the muscle/lumen were calculated to determine whether BBTI could provide an adequate thrombus signal for diagnosis. Two blinded readers assessed the randomized BBTI images from all participants and made independent decisions on the presence or absence of thrombus at the segment level. Images obtained by CE-CMRV were also randomized and assessed by the two readers. Using the consensus CE-CMRV as a reference, the sensitivity, specificity, positive and negative predictive values, and accuracy of BBTI, as well as its diagnostic agreement with CE-CMRV, were calculated. Additionally, diagnostic confidence and interobserver diagnostic agreement were evaluated. Results The thrombi in the acute phase exhibited iso- or hyperintense signals on the BBTI images. All the healthy subjects were correctly identified from the participants based on the segment level. The diagnostic confidence of BBTI was comparable to that of CE-CMRV (3.69 ± 0.52 vs. 3.70 ± 0.47). High overall sensitivity (95.2%), SP (98.6%), positive predictive value (96.0%), negative predictive value (98.3%), and accuracy (97.7%), as well as excellent diagnostic and interobserver agreements, were achieved using BBTI. Conclusion BBTI is a reliable, contrast-free technique for the diagnosis of acute DVT at 1.5 T